Current Projects:
Multiple projects are available pertaining to the listed keywords. Students are also welcome to pitch and pursue their own projects, as relevant to the lab's core research areas. To learn more, please schedule a meeting with Prof. Majumder.
Contact: maimuna.majumder [at] childrens.harvard.edu (maimuna[dot]majumder[at]childrens[dot]harvard[dot]edu)
Location: Longwood
We seek to understand the quantitative control of gene expression in development and the resulting “tipping points” in disease, knowledge that can lead to more precise, controlled therapies for a range of rare and common disorders. Towards these goals, we combine functional genomics and computational modeling using stem cell-derived in vitro models of development. A common theme across projects is the use of highly quantitative tools and approaches.
Current Projects:
Building a quantitative toolkit for modulating gene dosage
Most of the observed variation in human traits and disease risk is driven by quantitative changes in gene expression levels (dosage), but we lack similarly quantitative tools to study the consequences of such changes experimentally. We have developed a chemical genetic approach to precisely modulate protein levels in human pluripotent stem cells and their derivatives. We are now working to expand the flexibility and scale of this toolkit.
Cell type specific effects of transcription factor dosage
Transcription factors (TFs), proteins that bind to noncoding regulatory DNA elements and modulate the production of RNA from target genes, are key drivers of developmental regulatory programs. We are studying the effects of quantitative changes in TF dosage in the human neural crest, a transient, embryonic cell population that gives rise to a fascinating array of cell types and tissues. These studies will provide insights into developmental disorders that affect organ systems as diverse as the face, as in craniofacial malformations, and the gut, such as the loss of enteric nervous system function in Hirschsprung’s disease.
Machine learning to decode gene regulatory mechanisms
Recent advances in machine learning and artificial intelligence have made substantial advances in learning the sequence features that predict cell type-specific chromatin and gene regulatory networks. However, the vast majority of these approaches rely on static, steady-state measurements. We are combining deep learning approaches with quantitative perturbations and functional genomic data to reveal hidden layers of the cis-regulatory code.
Contact: sahin.naqvi [at] childrens.harvard.edu (sahin[dot]naqvi[at]childrens[dot]harvard[dot]edu)
Location: Longwood
Pancreatic cancer has notoriously ineffective treatment options and will take the lives of over 50,000 individuals in the U.S. This year, we have a vibrant team applying innovative bench and computational approaches to pioneer a cancer “interception” strategy: a way to restrain or revert pancreas precursor lesions before they have a chance to become cancer.
Current projects:
Epigenetic determinants of pancreas cell identity
The identity of acinar cells, the cell of origin of pancreatic cancer, is maintained by a gene regulatory network of transcription factors and epigenetic states. How are these regulators of normal cell identity disrupted by stressors that promote cancer (inflammation, obesity, and an oncogenic Kras mutation) and can we target these regulators to stabilize normal cell identity for cancer interception? Interested students will have access to a complement of mouse models, human pancreas surgical specimens, new transcriptomic and epigenomic pipelines developed in the lab, and functional tools to help answer these questions.
“Amnesia” of prior inflammatory stress
Cells that recover from inflammation are thought to harbor epigenetic scars that distinguish them from naïve cells. These scars may explain why pancreatitis is a risk factor for pancreatic cancer. During pancreatitis, acinar cells transiently undergo metaplasia but then completely recover histologically. Though they appear normal, these recovered acinar cells have durable epigenetic marks of metaplasia that may facilitate cancer initiation. Interested students can (i) define these epigenetic marks and (ii) determine whether we can pharmacologically reverse these epigenetic marks to achieve “amnesia” of pancreatitis as an interception strategy for cancer.
Reprogramming the immune microenvironment in pancreas cancer formation
Pancreatic cancer is refractory to immunotherapy due to active evasion mechanisms. In this project, students will study how stressors 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 pancreas biology and disease. However, tools to functionally validate these pathways in the pancreas are limited. In this project, students will help establish in vivo 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 (snissim[at]bwh[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
Programming innate immune tolerance within tissues
Macrophages are key regulators of tissue inflammation. Our lab has recently identified a multicellular circuit that programs tolerogenic memory in intestinal macrophages (Mertens, Immunity 2024). We are interested in better understanding the epigenetic and metabolic basis of innate immune memory with the goal of programming tissue circuits that elicit durable immune tolerance.
Contact: rnowarski [at] bwh.harvard.edu (rnowarski[at]bwh[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
Immune suppression in metastatic cancer
Utilizing the power of lymph nodes to generate long-lasting, systemic anti-cancer immune responses has the potential to eradicate metastatic cancers from patients as seen with the recent success of immunotherapy in a subset of patients. The presence of lymph node metastases, however, brings with it a worse prognosis and the suppression of anti-cancer immune responses. Our laboratory has shown that—beyond being a biomarker of the aggressiveness of the cancer—lymph node metastases play previously unrecognized roles in cancer progression, including by escaping the lymph node and seeding distant metastases as well as leading to the suppression of anti-cancer immune responses. We have ongoing projects to define mechanisms of the suppression of anti-cancer immune responses in metastatic cancer in order to design therapies to overcome this suppression.
Developing the first drugs that target lymphatic vessel function
Lymphatic diseases affect millions of Americans and hinder hundreds of millions worldwide. It is estimated that up to 7 million Americans and 100 million worldwide have incurable, debilitating and progressive lymphedema. Currently, there are few treatments and no cures. The lymphatic system has been shown to play a role in or be affected by a variety of disease processes, including bacterial and filarial infections, inflammatory bowel disease, metabolic syndrome, congestive heart failure, hypertension, diabetes and neurodegenerative diseases, including Alzheimer’s disease. Further, the recovery from many pathologies, including myocardial infarction, also requires the lymphatic system. Altering the growth and function of the lymphatic system in these diseases and repair processes is a promising therapeutic strategy. However, there are no FDA-approved drugs indicated to enhance lymphatic function. In short, the field of Lymphatic Medicine is in its infancy. We have ongoing projects to identify and validate molecular targets on the lymphatic system, specifically on lymphatic muscle cells, as a first step in developing the first drugs to improve lymphatic function.
Contact: tpadera [at] mgh.harvard.edu (tpadera[at]mgh[dot]harvard[dot]edu)
Location: MGH Main Campus
Current Projects:
The so-called inactive X chromosome (Xi), which is present only in females, and the Y, which is present only in males, encode master regulators of transcription at multiple levels. How do Xi and Y influence molecular sex differences at the levels of DNA methylation, chromatin modification, chromatin accessibility, proteomics, and metabolomics? Our recent studies have led us to rethink the genetics of the human X chromosome and its role in sex biases in disease. Could Xi hold the key to the "female protective effect" in autism and beyond? We have found that expression from Xi is conserved across human cell types. How conserved is Xi expression across species and across the entire body?
Contact: dcpage [at] wi.mit.edu (dcpage[at]wi[dot]mit[dot]edu) | Susan Tocio, page_admin [at] wi.mit.edu (page_admin[at]wi[dot]mit[dot]edu)
Location: Whitehead Institute
How do mutations arise in the first place in your body? How can we detect rare mutations in genome sequencing data? Can we sequence the whole genome of a single cell? Can we use somatic mutations as barcodes to understand developmental trajectories? We develop and apply computational methods to analyze DNA sequencing data, working with the latest technologies and top biology labs. Our applications are in cancer and brain-related diseases. We are also part of a new $140 million NIH consortium that is generating an enormous amount of genomic data from human tissues to profile the landscape of somatic mutations in non-cancer cells.
Current Projects:
Algorithms for detection of low-frequency mutation, including structural alteration
Analysis of RNA splicing using long-read data
Assembly of individual-specific genomes
Matrix decomposition-based methods for mutational signatures
Analysis of circulating tumor DNA for cancer detection/monitoring
Large-scale collaborative projects analyzing thousands of genomes
Contact: peter_park [at] hms.harvard.edu (peter_park[at]hms[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
The student will be contributing to our work on the mechanism of action of anti-Müllerian Hormone in the ovary for applications such as contraception, treatment of infertility, and prevention of ovarian aging. They will be studying the basic biology of the ovary, and particularly how AMH affects both follicular growth and suppresses ovarian development using in vitro and in vivo models. Students may be involved in scRNAseq data analysis, use of tissue and organ culture models, and ovarian histology including analysis of human specimens.
Contact: dpepin [at] mgh.harvard.edu (dpepin[at]mgh[dot]harvard[dot]edu)
Location: MGH Main Campus
Current Projects:
Space flight
Countermeasure development to maintain human health and performance during deep space and extraterrestrial exploration: exercise and simulated gravitational stress are key components in maintaining human performance but currently insufficient on their own during stay in extended weightlessness and unproven during extended stay in partial gravity. Project aims include technology and hardware development as well as human testing to mature interventions for implementation in space missions.
Drone Ambulances (MEDEVAC and CASEVAC)
It is known that casualty (hemorrhage, poly-trauma, etc.) have reduced tolerance for acceleration and vibration during aerial evacuation, we accommodate by modifying flight trajectory and providing in-flight care. Safe development of drone ambulances involves automation of patient triage, monitoring, and decision for evacuation strategy. This line of research involves sensor development, algorithms to predict physiological outcome (cardiovascular compensatory / reserve capacity prediction), field care, triage, and enroute care.
Contact: lgpeters [at] mit.edu (lgpeters[at]mit[dot]edu)
Location: MIT
Current Projects:
Color / size / duration of phosphenes in a non-human primate model of artificial vision
Work in concert with PI, post-doc and lab technician to measure characteristics of the visual effects from microstimulation of the LGN in an awake behaving primate model. 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.
Perception in Artificial Vision
Sighted human subjects are used to measure different aspects of visual perception under a simulation of artificial vision. A sufficiently motivated student will be able to lead this project. Students will be required to obtain clearance to work with human subjects.
Spatial transcriptomics of human LGN
A primarily computational project to use R and python to analyze results from spatial transcriptomic data of human LGN. Students would work in close cooperation with a post-doc in the lab.
Contact: pezaris.john [at] mgh.harvard.edu (pezaris[dot]john[at]mgh[dot]harvard[dot]edu)
Location: MGH Main Campus
Current Projects:
Time-resolved cryo-vitrification
Cell signaling requires coordinated nanoscale protein and membrane motions triggered by an extracellular stimulus. However, current imaging methods lack the temporal resolution to observe these dynamics. To this end, we are developing tools to cryo-vitrify biological samples at ultrafast time delays post-stimulation for subsequent super-resolution optical and electron microscopy. With these methods, we will determine the nanoscale molecular interactions governing liquid-liquid phase separation and GPCR signaling over time.
Multicolor electron microscopy
We are developing multicolor electron microscopy to achieve combined molecular and ultrastructural readout in a single sample with nanoscale spatial resolution. In this technique, biomolecules are labeled with special probes – cathodophores – whose optical emission is induced directly by the electron beam via a process termed cathodoluminescence. This approach will juxtapose membrane ultrastructure with locations of biomolecules, elucidating how their colocalization informs function in cells and tissues.
Contact: maxim_prigozhin [at] harvard.edu (maxim_prigozhin[at]harvard[dot]edu)
Location: Cambridge
Current Projects:
Understanding glaucoma immunopathology using microfluidics
We have developed a prototype immunocompetent microfluidic model to examine the crosstalk between circulating leukocytes and retinal microglia under the influence of various molecules involved in glaucoma disease progression. The student will continue develop this model in collaboration with Massachusetts Eye and Ear and examine neuroinflammation to fight blindness.
Building a whole retina using microfluidics
We hope to completely transform the stem cell derived retinal organoid culture to a microfluidics-based method to create stratified layers of neurons mimicking the retina for disease modeling and drug screening.
Biomechanical modeling of nanoparticle-leukocyte interactions
We hope to model the entire process of leukocyte extravasation computationally and account for the effect of nanoparticles to benefit the design of cell therapies.
Contact: qmqi [at] mit.edu (qmqi[at]mit[dot]edu) | agoertz [at] mit.edu (agoertz[at]mit[dot]edu)
Location: MIT
Current Projects:
Chemical Biology in the Extracellular Matrix
Collagen is the most abundant protein in humans and the major component of the extracellular matrix. We revealed the fundamental bases for the conformational stability of the collagen triple helix. Using that knowledge, we developed a collagen-mimetic peptide that anneals tightly and specifically to damaged collagen in fibrotic tissue and the tumor microenvironment. Now, we seek to exploit our peptidic “pylon” to anchor PET/MR probes and target drug delivery in preclinical contexts.
Ribonucleases as Cancer Chemotherapeutic Agents
By catalyzing the degradation of RNA, ribonucleases act at the crossroads of transcription and translation. We enabled a human secretory ribonuclease to evade an endogenous inhibitor protein and thereby endowed the enzyme with toxicity for cancer cells. That ribonuclease has been used to treat solid tumors in the clinic. We now seek to enhance its biomedical attributes and further elaborate on the biological roles of secretory ribonucleases.
Delivery of Therapeutic Proteins into Human Cells
Biologic drugs are restricted to extracellular targets, leaving ¾ of disease-relevant proteins undruggable. To overcome this limitation, we developed cationic and boronic acid-based pendants to transport proteins into cells. Recently, we discovered how to “mask” protein carboxyl groups by esterification with tuned diazo compounds. These protein “prodrugs” can enter the cytosol, where human esterases hydrolyze the nascent esters. We are especially interested in delivering PTEN, which is a phosphatase that is often deficient in human cancer cells.
Contact: rtraines [at] mit.edu (rtraines[at]mit[dot]edu)
Location: MIT
The Rajpurkar Lab at Harvard Medical School pioneers advanced artificial intelligence systems for medical applications, with a focus on developing generalist AI models capable of reasoning across multiple medical tasks and modalities. Our research spans medical image analysis, natural language processing of clinical text, multimodal learning, and human-AI collaboration in healthcare. Key areas include AI-assisted radiology reporting, self-supervised learning for disease detection, and the development of "AI copilots" to augment clinical workflows. We aim to create AI systems that can enhance medical decision-making, improve diagnostic accuracy, and ultimately advance patient care through the responsible integration of AI in clinical practice.
Current Projects:
Generalist Medical AI for Diagnosis Develop an AI system capable of reasoning through various medical tasks across multiple data modalities (images, text, sensors) to provide comprehensive medical analysis and diagnosis.
Self-Supervised Learning for Disease Detection Advance self-supervised and pre-trained adaptable models for detecting diseases in medical imaging (e.g. chest X-rays, CT scans) and other data types like ECGs without requiring explicit labels.
AI Copilot for Radiology Report Generation Create an AI system to generate initial drafts of radiology reports for clinician review and refinement. Investigate the technical, behavioral, and ethical implications of AI assistance in clinical workflows.
Contact: pranav_rajpurkar [at] hms.harvard.edu (pranav_rajpurkar[at]hms[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
Machine learning diagnosis of Alzheimer's disease from brain imaging data
This project aims to develop novel procedures for the identification of Alzheimer's disease brain pathology early in the course of the disease.
Examination of factors related to cognitive resilience in late age using brain imaging
This project aims to determine neural factors that contribute to optimal cognitive function in older adults very late in life when most of their peers exhibit a decline in function.
Examination of mechanisms of brain aging and neurodegeneration
This project aims to determine genetic, lifestyle, and medical factors that contribute to healthy and degenerative brain aging.
Contact: dsalat [at] mgh.harvard.edu (dsalat[at]mgh[dot]harvard[dot]edu)
Location: Charlestown
Current Projects:
Importance of chromatin compaction for DNA repair kinetics
TOPAS-nBio is a Monte Carlo code to describe the nanometer scale response of cells to radiation developed in our lab. TOPAS-nBio can generate models of DNA with varying compaction based on Hi-C data. The code further includes a model of DNA repair kinetics. The projects aim to study how DNA compaction impacts the DNA damage induction from radiation and the speed and performance of DNA repair
Agent-based model for oxygen depletion in FLASH radiation therapy
Ultra high dose rate (FLASH) irradiations have been shown to reduce the damage to some healthy tissues. One hypothesis is that the oxygen tension within the tissue varies greatly with distance from vasculature, creating niches of low oxygen concentration which can become temporarily hyoxic when irradiated and thereby reducing DNA damage. Student projects will develop an agent based model to capture the oxygen tension across different tissues and test the hypothesis that low-oxygen niches cause the FLASH tissue sparing
Modeling nanoparticles as radiation enhancers
Metallic nanoparticles (MNPs) can greatly enhance the efficacy of radiation therapy. Several models have tried to explain the effect with a highly localized increase in radiation released around the MNPs. This assumption, however, breaks down with the latest generation of MNPs due to their localization, released energy, or low concentration. Projects include TOPAS-nBio Monte Carlo simulations and design of nanoparticle uptake and effects at the cell scale.
Contact: jschuemann [at] mgh.harvard.edu (jschuemann[at]mgh[dot]harvard[dot]edu)
Location: 125 Nashua St
We combine statistical genetics, computational and functional genomics, and system biology approaches to understand the causal regulatory mechanisms, genes, pathways, and cell types that affect complex retina-related diseases, including primary open angle glaucoma (POAG) and age-related macular degeneration (AMD), and drug response, and to detect disease subtypes in large biobanks. We collaborate with clinician scientists in the Ocular Genomics Institute at Mass Eye and Ear (https://oculargenomics.meei.harvard.edu/).
Current Projects:
Integrating functional and single cell genomics data with genetic association studies to uncover causal mechanisms of complex retinal diseases
We are creating comprehensive transcriptome, genetic regulation (expression and splicing QTLs), and chromatin accessibility (ATAC-seq) maps for healthy human eye tissues, including retina, optic nerve head, and the anterior segment at tissue and single cell levels. Project opportunities entail contributing to this effort and integrating these functional genomic data with genome-wide association study (GWAS) loci for glaucoma to identify underlying causal regulatory mechanisms and genes in relevant eye tissues. We are also working on computational methods to detect genetic regulation at the single cell level in eye tissues using single cell RNA-seq and whole genome sequencing data to gain insight into glaucoma mechanisms at the cellular resolution and identify genetic modifiers.
Pharmacogenetic studies of drug treatment response and drug repurposing opportunities
We are working on several pharmacogenomic projects aimed at identifying common and rare variants associated with response to drug treatments in diabetic retinopathy patients and retina vein occlusion patients. Project opportunities include analyzing whole exome sequencing data to detect rare deleterious mutations associated with drug response, and follow-up integrative functional genomic analyses of rare and common variant association discoveries. We are also working on methods that integrate transcriptomics and genomics data to identify FDA-approved drugs that can be repurposed for common retinal diseases and predict adverse effects.
Contact: ayellet_segre [at] meei.harvard.edu (ayellet_segre[at]meei[dot]harvard[dot]edu)
Location: Mass Eye & Ear, Boston
We combine genomics, chemical biology, and nanotechnology to construct accessible and widely useful cross-disciplinary platforms that enable us and others to profile and control cells and their interactions. Working with partners around the world, they apply these technologies to dissect human diseases toward understanding links between cellular responses and clinical observations to guide preventions and cures.
Current Projects:
The Shalek lab collaboratively develops broadly enabling technologies and applies them to characterize, model, and rationally control complex multicellular systems. Current studies seek to methodically dissect connections between human tissue responses and disease, including how: immune cells coordinate balanced responses to environmental changes with tissue-resident cells; host cell-pathogen interactions evolve across time and tissues during pathogenic infection; and tumor cells evade homeostatic immune activity. From these observations and those of others, we aim to construct a unified understanding of how disease alters tissue function at the cellular level and realize therapeutic and prophylactic interventions to reestablish or maintain human health.
Contact: shalek [at] mit.edu (shalek[at]mit[dot]edu)
Location: MIT
Current Projects:
Our lab is a highly interdisciplinary and collaborative environment that combines precise 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 base editing and prime editing screening and biobank genetics datasets to understand and develop treatments for cardiovascular disease risk factors.
Contact: rsherwood [at] bwh.harvard.edu (rsherwood[at]bwh[dot]harvard[dot]edu)
Location: Longwood (New Research Building)
Professor Jian Shu's lab at MGH and Harvard aims to develop a 'Google Translate' for multi-modal data translation and prediction in biomedicine. The Shu lab is pushing boundaries by building generative AI models for digital twins of biology, expanding beyond molecular structure prediction into single-cell genomics and other modalities and scales—from the cellular level to tissues and whole organisms across species and disease settings.
Current Projects:
Image2Omics
Generating multi-omics and sequencing data from low-cost imaging (similar to Google's AlphaFold but for single-cell genomics)
Omics2Images
Generating cellular and tissue images from omics and sequencing data (akin to OpenAI's DALL-E)
AI-Generated Cells
De novo design of gene circuits and cells through massively scalable perturbations combined with genotype-phenotype mapping
Diseases
Applying our platform technologies to study various diseases, such as pregnancy complications, women’s health, aging, brain health, and infectious diseases
Non-Model Organisms
Studying organisms with exceptional features and functions to advance human health.
Contact: jian.shu [at] mgh.harvard.edu (jian[dot]shu[at]mgh[dot]harvard[dot]edu)
Location: MGH
The Smillie lab is a creative, fun, and rigorous group of scientists located at MGH (Boston). We develop innovative computational methods to understand host-microbiome interactions. This work integrates diverse fields, such as the gut microbiome, tissue biology, immunology, genetics, and evolution - all unified by the underlying biology. Our recent work is described below. Graduate students are also encouraged to develop new research projects based on their own interests (with guidance from Chris Smillie).
Current Projects:
Microbiome evolution during disease
We performed the first large-scale analysis of microbiome evolution during inflammatory disease. Surprisingly, we discovered many bacterial lineages that are adapted to inflammatory disease. We are studying the functions of these lineages in IBD and other diseases, such as cancer and Parkinson's disease
Microbiome GWAS
We performed one of the first microbiome GWAS of disease, revealing SNPs within the gut microbiota that are strongly associated with intestinal inflammation. We are interested in using large language models (LLMs) to interpret these SNPs, with the goal of understanding how they impact protein functions
Single-cell RNA-seq of bacteria
We are collaborating with the Hung lab to develop a highly scalable method for single-cell RNA-seq profiling of bacteria, which we are now applying to infectious disease and the gut microbiome. We are developing new computational methods to interpret transcriptional variation within the gut microbiota
Spatial transcriptomics
We have used spatial transcriptomics to study the intestine during fibrosis and inflammatory disease. This research has led to new insights into the genetic causes of disease, as well as the cell-cell interactions that support inflammation. We are interested in developing new methods to extend this work to other diseases
Deep learning and large language models
We are interested in using machine learning to explore and demystify the microbiome. The gut microbiome contains hundreds of species, which collectively encode hundreds of millions of unique proteins. Most are unannotated. We are applying recent advances in deep learning to these large metagenomics datasets.
If any of these research directions sound exciting to you, please do not hesitate to reach out.
Contact: csmillie [at] mgh.harvard.edu (csmillie[at]mgh[dot]harvard[dot]edu) | Thomas Cheng (HST student), nhcheng [at] mit.edu (nhcheng[at]mit[dot]edu)
Location: MGH Main Campus
Current Projects:
The mission of the Center for Space Medicine Research is to advance human health in space and on Earth. Our research aims to address the challenges of long-duration human spaceflight, and to pioneer biomedical discoveries using the microgravity environment that can lead to new therapeutic innovations for patients on Earth. Our group has several opportunities for students to engage with on-going research projects in collaboration with NASA and commercial spaceflight companies.
Contact: astankovic1 [at] mgh.harvard.edu (astankovic1[at]mgh[dot]harvard[dot]edu)
Location: MGH / HMS
Current Projects:
Integrated, dynamic B0 and flip-angle shimming using multi-coil arrays
Address two longstanding obstacles to widespread use of 7 Tesla MRI for routine clinical exams: static (B0) and radiofrequency (RF) field inhomogeneity inside the body. Design RF coil and B0 shim hardware and associated electronics. Compute "Universal" RF pulses to achieve uniform image contrast over the whole head and neck.
Hybrid TMS/MRI system for regionally tailored causal mapping of human cortical circuits and connectivity
Develop coil hardware, high power electronics, and image acquisition approaches for interleaved transcranial magnetic stimulation (TMS) and MRI image encoding using the same array of inductive coils placed close to the head.
Contact: jstockmann [at] mgh.harvard.edu (jstockmann[at]mgh[dot]harvard[dot]edu)
Location: Charlestown
Current Projects:
Nanosensor-Enabled 3D Chemical Imaging for the Diagnosis and Treatment of Bladder Cancer
This project aims to develop a sensor platform for the detection of bladder cancer via cell and tissue biomarkers. The student will be engineering nanoparticles using corona phase molecular recognition to enable selective binding of target molecules and subsequently changes in nanoparticle fluorescence. Promising sensors, in terms of selectivity and sensitivity, will then be grafted onto medical catheters to enable spatial 3D cancer biomarker maps in vivo with applications for disease diagnosis and surgical planning. This work will be performed in collaboration with physician mentors from the Brigham and Women's Hospital at Harvard Medical School.
Nanosensor Chemical Cytometry for the Measurement of Cellular Chemical Signals in Therapeutics Manufacturing
Our project develops a detection platform to the study intracellular and pericellular biochemical signals from living cells and microbes in a rapid, non-destructive and label-free manner. We have recently developed Nanosensor Chemical Cytometry (NCC) that is able to measure single cell biochemical signals, allowing for the study of cellular heterogeneity within a population. This powerful approach allows for the measurement of distribution cellular states that would be lost in conventional methods. One potential application would be to monitor the quality of cellular therapeutics in the process of production, allowing for dynamic optimization in the manufacturing environment. This thesis will involve sensor engineering, platform development, cellular studies and computational analysis.
Contact: strano [at] mit.edu (strano[at]mit[dot]edu) | srgoffice [at] mit.edu (srgoffice[at]mit[dot]edu)
Location: MIT
Current Projects:
Development and Clinical Translation of Dynamic µOCT (DµOCT)
DµOCT is a new form of non-contact, label free microscopy that provides detailed information on subcellular microstructure and metabolic activity. Projects are available developing novel ways to conduct DµOCT in living patients for a variety of applications in otolaryngology, cardiovascular disease, dermatology, and early cancer detection.
Development and Clinical Translation of Wireless Capsule Endomicroscopy
Projects are available developing wireless, swallowable microscopic imaging technologies. Once swallowed, these devices conduct gastrointestinal tract microscopy and utilize this information to guide and implement local therapy. Applications include early cancer interception and inflammatory bowel disease diagnosis and treatment.
Development and Clinical Translation of Multimodality Optical Imaging Devices
Projects are available developing catheters, tethered capsules, and microendoscopes that combine structural microscopic imaging using optical coherence tomography (OCT) and molecular imaging through fluorescence, reflectance, and/or Raman spectroscopy. Applications include intravascular imaging for the diagnosis of vulnerable coronary plaque and early detection of high-risk Barrett's esophagus progressors.
Contact: gtearney [at] mgb.org (gtearney[at]mgb[dot]org) | Sarita Mukhiya, SMUKHIYA [at] mgh.harvard.edu (SMUKHIYA[at]mgh[dot]harvard[dot]edu)
Location: MGH Main Campus
Current Projects:
Spatial transcriptomics analysis of cancer
Understanding the role of repeat elements in cancer cell plasticity
Developing novel antibody therapeutics targeting metastasis
Contact: dting1 [at] mgh.harvard.edu (dting1[at]mgh[dot]harvard[dot]edu)
Location: MGH Main Campus / Charlestown
We are a basic and translational research laboratory that focuses on mechanisms of blood cancer development and translate our findings to new therapeutic options for patients. We use a variety of models including cell lines, CRISPR engineered mouse transplant models, transgenic and patient derived xenograft models and primary patient samples. We have been interested in dissecting and targeting mechanisms by which chromatin complexes, including the cohesin complex, mediate clonal dominance and disease progression in clonal hematopoiesis of indeterminate potential (CHIP), myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML).
Current Projects:
Role of transposable elements during the development of blood cancers and adaptation to inflammation and source of neoantigens.
R loops as regulators of clonal dominance in cohesin-mutant MDS.
Dissecting the mechanisms of dependency of Cohesin-mutant myeloid malignancies on splicing.
Contact: zuzana_tothova [at] dfci.harvard.edu (zuzana_tothova[at]dfci[dot]harvard[dot]edu)
Location: Longwood / Dana-Farber Cancer Institute
Current Projects:
Oral Delivery of Biologics
Devices for oral macromolecule delivery, tissue models of the gastrointestinal tract.
Ingestible Electroceuticals
Device development for stimulation for hormone modulation and diagnostic interventions.
Implants for long-acting drug release
Development of new materials, devices and drug releasing systems capable of supporting 1-10year drug release.
Materials Science
Materials synthesis and evaluation for injectable and ingestible applications.
Contact: cgt20 [at] mit.edu (cgt20[at]mit[dot]edu) | Katherine Eisenbach, keisenba [at] mit.edu (keisenba[at]mit[dot]edu)
Location: MIT
Additional keywords: Medical devices, Clinical translation, deep learning
Our lab, at the Center for Biomedical OCT Research, is focused on optical coherence tomography (OCT) and other optical imaging technologies. Our projects rely on rigorous physics and mathematical concepts, while also involving hands-on hardware development and direct clinical translation. Many of our students, from departments like MIT HST, MIT EECS and Harvard SEAS, have the opportunity to develop new imaging techniques, then build up imaging hardware, and finally translate it in real life either to the clinic via MGH or through live animal studies. Optical imaging techniques are pervasive in biomedical engineering, from basic science to directly diagnosing and treating patients. While our lab is primarily focused on OCT, the knowledge and skills you will gain are highly translatable to all areas of imaging, even non-optical methods such as ultrasound and MRI.
Current Projects:
Optical intravascular elastography for assessment of atherosclerotic plaque biomechanics
The most prevalent type of heart disease is caused by atherosclerosis, the thickening of the vessel wall and creation of atherosclerotic plaque. Biomechanical characterization of plaques can enable the stratification of these lesions and the development of clinical studies to determine optimal treatment strategies. In this project, we will develop hardware and signal processing–including Fourier-domain analysis and programming in MATLAB, catheter engineering and custom laser development–for all-optical intravascular elastography using phase-sensitive ultra-fast optical coherence tomography
High-resolution, adaptive-optics retinal imaging for early detection of Alzheimer’s diseases
We are developing new capabilities in state-of-the-art functional imaging with adaptive-optics optical coherence tomography to determine if subtle structural and metabolic changes occur in early Alzheimer’s disease. If successful, these capabilities could be translated to conventional imaging systems in the clinic to enable population-level screening for Alzheimer’s disease. This project includes development of signal and image processing methods–including deep learning, working with unique and powerful custom imaging hardware, and imaging in animal models and human subjects
Tracking degeneration of individual neurons in the living retina with computational adaptive optics
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–combining conventional signal processing and deep learning–that could enable high-resolution imaging in patients with minimal modifications to existing imaging systems in the clinic
Physics-informed functional imaging to enhance label-free contrast in optical coherence tomography
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. These developments will be implemented in MATLAB, used in intravascular imaging and ophthalmic applications, and tested in animal models and patients.
Contact: uribepnr [at] mit.edu (uribepnr[at]mit[dot]edu)
Location: MGH Main Campus and MGB Assembly Row Campus (Somerville)
Current Projects:
Photoacoustic Imaging for Clinical Translation
This project focuses on developing and translating advanced photoacoustic imaging (PAI) systems for clinical use. By designing novel laser sources integrated with ultrasound, we will create compact, cost-effective PAI systems. The aim is to improve diagnostics and treatment monitoring for conditions such as cancer, lymphatic, and cardiovascular diseases. Graduate students will contribute to laser system development, integration, and clinical trials, gaining hands-on experience in cutting-edge biomedical imaging technology
Optical Coherence Tomography and Spectroscopic Methods for AMD Diagnosis and Treatment
Age-related macular degeneration (AMD) is a leading cause of vision loss, but current diagnostics are limited in predicting disease progression. This project integrates optical coherence tomography (OCT) with spectroscopic methods to create a non-invasive platform for analyzing diseased retinas. The goal is to improve early AMD diagnosis and identify new therapeutic strategies. Graduate students will work at the intersection of optical imaging and biomedicine, contributing to innovative approaches in vision science.
Contact: dveysset [at] mgh.harvard.edu (dveysset[at]mgh[dot]harvard[dot]edu)
Location: MGH
Several exciting projects involving integrative genomics are currently available. Both wet-lab and dry-lab (for students with strong background in R/python) opportunities are available.
Current Projects:
Use of functional genetics (CRISPR screening) to identify new drug targets in prostate and kidney cancer
Genomic and transcriptomic studies of prostate and kidney cancers
Molecular biology studies to understand the mechanisms of oncogenesis and vulnerabilities in genitourinary cancers
Genomic and molecular biology studies to understand sex differences in cancer
Contact: srinivas.viswanathan [at] dfci.harvard.edu (srinivas[dot]viswanathan[at]dfci[dot]harvard[dot]edu) | Janet Quinn, janet_quinn [at] dfci.harvard.edu (janet_quinn[at]dfci[dot]harvard[dot]edu)
Location: Longwood
The overarching goals of the Walensky Laboratory are to: operate at the interface of chemistry, biology, biotechnology, and translational medicine to drive fundamental basic science discovery; provide a vibrant and multidisciplinary laboratory environment for postdoctoral and graduate training; and maintain laser focus on harnessing the fresh scientific insights and trainee talent to advance new treatments for our patients.
Current 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 (loren_walensky[at]dfci[dot]harvard[dot]edu) | Clara Brotzen-Smith, clara_brotzen-smith [at] dfci.harvard.edu (clara_brotzen-smith[at]dfci[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
We develop new technologies that address unmet needs in diagnostics. Our projects are centered around biomarker discovery, ultrasensitive protein detection, and extracellular vesicles. We aim to develop new diagnostics technologies for neurodegenerative diseases (Alzheimer's, Parkinson's), multiple cancers (breast, ovarian, pancreatic), and infectious diseases (HIV, TB, long COVID).
Contact: dwalt [at] bwh.harvard.edu (dwalt[at]bwh[dot]harvard[dot]edu) | Ceara Buzzell, cbuzzell [at] bwh.harvard.edu (cbuzzell[at]bwh[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
ThermoOmniFlux: error robust, rapid sequential multi-omics multiplex detection with thermal barcodes
We introduce the concept of ThermoOmniFlux, a versatile platform capable of not only reading gene mutations but also analyzing RNA expression levels and proteins using unique melting temperature (Tm) patterns with an error-robust encoding scheme.
Engineering asymmetric lipid vesicles for drug delivery
To protect therapeutics from degradation and enable cell-specific targeting, they are often encapsulated into drug delivery vehicles such as lipid nanoparticles, viral vectors or lipid vesicles. However, there is no universal drug delivery vehicle that can deliver any type of therapeutics including small molecules, nucleic acids and proteins. We work on engineering and understanding the properties of asymmetric vesicles, which might be a universal type of delivery vehicle that can deliver therapeutics including nucleic acids and proteins to potentially enable new treatment options for human disease.
Contact: weitz [at] seas.harvard.edu (weitz[at]seas[dot]harvard[dot]edu) | Hong Li, hongli [at] seas.harvard.edu (hongli[at]seas[dot]harvard[dot]edu)
Location: Cambridge
Current Projects:
My lab works on automating and the interpretation of clinical neurophysiology diagnostic tests, including EEG done to evaluate suspected epilepsy, EEG in the ICU setting for prognosis and detection of harmful types of brain activity like seizures, and polysomnography done to evaluate sleep disorders. We also work to extract hidden information about health and disease from EEG signals. Finally, the lab does large-scale EHR phenotyping and NLP work to support the EEG studies. Our studies include the lifespan including neonates, children, and adults.
Contact: bwestove [at] bidmc.harvard.edu (bwestove[at]bidmc[dot]harvard[dot]edu) | Alex Phan, dphan4 [at] bidmc.harvard.edu (dphan4[at]bidmc[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
Dynamic Regulation of the Tumor:immune interface in glioblastoma
This project aims to address the evolution of the tumor:immune interface in GBM (and potentially other tumor types), with the goal of identifying cell surface targets for targeted immunotherapies (BiTEs, mRNA vaccines). A second aspect of the project will aim to defining the dynamic response of this system to chemotherapies and other treatments with the goal of establishing combinatorial therapies.
Contact: fwhite [at] mit.edu (fwhite[at]mit[dot]edu)
Location: MIT / Koch Institute
Current Projects:
The immune system mounts destructive responses to protect the host from diverse threats. However, a trade-off emerges: if immune responses cause too much damage, they can compromise host tissue function. Conversely, if they fail to generate sufficient damage, the host may succumb to a given threat. How is the optimal balance achieved? The Wong lab investigates how cells communicate with one another and their surrounding tissue environment to accurately control the magnitude of immune responses, both in time and space. Many of our ongoing projects focus on T cells as a paradigm because subtle shifts in their control can lead to widely divergent host outcomes, including the successful elimination of threats, the induction of tolerance against foreign entities (e..g, commensal organisms), and the initiation of autoimmune disorders.
Contact: wonghs [at] mit.edu (wonghs[at]mit[dot]edu)
Location: MIT / Ragon Institute
Deciphering immune mechanisms underlying the spontaneous regression of chronic lymphocytic leukemia. Spontaneous regression of cancer is a rare phenomenon whose underlying mechanism remains unknown. We assembled a unique cohort of 50 patients with spontaneously regressing leukemia and generated extensive bulk and single-cell sequencing (RNA/TCR/ATAC-seq) datasets from longitudinal peripheral blood and bone marrow samples. Through integrating computational analysis of these datasets with functional studies this project will shed light on the etiology of this rare phenomenon and provide novel insights into cancer heterogeneity and therapeutics.
Current Projects:
Cancer antigen discovery
We integrate genomics and immunopeptidomics to identify novel species of antigens that can generate immune responses in patients with blood malignancies and solid tumors
T-cell responses to cancer neoantigens
We employ cellular assays as well as library based approaches to detect immune responses to cancer neoantigens following personalized cancer vaccines
Evaluation of immune responses following clinical trials of cancer immunotherapy
Contact: catherine_wu [at] dfci.harvard.edu (catherine_wu[at]dfci[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
Synthetic gene circuits for cancer immunotherapy
We have developed a synthetic biology platform that turns cancer cells against themselves. New projects primarily focus on bridging the gap between lab prototypes and clinically actionable medicines to maximize the potential for benefiting patients.
Sense-and-respond gene circuits to enhance CAR-T cell function
We are developing various types of sensors for CAR-T cells to detect their microenvironment and behave more intelligently. New projects focus on implementing useful cellular sensors and computational devices for T cells to maximize their potential to overcome tumor microenvironment suppression and their limited proliferation capacity.
AI for medicine
We have developed deep learning and reinforcement learning-inspired algorithms that allow us to explore a vast space of biological sequence design. New projects focus on leveraging these algorithms to develop novel tissue specific- and disease specific- regulatory elements for triggering disease-focused gene therapies.
Foundational synthetic biology
We are constantly exploring design ideas for the following research directions: 1) What is the best balance between harnessing existing cellular programs (bio-inspired) and building new devices from the ground up? 2) How to most efficiently build computation devices (e.g., logic gates, memory circuits, toggle switches, non-linear classifiers) for biomedical applications? 3) How to not only build proof-of-concept circuits but also develop clinically actionable circuits?
Contact: ming-ru_wu [at] dfci.harvard.edu (ming-ru_wu[at]dfci[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
Automated acute ischemic stroke infarct segmentation
Refine/develop machine learning algorithms to segment/predict acute stroke lesions using multiple modalities on either CT or MRI.
Automated white matter lesion segmentation
Refine/develop machine learning algorithms to segment white matter lesions on FLAIR MRI
Brain connectivity
Investigate structural and functional connectivity changes in patients with disorders of consciousness
Explainable AI Investigate explainable
AI methods to evaluate healthcare-related machine learning algorithms
Contact: ona.wu [at] mgh.harvard.edu (ona[dot]wu[at]mgh[dot]harvard[dot]edu)
Location: Charlestown
Dr. Yang's lab is focused on adapting AI, mostly LLMs, to extract information from unstructured and semi-structured data in longitudinal electronic health records (EHRs) for clinical phenotype extraction and patient outcome prediction. We have a computation cluster with 8xH100 GPUs hosted by our Division and access to all MGB EHR data, plus the FDA-funded Sentinel system with 21 million lives with EHR and claims data. Our group also collected over 90 open accessed clinical datasets for NLP and LLMs research (please see our recent NEJM AI paper for details: https://ai.nejm.org/doi/abs/10.1056/AIra2400012).
Current Projects:
Applications of Large Language Models (LLMs) in Healthcare, utilizing LLMs to extract clinical information and predict patients status/disease through electronic health records
Building clinical LLMs and real-world clinical evaluation benchmarks for LLMs
Novel natural language processing (NLP) and AI algorithms and software for healthcare applications
Contact: jyang66 [at] bwh.harvard.edu (jyang66[at]bwh[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
Analysis of microscopy data
Algorithms for high-throughput, automated analysis of optical and X-ray microscopy datasets, including cross-modal registration and axon segmentation.
Multi-scale tractography
Algorithms that can take advantage of data across any combination of modalities and scales to improve reconstruction of connectional anatomy.
Contact: ayendiki [at] mgh.harvard.edu (ayendiki[at]mgh[dot]harvard[dot]edu)
Location: Charlestown, Virtual
Current Projects:
Laser nanotechnology and single-cell tracking analysis
Novel analysis platforms integrating imaging, sequencing, flow cytometry, and molecular assays for basic sciences, drug screening, and diagnostics, using single-cell barcoding by laser-emitting nanoparticles.
Optical elastography and biomechanics
Development and applications of optical coherence elastography and Brillouin microscopy.
Contact: syun [at] mgh.harvard.edu (syun[at]mgh[dot]harvard[dot]edu)
Location: Landsdowne St., Cambridge
Current Projects:
The Zhou Lab integrates protein engineering, chemical biology, and synthetic biology approaches to develop novel biologics for sensing and modulating cell signaling pathways, with a particular focus on cell surface and extracellular proteins. We have a variety of available projects in therapeutic protein engineering, immune signaling reprogramming, in vivo macromolecular delivery, and targeted membrane and extracellular protein degradation.
Our pathways and disease models of interest include:
Contact: xin_zhou1 [at] dfci.harvard.edu (xin_zhou1[at]dfci[dot]harvard[dot]edu)
Location: Longwood
Our overarching goal is to lay the foundations for AI that contribute to the scientific understanding of medicine and therapeutic design, eventually enabling AI to learn on its own and acquire knowledge autonomously. We focus on foundational innovation in artificial intelligence and machine learning with an emphasis on AI systems that are informed by geometry, structure, and grounded in medical knowledge. This involves building AI models, including pre-trained, self-supervised, multi-purpose, and multi-modal models trained at scale to enable broad generalization.
Current Projects:
AI for Medicine | Individualized Diagnosis and Treatment
The state of a person is described with increasing precision incorporating modalities like genetic code, cellular atlases, molecular datasets, and therapeutics—the challenge is how to reason over these data to develop powerful disease diagnostics and empower new kinds of therapies. Our research creates new avenues for fusing knowledge and patient data to give the right patient the right treatment at the right time and have medicinal effects that are consistent from person to person and with results in the laboratory.
AI for Science | Therapeutic Science
For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. We are using AI to change that. The natural world is interconnected, from the various facets of genome regulation to the molecular and organismal levels. These interactions across different levels yield a bewildering degree of complexity. Our research seeks to disentangle this complexity, developing AI models that advance drug design and help develop new kinds of therapies.
Contact: marinka [at] hms.harvard.edu (marinka[at]hms[dot]harvard[dot]edu)
Location: Longwood
Current Projects:
We are developing computational tools to model brain development in the fetal and postnatal stages. We use high-resolution postmortem MRI and in vivo brain scans to quantitatively characterize differences between healthy and interrupted / abnormal growth. Some examples of tasks that we are currently focusing on are surface reconstruction, anatomical annotation, spatial registration, and multi-modality image fusion.
Contact: lzollei [at] mgh.harvard.edu (lzollei[at]mgh[dot]harvard[dot]edu)
Location: Charlestown