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2023-24 Takeda Fellows: Advancing research at the intersection of AI and health | MIT News

The 2023-24 Takeda Fellows at the Massachusetts Institute of Technology (MIT) are driving advancements in the field of artificial intelligence (AI) and health. These 13 graduate students, supported by Takeda, will engage in groundbreaking research that spans various areas, including remote health monitoring and ingestible devices for diagnostics. The MIT-Takeda Program, now in its fourth year, brings together diverse disciplines, merges theory with practical implementation, and fosters collaborations between academia and industry to harness the power of AI for the betterment of human health and drug development. This article presents an overview of the research projects undertaken by each of the Takeda Fellows, showcasing their potential to make significant contributions to medicine and global healthcare.

2023-24 Takeda Fellows: Advancing research at the intersection of AI and health

The MIT School of Engineering has selected 13 exceptional individuals as Takeda Fellows for the 2023-24 academic year. These graduate students will conduct groundbreaking research at the intersection of artificial intelligence (AI) and health. With the support of Takeda, a leading pharmaceutical company, these fellows will contribute to advancing various fields, from remote health monitoring and image-guided neurosurgery to drug discovery and cancer research.

2023-24 Takeda Fellows: Advancing research at the intersection of AI and health | MIT News

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Adam Gierlach

Adam Gierlach, a PhD candidate in the Department of Electrical Engineering and Computer Science, focuses his research on the development of innovative biotechnology and machine learning. Specifically, Gierlach is working on creating ingestible devices for advanced diagnostics and therapeutics. His previous work involved the development of a non-invasive, ingestible device for long-term gastric recordings. Building on this foundation, Gierlach’s Takeda Fellowship will support the development of smart, energy-efficient, and ingestible devices powered by application-specific integrated circuits. These devices have the potential to identify, characterize, and even correct gastrointestinal diseases. Gierlach’s research will contribute to a better understanding of gut-brain axis dysfunctions in prevalent disorders such as Parkinson’s disease and autism spectrum disorder.

Vivek Gopalakrishnan

Vivek Gopalakrishnan, a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology, focuses on the development of biomedical machine-learning methods for the study and treatment of human disease. His research aims to advance approaches for minimally invasive, image-guided neurosurgery as an alternative to open brain and spinal procedures. With the support of a Takeda Fellowship, Gopalakrishnan will develop real-time computer vision algorithms that extract and fuse information from multimodal neuroimaging data. These algorithms have the potential to reconstruct 3D neurovasculature from X-ray angiography, enhancing precision in device deployment and enabling more accurate localization of healthy versus pathologic anatomy.

Hao He

Hao He, a PhD candidate in the Department of Electrical Engineering and Computer Science, focuses on the intersection of generative AI, machine learning, and their applications in medicine and human health. He is particularly interested in passive, continuous, remote health monitoring to support virtual clinical trials and healthcare management. He’s working on developing trustworthy AI models that provide equitable access and fair performance across different demographic groups. With the help of a Takeda Fellowship, He aims to address existing gaps in performance by developing novel technology for passive monitoring of sleep stages. By using generative AI and multi-modality/multi-domain learning, He’s project seeks to learn robust features that are invariant to different subpopulations, ultimately delivering advanced, equitable healthcare services to all people.

Chengyi Long

Chengyi Long, a PhD candidate in the Department of Civil and Environmental Engineering, integrates the fields of physics, mathematics, and computer science to conduct research in ecology. His work focuses on understanding the interplay between external perturbations and internal community dynamics in microbial systems. Long’s research aims to develop AI-assisted platforms that can anticipate the changing behavior of microbial systems. This research has the potential to differentiate between healthy and unhealthy hosts, design probiotics, and contribute to bio solutions for health management. By bridging the gap between ecology and AI, Long’s work holds promise for making powerful contributions to medicine and global health.

Omar Mohd

Omar Mohd, a PhD candidate in the Department of Electrical Engineering and Computer Science, is focused on developing spatial profiling technologies for microRNAs. Spatial transcriptomics, an emerging field, aims to understand diseases such as cancer by examining the relative locations of cells and their contents within tissues. Mohd’s research aims to measure the spatial variations of microRNAs within tissue samples to gain new insights into drug resistance in cancer. With the support of a Takeda Fellowship, Mohd is developing open-source AI programs to detect cancer epithelial cells and correlate their abundance with the spatial variations of microRNAs. His research contributes to advancements in microsystem technology, AI-based image analysis, and cancer treatment.

Sanghyun Park

Sanghyun Park, a PhD candidate in the Department of Mechanical Engineering, integrates AI and biomedical engineering to address complex health challenges. His research focuses on in-situ forming implants (ISFIs) for long-term drug delivery. Using his expertise in polymer physics, drug delivery, and rheology, Park is working on understanding the compaction mechanism of drug particles in ISFI formulations. Through comprehensive modeling and in-vitro characterization studies, he aims to develop optimal properties for long-term drug delivery. Park’s research has the potential to revolutionize drug delivery systems, improve patient outcomes, and benefit the pharmaceutical industry.

Huaiyao Peng

Huaiyao Peng, a PhD candidate in the Department of Biological Engineering, is advancing AI techniques for the development of pre-cancer organoid models of high-grade serous ovarian cancer (HGSOC). HGSOC is a difficult-to-treat and lethal cancer, and Peng’s research aims to gain new insights into the progression and effective treatments for this disease. Using cells from serous tubal intraepithelial carcinoma lesions found in HGSOC patients, Peng examines the cellular and molecular changes in response to treatment with small molecule inhibitors. Through this research, Peng aims to identify potential biomarkers and therapeutic targets, ultimately improving the clinical outcomes for HGSOC patients. His work has the potential to advance cancer treatment and contribute to personalized medicine.

Priyanka Raghavan

Priyanka Raghavan, a PhD candidate in the Department of Chemical Engineering, focuses on predictive chemistry by integrating computational and experimental approaches. Raghavan is developing novel models to predict small-molecule substrate reactivity and compatibility for underexplored reactions. Her work combines low-data and multi-task machine learning approaches with synthetic chemistry and robotic laboratory automation. With the support of a Takeda Fellowship, Raghavan aims to create an autonomous, closed-loop system for the discovery of high-yielding organic small molecules. By identifying new reactions and novel scaffolds, her research could significantly improve early-stage, small-molecule discovery and accelerate the drug-discovery process.

Oscar Wu

Oscar Wu, a PhD candidate in the Department of Chemical Engineering, integrates quantum chemistry and deep learning methods for small-molecule screening in drug development. His research focuses on identifying reliable methods for calculating the reactivity of drug-like molecules. With the support of a Takeda Fellowship, Wu is developing open-source software for high-throughput quantum chemistry calculations. Additionally, he aims to develop deep learning models that predict the oxidative stability of active pharmaceutical ingredients. Wu’s work has the potential to transform and accelerate the drug-discovery process, benefiting the pharmaceutical industry, medical fields, and ultimately, patients.

These exceptional individuals selected as Takeda Fellows for the 2023-24 academic year demonstrate the cutting-edge research at the intersection of AI and health. From advanced diagnostics and image-guided neurosurgery to drug delivery and cancer research, their work has the potential to significantly impact medicine, improve patient outcomes, and contribute to innovative applications of AI in healthcare. Through their contributions, they bring us closer to a future where AI plays a central role in advancing human health.

Source: https://techtoday.co/2023-24-takeda-fellows-advancing-research-at-the-intersection-of-ai-and-health-mit-news/