School of Engineering

The MIT-Takeda Program, a collaboration between MIT’s School of Engineering and Takeda Pharmaceuticals Company, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry.

With the aim of building a community dedicated to the next generation of AI and system-level breakthroughs, the MIT-Takeda Program is also creating educational opportunities. Every year Takeda funds fellowships to support graduate students pursuing research related to health and AI. This year’s Takeda Fellows, described below, are working on projects ranging from electronic health record systems and robotic control to pandemic preparedness and traumatic brain injuries.

Camille C. Farruggio

Farruggio is a PhD candidate in the Department of Materials Science and Engineering whose research leverages AI and machine learning, including regression modeling, to help realize the promise of cells-as-medicine applications. As a Takeda Fellow, she seeks to develop a holistic understanding of the culture conditions and cell attributes that modulate and predict cell efficacy as therapeutic treatments and solve existing technology bottlenecks in the production of cell therapies.

Wenhao Gao

Gao is a PhD candidate in the Department of Chemical Engineering who aims to accelerate biological and chemical discovery processes. His work specifically focuses on AI for health sciences and cutting-edge applications of machine learning for molecular discovery and drug development. Gao’s research, supported by a Takeda Fellowship, seeks to create a more efficient process, using AI algorithms to advance de novo design methods and organic synthesis for accelerated drug development.

Samuel Goldman

Goldman is a PhD candidate in the Computational and Systems Biology Program whose research interests lie at the intersection of biology, analytical chemistry, and machine learning. Specifically, Goldman uses mass spectrometry data and generative deep learning to elucidate the structures of unknown molecules in biological samples, with important implications for drug discovery. As a Takeda Fellow, he will build new computational tools to characterize and measure unknown small molecule metabolites in a cellular mixture.

Sarah Gurev

Gurev is a PhD candidate in the Department of Electrical Engineering and Computer Science. Her research seeks to address the challenges of pandemic preparedness and the prediction of viral immune evasion. As a Takeda Fellow, Gurev will advance her work at the intersection of computational approaches and experimental screening to develop new models of antibody escape.

R’mani Haulcy

Haulcy is a PhD candidate in the Department of Electrical Engineering and Computer Science whose work bridges the fields of AI and health to create cutting-edge AI-based assessments of cognitive impairment in speech and language disorders. Supported by a Takeda Fellowship, Haulcy will develop new tools for speech processing focused on the measurement of health-related speech biomarkers, specifically examining the speech of subjects with frontotemporal dementia and primary progressive aphasia.

Velina Kozareva

Kozareva is a PhD candidate in the Computational and Systems Biology Program whose research focuses on developing machine learning methods to integrate multi-omic data in heterogeneous diseases. As a Takeda Fellow, Kozareva aims to develop computational methods to simultaneously identify subtypes of heterogeneous diseases and the causal mechanisms that drive each subtype, with an initial focus on amyotrophic lateral sclerosis.

Yang Liu

Liu is a PhD candidate in the Department of Electrical Engineering and Computer Science whose current work focuses on AI for health records and computational imaging/photography, which lies at the confluence of computer science, optics, biomedical/neuroscience, hardware design, and software design. Liu’s Takeda Fellowship will support his current research, a collaborative project that aims to address the connected challenges of delivering health care and maintaining health-care records in resource-constrained settings.

Luke Murray

Murray is a PhD candidate in the Department of Electrical Engineering and Computer Science whose work is focused on electronic health record (EHR) systems, which have revolutionized health care and hold tremendous potential for clinical diagnosis, operations, and research, but also suffer from serious shortcomings. Through his Takeda Fellowship, Murray will tackle a primary EHR limitation: disparate interfaces that fragment the clinical workflow into time-consuming, error-prone processes that require clinicians to spend more time interacting with EHRs than with patients.

Mark Olchanyi

Olchanyi is a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology whose research seeks to advance our knowledge of traumatic brain injuries (TBIs). Olchanyi’s research, supported by a Takeda Fellowship, will apply deep learning to study in vivo imaging-based TBI biomarkers, with a particular focus on subcortical white matter lesions in acute TBIs resulting in disorders of consciousness.

Krista Pullen

Pullen is a PhD candidate in the Department of Biological Engineering whose research is situated at the intersection of vaccine immunology and machine learning. With the support of a Takeda Fellowship, Pullen will develop and validate the application of cross-species modeling in the context of vaccine immunology to enable the prediction of human efficacy from preclinical data.

Georgia Thomas

Thomas is a PhD candidate in the Harvard-MIT Program in Health Sciences and Technology whose research explores the underlying physics of optical imaging, with the goal of expanding its capacity to address important medical challenges. As a Takeda Fellow, Thomas will advance her work to create innovative tools to better understand and treat coronary atherosclerosis, a disease affecting over 18 million people in the United States alone.

A. Michael West Jr.

West is a PhD candidate in the Department of Mechanical Engineering whose research integrates robotics, AI, and health care to improve robotic rehabilitation and advance human-robot interactions. Specifically, his work explores the human neuromotor control of movement, with the goal of enhancing robot control and performance. As a Takeda Fellow, West will study the functionality of the human hand and its ability to manipulate objects and tools.

Launched in October 2020, the MIT and Accenture Convergence Initiative for Industry and Technology underscores the ways in which industry and technology can collaborate to spur innovation. The five-year initiative aims to achieve its mission through research, education, and fellowships. To that end, Accenture has once again awarded five annual fellowships to MIT graduate students working on research in industry and technology convergence who are underrepresented, including by race, ethnicity, and gender.

This year’s Accenture Fellows work across research areas including telemonitoring, human-computer interactions, operations research,  AI-mediated socialization, and chemical transformations. Their research covers a wide array of projects, including designing low-power processing hardware for telehealth applications; applying machine learning to streamline and improve business operations; improving mental health care through artificial intelligence; and using machine learning to understand the environmental and health consequences of complex chemical reactions.

As part of the application process, student nominations were invited from each unit within the School of Engineering, as well as from the Institute’s four other schools and the MIT Schwarzman College of Computing. Five exceptional students were selected as fellows for the initiative’s third year.

Drew Buzzell is a doctoral candidate in electrical engineering and computer science whose research concerns telemonitoring, a fast-growing sphere of telehealth in which information is collected through internet-of-things (IoT) connected devices and transmitted to the cloud. Currently, the high volume of information involved in telemonitoring — and the time and energy costs of processing it — make data analysis difficult. Buzzell’s work is focused on edge computing, a new computing architecture that seeks to address these challenges by managing data closer to the source, in a distributed network of IoT devices. Buzzell earned his BS in physics and engineering science and his MS in engineering science from the Pennsylvania State University.

Mengying (Cathy) Fang is a master’s student in the MIT School of Architecture and Planning. Her research focuses on augmented reality and virtual reality platforms. Fang is developing novel sensors and machine components that combine computation, materials science, and engineering. Moving forward, she will explore topics including soft robotics techniques that could be integrated with clothes and wearable devices and haptic feedback in order to develop interactions with digital objects. Fang earned a BS in mechanical engineering and human-computer interaction from Carnegie Mellon University.

Xiaoyue Gong is a doctoral candidate in operations research at the MIT Sloan School of Management. Her research aims to harness the power of machine learning and data science to reduce inefficiencies in the operation of businesses, organizations, and society. With the support of an Accenture Fellowship, Gong seeks to find solutions to operational problems by designing reinforcement learning methods and other machine learning techniques to embedded operational problems. Gong earned a BS in honors mathematics and interactive media arts from New York University.

Ruby Liu is a doctoral candidate in medical engineering and medical physics. Their research addresses the growing pandemic of loneliness among older adults, which leads to poor health outcomes and presents particularly high risks for historically marginalized people, including members of the LGBTQ+ community and people of color. Liu is designing a network of interconnected AI agents that foster connections between user and agent, offering mental health care while strengthening and facilitating human-human connections. Liu received a BS in biomedical engineering from Johns Hopkins University.

Joules Provenzano is a doctoral candidate in chemical engineering. Their work integrates machine learning and liquid chromatography-high resolution mass spectrometry (LC-HRMS) to improve our understanding of complex chemical reactions in the environment. As an Accenture Fellow, Provenzano will build upon recent advances in machine learning and LC-HRMS, including novel algorithms for processing real, experimental HR-MS data and new approaches in extracting structure-transformation rules and kinetics. Their research could speed the pace of discovery in the chemical sciences and benefits industries including oil and gas, pharmaceuticals, and agriculture. Provenzano earned a BS in chemical engineering and international and global studies from the Rochester Institute of Technology.

The MIT School of Engineering and Pillar VC today announced the MIT-Pillar AI Collective, a one-year pilot program funded by a gift from Pillar VC that will provide seed grants for projects in artificial intelligence, machine learning, and data science with the goal of supporting translational research. The program will support graduate students and postdocs through access to funding, mentorship, and customer discovery.

Administered by the MIT Deshpande Center for Technological Innovation, the MIT-Pillar AI Collective will center on the market discovery process, advancing projects through market research, customer discovery, and prototyping. Graduate students and postdocs will aim to emerge from the program having built minimum viable products, with support from Pillar VC and experienced industry leaders.

“We are grateful for this support from Pillar VC and to join forces to converge the commercialization of translational research in AI, data science, and machine learning, with an emphasis on identifying and cultivating prospective entrepreneurs,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Pillar’s focus on mentorship for our graduate students and postdoctoral researchers, and centering the program within the Deshpande Center, will undoubtedly foster big ideas in AI and create an environment for prospective companies to launch and thrive.” 

Founded by Jamie Goldstein ’89, Pillar VC is committed to growing companies and investing in personal and professional development, coaching, and community.

“Many of the most promising companies of the future are living at MIT in the form of transformational research in the fields of data science, AI, and machine learning,” says Goldstein. “We’re honored by the chance to help unlock this potential and catalyze a new generation of founders by surrounding students and postdoctoral researchers with the resources and mentorship they need to move from the lab to industry.”

The program will launch with the 2022-23 academic year. Grants will be open only to MIT faculty and students, with an emphasis on funding for graduate students in their final year, as well as postdocs. Applications must be submitted by MIT employees with principal investigator status. A selection committee composed of three MIT representatives will include Devavrat Shah, faculty director of the Deshpande Center, the Andrew (1956) and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society; the chair of the selection committee; and a representative from the MIT Schwarzman College of Computing. The committee will also include representation from Pillar VC. Funding will be provided for up to nine research teams.

“The Deshpande Center will serve as the perfect home for the new collective, given its focus on moving innovative technologies from the lab to the marketplace in the form of breakthrough products and new companies,” adds Chandrakasan. 

“The Deshpande Center has a 20-year history of guiding new technologies toward commercialization, where they can have a greater impact,” says Shah. “This new collective will help the center expand its own impact by helping more projects realize their market potential and providing more support to researchers in the fast-growing fields of AI, machine learning, and data science.”