The School of Engineering has chosen 13 recent Takeda Fellows for the 2023-24 academic yr. With support from Takeda, the graduate students will conduct pathbreaking research starting from distant health monitoring for virtual clinical trials to ingestible devices for at-home, long-term diagnostics.

Now in its fourth yr, the MIT-Takeda Program, a collaboration between MIT’s School of Engineering and Takeda, fuels the event and application of artificial intelligence capabilities to profit human health and drug development. A part of the Abdul Latif Jameel Clinic for Machine Learning in Health, this system coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry.

The 2023-24 Takeda Fellows are:

Adam Gierlach

Adam Gierlach is a PhD candidate within the Department of Electrical Engineering and Computer Science. Gierlach’s work combines modern biotechnology with machine learning to create ingestible devices for advanced diagnostics and delivery of therapeutics. In his previous work, Gierlach developed a non-invasive, ingestible device for long-term gastric recordings in free-moving patients. With the support of a Takeda Fellowship, he’ll construct on this pathbreaking work by developing smart, energy-efficient, ingestible devices powered by application-specific integrated circuits for at-home, long-term diagnostics. These revolutionary devices — able to identifying, characterizing, and even correcting gastrointestinal diseases — represent the vanguard of biotechnology. Gierlach’s modern contributions will help to advance fundamental research on the enteric nervous system and help develop a greater understanding of gut-brain axis dysfunctions in Parkinson’s disease, autism spectrum disorder, and other prevalent disorders and conditions.

Vivek Gopalakrishnan

Vivek Gopalakrishnan is a PhD candidate within the Harvard-MIT Program in Health Sciences and Technology. Gopalakrishnan’s goal is to develop biomedical machine-learning methods to enhance the study and treatment of human disease. Specifically, he employs computational modeling to advance recent approaches for minimally invasive, image-guided neurosurgery, offering a secure alternative to open brain and spinal procedures. With the support of a Takeda Fellowship, Gopalakrishnan will develop real-time computer vision algorithms that deliver high-quality, 3D intraoperative image guidance by extracting and fusing information from multimodal neuroimaging data. These algorithms could allow surgeons to reconstruct 3D neurovasculature from X-ray angiography, thereby enhancing the precision of device deployment and enabling more accurate localization of healthy versus pathologic anatomy.

Hao He

Hao He’s a PhD candidate within the Department of Electrical Engineering and Computer Science. His research interests lie on the intersection of generative AI, machine learning, and their applications in medicine and human health, with a specific emphasis on passive, continuous, distant health monitoring to support virtual clinical trials and health-care management. More specifically, He goals to develop trustworthy AI models that promote equitable access and deliver fair performance independent of race, gender, and age. In his past work, He has developed monitoring systems applied in clinical studies of Parkinson’s disease, Alzheimer’s disease, and epilepsy. Supported by a Takeda Fellowship, He’ll develop a novel technology for the passive monitoring of sleep stages (using radio signaling) that seeks to deal with existing gaps in performance across different demographic groups. His project will tackle the issue of imbalance in available datasets and account for intrinsic differences across subpopulations, using generative AI and multi-modality/multi-domain learning, with the goal of learning robust features which are invariant to different subpopulations. He’s work holds great promise for delivering advanced, equitable health-care services to all people and will significantly impact health care and AI.

Chengyi Long

Chengyi Long is a PhD candidate within the Department of Civil and Environmental Engineering. Long’s interdisciplinary research integrates the methodology of physics, mathematics, and computer science to research questions in ecology. Specifically, Long is developing a series of doubtless groundbreaking techniques to elucidate and predict the temporal dynamics of ecological systems, including human microbiota, that are essential subjects in health and medical research. His current work, supported by a Takeda Fellowship, is concentrated on developing a conceptual, mathematical, and practical framework to know the interplay between external perturbations and internal community dynamics in microbial systems, which can function a key step toward finding bio solutions to health management. A broader perspective of his research is to develop AI-assisted platforms to anticipate the changing behavior of microbial systems, which can help to distinguish between healthy and unhealthy hosts and design probiotics for the prevention and mitigation of pathogen infections. By creating novel methods to deal with these issues, Long’s research has the potential to supply powerful contributions to medicine and global health.

Omar Mohd

Omar Mohd is a PhD candidate within the Department of Electrical Engineering and Computer Science. Mohd’s research is concentrated on developing recent technologies for the spatial profiling of microRNAs, with potentially essential applications in cancer research. Through modern combos of micro-technologies and AI-enabled image evaluation to measure the spatial variations of microRNAs inside tissue samples, Mohd hopes to achieve recent insights into drug resistance in cancer. This work, supported by a Takeda Fellowship, falls throughout the emerging field of spatial transcriptomics, which seeks to know cancer and other diseases by examining the relative locations of cells and their contents inside tissues. The last word goal of Mohd’s current project is to search out multidimensional patterns in tissues which will have prognostic value for cancer patients. One worthwhile component 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 cancer epithelial cells from other cell types in a tissue sample and to correlate their abundance with the spatial variations of microRNAs. Through his research, Mohd is making modern contributions on the interface of microsystem technology, AI-based image evaluation, and cancer treatment, which could significantly impact medicine and human health.

Sanghyun Park

Sanghyun Park is a PhD candidate within the Department of Mechanical Engineering. Park makes a speciality of the mixing of AI and biomedical engineering to deal with complex challenges in human health. Drawing on his expertise in polymer physics, drug delivery, and rheology, his research focuses on the pioneering field of in-situ forming implants (ISFIs) for drug delivery. Supported by a Takeda Fellowship, Park is currently developing an injectable formulation designed for long-term drug delivery. The first goal of his research is to unravel the compaction mechanism of drug particles in ISFI formulations through comprehensive modeling and in-vitro characterization studies utilizing advanced AI tools. He goals to achieve a radical understanding of this unique compaction mechanism and apply it to drug microcrystals to attain properties optimal for long-term drug delivery. Beyond these fundamental studies, Park’s research also focuses on translating this data into practical applications in a clinical setting through animal studies specifically aimed toward extending drug release duration and improving mechanical properties. The modern use of AI in developing advanced drug delivery systems, coupled with Park’s worthwhile insights into the compaction mechanism, could contribute to improving long-term drug delivery. This work has the potential to pave the way in which for effective management of chronic diseases, benefiting patients, clinicians, and the pharmaceutical industry.

Huaiyao Peng

Huaiyao Peng is a PhD candidate within the Department of Biological Engineering. Peng’s research interests are focused on engineered tissue, microfabrication platforms, cancer metastasis, and the tumor microenvironment. Specifically, she is advancing novel AI techniques for the event of pre-cancer organoid models of high-grade serous ovarian cancer (HGSOC), an especially lethal and difficult-to-treat cancer, with the goal of gaining recent insights into progression and effective treatments. Peng’s project, supported by a Takeda Fellowship, shall be one among the primary to make use of cells from serous tubal intraepithelial carcinoma lesions present in the fallopian tubes of many HGSOC patients. By examining the cellular and molecular changes that occur in response to treatment with small molecule inhibitors, she hopes to discover potential biomarkers and promising therapeutic targets for HGSOC, including personalized treatment options for HGSOC patients, ultimately improving their clinical outcomes. Peng’s work has the potential to bring about essential advances in cancer treatment and spur modern recent applications of AI in health care. 

Priyanka Raghavan

Priyanka Raghavan is a PhD candidate within the Department of Chemical Engineering. Raghavan’s research interests lie on the frontier of predictive chemistry, integrating computational and experimental approaches to construct powerful recent predictive tools for societally essential applications, including drug discovery. Specifically, Raghavan is developing novel models to predict small-molecule substrate reactivity and compatibility in regimes where little data is on the market (probably the most realistic regimes). A Takeda Fellowship will enable Raghavan to push the boundaries of her research, making modern use of low-data and multi-task machine learning approaches, synthetic chemistry, and robotic laboratory automation, with the goal of making an autonomous, closed-loop system for the invention of high-yielding organic small molecules within the context of underexplored reactions. Raghavan’s work goals to discover recent, versatile reactions to broaden a chemist’s synthetic toolbox with novel scaffolds and substrates that might form the idea of essential drugs. Her work has the potential for far-reaching impacts in early-stage, small-molecule discovery and will help make the lengthy drug-discovery process significantly faster and cheaper.

Zhiye Song

Zhiye “Zoey” Song is a PhD candidate within the Department of Electrical Engineering and Computer Science. Song’s research integrates cutting-edge approaches in machine learning (ML) and hardware optimization to create next-generation, wearable medical devices. Specifically, Song is developing novel approaches for the energy-efficient implementation of ML computation in low-power medical devices, including a wearable ultrasound “patch” that captures and processes images for real-time decision-making capabilities. Her recent work, conducted in collaboration with clinicians, has centered on bladder volume monitoring; other potential applications include blood pressure monitoring, muscle diagnosis, and neuromodulation. With the support of a Takeda Fellowship, Song will construct on that promising work and pursue key improvements to existing wearable device technologies, including developing low-compute and low-memory ML algorithms and low-power chips to enable ML on smart wearable devices. The technologies emerging from Song’s research could offer exciting recent capabilities in health care, enabling powerful and cost-effective point-of-care diagnostics and expanding individual access to autonomous and continuous medical monitoring.

Peiqi Wang

Peiqi Wang is a PhD candidate within the Department of Electrical Engineering and Computer Science. Wang’s research goals to develop machine learning methods for learning and interpretation from medical images and associated clinical data to support clinical decision-making. He’s developing a multimodal representation learning approach that aligns knowledge captured in large amounts of medical image and text data to transfer this data to recent tasks and applications. Supported by a Takeda Fellowship, Wang will advance this promising line of labor to construct robust tools that interpret images, learn from sparse human feedback, and reason like doctors, with potentially major advantages to essential stakeholders in health care.

Oscar Wu

Haoyang “Oscar” Wu is a PhD candidate within the Department of Chemical Engineering. Wu’s research integrates quantum chemistry and deep learning methods to speed up the strategy of small-molecule screening in the event of latest drugs. By identifying and automating reliable methods for locating transition state geometries and calculating barrier heights for brand spanking new reactions, Wu’s work could make it possible to conduct the high-throughput ab initio calculations of response rates needed to screen the reactivity of enormous numbers of energetic pharmaceutical ingredients (APIs). A Takeda Fellowship will support his current project to: (1) develop open-source software for high-throughput quantum chemistry calculations, specializing in the reactivity of drug-like molecules, and (2) develop deep learning models that may quantitatively predict the oxidative stability of APIs. The tools and insights resulting from Wu’s research could help to remodel and speed up the drug-discovery process, offering significant advantages to the pharmaceutical and medical fields and to patients.

Soojung Yang

Soojung Yang is a PhD candidate within the Department of Materials Science and Engineering. Yang’s research applies cutting-edge methods in geometric deep learning and generative modeling, together with atomistic simulations, to raised understand and model protein dynamics. Specifically, Yang is developing novel tools in generative AI to explore protein conformational landscapes that supply greater speed and detail than physics-based simulations at a substantially lower cost. With the support of a Takeda Fellowship, she is going to construct upon her successful work on the reverse transformation of coarse-grained proteins to the all-atom resolution, aiming to construct machine-learning models that bridge multiple size scales of protein conformation diversity (all-atom, residue-level, and domain-level). Yang’s research holds the potential to offer a robust and widely applicable recent tool for researchers who seek to know the complex protein functions at work in human diseases and to design drugs to treat and cure those diseases.

Yuzhe Yang

Yuzhe Yang is a PhD candidate within the Department of Electrical Engineering and Computer Science. Yang’s research interests lie on the intersection of machine learning and health care. In his past and current work, Yang has developed and applied modern machine-learning models that address key challenges in disease diagnosis and tracking. His many notable achievements include the creation of one among the primary machine learning-based solutions using nocturnal respiratory signals to detect Parkinson’s disease (PD), estimate disease severity, and track PD progression. With the support of a Takeda Fellowship, Yang will expand this promising work to develop an AI-based diagnosis model for Alzheimer’s disease (AD) using sleep-breathing data that’s significantly more reliable, flexible, and economical than current diagnostic tools. This passive, in-home, contactless monitoring system — resembling an easy home Wi-Fi router — may also enable distant disease assessment and continuous progression tracking. Yang’s groundbreaking work has the potential to advance the diagnosis and treatment of prevalent diseases like PD and AD, and it offers exciting possibilities for addressing many health challenges with reliable, inexpensive machine-learning tools.