The graduate students, including an HST MEMP PhD candidate, will aim to commercialize innovations in AI, machine learning, and data science.
School of Engineering
MIT-Pillar AI Collective has announced three inaugural fellows for the fall 2023 semester. With support from the program, the graduate students, who are in their final year of a master’s or PhD program, will conduct research in the areas of artificial intelligence, machine learning, and data science with the aim of commercializing their innovations.
Launched by MIT’s School of Engineering and Pillar VC in 2022, the MIT-Pillar AI Collective supports faculty, postdocs, and students conducting research on AI, machine learning, and data science. Supported by a gift from Pillar VC and administered by the MIT Deshpande Center for Technological Innovation, the mission of the program is to advance research toward commercialization.
The fall 2023 MIT-Pillar AI Collective Fellows are:
Alexander Andonian SM ’21 is a PhD candidate in electrical engineering and computer science whose research interests lie in computer vision, deep learning, and artificial intelligence. More specifically, he is focused on building a generalist, multimodal AI scientist driven by generative vision-language model agents capable of proposing scientific hypotheses, running computational experiments, evaluating supporting evidence, and verifying conclusions in the same way as a human researcher or reviewer. Such an agent could be trained to optimally distill and communicate its findings for human consumption and comprehension. Andonian’s work holds the promise of creating a concrete foundation for rigorously building and holistically testing the next-generation autonomous AI agent for science. In addition to his research, Andonian is the CEO and co-founder of Reelize, a startup that offers a generative AI video tool that effortlessly turns long videos into short clips — and originated from his business coursework and was supported by MIT Sandbox. Andonian is also a founding AI researcher at Poly AI, an early-stage YC-backed startup building AI design tools. Andonian earned an SM from MIT and a BS in neuroscience, physics, and mathematics from Bates College.
Daniel Magley is a Medical Engineering and Medical Physics (MEMP) PhD candidate in the Harvard-MIT Program in Health Sciences and Technology (HST) who is passionate about making a healthy, fully functioning mind and body a reality for all. His leading-edge research is focused on developing a swallowable wireless thermal imaging capsule that could be used in treating and monitoring inflammatory bowel diseases and their manifestations, such as Crohn’s disease. Providing increased sensitivity and eliminating the need for bowel preparation, the capsule has the potential to vastly improve treatment efficacy and overall patient experience in routine monitoring. The capsule has completed animal studies and is entering human studies at Mass General Brigham, where Magley leads a team of engineers in the hospital’s largest translational research lab, the Tearney Lab. Following the human pilot studies, the largest technological and regulatory risks will be cleared for translation. Magley will then begin focusing on a multi-site study to get the device into clinics, with the promise of benefiting patients across the country. Magley earned a BS in electrical engineering from Caltech.
Madhumitha Ravichandra is a PhD candidate interested in advancing heat transfer and surface engineering techniques to enhance the safety and performance of nuclear energy systems and reduce their environmental impacts. Leveraging her deep knowledge of the integration of explainable AI with high-throughput autonomous experimentation, she seeks to transform the development of radiation-hardened (rad-hard) sensors, which could potentially withstand and function amidst radiation levels that would render conventional sensors useless. By integrating explainable AI with high-throughput autonomous experimentation, she aims to rapidly iterate designs, test under varied conditions, and ensure that the final product is both robust and transparent in its operations. Her work in this space could shift the paradigm in rad-hard sensor development, addressing a glaring void in the market and redefining standards, ensuring that nuclear and space applications are safer, more efficient, and at the cutting edge of technological progress. Ravichandran earned a BTech in mechanical engineering from SASTRA University, India.
* Originally published in MIT News.