The Faculty of Engineering has chosen 13 new Takeda Fellows for the 2023-24 educational yr. With help from Takeda, the graduate college students will conduct pathbreaking analysis starting from distant well being monitoring for digital medical trials to ingestible gadgets for at-home, long-term diagnostics.
Now in its fourth yr, the MIT-Takeda Program, a collaboration between MIT’s Faculty of Engineering and Takeda, fuels the event and utility of synthetic intelligence capabilities to learn human well being and drug growth. A part of the Abdul Latif Jameel Clinic for Machine Studying in Well being, this system coalesces disparate disciplines, merges concept 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 within the Division of Electrical Engineering and Laptop 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 good, energy-efficient, ingestible gadgets powered by application-specific built-in circuits for at-home, long-term diagnostics. These revolutionary gadgets — able to figuring out, characterizing, and even correcting gastrointestinal ailments — signify the forefront of biotechnology. Gierlach’s modern contributions will assist to advance elementary analysis 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 within the Harvard-MIT Program in Well being Sciences and Expertise. Gopalakrishnan’s objective is to develop biomedical machine-learning strategies to enhance the research and therapy of human illness. Particularly, he employs computational modeling to advance new approaches for minimally invasive, image-guided neurosurgery, providing a secure different to open mind and spinal procedures. With the help of a Takeda Fellowship, Gopalakrishnan will develop real-time laptop imaginative and prescient algorithms that ship high-quality, 3D intraoperative picture steerage by extracting and fusing data from multimodal neuroimaging knowledge. These algorithms may permit 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’s a PhD candidate within the Division of Electrical Engineering and Laptop Science. His analysis pursuits lie on the intersection of generative AI, machine studying, and their purposes in drugs and human well being, with a selected emphasis on passive, steady, distant well being monitoring to help digital medical trials and health-care administration. Extra particularly, He goals to develop reliable AI fashions that promote equitable entry and ship truthful efficiency impartial of race, gender, and age. In his previous work, He has developed monitoring techniques utilized in medical research of Parkinson’s illness, Alzheimer’s illness, and epilepsy. Supported by a Takeda Fellowship, He’ll 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 issue of imbalance in out there datasets and account for intrinsic variations throughout subpopulations, utilizing generative AI and multi-modality/multi-domain studying, with the objective of studying sturdy options which are invariant to completely different subpopulations. He’s work holds nice promise for delivering superior, equitable health-care companies to all folks and will considerably influence well being care and AI.
Chengyi Lengthy
Chengyi Lengthy is a PhD candidate within the Division of Civil and Environmental Engineering. Lengthy’s interdisciplinary analysis integrates the methodology of physics, arithmetic, and laptop science to research questions in ecology. Particularly, Lengthy is growing a sequence of doubtless groundbreaking methods to elucidate and predict the temporal dynamics of ecological techniques, together with human microbiota, that are important topics in well being and medical analysis. His present work, supported by a Takeda Fellowship, is targeted on growing a conceptual, mathematical, and sensible framework to know the interaction between exterior perturbations and inside group dynamics in microbial techniques, which can function a key step towards discovering bio options to well being administration. A broader perspective of his analysis is to develop AI-assisted platforms to anticipate the altering habits of microbial techniques, 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, Lengthy’s analysis has the potential to supply highly effective contributions to drugs and world well being.
Omar Mohd
Omar Mohd is a PhD candidate within the Division of Electrical Engineering and Laptop Science. Mohd’s analysis is targeted on growing new applied sciences for the spatial profiling of microRNAs, with probably essential purposes in most cancers analysis. Via modern mixtures 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 throughout the rising subject of spatial transcriptomics, which seeks to know most cancers and different ailments by inspecting the relative places of cells and their contents inside tissues. The final word objective of Mohd’s present mission is to search out multidimensional patterns in tissues which will have prognostic worth for most cancers sufferers. One helpful part of his work is an open-source AI program developed with collaborators at Beth Israel Deaconess Medical Heart and Harvard Medical Faculty 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. Via his analysis, Mohd is making modern contributions on the interface of microsystem know-how, AI-based picture evaluation, and most cancers therapy, which may considerably influence drugs and human well being.
Sanghyun Park
Sanghyun Park is a PhD candidate within the Division of Mechanical Engineering. Park specializes within the integration of AI and biomedical engineering to handle complicated challenges in human well being. Drawing on his experience in polymer physics, drug supply, and rheology, his analysis focuses on the pioneering subject of in-situ forming implants (ISFIs) for drug supply. Supported by a Takeda Fellowship, Park is at the moment growing an injectable formulation designed for long-term drug supply. The first objective of his analysis is to unravel the compaction mechanism of drug particles in ISFI formulations by means 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. Past these elementary research, Park’s analysis additionally focuses on translating this information into sensible purposes in a medical setting by means of animal research particularly aimed toward extending drug launch length and bettering mechanical properties. The modern use of AI in growing superior drug supply techniques, coupled with Park’s helpful insights into the compaction mechanism, may contribute to bettering long-term drug supply. This work has the potential to pave the best way for efficient administration of persistent ailments, benefiting sufferers, clinicians, and the pharmaceutical trade.
Huaiyao Peng
Huaiyao Peng is a PhD candidate within the Division of Organic Engineering. Peng’s analysis pursuits are targeted on engineered tissue, microfabrication platforms, most cancers metastasis, and the tumor microenvironment. Particularly, she is advancing novel AI methods for the event of pre-cancer organoid fashions of high-grade serous ovarian most cancers (HGSOC), an particularly deadly and difficult-to-treat most cancers, with the objective of gaining new insights into development and efficient therapies. Peng’s mission, supported by a Takeda Fellowship, can be one of many first to make use of cells from serous tubal intraepithelial carcinoma lesions discovered within the fallopian tubes of many HGSOC sufferers. By inspecting the mobile and molecular adjustments that happen in response to therapy with small molecule inhibitors, she hopes to determine potential biomarkers and promising therapeutic targets for HGSOC, together with customized therapy choices for HGSOC sufferers, finally bettering their medical outcomes. Peng’s work has the potential to result in essential advances in most cancers therapy and spur modern new purposes of AI in well being care.
Priyanka Raghavan
Priyanka Raghavan is a PhD candidate within the Division of Chemical Engineering. Raghavan’s analysis pursuits lie on the frontier of predictive chemistry, integrating computational and experimental approaches to construct highly effective new predictive instruments for societally essential purposes, together with drug discovery. Particularly, Raghavan is growing novel fashions to foretell small-molecule substrate reactivity and compatibility in regimes the place little knowledge is accessible (essentially the most practical regimes). A Takeda Fellowship will allow Raghavan to push the boundaries of her analysis, making modern use of low-data and multi-task machine studying approaches, artificial chemistry, and robotic laboratory automation, with the objective of making an autonomous, closed-loop system for the invention of high-yielding natural small molecules within the context of underexplored reactions. Raghavan’s work goals to determine new, versatile reactions to broaden a chemist’s artificial toolbox with novel scaffolds and substrates that might kind the premise of important medication. Her work has the potential for far-reaching impacts in early-stage, small-molecule discovery and will assist make the prolonged drug-discovery course of considerably sooner and cheaper.
Zhiye Track
Zhiye “Zoey” Track is a PhD candidate within the Division of Electrical Engineering and Laptop Science. Track’s analysis integrates cutting-edge approaches in machine studying (ML) and {hardware} optimization to create next-generation, wearable medical gadgets. Particularly, Track 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 pictures for real-time decision-making capabilities. Her latest work, performed in collaboration with clinicians, has centered on bladder quantity monitoring; different potential purposes embrace blood strain monitoring, muscle analysis, and neuromodulation. With the help of a Takeda Fellowship, Track 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 good wearable gadgets. The applied sciences rising from Track’s analysis may provide thrilling new capabilities in well being 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 within the Division of Electrical Engineering and Laptop Science. Wang’s analysis goals to develop machine studying strategies for studying and interpretation from medical pictures and related medical knowledge to help medical decision-making. He’s growing a multimodal illustration studying method that aligns data captured in giant quantities of medical picture and textual content knowledge to switch this information to new duties and purposes. Supported by a Takeda Fellowship, Wang will advance this promising line of labor to construct sturdy instruments that interpret pictures, study from sparse human suggestions, and purpose like medical doctors, with probably main advantages to essential stakeholders in well being care.
Oscar Wu
Haoyang “Oscar” Wu is a PhD candidate within the Division of Chemical Engineering. Wu’s analysis integrates quantum chemistry and deep studying strategies to speed up the method of small-molecule screening within the growth of latest medication. 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 doable to conduct the high-throughput ab initio calculations of response charges wanted to display screen the reactivity of enormous numbers of lively pharmaceutical elements (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 analysis may assist to remodel and speed up the drug-discovery course of, providing vital advantages to the pharmaceutical and medical fields and to sufferers.
Soojung Yang
Soojung Yang is a PhD candidate within the Division of Supplies Science and Engineering. Yang’s analysis applies cutting-edge strategies in geometric deep studying and generative modeling, together with atomistic simulations, to higher perceive and mannequin protein dynamics. Particularly, Yang is growing novel instruments in generative AI to discover protein conformational landscapes that provide higher velocity and element than physics-based simulations at a considerably decrease value. With the help of a Takeda Fellowship, she is 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 dimension scales of protein conformation variety (all-atom, residue-level, and domain-level). Yang’s analysis holds the potential to supply a robust and broadly relevant new instrument for researchers who search to know the complicated protein features at work in human ailments and to design medication to deal with and treatment these ailments.
Yuzhe Yang
Yuzhe Yang is a PhD candidate within the Division of Electrical Engineering and Laptop Science. Yang’s analysis pursuits lie on the intersection of machine studying and well being care. In his previous and present work, Yang has developed and utilized modern machine-learning fashions that tackle key challenges in illness analysis and monitoring. His many notable achievements embrace the creation of one of many first machine learning-based options utilizing nocturnal respiration indicators to detect Parkinson’s illness (PD), estimate illness severity, and monitor PD development. With the help of a Takeda Fellowship, Yang will increase this promising work to develop an AI-based analysis mannequin for Alzheimer’s illness (AD) utilizing sleep-breathing knowledge that’s considerably extra dependable, versatile, and economical than present diagnostic instruments. This passive, in-home, contactless monitoring system — resembling a easy house Wi-Fi router — may also allow distant illness evaluation and steady development monitoring. Yang’s groundbreaking work has the potential to advance the analysis and therapy of prevalent ailments like PD and AD, and it gives thrilling potentialities for addressing many well being challenges with dependable, inexpensive machine-learning instruments.
