The Faculty of Engineering has chosen 13 new Takeda Fellows for the 2023-24 tutorial yr. With assist 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 software of synthetic intelligence capabilities to learn human well being and drug improvement. A part of the Abdul Latif Jameel Clinic for Machine Studying in Well being, this system coalesces disparate disciplines, merges idea and sensible implementation, combines algorithm and {hardware} improvements, and creates multidimensional collaborations between academia and business.
The 2023-24 Takeda Fellows are:
Adam Gierlach
Adam Gierlach is a PhD candidate within the Division of Electrical Engineering and Pc Science. Gierlach’s work combines revolutionary 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 gadget for long-term gastric recordings in free-moving sufferers. With the assist 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 — able to figuring out, characterizing, and even correcting gastrointestinal illnesses — symbolize the forefront of biotechnology. Gierlach’s revolutionary contributions will assist to advance basic 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 situations.
Vivek Gopalakrishnan
Vivek Gopalakrishnan is a PhD candidate within the Harvard-MIT Program in Well being Sciences and Expertise. Gopalakrishnan’s aim is to develop biomedical machine-learning strategies to enhance the research and remedy of human illness. Particularly, he employs computational modeling to advance new approaches for minimally invasive, image-guided neurosurgery, providing a protected various to open mind and spinal procedures. With the assist of a Takeda Fellowship, Gopalakrishnan will develop real-time pc imaginative and prescient algorithms that ship high-quality, 3D intraoperative picture steering by extracting and fusing data from multimodal neuroimaging knowledge. These algorithms might enable surgeons to reconstruct 3D neurovasculature from X-ray angiography, thereby enhancing the precision of gadget 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 Pc 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 assist digital medical trials and health-care administration. Extra 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 programs utilized in medical research of Parkinson’s illness, Alzheimer’s illness, and epilepsy. Supported by a Takeda Fellowship, He’ll develop a novel expertise for the passive monitoring of sleep phases (utilizing radio signaling) that seeks to handle present gaps in efficiency throughout completely different demographic teams. His undertaking will sort out the issue of imbalance in accessible 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 providers to all individuals and will considerably affect 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 pc science to analyze questions in ecology. Particularly, Lengthy is growing a sequence of probably groundbreaking strategies to elucidate and predict the temporal dynamics of ecological programs, 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 neighborhood dynamics in microbial programs, 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 programs, 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 Pc 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 revolutionary 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 inside the rising discipline of spatial transcriptomics, which seeks to know most cancers and different illnesses by analyzing the relative areas of cells and their contents inside tissues. The final word aim of Mohd’s present undertaking is to search out multidimensional patterns in tissues which will have prognostic worth for most cancers sufferers. One useful element 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 sorts in a tissue pattern and to correlate their abundance with the spatial variations of microRNAs. Via his analysis, Mohd is making revolutionary contributions on the interface of microsystem expertise, AI-based picture evaluation, and most cancers remedy, which might considerably affect 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 discipline 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 first aim of his analysis is to unravel the compaction mechanism of drug particles in ISFI formulations via complete modeling and in-vitro characterization research using superior AI instruments. He goals to achieve an intensive understanding of this distinctive compaction mechanism and apply it to drug microcrystals to attain properties optimum for long-term drug supply. Past these basic research, Park’s analysis additionally focuses on translating this information into sensible purposes in a medical setting via animal research particularly geared toward extending drug launch period and enhancing mechanical properties. The revolutionary use of AI in growing superior drug supply programs, coupled with Park’s useful insights into the compaction mechanism, might contribute to enhancing long-term drug supply. This work has the potential to pave the way in which for efficient administration of continual illnesses, benefiting sufferers, clinicians, and the pharmaceutical business.
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 strategies 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 aim of gaining new insights into development and efficient therapies. Peng’s undertaking, supported by a Takeda Fellowship, shall 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 analyzing the mobile and molecular modifications that happen in response to remedy with small molecule inhibitors, she hopes to determine potential biomarkers and promising therapeutic targets for HGSOC, together with customized remedy choices for HGSOC sufferers, finally enhancing their medical outcomes. Peng’s work has the potential to result in essential advances in most cancers remedy and spur revolutionary 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 out there (probably the most lifelike regimes). A Takeda Fellowship will allow Raghavan to push the boundaries of her analysis, making revolutionary use of low-data and multi-task machine studying approaches, artificial chemistry, and robotic laboratory automation, with the aim 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 Tune
Zhiye “Zoey” Tune is a PhD candidate within the Division of Electrical Engineering and Pc Science. Tune’s analysis integrates cutting-edge approaches in machine studying (ML) and {hardware} optimization to create next-generation, wearable medical gadgets. Particularly, Tune 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 photos 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 strain monitoring, muscle prognosis, and neuromodulation. With the assist of a Takeda Fellowship, Tune will construct on that promising work and pursue key enhancements to present wearable gadget 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 Tune’s analysis might supply 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 Pc Science. Wang’s analysis goals to develop machine studying strategies for studying and interpretation from medical photos and related medical knowledge to assist medical decision-making. He’s growing a multimodal illustration studying strategy that aligns information captured in massive 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 photos, be taught from sparse human suggestions, and motive like docs, 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 improvement of recent 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 might make it attainable to conduct the high-throughput ab initio calculations of response charges wanted to display screen the reactivity of huge numbers of energetic pharmaceutical components (APIs). A Takeda Fellowship will assist his present undertaking 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 might 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 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 pace and element than physics-based simulations at a considerably decrease value. With the assist 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 range (all-atom, residue-level, and domain-level). Yang’s analysis holds the potential to supply a robust and extensively relevant new instrument for researchers who search to know the complicated protein capabilities at work in human illnesses and to design medication to deal with and treatment these illnesses.
Yuzhe Yang
Yuzhe Yang is a PhD candidate within the Division of Electrical Engineering and Pc 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 revolutionary machine-learning fashions that tackle key challenges in illness prognosis and monitoring. His many notable achievements embody the creation of one of many first machine learning-based options utilizing nocturnal respiration alerts to detect Parkinson’s illness (PD), estimate illness severity, and observe PD development. With the assist of a Takeda Fellowship, Yang will increase this promising work to develop an AI-based prognosis 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 residence Wi-Fi router — can even allow distant illness evaluation and steady development monitoring. Yang’s groundbreaking work has the potential to advance the prognosis and remedy of prevalent illnesses like PD and AD, and it provides thrilling prospects for addressing many well being challenges with dependable, reasonably priced machine-learning instruments.