The School of Engineering has chosen 13 new Takeda Fellows for the 2023-24 educational 12 months. With help from Takeda, the graduate college students will conduct pathbreaking research starting from distant health monitoring for digital medical trials to ingestible gadgets for at-home, long-term diagnostics.
Now in its fourth 12 months, the MIT-Takeda Program, a collaboration between MIT’s School of Engineering and Takeda, fuels the growth and utility of synthetic intelligence capabilities to profit human health and drug growth. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges idea and sensible implementation, combines algorithm and {hardware} improvements, and creates multidimensional collaborations between academia and trade.
The 2023-24 Takeda Fellows are:
Adam Gierlach
Adam Gierlach is a PhD candidate in the Department of Electrical Engineering and Computer Science. Gierlach’s work combines modern biotechnology with machine studying to create ingestible gadgets for superior diagnostics and supply of therapeutics. In his earlier work, Gierlach developed a non-invasive, ingestible system for long-term gastric recordings in free-moving sufferers. With the help of a Takeda Fellowship, he’ll construct on this pathbreaking work by growing sensible, energy-efficient, ingestible gadgets powered by application-specific built-in circuits for at-home, long-term diagnostics. These revolutionary gadgets — succesful of figuring out, characterizing, and even correcting gastrointestinal ailments — signify the forefront of biotechnology. Gierlach’s modern contributions will assist to advance elementary research on the enteric nervous system and assist develop a greater understanding of gut-brain axis dysfunctions in Parkinson’s illness, autism spectrum dysfunction, and different prevalent problems and circumstances.
Vivek Gopalakrishnan
Vivek Gopalakrishnan is a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology. Gopalakrishnan’s aim is to develop biomedical machine-learning strategies to enhance the research and remedy of human illness. Specifically, he employs computational modeling to advance new approaches for minimally invasive, image-guided neurosurgery, providing a secure various to open mind and spinal procedures. With the help of a Takeda Fellowship, Gopalakrishnan will develop real-time pc imaginative and prescient algorithms that ship high-quality, 3D intraoperative picture steerage by extracting and fusing info from multimodal neuroimaging information. These algorithms may enable surgeons to reconstruct 3D neurovasculature from X-ray angiography, thereby enhancing the precision of system deployment and enabling extra correct localization of wholesome versus pathologic anatomy.
Hao He
Hao He is a PhD candidate in the Department of Electrical Engineering and Computer Science. His research pursuits lie at the intersection of generative AI, machine studying, and their purposes in medication and human health, with a specific emphasis on passive, steady, distant health monitoring to help digital medical trials and health-care administration. More particularly, He goals to develop reliable AI fashions that promote equitable entry and ship honest efficiency impartial of race, gender, and age. In his previous work, He has developed monitoring methods utilized in medical research of Parkinson’s illness, Alzheimer’s illness, and epilepsy. Supported by a Takeda Fellowship, He will develop a novel know-how for the passive monitoring of sleep levels (utilizing radio signaling) that seeks to handle current gaps in efficiency throughout completely different demographic teams. His mission will deal with the downside of imbalance in out there datasets and account for intrinsic variations throughout subpopulations, utilizing generative AI and multi-modality/multi-domain studying, with the aim of studying sturdy options which might be invariant to completely different subpopulations. He’s work holds nice promise for delivering superior, equitable health-care companies to all folks and may considerably affect health care and AI.
Chengyi Long
Chengyi Long is a PhD candidate in the Department of Civil and Environmental Engineering. Long’s interdisciplinary research integrates the methodology of physics, arithmetic, and pc science to analyze questions in ecology. Specifically, Long is growing a collection of probably groundbreaking strategies to elucidate and predict the temporal dynamics of ecological methods, together with human microbiota, that are important topics in health and medical research. His present work, supported by a Takeda Fellowship, is targeted on growing a conceptual, mathematical, and sensible framework to grasp the interaction between exterior perturbations and inside neighborhood dynamics in microbial methods, which can function a key step towards discovering bio options to health administration. A broader perspective of his research is to develop AI-assisted platforms to anticipate the altering conduct of microbial methods, which can assist to distinguish between wholesome and unhealthy hosts and design probiotics for the prevention and mitigation of pathogen infections. By creating novel strategies to handle these points, Long’s research has the potential to supply highly effective contributions to medication and international health.
Omar Mohd
Omar Mohd is a PhD candidate in the Department of Electrical Engineering and Computer Science. Mohd’s research is targeted on growing new applied sciences for the spatial profiling of microRNAs, with probably necessary purposes in most cancers research. Through modern combos of micro-technologies and AI-enabled picture evaluation to measure the spatial variations of microRNAs inside tissue samples, Mohd hopes to achieve new insights into drug resistance in most cancers. This work, supported by a Takeda Fellowship, falls inside the rising area of spatial transcriptomics, which seeks to grasp most cancers and different ailments by analyzing the relative places of cells and their contents inside tissues. The final aim of Mohd’s present mission is to seek out multidimensional patterns in tissues which will have prognostic worth for most cancers sufferers. One precious element of his work is an open-source AI program developed with collaborators at Beth Israel Deaconess Medical Center and Harvard Medical School to auto-detect most cancers epithelial cells from different cell varieties in a tissue pattern and to correlate their abundance with the spatial variations of microRNAs. Through his research, Mohd is making modern contributions at the interface of microsystem know-how, AI-based picture evaluation, and most cancers remedy, which may considerably affect medication and human health.
Sanghyun Park
Sanghyun Park is a PhD candidate in the Department of Mechanical Engineering. Park makes a speciality of the integration of AI and biomedical engineering to handle complicated challenges in human health. Drawing on his experience in polymer physics, drug supply, and rheology, his research focuses on the pioneering area of in-situ forming implants (ISFIs) for drug supply. Supported by a Takeda Fellowship, Park is at present growing an injectable formulation designed for long-term drug supply. The main aim of his research is to unravel the compaction mechanism of drug particles in ISFI formulations by way of complete modeling and in-vitro characterization research using superior AI instruments. He goals to achieve a radical understanding of this distinctive compaction mechanism and apply it to drug microcrystals to attain properties optimum for long-term drug supply. Beyond these elementary research, Park’s research additionally focuses on translating this information into sensible purposes in a medical setting by way of animal research particularly aimed at extending drug launch length and bettering mechanical properties. The modern use of AI in growing superior drug supply methods, coupled with Park’s precious insights into the compaction mechanism, may contribute to bettering long-term drug supply. This work has the potential to pave the approach for efficient administration of persistent ailments, benefiting sufferers, clinicians, and the pharmaceutical trade.
Huaiyao Peng
Huaiyao Peng is a PhD candidate in the Department of Biological Engineering. Peng’s research pursuits are centered on engineered tissue, microfabrication platforms, most cancers metastasis, and the tumor microenvironment. Specifically, she is advancing novel AI strategies for the growth of pre-cancer organoid fashions of high-grade serous ovarian most cancers (HGSOC), an particularly deadly and difficult-to-treat most cancers, with the aim of gaining new insights into development and efficient therapies. Peng’s mission, supported by a Takeda Fellowship, shall be one of the first to make use of cells from serous tubal intraepithelial carcinoma lesions present in the fallopian tubes of many HGSOC sufferers. By analyzing the mobile and molecular modifications that happen in response to remedy with small molecule inhibitors, she hopes to establish potential biomarkers and promising therapeutic targets for HGSOC, together with customized remedy choices for HGSOC sufferers, in the end bettering their medical outcomes. Peng’s work has the potential to result in necessary advances in most cancers remedy and spur modern new purposes of AI in health care.
Priyanka Raghavan
Priyanka Raghavan is a PhD candidate in the Department of Chemical Engineering. Raghavan’s research pursuits lie at the frontier of predictive chemistry, integrating computational and experimental approaches to construct highly effective new predictive instruments for societally necessary purposes, together with drug discovery. Specifically, Raghavan is growing novel fashions to foretell small-molecule substrate reactivity and compatibility in regimes the place little information is out there (the most life like regimes). A Takeda Fellowship will allow Raghavan to push the boundaries of her research, making modern use of low-data and multi-task machine studying approaches, artificial chemistry, and robotic laboratory automation, with the aim of creating an autonomous, closed-loop system for the discovery of high-yielding natural small molecules in the context of underexplored reactions. Raghavan’s work goals to establish new, versatile reactions to broaden a chemist’s artificial toolbox with novel scaffolds and substrates that would kind the foundation of important medicine. Her work has the potential for far-reaching impacts in early-stage, small-molecule discovery and may assist make the prolonged drug-discovery course of considerably quicker and cheaper.
Zhiye Song
Zhiye “Zoey” Song is a PhD candidate in the Department of Electrical Engineering and Computer Science. Song’s research integrates cutting-edge approaches in machine studying (ML) and {hardware} optimization to create next-generation, wearable medical gadgets. Specifically, Song is growing novel approaches for the energy-efficient implementation of ML computation in low-power medical gadgets, together with a wearable ultrasound “patch” that captures and processes photographs for real-time decision-making capabilities. Her current work, performed in collaboration with clinicians, has centered on bladder quantity monitoring; different potential purposes embody blood stress monitoring, muscle prognosis, and neuromodulation. With the help of a Takeda Fellowship, Song will construct on that promising work and pursue key enhancements to current wearable system applied sciences, together with growing low-compute and low-memory ML algorithms and low-power chips to allow ML on sensible wearable gadgets. The applied sciences rising from Song’s research may supply thrilling new capabilities in health care, enabling highly effective and cost-effective point-of-care diagnostics and increasing particular person entry to autonomous and steady medical monitoring.
Peiqi Wang
Peiqi Wang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Wang’s research goals to develop machine studying strategies for studying and interpretation from medical photographs and related medical information to help medical decision-making. He is growing a multimodal illustration studying method that aligns data captured in giant quantities of medical picture and textual content information to switch this information to new duties and purposes. Supported by a Takeda Fellowship, Wang will advance this promising line of work to construct sturdy instruments that interpret photographs, study from sparse human suggestions, and cause like medical doctors, with probably main advantages to necessary stakeholders in health care.
Oscar Wu
Haoyang “Oscar” Wu is a PhD candidate in the Department of Chemical Engineering. Wu’s research integrates quantum chemistry and deep studying strategies to speed up the course of of small-molecule screening in the growth of new medicine. By figuring out and automating dependable strategies for locating transition state geometries and calculating barrier heights for brand spanking new reactions, Wu’s work may make it attainable to conduct the high-throughput ab initio calculations of response charges wanted to display the reactivity of giant numbers of lively pharmaceutical components (APIs). A Takeda Fellowship will help his present mission to: (1) develop open-source software program for high-throughput quantum chemistry calculations, specializing in the reactivity of drug-like molecules, and (2) develop deep studying fashions that may quantitatively predict the oxidative stability of APIs. The instruments and insights ensuing from Wu’s research may assist to rework and speed up the drug-discovery course of, providing important advantages to the pharmaceutical and medical fields and to sufferers.
Soojung Yang
Soojung Yang is a PhD candidate in the Department of Materials Science and Engineering. Yang’s research applies cutting-edge strategies in geometric deep studying and generative modeling, together with atomistic simulations, to raised perceive and mannequin protein dynamics. Specifically, Yang is growing novel instruments in generative AI to discover protein conformational landscapes that supply higher pace and element than physics-based simulations at a considerably decrease value. With the help of a Takeda Fellowship, she’s going to construct upon her profitable work on the reverse transformation of coarse-grained proteins to the all-atom decision, aiming to construct machine-learning fashions that bridge a number of measurement scales of protein conformation variety (all-atom, residue-level, and domain-level). Yang’s research holds the potential to offer a robust and extensively relevant new instrument for researchers who search to grasp the complicated protein features at work in human ailments and to design medicine to deal with and remedy these ailments.
Yuzhe Yang
Yuzhe Yang is a PhD candidate in the Department of Electrical Engineering and Computer Science. Yang’s research pursuits lie at the intersection of machine studying and health care. In his previous and present work, Yang has developed and utilized modern machine-learning fashions that handle key challenges in illness prognosis and monitoring. His many notable achievements embody the creation of one of the first machine learning-based options utilizing nocturnal respiratory alerts to detect Parkinson’s illness (PD), estimate illness severity, and monitor PD development. With the help of a Takeda Fellowship, Yang will develop this promising work to develop an AI-based prognosis mannequin for Alzheimer’s illness (AD) utilizing sleep-breathing information that’s considerably extra dependable, versatile, and economical than present diagnostic instruments. This passive, in-home, contactless monitoring system — resembling a easy dwelling Wi-Fi router — will even allow distant illness evaluation and steady development monitoring. Yang’s groundbreaking work has the potential to advance the prognosis and remedy of prevalent ailments like PD and AD, and it presents thrilling prospects for addressing many health challenges with dependable, reasonably priced machine-learning instruments.