Massachusetts Institute of TechnologyUnited States
Abhijit Banerjee is a Professor in the Department of Economics at the Massachusetts Institute of Technology (MIT). His research focuses on development economics, public health policy, and social protection programs, particularly addressing poverty and inequality in low- and middle-income countries. He has conducted large-scale experiments evaluating interventions such as conditional cash transfers, universal basic income, and healthcare policies. Key areas of interest include the design and implementation of anti-poverty programs, behavioral economics, and the long-term health impacts of infectious diseases like COVID-19. His work often integrates experimental methods to assess policy effectiveness, with contributions to global health studies and economic development strategies. Notably, his research on the RECOVERY trial evaluates treatments for hospitalized COVID-19 patients, while other studies explore financial spillover effects of electronic government transfers in Indonesia and savings behavior interventions in Chile. Banerjee has collaborated on projects analyzing post-COVID-19 health outcomes, labor market dynamics in India, and the role of trusted messengers in public health communication during crises. Despite the breadth of his contributions, specific details about his educational background, grants, or lab affiliations are not explicitly provided in the source text.
Massachusetts Institute of TechnologyUnited States
Joshua D. Angrist is the Ford Professor of Economics at the Massachusetts Institute of Technology, where he has been a faculty member since 1996. He is also a co-founder and director of MIT's Blueprint Labs and a Research Associate at the National Bureau of Economic Research. Angrist shares the 2021 Nobel Prize in Economic Sciences with David Card and Guido Imbens for their methodological contributions to the analysis of causal relationships. Angrist received his B.A. from Oberlin College in 1982 and completed his Ph.D. in Economics at Princeton University in 1989. Prior to joining MIT, he taught at Harvard University and the Hebrew University of Jerusalem. His academic journey began somewhat unconventionally, as he left high school early after 11th grade, worked for over a year, and only later discovered his passion for economics through an inspiring teacher at Oberlin. Angrist's research focuses on developing and applying innovative econometric methods to answer important economic questions using natural experiments. His work spans labor economics, education economics, and causal inference methodology. He is particularly known for his contributions to instrumental variables methods and the Local Average Treatment Effect (LATE) framework developed with Guido Imbens. His research explores the economics of education and school reform, the impact of social programs on labor markets, and the effects of immigration and regulation. His recent publications reveal a continued focus on causal inference methods applied to education policy questions, labor market issues, and health economics. The trend shows increasing sophistication in research design, with particular attention to addressing selection bias and developing methods for external validity. His work spans theoretical econometric contributions alongside empirical applications in education, labor markets, and health. Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2021) Fama Prize for Graduate Education (2018) Fellow of the American Academy of Arts and Sciences Fellow of the Econometric Society Angrist is deeply committed to teaching and mentoring. He has developed influential econometrics textbooks including 'Mostly Harmless Econometrics' and 'Mastering Metrics' with Jörn-Steffen Pischke. At MIT, he teaches courses including Labor Economics I (14.661), Econometric Data Science (14.32), and Labor Economics and Public Policy (14.64). He emphasizes selecting UROP students who have mastered foundational economics through courses like 14.64 and 14.32. Beyond MIT, Angrist co-founded Avela, a software startup using cutting-edge research to help schools improve enrollment and operations. Angrist co-founded and directs MIT's Blueprint Labs, which brings together researchers from economics, computer science, and education to develop innovative solutions for educational challenges. Through Blueprint Labs and Avela, his work bridges academic research with practical applications in education policy and technology.
Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.
University of Illinois Urbana-ChampaignUnited States
Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .
Stefanos Zenios is Professor at Stanford Graduate School of Business with dual appointments in Entrepreneurship and Operations, Information & Technology . He directs Stanford GSB’s Center for Entrepreneurial Studies and co-directs the Doerr School of Sustainability ’s Program in Ecopreneurship. His Startup Garage course has launched companies like DoorDash while alumni have raised over $3B in venture capital. He co-authored the foundational Biodesign textbook and previously served as Editor-in-Chief of Operations Research . PhD, MIT Operations Research (1996) MA, University of Cambridge (1996) BA, University of Cambridge (1992) Research Focus Dr. Zenios’ work bridges operations research and innovation management , with significant contributions to: Healthcare operations : Administrative cost reduction, organ allocation optimization, and clinical workflow improvements Entrepreneurship : Venture creation frameworks and experiential education Ecopreneurship : Sustainable business model innovation His Precedents Thinking framework (HBR 2025) has gained global recognition for solving systemic inefficiencies. Current research includes the biennial Search Fund Study with Peter Kelly. Scientific Recognition INFORMS Fellow Multiple Best Paper Awards, INFORMS CAREER Award, National Science Foundation (2000) Dhirubhai Ambani Faculty Fellow (2017–2020) George E. Nicholson Award (1997) He has received extensive media coverage in Fast Company , Time Magazine , and Stanford Business . His research has been featured in over 150 press mentions including JAMA , Harvard Business Review , and Health Affairs .
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Mayo Clinic College of Medicine and ScienceUnited States
Mark R. Pittelkow is a Professor of Dermatology at the Mayo Clinic, holding a primary appointment as a Consultant in the Department of Dermatology. He completed his MD at Mayo Medical School and advanced training through Mayo Graduate School of Medicine and Northwestern University. His research focuses on cutaneous lymphomas, photoprotection, and skin cancer, with leadership roles in international guidelines (ISCL/EORTC). He contributes to clinical trials, medical education, and interdisciplinary research through affiliations like the Mayo Clinic Comprehensive Cancer Center and Regenerative Biotherapeutics. Education: MD (Mayo Medical School), MS (Biological Sciences, Northwestern), BA (Northwestern University). Research spans cutaneous lymphoma classification, immunology, and translational dermatology. Notable publications address staging systems for mycosis fungoides/Sezary syndrome and photoprotection strategies. His work integrates clinical practice with molecular mechanisms, such as JAK inhibitor responses in lichen planus. Labs/Teams: Collaborates with Mayo Clinic Cancer Center and interdisciplinary groups in immunotherapy and regenerative medicine. Supervises clinical trials and postdoctoral training programs.
Lyle Ungar is a Professor at the Department of Computer and Information Science at the University of Pennsylvania . He is affiliated with multiple graduate groups, including Genomics and Computational Biology in the School of Medicine , Operations, Information and Decisions in the Wharton School , and Psychology in the School of Arts and Sciences . His research focuses on explainable machine learning , deep learning , and natural language processing for psychology and medical research , analyzing social media and sensor data to understand well-being, empathy, and stress. His work spans bioinformatics , applied economics , and group decision-making . Recent publications examine LLM-based tutoring , cross-cultural translation , and AI in palliative care , showing trends in reinforcement learning , mobile health , and health data analytics . He has contributed to Google Scholar , PubMed , and DBLP with over 15 papers since 2023. Scientific Awards : 2019 Alan I. Leshner Leadership Institute Public Engagement Fellow His students include Vitoria Aquino Guardieiro , Yihao Li , and co-advised researchers like Shreya Havaldar with Eric Wong. He leads projects at interdisciplinary centers such as the Annenberg Public Policy Center , Center for Cognitive Neuroscience , and Institute for Translational Medicine .
Jenna Wiens is an Associate Professor of Computer Science and Engineering at the University of Michigan's College of Engineering. She serves as Associate Director of the Artificial Intelligence Lab and co-Director of AI & Digital Health Innovation. Leading the MLD3 research group, her work focuses on machine learning and AI applications for healthcare data. PhD from MIT (2014) under John Guttag NSF CAREER Award recipient (2016) Humboldt Foundation Carl Friedrich von Siemens Award (2024) Her research addresses four key technical thrusts: Time-series analysis for predicting clinical outcomes Robust machine learning against spurious correlations Decision-making with causal inference and offline reinforcement learning Human-AI collaboration frameworks Notable methodological contributions include the FIDDLE preprocessing pipeline for clinical time-series data, foundational work on survival analysis, and novel approaches to model selection in healthcare reinforcement learning. Her work has led to real-world AI deployments in infection prevention and patient risk stratification. Scientific honors include: Forbes 30 Under 30 (2015) MIT Tech Review 35 Innovators Under 35 (2017) Sloan Research Fellowship in Computer Science (2020) Sarah Goddard Power Award (2023) Wiens collaborates with clinicians across disciplines, emphasizing clinician-in-the-loop AI systems and ethical implementation in healthcare workflows.
David H. Sherman is the Hans W. Vahlteich Professor of Medicinal Chemistry at the University of Michigan, holding joint appointments in the College of Pharmacy (Department of Medicinal Chemistry), Medical School (Microbiology & Immunology), and College of Literature, Science, and the Arts (Chemistry). He leads the Sherman Lab at the Life Sciences Institute and co-founded the Natural Products Discovery Core. His research focuses on natural product discovery, biosynthetic pathways, and drug development for infectious diseases, cancer, and neurological disorders. Education: PhD in Synthetic Organic Chemistry from Columbia University (1981), BA in Chemistry from UC Santa Cruz (1978). Postdoctoral research at MIT (1984). Research interests include microbial secondary metabolites, enzymatic catalysis (e.g., C-H functionalization, polyketide assembly), and high-throughput drug screening. He pioneered a microbial natural product library with over 50,000 samples. Current projects emphasize developing macrolide antibiotics and advancing compounds toward clinical trials through the Natural Products Biosciences Initiative. Collaborations span global institutions, with a focus on biodiversity conservation and capacity-building in low-income nations. He has mentored 67 PhD students, 60 postdocs, and 85+ undergraduates, fostering interdisciplinary training in chemical biology and microbial biochemistry. Labs/Teams: Sherman Lab (Life Sciences Institute), Center Member at Samuel and Jean Frankel Cardiovascular Center, Center for Computational Medicine and Bioinformatics, Rogel Cancer Center.
Allison Koenecke is an Assistant Professor of Information Science at Cornell Tech and a field faculty member in Computer Science at Cornell University. Previously, she was a postdoctoral researcher at Microsoft Research New England and completed her PhD at Stanford University’s Institute for Computational & Mathematical Engineering. Her research focuses on algorithmic fairness, computational social science, and causal inference in public health, addressing disparities in automated systems like speech recognition and policy decision-making. Education : PhD, Stanford Institute for Computational & Mathematical Engineering MA/MS, Stanford University BA/BS, Massachusetts Institute of Technology Research Interests : Dr. Koenecke’s work bridges economics and computer science, emphasizing equity in AI systems. Key areas include: Algorithmic bias in speech recognition (e.g., racial disparities in voice assistants) Fairness in policy tools like environmental justice data systems Causal analysis in public health interventions Ethical implications of large language models in education Media & Impact : Her research has been featured in outlets like New York Times , Science , and Scientific American . Notable studies include exposing racial gaps in speech-to-text systems and advocating for inclusive dataset development. She also explores societal impacts of AI in education and governance. Awards : Sloan Research Fellow in Computer Science Forbes 30 Under 30 in Science NSF Awards Cornell CIS Teaching Excellence Award (2024) Teaching & Outreach : She teaches courses like Designing Fair Algorithms and Data Science for Global Development , emphasizing interdisciplinary collaboration. Her PhD Professionalization course addresses hidden curricula in academia. She advises on AI ethics for nonprofits, tech companies, and government agencies.
Brooke Hudspeth, PharmD, is an Associate Professor and Chief Practice Officer at the University of Kentucky (UK) College of Pharmacy. She oversees community pharmacy initiatives and serves as acting President of the Kentucky Pharmacists Association (KPhA). Her roles include mentoring pharmacy students and residents as a preceptor, and leading collaborative projects with organizations like the American Pharmacists Association Foundation, Kroger Co., and the CDC. Educationally, she holds a Doctor of Pharmacy from the UK College of Pharmacy, alongside PGY1/PGY2 Community-based Pharmacy Practice Residencies from the same institution. Her research focuses on diabetes prevention, obesity, and pharmacist-driven clinical services, particularly through expanding the National Diabetes Prevention Program (NDPP) to five U.S. states via a $7.5M CDC grant. Research interests emphasize health disparities, vaccine accessibility, and optimizing pharmacist roles in self-care conditions. Notable projects include influenza point-of-care testing and pharmacy partnerships during the pandemic. Her work bridges academic innovation with real-world healthcare challenges, advocating for policy changes to enhance pharmacist payment for clinical services. Despite no explicitly listed awards, her impactful grant-funded projects reflect significant contributions to public health. She advises students and residents while leading residency expansion initiatives. Collaborations with major institutions underscore her role in advancing pharmacy practice and education nationally. Hudspeth’s lab/team is embedded within UK’s College of Pharmacy, focusing on translational research to improve patient care through pharmacist-led interventions. Future work includes expanding telehealth pharmacy services and addressing healthcare workforce shortages.
Susan A. Murphy is the Mallinckrodt Professor of Statistics and of Computer Science at Harvard University, with affiliations to the Kempner Institute. She leads the Statistical Reinforcement Learning Lab, focusing on developing algorithms to inform sequential decision-making in health, particularly for Just-in-Time Adaptive Interventions (JITAIs) and micro-randomized trials (MRTs). Her work is funded by NIH institutes, including NIDA, NHLBI, and NIBIB. Dr. Murphy has been awarded a MacArthur Fellowship (2013) and is a member of the National Academy of Medicine (2014) and the National Academy of Sciences (2016). Her research integrates statistical methods with computer science techniques to optimize mobile health interventions. She collaborates with d3Lab and mDOT on projects like HeartSteps and Sense2Stop, evaluating real-time treatment policies. Notable contributions include advancing MRT designs, sample size calculations, and reinforcement learning algorithms for personalized healthcare. Dr. Murphy advises a large team of postdocs, graduate students, and undergraduates, many of whom hold academic and industry roles globally. She emphasizes engagement in digital interventions, balancing personalization with ethical considerations. Her lab’s work spans algorithm development, clinical trial design, and causal inference, aiming to improve health outcomes through adaptive interventions.
University of North Carolina at Chapel HillUnited States
Tanya P. Garcia, PhD is an Associate Professor of Biostatistics at the Gillings School of Public Health and Research Faculty in the UNC Neurology Huntington Disease Program at the University of North Carolina at Chapel Hill . She leads the Methods for INcomplete Data (MIND) Lab , focusing on statistical methods for handling censored, missing, or incomplete data in neurodegenerative disease progression studies. Education: PhD in Statistics, Texas A&M University MS in Statistics, University of Western Ohio MS in Industrial Engineering and Operations Research, UC Berkeley Research Interests: Specializing in High-Dimensional Variable Selection , Longitudinal Data Analysis , and Neurodegenerative Disease Modeling , her work develops reproducible statistical methods for Huntington's disease progression, improving clinical trial design and biomarker identification. Scientific Awards: American Statistical Association Fellow (2024) Landis Award for Outstanding Mentorship (2024) Roy R. Kuebler Award (2024) Gertrude M. Cox Award (2024) Leadership & Mentorship: Director of the MIND Lab, Chair-Elect of the Biometrics Section of ASA, and Tyson Academic Leadership Fellow (2023–2024). Her lab alumni have secured prestigious positions at institutions like Wake Forest University and Baylor University.
Tara McAllister is an Associate Professor and Director of the Doctoral Program in Communicative Sciences and Disorders at New York University’s Steinhardt School. She leads the Biofeedback Intervention Technology for Speech (BITS) Lab , focusing on speech learning mechanisms and biofeedback treatments for speech disorders. Her work emphasizes acoustic and ultrasound biofeedback efficacy in resolving residual speech sound disorders, particularly in children. McAllister directs development of the staRt iOS app, expanding access to biofeedback training. She holds degrees from Harvard, MIT, and Boston University, with clinical expertise in speech-language pathology. Education: A.B./A.M., Linguistics, Harvard University (2003) M.S., Communication Disorders, Boston University (2007) Ph.D., Linguistics, MIT (2009) Research Interests: Speech motor control, perception-production links, bilingual phonological development, and technology-driven interventions. Her NIH-funded studies investigate biofeedback applications for speech disorders and crowdsourcing methodologies for perceptual analysis. Grants & Labs: NIH/NIDCD-funded BITS Lab research staRt app development since 2014 Teaching: Courses include Critical Evaluation of Research and Speech Science Instrumentation , emphasizing evidence-based practices in communication sciences.
