Iman Aganj - Laboratory for Computational Neuroimaging (LCN)

medical image segmentation, brain connectivity analysis

Currrent Projects:

Medical image segmentation
We'd like to use computational analysis to segment brain structures, such as the locus coeruleus, from available T1/T2/diffusion MRI images.

Brain connectivity analysis
We'd like to find relationships between structural and functional connectivity of the human brain and neurodegenerative disease.

Code optimization
We'd like to optimize existing code so they run faster on CPU and GPU.  

Contact: iaganj [at] mgh.harvard.edu

Polina Anikeeva - Bioelectronics Lab

neural engineering, brain-body communication, neuroscience, multifunctional fiber-based neural probes, magnetic nanotransducers, wireless electronics

Currrent Projects:

Design and implement multifunctional low-power wireless probes to enable manipulation and sensing of cell signaling in central and peripheral neural circuits during complex behavioral experiments in animal models of neurological and psychiatric disorders. 

Investigate fundamental principles of brain-body communication in models of neurodevelopmental disorders, neurodegenerative disease, or affective disorders using implanted multifunctional neural probes. 

Translate multifunctional neurotechnology from applications in small animal models to preclinical studies of anesthesia and consciousness. Apply statistical methods to analyze multimodal datasets made available through neurotechnology innovation. Collaboration between Brown, Miller, and Anikeeva labs.

Contact: anikeeva [at] mit.edu

Sylvan Baca - Baca Lab

 liquid biopsy, machine learning, computational epigenomics, oncology

Our lab applies develops and applies computational methods, with a focus on epigenomics, to advance precision oncology. We have several projects that are well-suited to students with a background in R and/or python. 

Currrent Projects:

Analysis of novel liquid biopsy datatypes
We are analyzing epigenetic features of cell-free DNA to learn about non-mutational mechanisms of treatment resistance in cancer. (eg: https://pubmed.ncbi.nlm.nih.gov/34907080/)

Cistrome-wide associations studies
We are applying a method for understanding how GWAS variants influence cancer risk through effects on the epigenome (eg: https://pubmed.ncbi.nlm.nih.gov/36071171/)

Contact: sbaca [at] partners.org and Caelin_Curley [at] DFCI.HARVARD.EDU

Robert Barry - Brain & Spinal Cord Laboratory

brain, spinal cord, functional MRI, 7 Tesla

Several projects are available that involve the development/validation of new methods to acquire, analyze and/or interpret brain and spinal cord data at an ultra-high field of 7 Tesla. This research is clinically-relevant to central nervous system diseases such as multiple sclerosis, amyotrophic lateral sclerosis, and spinal cord injury.

Contact: Robert.Barry [at] mgh.harvard.edu

Ross Berbeco

nanoparticles, imaging, radiation, MRI, x-ray

Currrent Projects:

Preclinical and clinical radiation therapy and imaging with novel nanoparticles
We are developing new nanoparticles for combination imaging (e.g. MRI, x-ray) and radiation therapy applications. One nanoparticle is in Phase 2 clinical testing at our institution and others are currently being evaluated preclinically. Projects can include lab bench work, animal research, computational studies, and image analysis.

Preclinical and clinical multi-energy imaging with novel detectors
We are engaged in the design, development, testing, and clinical evaluation of novel multi-layer flat-panel imagers for multi-spectral applications. Projects include Monte Carlo simulations, reconstruction algorithm design, and image analysis.

Contact: ross_berbeco [at] dfci.harvard.edu

Sangeeta Bhatia - Laboratory for Multiscale Regenerative Technologies (LMRT)

 liver disease, regeneration, nanotechnology, cancer, infection, diagnostics, therapeutic delivery

Available projects are always slightly 'organic' in that they evolve with the trainee and with the data, over time. We do have project ideas that encompass our core areas of 1) human liver disease modeling and intervention, and 2) the detection and treatment of cancer and infection. Some more specific topics include the design of nanomaterial-mediated antimicrobial delivery to combat rising antibiotic resistance (Ngambenjawong, ACS Nano, 2022), development of point-of-care tests for pneumonia or other microbial infections (Anahtar, PNAS, 2022), harnessing protease activity to detect, diagnose, and discover tumor biology (Soleimany/Kirkpatrick, Nature Comm, in press), and gene editing of human hepatocytes / organoids used in liver regeneration (in vitro and in vivo) with syn bio circuits and electrical stimulation platforms (Chhabra, PNAS, 2022).    

Contact: sbhatia [at] mit.edu and hfleming [at] mit.edu (Heather Fleming)

Berkin Bilgic - MRI Acquisition and Reconstruction Lab

MRI, medical imaging, deep learning, signal processing

Currrent Projects:

Efficient MRI through improved acquisition and reconstruction
We develop data acquisition and image reconstruction strategies that synergistically employ MR physics, cutting edge hardware, signal processing and deep learning algorithms to push the limits of spatial and temporal resolution for more efficient clinical and neuroscientific imaging. 

Contact: bbilgic [at] mgh.harvard.edu

Giorgio Bonmassar - AbiLab

MRI safety, EEG/fMRI, TMS, micro-TMS, Deep Brain Stimulation, electrodes, novel ephys systems

Currrent Projects:

The proposed research aims at designing, developing, and testing novel NiTi-based on MRI-compatible metamaterial technology for depth and an ECoG electrode set for the Neuropace System for patients suffering from epilepsy.  The development of such novel technology could result in significant benefits for patients that suffer from some medically refractory pathological conditions such as Parkinson’s disease, epilepsy, and stroke.

The proposed research aims at designing and developing at the Center for Nanoscale Systems at Harvard University, and testing novel micro--magnetic stimulation systems for next-generation Nervous System Stimulation, both in basic research and in clinical applications.  The development of such novel technology could result in significant benefits to basic neuroscience research as it will pair magnetic stimulators with Calcium channels optical sensors to measure onsite efficacy, as well as fMRI for the large network response. 

Contact: giorgio.bonmassar [at] mgh.harvard.edu and PPANTAZOPOULOS [at] mgh.harvard.edu (Pamela Pantazopoulos)

Brett Bouma

biophotonics, biomedical optics, optical imaging, OCT

The physics of light propagation and interactions in biological tissues underpins our engineering of new methods and instruments for disease diagnosis and intervention. We have multiple projects including elements of novel laser and fiber optic development, signal and image processing, and automated image interpretation and quantification.

Currrent Projects:

White matter tract imaging
White matter tracts in the brain feature prominent optical anisotropy. Polarimetric imaging through a narrow-gauge probe offers a pathway to resolve the local neuroanatomy in the probe’s vicinity and assist guiding neurosurgical procedures. This project will develop novel algorithms that reconstruct the optical anisotropy from measurements at multiple imaging angles.

Retinal polarimetry
The polarimetric properties of the retinal nerve fiber layer offer valuable insight into a broad range of retinal and neurodegenerative diseases. This project will refine a compact add-on module and suitable reconstruction algorithms to enable the investigation of these important polarimetric signatures with existing retinal optical coherence tomography instruments.

Imaging through multimode fiber
A segment of multimode fiber can serve as a hair-thin endoscope when the mode-mixing induced by propagation through the fiber is correctly compensated. This project will develop strategies for efficiently retrieving and tracking the required fiber calibration to enable coherent confocal imaging through a dynamically bending fiber.

Optical intravascular elastography
Atherosclerosis, the thickening of the coronary artery wall, is a leading cause of mortality. Biomechanical characterization of plaques can enable the stratification of these lesions and the development of clinical studies to determine optimal treatment strategies. We seek to develop hardware and signal processing for all-optical intravascular elastography using phase-sensitive ultra-fast optical coherence tomography.

Computational adaptive optics imaging of the living retina
High-resolution imaging of the retina with adaptive optics has opened the door to imaging individual neurons involved in the transmission of visual information from the retina into the brain. Degeneration of these neurons play a critical role in many sight-robbing diseases, but current adaptive optics instrumentation remain limited to the research lab. In this project, we will develop a computational adaptive optics technique that could enable high-resolution imaging of the living retina with minimal modifications to existing imaging systems in the clinic.

Physics-informed functional imaging
Functional extensions of optical coherence tomography provide enhanced contrast in a vast array of clinical applications, but suffer from low spatial resolution and image quality. In this project, we will develop a novel signal processing framework based on the physics of image formation and the statistics of the signal to dramatically enhance the resolution of angiographic, spectroscopic, and polarization-sensitive optical coherence tomography.

Contact: bbouma [at] mit.edu

Emery Brown - Neuroscience Statistics Research Laboratory

neuroscience, anesthesia, statistics, math scientists

Contact: enb [at] neurostat.mit.edu and rv25 [at] mit.edu

Sydney Cash

BCI, neuromodulation, neurosimulation, neurotechnology, single unit, epilepsy

Currrent Projects:

Neurostimulation
1.    Electrical stimulation and its effect on oscillations in the human brain 
2.    Comparing stimulation during a cognitive task versus without
3.    Examining possible effects of plasticity with stimulation
4.    Closed-Loop stimulation: Algorithms for real-time detection of neural activity

New Devices
1.    Testing new electrodes in the operating room
2.    Modelling high-density electrodes relative to real human neural cells
3.    Improving software to record neural activity using new devices 

Human Neurophysiology
1.    Modeling highly localized neuronal activity
2.     Understanding the relationship of local field potentials and spikes with high-density sampling electrodes
3.    Examining Neuropixels waveforms

Tumor studies
1.    Using imaging to characterize the 3D mapping of tumors and tumor boundaries and comparing that to the electrode locations and neural activity
2.    Looking at connectivity measures at different locations relative to the tumor location and the electrodes

Cognition
1.    Understanding the basis of spontaneous language generation
2.    Investigations into distributed processing of sensory stimuli
3.    Examining how Time is represented in the brain

Contact: scash [at] mgh.harvard.edu and ychou4 [at] mgh.harvard.edu

Ciprian Catana - Integrated PET-MR Imaging Laboratory

simultaneous PET/MRI, machine learning, imaging hardware, prostate cancer, pulmonary fibrosis

Currrent Projects:

Development of the Human Dynamic Neurochemical Connectome Scanner
The goals of this project are to build, integrate and test the hardware and software components for a next generation 7 Tesla MR-compatible PET insert with dramatically improved sensitivity and perform proof-of-principle studies in healthy volunteers.

PET-MR Imaging of pulmonary fibrosis
This project aims to apply PET-MR imaging to quantify molecular abnormalities in the lungs of idiopathic pulmonary fibrosis patients and determine if such measures can predict the pace of disease progression and determine whether the patient is responding to anti-fibrotic therapy.

Multimodal MR-PET Machine Learning Approaches for Primary Prostate Cancer Characterization
We are using machine learning approaches to characterize the aggressiveness of the tumors from multimodal PET and MRI data in patients undergoing radical prostatectomy.

Contact: ccatana [at] mgh.harvard.edu

Elliot Chaikof - Chaikof Lab

drug delivery, drug discovery, tissue engineering, genome engineering, innate and adaptive immunity, artificial organs

Currrent Projects:

Delivery Technologies
Developing protein-based nanoparticles for efficient delivery of therapeutic macromolecules. We are developing protein polymer-based, cell type-specific delivery systems to enhance the efficacy and safety of genome editors, RNA therapeutics, and other macromolecular cargo for in vivo applications.

Engineering Living Tissues
Cell and tissue engineering for applications in regenerative medicine. We are investigating genetic pathways that could serve as rational targets to improve the long-term success of organ, tissue, and cell transplantation through multiplex genome editing and developing new additive manufacturing approaches to accelerate organ fabrication. 

Modulating Innate Immunity
Defining modulators of innate immunity and tissue repair. We are studying transcription factor protein-protein interactions that promote gut tissue integrity and are developing small molecules that target these interactions as immune-modulating therapeutics with relevance to inflammatory bowel disease.

Glycobiology & Glycotherapeutics
Defining Clinically Relevant Protein-Glycan Interactions. We are developing tools to study the roles of glycan-protein interactions in innate immunity, thrombosis, and cancer and identifying molecules that target these interactions as therapeutic agents.

Biologically Inspired Artificial Organs
Biomolecular engineering to improve the performance of implanted devices. We are developing schemes that regenerate selective molecular constituents after device implantation to extend the lifetime of bioactive films and enhance clinically related performance characteristics of blood contacting devices.

Contact: echaikof [at] bidmc.harvard.edu

Arup Chakraborty

immunology, virology, vaccines, statistical physics, phase separation, transcription

Contact: arupc [at] mit.edu

Yee-Ming Chan - Pediatric Reproductive Hormone (PReproHormone) Program

pediatric reproductive endocrinology, delayed puberty, differences of sex development, transgender health

Currrent Projects:

Genetics of delayed puberty
We are studying the role of both common and rare genetic variants in causing self-limited delayed puberty (constitutional delay of puberty) to understand the pathways that determine pubertal timing.

Genetic testing for differences of sex development (DSD)/intersex conditions
We are analyzing rare and common genetic variant causes of DSD's and also studying the impact on parents of receiving genetic testing results.

Physical effects of gender-affirming hormonal treatments
As part of the four-site Trans Youth Care (TYC) study, our group is analyzing data from the largest longitudinal cohort in the US of transgender and nonbinary youth receiving hormonal treatments for gender affirmation (GnRH agonists for pubertal blockade and sex steroids for pubertal induction). We are focusing on physical outcomes (growth, bone mineralization, metabolic changes), and other investigators are focusing on psychosocial outcomes

Contact: Yee-Ming.Chan [at] childrens.harvard.edu

Luke Chao - Chao Lab

cryo-EM, cryo-ET, in vitro reconstitution

Currrent Projects:

Subtomogram averaging of membrane protein assemblies in situ (in bacteria, mitochondria, mammalian cells) - computationally intense.

Numerous single particle and in vitro reconstitution projects - more experimental/wet-lab intense.   

Contact: luke_chao [at] hms.harvard.edu and finn [at] molbio.mgh.harvard.edu

Jianzhu Chen - Chen Lab

Immunology, NK cell therapy, macrophage reprogramming

Currrent Projects: 

Development of the next generation CAR-NK cells for cancer therapy
Through expression of CAR, NK cells can be directed to kill tumor cells. To achieve long-term therapeutic efficacy, we are developing CARs targeting tumor driver mutations in memory-like NK cells that show long-term persistence in patients. We are also interested in expressing other molecules in CAR-NK cells in order to induce endogenous T cell responses against neoantigens released from tumor cells.

Reprogramming macrophages for disease intervention
Macrophages are a key immune cell type in tissue homeostasis, inflammation and immune responses. Dysregulation of macrophages are involved in many chronic diseases. We have identified small molecules that can reprogram macrophages to different functional states. We are interested in elucidating the underlying molecular mechanisms and exploring their translation for treating metabolic and neurological diseases.

Contact:  jchen [at] mit.edu

Jingyuan Chen - Neuroimaging of Brain Dynamics Lab

multi-modality, neuroimaging, PET-MRI, fMRI, signal processing, brain dynamics

Currrent Projects:

Sleep-wake aerobic glycolysis, memory consolidation and reduced sleep spindles in Schizophrenia
This project will integrate simultaneous EEG-PET-MR imaging to track sleep-wake changes of aerobic glycolysis, and apply it to test the role of abnormal sleep spindles in causing cognitive deficits in Schizophrenia. 

Precision metabolic mapping of brain networks using simultaneous functional PET-MRI
Recent studies have shown that fMRI is capable of mapping and fingerprinting individualized brain functional architecture. This project seeks to probe the energetic underpinnings of the individual-specific brain network patterns.  "

Contact: jechen [at] mgh.harvard.edu

Leo Cheng

NMR, metabolomics, cancer, Alzheimer

Currrent Projects:

Metabolomic characterization of normal aging and Alzheimer's disease under anesthesia- surgery stimuli
(1) Characterize age-associated metabolomic changes ex vivo using tissues from different brain regions and blood serum obtained from wild-type and AD mice after administration of anesthesia-surgery stimuli; 
(2) Develop multimodality in vivo MR protocols for non-invasive evaluations of age- and AD-associated brain metabolic changes induced by anesthesia-surgery stimuli; and
(3) Investigate the effect of an AD protective intervention on metabolomic changes after administration of anesthesia-surgery stimuli.

Novel metabolomic contrast probes for human lung cancer characterization
1) To evaluate the efficacy of tissue-serum LuCa MRS metabolomic probes identified in a successful preliminary project, by comparison with an additional 200 pairs of tissue and serum specimens and 200 serum samples from matched healthy controls, 
2) To measure tissue-serum LuCa MRS metabolomic probes with mass spectrometry (MS) and MS imaging (MSI) to associate the probes with LuCa pathologies and identify serum MS LuCa probes, and 
3) To test LuCa metabolomics probes using 200 serum samples collected before LuCa detection and evaluate LuCa metabolomic probe health- and cost-effectiveness as compared to existing advanced tests.

Contact:  lcheng [at] mgh.harvard.edu 

Kwanghun Chung

neuroscience, bioimaging, spatial biology, tissue engineering

Contact: khchung [at] mit.edu

George Church - gclab

stem cells, organoids, multiplex, aging, genome editing, super-resolution imaging, technology development 

Currrent Projects:

Aging reversal
We continue to explore how multiplexed gene therapies can be delivered to optimally impact multiple tissues and multiple age-related diseases simultaneously. 

Resistance to all viruses
By changing the ribosomal genetic code we have made one organism (E.coli) resistant to all viruses.  We are now attempting to extend this to other species, notably, human stem cells for enhanced transplants and cell therapies. 

Reading and writing brain connectomes
We have progress on super-resolution imaging and applying it to DNA, RNA, synaptic proteins and cell-specific barcodes.  This allows integration of epigenetic state of each cell and its ~8000 (synaptic) neighbors.

In vitro reproduction and multiple novel alleles
We are studying embryonic and fetal development of animals in vitro -- including editing diverse alleles from extinct keystone species and synthetic or rare natural alleles protective from radiation, senescence, pathogens, cancer, etc.  

Contact: gchurch [at] genetics.med.harvard.edu and church_lab_admin [at] hms.harvard.edu  

Michael Cima

medical product development

Currrent Projects:

Hydration status
This project involves a first-in-class measurement technology for assessment of hydration status with clinical validation.  Despite what you read on the web, there are no such measurements that have proven to be clinically useful.  Over one third of hospital admissions involve disregulation of hydration state (too much or too little water).  We have just been awarded funding for a trial in ESRD patients with the objective of predicting adverse events during dialysis.  Thus, the project involves both technology development and clinical trial experience.  

Neuroscience
This project involves microsampling of of brain interstitial fluid in behaving animals.  The microsampling technology developed in my lab has been partnered with the mass spec capabilities of the White lab to discover the chemical basis of neurological disorders.

Contact: mjcima [at] mit.edu

Jim Collins - Collins Lab

synthetic biology, antibiotics, deep learning

Currrent Projects:

Harnessing synthetic biology to develop novel RNA therapeutics

Harnessing deep learning to discover and design novel antibiotics

Contact:  jimjc [at] mit.edu

Adrian Dalca - Laboratory for Computational Neuroimaging (LCN)

machine learning, deep learning, medical images, neuroimage

Currrent Projects:

Large machine learning models for solving multiple imaging tasks
We are working on ways to integrate information across different processing tasks and datasets to solve multiple popular/important imaging tasks (segmentation, registration, diagnosis) at once, improving easy of use, accuracy, speed, and data dependency.

Machine learning models for general rapid interactive segmentation
We are designing new interactive systems that enable very fast and easy manual segmentation pipelines of *any* medical image. The systems will learn across domains to rapidly predict what a user is trying to segment as they segment.

Integrating anatomical or clinical knowledge in machine learning models
We are working on general ways to integrate medical knowledge and external clinical attributes into deep learning models for various analyses.

Contact: adalca [at] mgh.harvard.edu

George Daley - Daley Lab 

stem cells, blood development, pluripotency, immunotherapy 

Multiple projects available for probing the molecular pathways intrinsic to hematopoietic development during embryogenesis, in order to define principles for guiding directed differentiation of hematopoietic stem and progenitor cells from pluripotent stem cells for applications in research and cell therapy.

Contact: GEORGE.DALEY [at] CHILDRENS.HARVARD.EDU and aubrey.plumb [at] childrens.harvard.edu

Alan Dandrea

DNA Repair, anti-cancer drugs, drug resistance

Currrent Projects:

Novel treatment of breast, ovarian, and pancreatic cancers
These cancers often have a defect in DNA repair by homologous recombination.  While they are initially sensitive to PARP inhibitors, they usually acquire resistance to these drugs by novel mechanisms.  The project for this HST student is to evaluate novel agents which can overcome drug resistance.

CRISPR screens through the D'Andrea laboratory and the Broad Institute
Our lab has used whole genome CRISPR screens to identify novel predictive biomarkers for new anti-cancer drugs.  This project will entail new CRISPR screens for new agents.

Use of Human and Mouse tumor Organoids to evalulate new DNA repair drugs and biomarkers
The project will entail the generation of organoid cultures and the use of the cultures in evaluating new anti-cancer agents and radiation therapies.

Contact: alan_dandrea [at] dfci.harvard.edu and SirishaV_Mukkavalli [at] DFCI.HARVARD.EDU

Brandon DeKosky - Immune Engineering Lab

​​​​​​​B cell receptors, T cell receptors, single-cell analysis, infectious diseases, high-throughput screening, infectious diseases, cancer therapeutics

Currrent Projects:

High-throughput antibody screening technologies and computational approaches for rapid drug discovery
The current low-throughput technologies used for antibody discover cannot deconvolute the molecular features of human immunity against the vast array of potential protein targets. This project will develop and implement new experimental and/or machine learning/AI approaches to comprehensively characterize human antibody immunity, and for improved drug discovery.  

Personalized T cell receptor therapies for cancer cures
T cells are known to protect effectively against cancers, but the identification of T cell receptors as drugs for individual patients remains too difficult for clinical use. This project will establish a rapid technology to screen and identify protective T cell receptors, and apply it to understand the key features of anti-cancer immune pressure in human patient samples and in animal models.

Vaccine and antibody drug development against infectious diseases
Infectious diseases like HIV-1, TB, and malaria present enormous global health burdens, and they remain a challenge because of sophisticated ways that each pathogen can evade our immune systems. These projects in the DeKosky lab will identify precision targets of antibodies and T cell receptors that can prevent and treat global infectious diseases.        

Contact: dekosky [at] mit.edu                  

Min Dong - Dong Lab

bacterial toxins, botulinum neurotoxin, C. difficile toxin, microbiome, neurogenic inflammation, protein structure and engineering

Our laboratory has a broad interest in microbial toxins, bacterial pathogenesis, microbial interactions with human/animal/insects, microbiome, enteric nervous system, and engineering therapeutic proteins. Along these lines of basic research, we are keen in developing microbial protein-inspired novel therapeutics for treating genetic diseases, cancer, pain, and other neurological disorders. We have a multi-disciplinary team crossing microbiology, bioinformatics, structural biology, protein engineering, cell biology, insect biology, and neuroscience fields. Recent achievements include identification of receptors for C. difficile toxin via genome-wide CRISPR-Cas9 screen, identification and characterization of novel toxins , and protein engineering of botulinum neurotoxins. 

Currrent Projects:
(1) to investigate molecular and structural mechanisms of toxins, effectors, and engineering therapeutic proteins. 

(2) to investigate microbiome-pathogen interactions, enteric nervous system, enteric infection, and urinary tract infection. 

(3) to develop novel therapeutic proteins for genetic editing and modulation of signaling networks in cells for treating genetic diseases, pain, neurological disorders, and cancer.  

Contact: min.dong [at] childrens.harvard.edu  

Shadi Esfahani - Institutue for Innovation in Imaging (I3)

molecular imaging, animal and human PET, theranostic, radiopharmaceutical design, cancer imaging and treatment

Currrent Projects:

Molecular Theranostics of Cancer
broad range of projects with applying molecular imaging probes that can target a specific component of the tumors and then radiolabel with a beta emitting radioisotope to treat the tumors effectively and eradicate them. We use mouse models of different types of cancers and apply solo or combination therapies and look more closely on histopathology to see the treatment effects. Plenty of opportunities to learn different wet lab techniques such as cell culture, tumor model generation, IHC and IF staining, flowcytometry, animal imaging, image analysis, and radiochemistry.     

Contact: esfahani.shadi [at] mgh.harvard.edu

Elazer Edelman - Edelman Lab

cardiovascular systems biology (cells, tissues, patients)

The Edelman Lab is home to a collaborative research environment with teams of clinicians, engineers, and scientists from both academia and industry that work together to create translatable solutions to clinically relevant problems. The lab uses elements of continuum mechanics, digital signal processing, molecular biology, and polymeric controlled release technology to tackle a variety of challenges. Projects range from examining the interplay between mechanical support devices and native physiology to the cellular and molecular mechanisms that transform stable coronary-artery disease to unstable coronary syndromes. Tissue-generated cells, for example, deliver growth factors and growth inhibitors for the study and potential treatment of accelerated arterial disease following angioplasty and bypass surgery.

Contact: ere [at] mit.edu

Christian Farrar

molecular imaging, MRI, machine learning

Currrent Projects:

Artificial Intelligence Boosted Evolution and Detection of Genetically Encoded Reporters for In Vivo Imaging
Cell and viral based therapies have the potential to revolutionize the treatment of many diseases. However, the optimization of such biological therapies depends critically on the ability to monitor the spread and persistence of the therapeutic agent and assess the tissue response. This project therefore proposes to develop and optimize a novel MRI reporter gene technology that allows for the imaging of cell and viral based therapeutics. The reporter gene technology will be demonstrated for monitoring oncolytic virotherapy in glioblastoma tumor models, however, the technology is generalizable to any cell or viral therapeutic.

Rapid and Quantitative CEST Imaging with Deep-Learning Magnetic Resonance Fingerprinting
Measurement of tissue pH and protein/metabolite concentrations using chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has been demonstrated to provide crucial insight into many disease pathologies, including tumors, stroke, renal disease, osteoarthritis, and heart failure, as well as to enable the early assessment of disease response to therapies.  However, clinical translation of these CEST-MRI methods has been hindered by the qualitative nature of the image contrast, the long image acquisition times, and the complex data processing required. This project therefore proposes to develop, optimize and translate to the clinic a novel Magnetic Resonance Fingerprinting method that allows for rapid and quantitative pH and protein/metabolite imaging

Contact: cfarrar [at] mgh.harvard.edu

Maria Angela Franceschini - Optics @ Martinos

 fNIRS, diffuse optics spectroscopy and imaging, microscopy

Currrent Projects:

Wearable wireless brain health tracker
We are developing low cost devices to monitor brain blood perfusion and oxygen metabolism for hospital applications and telehealth. The project spans devices design and realization, algorithms development and validation studies in humans.

Non invasive optical continuous blood pressure monitor
We are developing novel continuous non invasive blood pressure monitors using speckle contracts and photoplethismography.  The project spans devices design and realization, algorithms development, including AI, and validation studies in humans.

Contact: mfranceschini [at] mgh.harvard.edu

James Fujimoto - Biomedical Optical Imaging and Biophotonics Lab

biomedical optics, optical coherence tomography, OCT, ophthalmology, cancer surgery

Currrent Projects:

Improving cancer surgery outcomes using nonlinear microscopy for real time histology
Breast cancer lumpectomy achieves good cosmetic outcomes, but up to ~30% of patients require repeat surgery because residual cancer is present on the surgical margin. Prostate cancer prostatectomy faces a similar challenge because the decision to remove neurovascular tissue must be balanced against risk of postoperative incontinence and impotence vs residual cancer on the surgical margin. Working with a multidisciplinary, team of engineers, pathologists, radiologists and surgeons at Beth Israel Deaconess Medical Center / Harvard Medical School, we are developing nonlinear microscopy for real time pathology and intraoperative cancer surgical guidance (see for example clinicaltrials.gov NCT02926729). This research training integrates engineering, system design, pathology, and clinical trial design, working in close collaboration with clinician scientists.

Ultrahigh-resolution and ultrahigh-speed optical coherence tomography (OCT) for diagnosis, monitoring, and investigation of retinal diseases 
Optical coherence tomography (OCT) was developed by our group and collaborators and is now a standard of care in ophthalmology with >20 million procedures per year. This project will focus on developing next generation OCT technology and computational analytical methods with a team of diverse engineers, scientists, and clinicians. The student will collaborate with ophthalmologists and research fellows at the New England Eye Center in translation studies using imaging technology developed at MIT. OCT data will be investigated using computational methods, including advanced OCT angiography and computer-assisted analysis of images, to develop imaging biomarkers for diagnosis, assessing disease progression and treatment response as well as understanding pathogenesis in vision impairing retinal diseases.

Improving gastrointestinal cancer screening / detection using optical imaging
Esophageal adenocarcinoma is among the most lethal cancers with a <20% five-year survival rate, and its incidence has increased several-fold in the last few decades. This program is developing tethered capsule imaging technology using next-generation, optical coherence tomography (OCT) and video, combined with computer vision methods in order to detect early neoplastic changes and reduce mortality. This technology does not require sedation endoscopy and will enable rapid, well tolerated and low cost screening which is scalable to different points of care. Students will design imaging devices, automated image analysis algorithms, lead clinical studies with gastroenterologists at the VA Boston Healthcare System, and develop expertise in medical device development and clinical translation.

Contact:  jgfuji [at] mit.edujnzhang [at] mit.edutweber [at] mit.edu and jwon8 [at] mit.edu

Guillermo Garcia-Cardena - Laboratory for Systems Mechanobiology

 biomechanics, blood vessels, cardiovascular, drug discovery

Currrent Projects:

Identification of novel cellular mechanosensors
This project seeks to identify novel mechanosensors activated in cells in response to flow. It combines a genome-wide CRISPR screen and the use in vitro flow systems.

Developing new systems to study interactions between blood vessels and cardiac muscle
This project aims to developed cardiac organoids generated with iPSC-derived cells cardiovascular with a perfused vasculature. It involves the use basic regenerative biology concepts, microfluidics and single-cell RNAseq. 

Contact:  guillermo_garcia-cardena [at] hms.harvard.edu 

Christopher Garris - Garris Laboratory

immunotherapy, cancer, dendritic cells, macrophage

Currrent Projects:

Therapeutic Myeloid Targeting Nanoparticles in Bladder Cancer
Test the ability of a novel combination therapy nanoparticle on re-educating tumor myeloid cells to support anti-tumor immunity.

Assessing the Role of Non-Canonical NFkB in Dendritic Cell Function in Cancer
Use genetic mouse models to test the role of the non-canonical NFkB signaling pathway in tumor immune recognition.

Contact: cgarris [at] mgh.harvard.edu 

Lee Gehrke - Laboratory for Viruses and Stem Cells

RNA virus, pathogenesis, rapid antigen diagnostics

Contact:  lgehrke [at] mit.edu 

Georg Gerber - Computational (micro)biology Lab

machine learning, deep learning, computational biology, microbiome, infectious diseases, statistics, time-series, dynamic systems, computer science, mathematics, microbiology

Currrent Projects:

Deep learning for the microbiome
The microbiome, or trillions of microbes living on and within us, is a complex and dynamic ecosystem that is crucial for human health, and when disrupted may contribute to a variety of diseases including infections, arthritis, allergies, cancer, heart and bowel disorders. We are looking for talented students to develop and apply novel deep learning methods that will further understanding of the microbiome. Applications include forecasting microbial population dynamics in the gut for rational design of therapies, predicting the impact of the microbiome on the onset or progression of human diseases, predicting interactions with the host immune system, elucidating host-microbial metabolic interactions, and discovering functions of uncharacterized microbial metabolites and proteins.

Contact: ggerber [at] bwh.harvard.edu

Marzyeh Ghassemi - HealthyML

robust private and fair machine learning for health

Contact: mghassem [at] mit.edu and fern [at] mit.edu

Wolfram Goessling

liver development, liver regeneration, liver cancer, metabolism, zebrafish disease models

Our research is driven by the needs of our patients to discover novel approaches to diagnose, prevent and treat liver disease. Our lab uses zebrafish in addition to mouse and in vitro approaches to investigate liver growth during development, regeneration, and cancer formation. New projects for prospective graduate students are available in all of these areas, and involve disease modeling of fatty liver, liver toxicity and carcinogenesis as well as in developmental and regenerative biology. We employ high resolution imaging and analysis of human and model organism tissues by transcriptomic and genomic approaches.

Contact: wolfram_goessling [at] hms.harvard.edu

Anna Greka - Greka Lab

disrupted cellular homeostasis, rare kidney disease, lipotoxicity, membrane proteins, trafficking, protein quality control, secretory proteins, metabolism, degenerative disorders

Currrent Projects:

Dissecting the roles of cargo receptors in protein trafficking and degradation
What are the fundamental mechanisms regulating the trafficking and degradation of misfolded protein cargoes? We are examining how a family of TMED proteins handle misfolded membrane and secretory protein cargoes including mucin 1, uromodulin, and rhodopsin. The work has implications for proteinopathies affecting the kidney, eye, brain, and beyond.

How does cellular exposure to specific lipids affect membrane proteins and intracellular signaling?
 We have pioneered a systematic approach to define cell states after exposure to specific lipids that might ultimately uncover targets that reflect the convergence of genetic and environmental risk for a host of human metabolic and degenerative diseases.

How can we harness next-generation technologies to probe disrupted homeostasis?
As members of the Kidney Disease Initiative, our team pilots new approaches to investigate cellular mechanisms underlying genetically defined rare diseases. 

Contact: agreka [at] broadinstitute.org and jshaw [at] broadinstitute.org (Jillian Shaw)

Rajiv Gupta - Advanced X-ray Imaging Science (AXIS) Center

x-ray sources, computed tomography, neuroradiology, acute stroke, traumatic brain injury, deep learning

Currrent Projects:

Distributed X-ray Source
Design and build a distributed x-ray source for motionless CT

Clinical Applications of a Novel Photon-counting CT

Multiple clinical projects in Neuroradiology

Contact: rgupta1 [at] mgh.harvard.edu and chynes [at] partners.org (Cyndi Hynes)

William Hahn - Hahn Lab

cancer, genetic dependency, therapeutic resistance

Students broadly work on projects studying cancer initiation, progression and the metastatic transition.  More specifically we identify and study a number of context specific cancer vulnerabilities including resistance to immuno-therapy.    

Contact: william_hahn [at] dfci.harvard.edu

Thomas Heldt

signal processing, mathematical modeling, patient monitoring, neurointensive care, biomedical instrumentation

Much of our recent work has focused on signal processing, physiological modeling, and biomedical device design to support our efforts in improving the care of patients with critical injuries to their brain and cerebrospinal fluid system.         

Contact: thomas [at] mit.edu

Miguel Hernan - CAUSALab

causal inference, AI, data science, health

Projects available on infectious diseases, cancer, cardiovascular disease, and mental health depending on student qualifications and interests.

Contact: mhernan [at] hsph.harvard.edu

Juan Eugenio Iglesias - Laboratory for Ex vivo Modeling of Neuroanatomy (LEMoN)

human neuroimaging, brain MRI, image analysis, machine learning

Currrent Projects:

Generative models of pathology for clinical brain MRI analysis
Our group has recently developed machine learning techniques for image analysis of brain MRI scans of any resolution and contrast, which has finally enabled the analysis of scans from our hospital PACS. While these techniques work remarkably well, we have so far trained them with relatively normal anatomy. The goal of this project is to learn to synthesize pathology during training (e.g., tumors, strokes) in order to enable the application of our methods in the wild.

Super-resolution of diffusion MRI with domain randomization and implicit neural representations (collaboration with Dr. Anastasia Yendiki)
Super-resolution of diffusion MRI enables the tracking of the thinner and more convoluted fiber tracts in the human brain. The goal of this project is to generally learn to super-resolve diffusion MRI data jointly in the spatial and angular domains, using a combination of domain randomization and implicit neural representations. Combining such algorithms with the unique ex vivo data generated with the newly developed Connectome 2.0 scanner at the Martinos should greatly improve our ability to analyze in vivo scans of living people.

Contact: jiglesiasgonzalez [at] mgh.harvard.edu

Felipe Jain - Depression Clinical and Research Program

mHealth, digital phenotyping, smartphones, depression, resilience, stress, caregivers, mobile applications

Currrent Projects:

Digital Phenotyping of Psychological States
The student will learn to apply machine learning approaches to analyzing big data (>5000 individuals) from smartphone sensors.  The goal will be to identify changes in mood and behavior.

Mobile Therapeutics for Family Caregivers
Family caregivers are often under tremendous psychological stress and exhibit higher rates of stress-related disorders: depression, anxiety, immune system dysfunction, cardiovascular dysfunction, accelerated cognitive decline.  The student may assist with the conduct of a randomized controlled trial of a mobile therapeutic and develop their own research data analysis project from the active (psychological questionnaire, ecological momentary assessment) or passive smartphone sensor (GPS, accelerometer, on/off events) data collected.  The student will learn principles of data science and analysis related to ""Big Data"" and gain exposure to a broad range of psychological therapies (mindfulness, guided imagery, cognitive therapy, behavioral activation, mentalization.)

You Design Your Mobile Therapy and Analytic Approach
The student may use the flexible smartphone platform developed by the lab, CareDoc, to design their own behavioral intervention to promote psychological health in a population of their choice.  They may choose to gather active questionnaire and ecological momentary assessment data, passive data, or even implement peer support groups supported by natural language processing.

Contact: felipe.jain [at] mgh.harvard.edu

Rakesh Jain - Edwin L. Steele Laboratories for Tumor Biology

tumor microenvironment, vascular biology, matrix biology, drug delivery, imaging, immunotherapy, molecular & cell biology, metabolism, brain tumors, pancreatic cancer, breast cancer, clinical translation

Currrent Projects:

Targeting the tumor microenvironment to improve cancer treatment
Our research goals are (i) to understand how the abnormal tumor microenvironment confers resistance to various cancer treatments (e.g., molecular therapeutics, nanotherapeutics, radiation and immunotherapy), (ii) to develop and test new strategies to overcome this resistance, and (iii) to translate these strategies from bench to bedside through multi-disciplinary clinical trials in patients with brain, breast, pancreatic, colon and ovarian cancers. This tight integration between bench and bedside and application of engineering/physical science principles to oncology is a hallmark of our research.   

Contact: jain [at] steele.mgh.harvard.edu and egarzon [at] mgh.harvard.edu (Elizabeth Garzon)

Alan Jasanoff

brain, neural circuit, molecular imaging, probe chemistry, synthetic biology, fMRI

Currrent Projects:

Brain-wide analysis of neural mechanisms
We apply a suite of innovative imaging probes to analyze neural circuit function in rodents and primates. Our molecular tools (see project 2) are detectable by noninvasive neuroimaging methods like MRI and functional ultrasound imaging; they provide readouts about how cell populations and neurochemical factors work together to mediate integrated aspects of brain function. We have particular interest in studying brain-wide mechanisms of decision-making, sensory processing, and intrinsic signaling dynamics. 

Creation of molecular tools for next-generation neuroimaging
We develop a diversity of chemical, nanotechnological, and genetic imaging probes for analysis of molecular and cellular function in intact, living organisms. We bring these molecular imaging technologies from concept to in vivo utility, with a strong focus on neural systems research (see project 1). We are interested both in basic neuroscience applications in animals, and in translational paths that may permit studies of human brain dynamics at the molecular level.

Contact: jasanoff [at] mit.edu and dballest [at] mit.edu (Diane Ballestas)

Meher Juttukonda - Cerebrovascular Aging and Spin Labeling (CASL) Lab

MRI, hemodynamics, vascular physiology, cerebrovascular disease, aging, Alzheimer's disease

Currrent Projects:

Cerebral hemodynamics in typical and abnormal aging
The human brain requires a constant supply of oxygen and other nutrients delivered through the bloodstream. Our lab is interested in applying noninvasive MRI approaches to understand cerebral hemodynamic function in health and disease. Projects include measuring cerebral perfusion and vascular reserve in older adults to assess the impact of risk factors for vascular disease and Alzheimer's disease on cerebrovascular function.

Noninvasive detection of altered capillary hemodynamics
Once oxygen-rich blood is delivered to the microvasculature, oxygen is offloaded from capillaries into brain tissue. Our lab is interested in noninvasive MRI approaches for detecting the presence of capillary flow disturbances. Projects include development of arterial spin labeling (ASL) approaches for better characterizing microvascular flow and investigating the impact on white matter lesion burden and progression.

Imaging white matter perfusion with high-field MRI
White matter poses unique challenges to perfusion imaging due to a lower vascular density and longer arterial transit times compared to gray matter. Our lab is interested in developing ASL MRI at 7 Tesla to address these challenges. Projects include the development of ASL with dynamic shimming approaches through collaboration with investigators at the Martinos Center to better characterize white matter cerebrovascular physiology.

Contact: mjuttukonda [at] mgh.harvard.edu

Jayashree Kalpathy-Cramer - Quantitative Translational Imaging in Medicine Laboratory (QTIM)

medical imaging, radiology, AI, deep learning, machine learning

Currrent Projects:

Deep learning analysis of digital histopathology to predict tumor phenotype and enhance precision medicine
The current clinical paradigm for cancer assessment involves manual evaluation of histopathologic (H/E) features, which are used to risk stratify patients for therapeutic decision making.  At present, H/E analysis is mostly centered around facets of tumor cell biology (e.g. tumor invasion, anaplasia, necrosis) and lack a rigorous assessment of the tumor microenvironment (TME), which recent studies suggest is an important determinant of treatment response and outcomes. This project will focus on developing deep-learning based image analysis methods to identify patterns in the tumor micro-environment that can be used to infer the gene expression and predict treatment outcomes.

Leveraging cell-free DNA and machine learning to enhance precision medicine for brain metastases
While precision medicine approaches for BM have demonstrated impressive intracranial responses, patients are not able to benefit from this treatment paradigm as molecular analysis of BM tissue is not usually feasible. To address this unmet need, we are developing multi-modal DL models, integrating cell-free DNA (cfDNA) genomic profiling and brain MRI, to develop rigorous and reproducible approaches that reflect the presence of targetable vulnerabilities within BM. Given wide availability of conventional MR and minimally invasive nature of cfDNA, our novel approach should be accessible to all patients and offers opportunities to non-invasively track BM evolution longitudinally.

Deep learning model monitoring in radiology
Deep learning models are poised to play an increasing role in clinical decision making within radiology. However, once a trained and validated model is deployed into the real world, the environment in which it operates is constantly evolving as imaging protocols, patient demographics, and disease prevalence shift. This project will explore methods to monitor automatically the model performance and develop and validate methods for determining when shifts in the input data may be giving rise to deterioration of model performance.

Generalizable deep learning models for radiological images
Radiological images, particularly volumetric modalities such as computed tomography (CT) and magnetic resonance imaging (MRI), contain huge amounts of information about patients and may be used to identify a large number of different abnormalities, and medical conditions and quantitative biomarkers. Despite this, most AI models operating on radiology images are narrow, and specialize on a single task. This project will explore how the use of unsupervised and self-supervised learning that exploit the specific characteristics of radiological images can learn representations that are re-usable for multiple tasks, thus increasing the efficiency of the process of creating deep learning models, and moving closer to more general artificial intelligence models within radiology.

Contact: jkalpathy-cramer [at] mgh.harvard.educpb28 [at] nmr.mgh.harvard.edu (Christopher Bridge) and akim46 [at] mgh.harvard.edu (Albert Kim)

Robert Langer - Langer Lab

tissue engineering, biomaterials, translational medicine

Currrent Projects:

Spinal Cord Injury 
We are looking for 1-2 graduate students to work on a new model of repair for spinal cord injury, involving novel surgical grafting methods, biomaterial scaffolds, and peripheral limb control using electrophysiological methods. 

Reach out if you are interested in joining an incredible team of scientists and engineers at the lab and working on this translational medicine project.

Contact: rlanger [at] mit.edu and shriyas [at] mit.edu

Laura Lewis

neuroimaging, sleep, neuroengineering, brain health

Projects focus on data analysis methods for imaging brain physiology in humans, and applying them to understand the fundamental role of sleep in brain health:
- Imaging fluid flow in the sleeping human brain: using advanced MRI techniques to study how cerebrospinal fluid flow washes through the brain during sleep;
- Technologies for fast imaging of neural activity in humans: working on fMRI analysis methods for imaging brain activity at fast timescales.

Contact: LLEWIS0 [at] mgh.harvard.edu

Tami Lieberman

human skin microbiome; microbial evolution

Currrent Projects:

Spatial stratification of the skin microbiome and its role in acne
The Lieberman Lab has recently reported that the each sebaceous follicle (pore) on human facial skin is dominated by just a single strain of C acnes -- despite coexistence of many C acnes strains on each person (Conwill et al 2022 Cell Host and Microbe). This paper studied only uninflamed follicles. This project is to explore if active acne lesions have different C acnes strains, other microbiome features, or inflammatory signals relative to other follicles on the same person, and will involve working with dermatologist John Barbieri at BWH to develop techniques for working with the low biomass in inflamed follicles (due to immune clearing) and may involve collaboration with the Shalek Lab for single-cell sequencing from low biomass samples.

The dynamics of bacteria and phage in the healthy facial skin microbiome
The Lieberman Lab has collected hundreds of swabs from the skin of healthy children at a K-8 school and their parents and has developed a library of over 7,000 bacterial isolates, including whole-genome sequencing, from these individuals. This project is to use this collection to study bacterial and phage dynamics, including the role of phage in interperson transmission in the skin microbiome. 

Mechanisms of probiotic-accelerated barrier repair
We have recently developed a model of skin epidermal barrier repair in mice, and identified that specific bacterial species accelerate barrier repair in this model. This project is to understand the microbial and host factors driving this response. 

We are also interested in designing a project around your interests in skin microbiomes, theoretical microbial evolution, and/or computational or experimental method development.

Contact:  tami [at] mit.edu

Bill Lotter - Lotter Lab

AI, medical imaging, computational neuroscience

Currrent Projects:

Predicting response to immune checkpoint inhibitors in lung cancer patients using imaging data
Immune checkpoint inhibitors (ICIs) are a promising form of anti-tumor therapy, yet its efficacy is highly variable across patients. This project aims to discover image-based biomarkers that are predictive of ICI response using rich, multimodal data. 

Psychophysics for improved human+AI medical image interpretation
AI-based products are increasingly being deployed to assist clinicians with medical image interpretation. While much focus is placed on AI standalone performance, the end clinical efficacy depends critically on the clinician + AI interaction. This project aims to discover guiding principles for this interaction using human psychophysics experiments. 

Modeling recovery from lesions in visual cortex using deep learning
Rehabilitation involving visual training can be used to help recover visual function in individuals with lesions in visual cortex, e.g. after a stroke. This project aims to model this recovery using deep neural networks. For instance, can we learn optimal rehabilitation stimuli or visual tasks using deep nets as a model system?"

Contact: lotterb [at] ds.dfci.harvard.edu

Michael Lu - MGH Cardiovascular Imaging Research Center

machine learning, deep learning, clinical trials, cardiovascular imaging

Currrent Projects:

Deep learning to estimate cardiovascular aging from chest CT
This funded project's goal is to develop a deep learning model to estimate “cardiovascular age” or the risk of a future heart attack or stroke based on a chest CT. Students will use large databases comprising over 30,000 individuals and will have access to in-house computational resources to develop and test models.
 
Clinical trial of deep learning to identify high-risk smokers for lung cancer screening CT
This funded clinical trial will test whether a deep learning tool (https://pubmed.ncbi.nlm.nih.gov/32866413/) that automates identification of high risk smokers based on their chest x-ray images can improve lung cancer screening participation. 

Deep learning to uncover the effect of somatic mutations on cardiovascular disease risk
The goal of this project is to identify somatic mutations and affected biological pathways that lead to cardiovascular disease risk using UK Biobank sequencing data from over 400k participants and new interpretable deep learning technology. Responsibilities will include developing the deep learning model, and studying the output to understand genes/biological functions captured by the model.
 
Use confounder-free pretraining approaches to develop deep learning models to estimate disease risk
We have previously developed deep learning models to estimate cardiovascular disease and lung cancer risk from chest x-rays. The purpose of this project is to develop models that are independent of established risk factors (age, sex, smoking, etc.) using a new technique called “confounder-free pretraining”. We will then test whether these models have greater added value to existing markers of disease risk.

Contact: mlu [at] mgh.harvard.edu and vraghu [at] mgh.harvard.edu (Vineet Raghu)

Ann Mullally - Mullally Lab

myeloid blood cancers, hematopoiesis, bone marrow niche

Multiple projects related to myeloid blood cancers

Contact: ann_mullally [at] dfci.harvard.edu 

Vitaly Napadow - Napadow Lab

pain, hyperscan, vagus nerve, psychedelics, meditation, acupuncture, MRI, EEG, brain

Currrent Projects:

Hyperscan functional MRI to assess the brain circuitry supporting therapeutic alliance
This project applies simultaneous, synchronized functional MRI and EEG of chronic pain patients and clinicians to better understand how the patient/clinician relationship influences clinical outcomes. Interventions to modulate this relationship, such as behavioral training and psychedelics (MDMA) are planned.

Respiratory-gated vagus nerve stimulation
We have a series of projects evaluating closed-loop approaches to neuromodulation, specifically vagus nerve stimulation. Applications include chronic pain, dyspepsia, depression, etc. Most projects involve neuroimaging.

Contact: vitaly [at] mgh.harvard.edu

Reza Nezafat - Cardiac MR Center at Beth Israel Deaconess Medical Center

medical imaging, cardiovascular disease, MRI, artificial intelligence

Our research focuses on the development and application of (a) cardiovascular magnetic resonance imaging (CMR) and (b) artificial intelligence (AI)-based solutions for improving imaging efficiency, quality, image analysis, interpretation, diagnosis, and prognosis of heart disease. My laboratory uses a multi-pronged approach based on engineering, physics, and cardiovascular medicine to fulfil our mission of advancing cardiovascular imaging through innovative research.

Currrent Projects:

Developing and applying AI-based CMR solutions and value-based AI in heart disease.

Novel exercise cardio-pulmonary MRI phenotyping of cardiovascular, pulmonary, and skeletal muscle physiology tissue composition, and energetics in heart failure.

Leveraging new and existing imaging and AI techniques to understand the mechanisms of ventricular arrhythmia and heart failure, 

Developing non-gadolinium MRI techniques to reduce cost and environmental pollution, and improve patient safety.

Contact: rnezafat [at] bidmc.harvard.edu

Sahar Nissim - Nissim Lab

pancreatic cancer, cell identity, single cell and spatial transcriptomics and epigenetics, cancer interception

Currrent Projects:

Epigenetic determinants of pancreas cell identity 
Normal cells undergo drastic changes as they progress to cancer. What are the epigenetic drivers of these changes, and can these be targeted for cancer interception? To answer these questions, students will help pioneer the wet bench and/or computational tools for spatial epigenetic analysis of the pancreas.

Reprogramming the immune microenvironment in pancreas cancer formation
Pancreatic cancer is notoriously refractory to immunotherapy due to active evasion mechanisms. In this project, students will study how perturbations that promote pancreatic cancer (including obesity, inflammation, and an oncogenic Kras mutation) reprogram the pancreas microenvironment to actively suppress immune surveillance.

CRISPR somatic genome engineering in mouse pancreas
Single cell transcriptomic and epigenomic studies identify candidate pathways that regulate biology and disease processes. However, tools to functionally validate these pathways in the pancreas are limited. In this project, students will help establish CRISPR-mediated genome engineering in the mouse pancreas that will be critical to validate strategies for pancreatic cancer interception and treatment.

Understanding metabolic dysfunction in the pancreas
The pancreas is composed of histologically distinct exocrine and endocrine compartments. Individuals diagnosed with pancreatic cancer often initially present with metabolic abnormalities including new-onset diabetes and severe weight loss, suggesting that an incipient cancer dysregulates adjacent endocrine pancreas cells. In this project, students will harness single cell and spatial transcriptomic tools to understand mechanisms of crosstalk between the exocrine and endocrine pancreas in the context of obesity, pancreatitis, and cancer.

Contact: snissim [at] bwh.harvard.edu

Roni Nowarski - Tissue Immunology Lab

immunology, mouse models, genetics, imaging

Projects usually involve studying how tissues adapt during local or systemic inflammation (e.g. colitis versus sepsis) with an emphasis on how the immune cells and stromal cells (e.g. fibroblasts) can control the outcomes. They usually involve studying the gut, the liver, and/or the central nervous system. Projects are open to whatever interests and questions trainees may have.   

Contact: rnowarski [at] bwh.harvard.edu and misraadi [at] mit.edu (Aditya Misra)

Ebru Oral - Harris Orthopaedic Laboratory

biomaterials, medical device, drug delivery, translation, AI, infectio

Currrent Projects:

Material development of drug delivery devices in ortho (polymeric particles and solid polymers)
Our goal is to develop polymeric delivery systems that are versatile in their delivery of single and dual drugs for prophylaxis and treatment of infection as well as pain management. Materials range from fast-acting liposomes to long-term delivery from device surfaces.

Characterization of periprosthetic infections in vitro and in preclinical functional (rat) models
Little is known about the initiation and propagation of infections in vivo. Our goal is to study standard and clinical bacterial populations (S aureus and S epi) in vivo and in vitro to develop appropriate testing models and treatments.

Diagnosis and prediction algorithms for infections
Our goal is to use ML and AI methods to expand our understanding of the factors affecting infection outcomes. Our approach is to use registry information (and genetic information) in addition to clinical imaging.

Characterization of immune response to bacteria and development of models and materials with immune activation
Our goal is to identify factors in the immune response for bacterial characterization and to use these factors for developing responsive materials for infection prophylaxis and treatment.

Contact: eoral [at] mgh.harvard.edu

David Page - Page Lab at Whitehead Institute

sex differences in health and disease, including heart disease, autism, autoimmune disease, and cancer

Currrent Projects:

Rethinking the genetics of the human X chromosome and its role in sex biases in diseases like autism and autoimmunity.  Could the X chromosome hold the key to the "female protective effect" in autism?

Computational and biochemical analyses of sex differences in metabolism in the heart, immune cells, adipose, and across the body.

Computational, genomic, and epigenomic studies of human preimplantation embryos, with the aim of understanding the beginnings of X chromosome inactivation and sex differences prior to the action of sex hormones.

Computational studies of the impact of gender-affirming therapies on gene expression in peripheral blood mononuclear cells.

Contact: dcpage [at] wi.mit.edu and page_admin [at] wi.mit.edu (Susan Tocio)

Peter Park

bioinformatics, computational genomics, cancer, neuroscience, DNA sequencing, single cell sequencing

Currrent Projects:

single cell DNA sequencing
methods development for identification of mutations from single cell whole-genome sequencing data and applications to neuroscience and cancer

mutational signatures
mathematical analysis of DNA mutations and applications to cancer therapy selection

repetitive sequences in the genome
methods development to identify variants in the repetitive regions using short and long-read sequencing and applications to cancer

Other projects available - see the lab home page

Contact: peter_park [at] hms.harvard.edu

Lonnie Petersen - Aerospace Physiology Lab

space physiology, countermeasure development, medical device technology

Currrent Projects:

GravitySuit
Wearable Lower Body Negative Pressure as integrative countermeasure for long duration spaceflight and extraterrestrial surface exploration. Project involves both hardware maturation towards flight certification (device development) and testing on healthy human subjects both in lab and ideally during weightlessness and partial gravity flights.

Random Positioning Device
Experimental platform to simulate weightlessness, partial Gravity, and hyper gravity on organoids, cells, plants, and material science. The project involves both computational modeling, hardware modification, and lab testing in preparation for space flight studies. 

Gravitational tolerance
Drone ambulances and other unmanned automated vehicles for human transportation is under development. While some information is available regarding G-tolerance in healthy humans, almost nothing is know about how much this tolerance is reduced in casualty. This DoD sponsored project will help define limits of exposure (G-stress, roll, pitch, yaw) particularly in trauma patients and will add critical information to the NATO safe-ride standarts.

Contact: lgpeters [at] mit.edu

John Pezaris - Visual Prosthesis Laboratory

artificial sight, lgn, lateral geniculate nucleus, visual prosthesis, phosphenes

Currrent Projects:

Optokinetic nystagmus in a simulation of artificial vision
Sighted human subjects are used to measure the learning rate of the OKN response in a simulation of artificial vision. Work in concert with PI and lab technician.  A sufficiently motivated student will be able to lead this project and work on additional, similar projects afterward. Students will be required to obtain clearance to work with human subjects.

Color / size / duration of phosphenes in a non-human primate model of artificial vision
Work in concert with PI, post-doc and lab technician.  Students would take one portion of the larger project. Students will be required to obtain clearance to work with non-human primate subjects.

Understanding the trajectory of fine-wire brush electrodes during implantation
Students will learn to construct fine-wire brush electrodes and photograph their penetration into agar in order to understand how to control electrode splay during insertion into brain tissue. These electrodes will be used in a visual prosthesis.

Contact: pezaris.john [at] mgh.harvard.edu

Jonathan Polimeni - High-Resolution and Ultra-High-Field Functional Imaging Laboratory

brain imaging, high-resolution functional MRI, ultra-high field MRI, human neuroscience, MRI technology development, laminar fMRI

Currrent Projects: 

In vivo neuroimaging of the human brain tissue’s mechanical response to neural activation.
This ongoing project, funded by two NIH grants, seeks to develop and apply new tools to measure the mechanical response of the cerebral cortex to focal neural activation elicited e.g. through visual stimulation. We are developing new approaches to measuring changes in tissue and surrounding cerebral spinal fluid expansion and compression driven by either neural or vascular responses. These measurements will help answer fundamental questions about brain physiology needed for understanding the brain’s glymphatic clearance system as well as advanced functional MRI methods.

Biophysical simulations of the functional MRI signal
In this project, funded by the BRAIN Initiative, we are evaluating our biophysical simulation framework based on realistic microvascular anatomy and dynamics derived from microscopic imaging of the cerebral cortex and its blood vessels. This powerful computational tool can be used to simulate the fMRI responses to many configurations of neuronal activity. There is an open position for a student interested to apply this framework to predict fMRI responses and compare them to measured experimental data.

Understanding the influence of vascular anatomy and physiology on the fMRI signal
All functional MRI signals are based on changes in blood flow, volume, and oxygenation, and so the anatomy of the blood vessels in the brain strongly influences the observed patterns of the functional MRI response. This project seeks to merge advanced vascular anatomical data and new methods for measuring vascular dynamics with high-resolution functional MRI data to better understand how the vasculature shapes the fMRI data. We will apply machine-learning tools to attempt to infer the underlying neuronal activity using the vascular data and the measured fMRI responses.

Accurate segmentation of the cerebral cortex
Accurate delineation of the cerebral cortex from structural MRI data is critical for detecting anatomical changes as well as for the interpretation of functional MRI data. However, adjacent tissue can often be mistaken for cortical gray matter. This project will utilize new multi-modal imaging approaches based on 7 Tesla MRI and advanced segmentation tools to improve the segmentation of the cortex for accurate cortical surface reconstruction.                        

Contact:  jonp [at] nmr.mgh.harvard.edu and egoldberg [at] mgh.harvard.edu

Julie Price - Pricelab

positron emission tomography (PET), pharmacokinetic modeling, Alzheimer's disease (AD), diabetes risk

Currrent Projects:

Multivariate statistical framework for biomarker mapping in Down Syndrome (DS)
We will apply a multivariate analysis framework that will better leverage the complexity and richness of multimodal neuroimaging datasets than commonly applied.  We expect this framework to provide improved spatiotemporal mapping of mis-folded protein accumulation and more sensitive detection of early and progressive AD-related pathophysiological brain changes in DS, relative to prior assessments.

Exploring mechanisms of altered peripheral glucose uptake in African American women
We use PET imaging and kinetic modeling methods to assess kinetic aspects of glucose metabolism in skeletal muscle and adipose tissue to better understand why young non-obese African American women may have a distinct form of lower insulin sensitivity, relative to young non-obese Caucasian women, that confers greater risk for the development of Type 2 diabetes.

Multimodal Dynamic Connectivity Analyses
This exploratory effort will focus on the application of analyses that incorporate kinetic information available from multimodal dynamic PET imaging and functional Magnetic Resonance imaging (MRI) data to provide more informed assessments of functional connectivity in neurodegeneration

Contact: jcprice [at] mgh.harvard.edu and FLFU [at] mgh.harvard.edu (Jessie Fu)

Maggie Qi

biomechanics, soft matter, biological microfluidics

Currrent Projects:

Retina-on-a-chip for ophthalmology research

Bomechanical modeling for drug carrier design

Contact: qmqi [at] mit.edu

Seward Rutkove - Rutkove Lab

neuromuscular, bioengineering, gravity, electrical impedance, nerve and muscle therapeutics

Contact: srutkove [at] bidmc.harvard.edu

Alex Shalek - Shalek Lab

single-cell technologies, technology development, cancer, TB, HIVE

We prefer students develop their own projects that speak to their passions with our help. Please contact to discuss more. 

Contact: shalek [at] mit.edu

Richard Sherwood

CRISPR, genome editing, cardiovascular disease, genetics, genomics

Our lab is a highly interdisciplinary and collaborative environment that combines CRISPR-Cas9 genome editing and genomic screening approaches with cutting edge machine learning and computational genetics approaches to understand how genomic variants contribute to complex human disease and to develop genetic treatments. We have a particular focus on using CRISPR screening and biobank genetics datasets to understand and develop treatments for cardiovascular disease risk factors.

Contact: rsherwood [at] bwh.harvard.edu

Jian Shu - ImageOmics Lab

imaging, single-cell omics, spatial omics, stem cell, reprogramming, aging, pregnancy

Decoding, modeling, and engineering multicellular systems
We are looking for multiple motivated research fellows at different levels (undergraduate, graduate, postdoc) who are interested in decoding and engineering complex multi-cellular systems at scale (e.g., seesaw modelWADDINGTON-OTRaman2RNA) by integrating advanced imaging, single-cell multi-omics, genome/cell engineering, and machine learning.

Contact: jian.shu [at] mgh.harvard.edu

Shriya Srinivasan - Biohybrid Organs and Neuroprosthetics (BIONICS) Lab

neuroprosthetics

Currrent Projects:

Physiology of Pain and Control Strategies
This project involves an electrophysiological study of pain signaling in the peripheral nervous system using computational methods. Then, it will involve engineering strategies to perform targeted neuromodulation. Optional development of implantable hardware. 

Surgical Reconstruction Strategies for Sensory Feedback
Using regenerative grafting methods in neuromuscular tissues, this project will develop grafts rich in mechanoreceptors of various kinds to restore sensory feedback, with a target of interfacing prostheses and/or exoskeletons.

EMG-controlled Exoskeletons
In the same way that power steering works, imagine developing controllers and hardware to control exoskeletons using EMG signals from peripheral limbs. This project will have applications in stroke and paralysis related dysfunction.

Contact: shriya_srinivasan [at] fas.harvard.edu

Chris Smillie - Smillie Lab

host-microbiome interactions, single-cell RNA-seq, spatial transcriptomics

We are a new research group at HMS, MGH, and the Broad Institute that uses genomics to study the human microbiome. We collaborate with other labs to generate large datasets and develop methods to analyze them. We are currently pursuing many exciting research directions, driven largely by trainee interests.

Currrent Projects:

Single-cell genomics of the human microbiome
We are working on the development of new methods for single-cell transcriptomics of bacteria. We are currently applying these methods to the human gut microbiome. There is a big opportunity to develop new computational methods for these datasets and to study bacterial heterogeneity on the human body.

Spatial transcriptomics of the human intestine
We are working on the application of spatial transcriptomics to intestinal tissue resections from IBD patients. We are developing new computational methods to discover interactions between cell types. Our goal is to understand what are the tissue drivers of inflammation.

Discovering host-microbiome interactions in large datasets
Gut bacteria have evolved exquisite mechanisms for manipulating our immune systems and the functioning of diverse cell types (e.g. epithelial cells and even neurons). We are interested in discovering the bacterial strains, genes, and molecules that drive these effects. We are developing unique ways of doing this that leverage large datasets.   

Contact: csmillie [at] mgh.harvard.edu or cssmillie [at] gmail.com

David Sontag - Clinical Machine Learning Group

machine learning, artificial intelligence, electronic medical records, disease progression, precision medicine, causal inference, human-AI interaction

Professor Sontag will be on sabbatical January-December 2023. As a result, we are unlikely to take new PhD students or MD-only students. However, MD-PhD students might be interested as the timing could work out well -- students could rotate this fall.

Contact: dsontag [at] csail.mit.edu

Kevin Staley - Pediatric Epilepsy Research Lab

epilepsy GABA chloride

Currrent Projects:

chloride microdomain plasticity
Do chloride currents at individual GABA synapses change direction as a consequence of long-term changes in displacement of chloride by anionic macromolecules?  Use 2 photon microscopy to image changes in chloride concentrations at labeled synapses over time under conditions that are likely to result in long term changes in the directions of GABA currents

Contact: staley.kevin [at] mgh.harvard.edu and staley.patricia [at] mgh.harvard.edu

Jason Stockmann

RF coils, B0 shimming, local field control, instrumentation for diffusion and functional MRI 

Currrent Projects:

Building AC/DC coils for local field control applications to arterial spin labeling to enable territory mapping of vessel-specific perfusion in the brain
Integrated coil array development for concurrent neuromodulation and MRI acquisitions (both diffusion and functional MRI). Develop single integrated coil array system that enables transcranial magnetic stimulation pulse delivery while doubling as a coil for MRI spatial encoding, diffusion encoding, and B0 shimming.

Contact: jstockmann [at] mgh.harvard.edu

Joelle Straehla - Straehla Lab

oncology, nanotechnology, drug delivery, pediatric brain tumor, tissue engineering, genetic screening

Currrent Projects:

Investigating the blood-tumor barrier in pediatric glioma
We are employing digital spatial profiling in patient tumor samples to determine structural and biological changes in tumor vasculature compared to normal brain tissue. Students would have the opportunity to analyze large datasets, perform correlative immunofluorescence, and assist in the design of functional assays based on this data.

Leveraging biologic regulators of nanoparticle delivery to cancer
We are performing pooled CRISPR screens to evaluate the impact of gene deactivation on nanoparticle delivery to cancer cells. Students would have the opportunity to assist with data analysis and design and conduct validation screens of single gene candidate biomarkers.

Contact: jstraehl [at] mit.edu

Steven Stufflebeam - Neuromind Lab

neuroimaging, machine learning, neuroscience, artificial intelligence, multimodal imaging, epilepsy, magnetoencephalography, MRI, PET

Currrent Projects:

Multimodal Imaging Machine Learning for Presurgical Mapping of Epilepsy Patients
Develop machine learning and AI techniques to help treat patients with epilepsy prior to surgery. Integrate magnetoencephalography, with invasive EEG, fMRI, PET, SPECT imaging to suggest best treatment and show similar patients for a data base. 

Contact: smstuff [at] mit.edu

Collin Stultz - Computational Cardiovascular Research Group

machine Learning for healthcare, cardiovascular risk stratification, explainable machine learning models

Contact: cmstultz [at] mit.edu and megumima [at] mit.edu (Megumi Masuda-Loos)

Atchar Sudhyadhom - Atchar Lab

medical physics, radiation therapy, MRI, proton therapy, radiation chemistry

Currrent Projects:

Real-time MRI of Radiation Chemistry in Patients
Atchar Lab is the first group to show images of radiation effects in real-time. The long-term goal of this project is to see the actual damage that radiation creates in patients using a clinical MR-linac machine. The research involves using an MR-linac, acquiring MR images, image reconstruction/post processing using conventional and machine learning methods.

MR Guided Proton Therapy
This project would involve developing methods to pinpoint proton therapy beam treatments in the body using only MRI scans of a patient. The research involves using a proton therapy machine, acquiring MR images, post processing using conventional and machine learning methods.

MR Functional Adaptive RT
The current state of the art in radiation therapy involves daily adaptation of patient treatments based off anatomic changes. The long-term goal is to acquire and adapt patient treatments to biological/functional changes rather than just anatomic information. This research involves developing in vivo clinically-feasible T1 mapping, hypoxia, and diffusion imaging and associated image processing methods by conventional and machine learning methods.

Contact: Atchar_Sudhyadhom [at] dfci.harvard.edu and nswarner [at] mit.edu (Noah Warner)

Gary Tearney - Tearney Lab

in vivo microscopy, medical devices, wireless medical technology, imaging capsules, catheters, endoscopes, coronary imaging, gastrointestinal imaging

Currrent Projects:

Wireless capsule endoscopes
Developing swallowable capsules that travel down the gastrointestinal tract, acquiring microscopic images of the mucosa for early cancer detection;

Multimodality intracoronary imaging
Developing microstructural and chemical/molecular (spectroscopy and fluorescence) coronary imaging systems and catheters;

Label-free metabolic microscopy
Developing and validating technologies for imaging metabolic activity in cells based on organelle transport;

Transnasal diagnostic toolset for evaluating the structure and function of the intestine
Developing and validating multiple medical devices for minimally invasive imaging, biopsy, and electrophysiological evaluation of the gut;

Transnasal echo-oximeter
Developing and validating a next generation transnasal photoacoustic catheter that evaluates central blood oxygenation and wedge pressure for critically ill patients.     

Contact: gtearney [at] mgh.harvard.edu and athrapp [at] mgh.harvard.edu

Zuzana Tothova - Tothova Lab

cancer, leukemia, epigenetics, cohesin, MDS 

Currrent Projects:

The role of splicing deregulation in cohesin-mutant myeloid malignancies
We have recently shown that cohesin-mutant cells are exquisitely sensitive to treatment with splicing modulators. The goal of this project is to characterize the effect of cohesin mutations in MDS and AML on RNA biogenesis and to characterize the nature of the interaction between the cohesin complex and SF3B complex. 

Elucidating mechanisms driving inflammation during cohesin-mutant MDS
pathogenesis

Description: We have recently published that cohesin and CTCF proteins are negative regulators of PDL1 expression, as well as Type 1 interferon and NFkappa B signaling.The goal of this project is to test the hypothesis that cohesin mutation-induced inflammation is necessary and sufficient for cohesin-mutant blood cancer development.

Therapeutic targeting of STAG1 in STAG2-mutant cancers
The goal of this project is to target STAG1, one of the strongest paralog dependencies in cancer. We are specifically focused on targeting loading of STAG1 vs STAG2-containing cohesin complexes on chromatin and development of a screening assay.

Contact: Zuzana_Tothova [at] dfci.harvard.edu

Srinivas Viswanathan - Viswanathan Lab

genomics, functional genetics, integrative genomics, cancer genetics

Our lab uses cutting-edge genomic and genome-scale functional genetic technologies to dissect the molecular underpinnings and vulnerabilities of cancer, with a focus on genitourinary cancers (particularly cancers of the prostate and kidney).  Several exciting projects involving integrative genomics are currently available. Most projects have opportunities for both wet-lab and dry-lab (bioinformatics) exposure.

Specific areas of research include the following: 
1. Use of functional genetic (CRISPR) screening and large-scale mass spectrometry to identify new drug targets in prostate cancer. 
2. Genome-scale genetic and drug-screening of kidney cancer cell line and organoid models in order to identify new therapies for various subtypes of cancer 
3. Molecular biology and biochemistry studies to understand the basic mechanisms by which oncogenes in prostate and kidney cancers drive these diseases, with a particular focus on core transcriptional programs driven by fusion oncogenes. 
4. Analysis of whole genome and transcriptome (bulk and single-cell) sequencing data from patients with prostate and kidney cancers.  

Contact: srinivas.viswanathan [at] dfci.harvard.edu

Loren Walensky - Walensky Laboratory of Cancer Chemical Biology

cancer, chemical biology, apoptosis, BCL-2 family, transcription, p53, chemoresistance, experimental therapeutics (anti-cancer, anti-bacterial, anti-viral), mechanistic dissection, multidisciplinary, clinical translation

The overarching goals of the Walensky Laboratory are to (1) operate at the interface of chemistry, biology, biotechnology, and translational medicine to drive fundamental basic science discovery, (2) provide a vibrant and multidisciplinary laboratory environment for postdoctoral and graduate training, and (3) maintain laser focus on harnessing the fresh scientific insights and trainee talent to advance new treatments for our patients.

Currrent Projects:

Dissecting and Targeting the BCL-2 Family Interaction Network in Health and Disease
Using novel chemical tools and multidisciplinary approaches (spanning protein biochemistry, mass spectrometry, structural biology, cell biology, and in vivo analyses), we interrogate the roles and mechanisms of BCL-2 family proteins in apoptosis regulation and noncanonical signaling pathways.

Development and Application of Stapled Peptide Technologies to Advance Prototype Therapeutics for Cancer and Infectious Diseases
By recapitulating the bioactive structure of peptide alpha-helices, we design, test, and translate next-generation therapeutics to target a host of pathologic protein interactions in cancer and infectious diseases, with an emphasis on overcoming drug resistance.

Contact: loren_walensky [at] dfci.harvard.edu and johna_olsoniii [at] dfci.harvard.edu (Jack Olson)

Hui Wang - Laboratory for Computational Neuroimaging (LCN)

neuroimaging, optics, microscopy, connectivity, vascular

Currrent Projects:

Volumetric optical microscopy to unravel cerebral microvascular architecture and the role in functional neuroimaging in human Alzheimer's disease
The goal of the project is to characterize the microvascular architecture changes in human brain samples of Alzheimer's disease (AD) using novel optical imaging techniques and leverage a computational vascular network model to predict the functional change of fMRI in AD patients.

Developing human cerebellum: from ex-vivo imaging to in-vivo prediction
This study aims to leverage an optical coherence tomography technique to establish high-resolution structure and connectivity maps of the developing cerebellum during the first-year of life and characterize normal neurodevelopment and developmental disorders under perinatal cerebellar injury in large cohorts. 

Ultra-resolution connectivity map in the human brain
The project aims to develop a novel polarization sensitive optical coherence tomography technique to reconstruct the 3D neural pathway at micrometer resolution in the human brain.

Contact: hwang47 [at] mgh.harvard.edu

Ona Wu - Clinical Computation Neuroimaging Lab

quantitative neuroimaging biomarkers, stroke, cardiac arrest, traumatic brain injury, consciousness, machine learning, diffusion tensor imaging, perfusion weighted imaging, functional MRI, MRI, CT

Currrent Projects:

Automated acute ischemic stroke infarct segmentation
Depending on student's experience, the student will refine/develop
machine learning algorithms to segment/predict acute stroke lesions using
multiple modalities on either CT or MRI. 

Automated white matter lesion segmentation
Depending on student's experience, the student will refine/develop
machine learning algorithms to segment white matter lesions on FLAIR
MRI. 

Contact: ona.wu [at] mgh.harvard.edu

Ramnik Xavier - Xavier Lab

autoimmunity, genotype-to-phenotype maps, gut microbiome function, deep mechanistic studies to chemical biology, mucosal immunity, tissue metabolism, spatial transcriptomics, development of new technologies to study the immune system, CRISPR base-editing

The Xavier Laboratory, spanning the Department of Molecular Biology at Massachusetts General Hospital and the Broad Institute, focuses on systematic characterization of genetic variants to understand the regulation of innate and adaptive immunity; initiation and resolution of tissue inflammation using single-cell and spatial transcriptomics; chemical biology to control and correct cellular disease phenotypes suggested by human genetics; molecular mechanisms that determine the roles of microbes in health and disease; and development of computational approaches to uncover patterns of human and microbial pathway activation during disease and treatment.    

Contact: xavier [at] molbio.mgh.harvard.edu 

Xin Yu - Translational NeuroImaging and Neural Control (TNNC)  Lab

coma, fMRI, spreading depression, vascular disease, conciousness, Alzheimer's Disease, Stroke, hydrocephalus, GCaMP, optogenetics

Currrent Projects:

Awake the unawakened
We are studying the neuronal basis of coma induction and emergence in animal models. We would like to investigate the neuronal network changes both in macro- and micro-level by conducting multi-modal imaging techniques including the recording of calcium signaling and high field MRI.

Simulate orthostatic stress
We will apply high-field functional MRI to investigate dynamic brain function in animal models, specifically using single-vessel fMRI. We are interested in understanding the brain functional changed due to orthostatic stress which can be linked to long-term space travel. 

Photons meet protons
We will combine fiber photometry-based brain dynamic signal recordings from genetically encoded sensors with advanced fMRI methods to study the neuro-glial-vascular coupling in normal and diseased animals (e.g. AD, hydrocephalus, Vascular cognitive impairment disease). 

Contact: xyu9 [at] mgh.harvard.edu

Xin Zhou

protein and cellular engineering

Currrent Projects:

Engineering artificial kinases for improving CAR-T therapy
Rewiring phospho-signaling pathways is a promising and unexplored direction for immunoengineering. In this project we are developing a modular method to engineer artificial, self-regualted kinases with redirected signaling specificity, based on a powerful technology called pY-TRAP. Successful implementation of the work will enable a new mechanism to improve CAR-T cells and generate a generic roadmap to developing tightly regulated enzymes for cell signaling engineering.

Devising novel BioPROTACs for manipulating extracellular receptors and cargos
Proteolysis-targeting chimeras (PROTACs) induce targeted protein degradation by recruiting a target protein to the ubiquitin-proteasome system; it has proven to be a powerful new therapeutic modality owning to advantages over traditional occupancy-based inhibitors with respect to modulating “undruggable” proteins. 
This project aims to engineer a novel modality of bioPROTACs based on tethering two cellular receptors. It will enable a modular approach to manipulate cell surface receptors and cargos for controlling immunotherapy and cancer signaling pathways. 

Constructing artificial proteases for targeted "proteome editing"
Our ability to characterize and control proteins lags far behind our ability to study the genome. We lack a protein-level counterpart to the gene-level CRISPR/Cas9 technology, and consequently, development of a modular molecular tool to conveniently manipulate endogenous proteins and their variants would be transformative. This project aims to engineer protein-directed, self-regulated proteases to enable the possibility of “proteome” and “proteoform” editing, and has broad implications across biology, biomedicine, and synthetic biology. 

Building conditional activated fluorescent proteins for biosensing
Developing biosensors for biological measurements is important at every scale of life, from molecules to cells to organisms. This project uses an innovative protein display strategy to develop next-generation fluorescent tools for biosensing. The engineered conditionally activated probes will be used to probe cell-cell interactions in the tumor microenvironment. 

Contact: xin_zhou1 [at] dfci.harvard.edu