Dr. Charmaine Demanuele is an Executive Director in the AI/ML Quantitative and Digital Sciences group within Global Biometrics and Data Management, Pfizer Research & Development. She leads a team of statisticians and data scientists that bring end-to-end clinical trial innovation with digital health technologies, imaging and statistical, AI/ML methodologies. Her group works across Pfizer’s portfolio to develop novel digital endpoints, and leverages models such as Bring-Your-Own-Device and Decentralized Clinical Trials to enable efficient, patient-centric trials and faster breakthroughs for patients. Prior to joining Pfizer, Charmaine was as a neuroscience research fellow at Harvard Medical School and Massachusetts General Hospital in Boston specialized in multimodal neuroimaging for psychiatric disorders such as schizophrenia.

Dr. Ezzeldin is the Associate Director for Advanced Technologies in the Office of Biostatistics and Pharmacovigilance (OBPV), in the Center for Biologics Evaluation and Research (CBER). Dr. Ezzeldin supported multiple programs in previous roles, for example, leading the digital health technology review team, and leading the natural history study for metachromatic leukodystrophy, HOME, among other initiatives. Currently, Dr. Ezzeldin leads the Biologics Effectiveness and Safety Innovative Methods Initiative (BEST IM), which aims to develop new and innovative methods for a semi-automated adverse events (AEs) reporting system for CBER-Regulated Biological Products.

Tala H. Fakhouri, Ph.D., MPH, is the Associate Director for Data Science and Artificial Intelligence in the Office of Medical Policy, Center for Drug Evaluation and Research at Food and Drug Administration. Dr. Fakhouri manages a team tasked with developing, coordinating, and implementing medical policy with a focus on data science and the use of Artificial Intelligence (AI) in drug development. These efforts include overseeing an AI policy group, as well as engaging external stakeholders and advancing the development of regulatory science around the use of AI in drug development. She also contributes to the development of medical policy related to real-world evidence and the use of digital health technologies for medical product development.

Usama Fayyad, PhD., is the executive director of Northeastern’s Institute for Experiential AI and a professor of the practice in the Khoury College of Computer Sciences. He continues to serve as chairman of Open Insights, a company he founded as a technology and consulting firm in 2008 to enable enterprises to extract greater value from their data assets. He co-founded OODA Health, Inc., and from 2017 to 2019 served as the company’s chief technology officer. Between 2013 and 2016 he was global chief data officer and group managing director at Barclays Bank in London.  

He has published more than 100 technical articles on data science, holds over 20 patents, and is a fellow of both the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery. He has also edited two influential books on data science and served as founding editor-in-chief on two key journals. He currently serves on the board advisory committee to Nationwide Building Society in the U.K. and on the advisory board of the WEF Global Center for Cybersecurity. Fayyad is an active angel investor/advisor in many early-stage tech startups across the U.S., Europe, and the Middle East. 

Asieh Golozar, Ph.D., is a physician-epidemiologist and biostatistician with more than 20 years of experience in health services research, real-world evidence generation, and evaluation of healthcare interventions within government, academia, and industry. She holds a PhD in epidemiology from the Johns Hopkins University School of Public Health and a Master of Health Sciences in biostatistics from JHU, supported by a postdoctoral research fellowship award with the National Cancer Institute’s Division of Cancer Epidemiology and Genetics. She earned her medical degree from the Tehran University of Medical Sciences. 

After receiving her PhD, Golozar joined the faculty at the JHU Department of Epidemiology, where she focused on cancer and diabetes epidemiology, the application of epidemiologic and statistical methods for robust synthesis of evidence from epidemiologic data, and applying evidence-based findings to strengthen public heath infrastructure and policies. In 2017, she moved into industry roles at Bayer AG, AstraZeneca, and Regeneron, where she led lifecycle management and real-world evidence generation activities in oncology, women’s health, and other therapeutic areas. 

As a professor of the practice, Golozar brings a comprehensive knowledge of healthcare systems to Northeastern. She also has the skills in research methodology and statistics required for addressing safety and effectiveness outcomes and risk assessment, and for generating high quality real-world evidence (RWE) from real-world data. She has played leading roles in large-scale private-public partnerships, including the Innovative Medicines Initiative (IMI) Big Data for Better Outcomes project and the IMI PIONEER Prostate Cancer Study-a-thon. 

Since 2018, Golozar has led the OHDSI Oncology Working Group, endeavoring to extend the OMOP common data model to support oncology use cases and advance oncology real-world evidence research. In 2021, Golozar received an OHDSI Titan Award for Clinical Application. In addition to her role at Northeastern, Golozar is an adjunct faculty member at the JHU School of Public Health and vice president of data science at Odysseus Data Services, a leading RWE technology vendor. 

Andy Houseman, Ph.D., is a Senior Project Leader / Team Leader in the Statistical Innovation Hub at Sanofi.  He has been at Sanofi for almost three years [as of June 2025], before which he held biomarker statistics positions at GSK.  In the more distant past he held faculty positions (research faculty at Harvard and Brown, tenured at Oregon State) before pivoting his career to work in the pharmaceutical industry.  He has published extensively in Biostatistics, Bioinformatics, and subject matter areas that utilize biostatistical methods.

Mark Van Der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor in Biostatistics and Statistics at the University of California, Berkeley. Mark research interests include censored data, causal inference, genomics, observational studies and adaptive designs. Mark has led the development of Targeted Learning, including Super Learning and Targeted maximum likelihood estimation (TMLE). Targeted Learning improves on typical current statistical practice by avoiding reliance on wrong model assumptions, and its capability to target any question of interest.  In 2005 Mark was awarded the Committee of Presidents of Statistical Societies (COPSS) Presidential Award in recognition of outstanding contributions to the statistics profession. He also received the 2004 Spiegelman Award and 2005 van Dantzig Award. He is co-founder of the international Journal of Biostatistics and Journal of Causal Inference. Mark has authored various books on Targeted Learning, Censored Data and Multiple Testing, published over 400 publications,  mentored 60 Ph.D students and 30 postdoctoral fellows.

David Madigan, Ph.D., came to Northeastern from Columbia University, where he served as executive vice president for arts and sciences and dean of the Faculty of Arts and Sciences. In those roles, he oversaw five schools with 27 departments and some 50 research centers and institutes. He led successful initiatives to expand lifelong learning programs, and to make Columbia’s faculty and student body more diverse. Notably, he spearheaded a program to ensure that Columbia’s first-generation students would have the mentoring and support they needed to succeed in a highly demanding academic environment.

At Columbia and in his previous position as Rutgers University’s Dean of Physical and Mathematical Sciences, Madigan developed new interdisciplinary learning and research collaborations, and forged industry partnerships to support student and faculty innovation. Under his leadership as department chair, Columbia’s statistics faculty ascended to the ranks of the nation’s top 10.

A fellow of the American Association for the Advancement of Science, Madigan has long been a leading researcher at the intersection of Big Data with healthcare innovation. While serving on the statistics faculty at the University of Washington, he was a member of the world-renowned Fred Hutchinson Cancer Research Center.

Madigan also brings a strong global perspective to Northeastern. A native of Ireland, he has led large-scale global collaborations involving academic and pharmaceutical company researchers and government regulators in the U.S. and Europe. Beyond the halls of academia, he has experience as an entrepreneur, innovation leader, researcher, and consultant, both in the U.S. and Ireland. He earned his BA and PhD from Trinity College Dublin.

Tommaso Mansi, Ph.D., is a member of the IEEE received his Ph.D. degree from INRIA Sophia Antipolis in 2010. He is currently serving as the VP of AI and Digital Health at Janssen: Pharmaceutical Companies of Johnson and Johnson. He then worked at Siemens Healthineers, prior to joining Janssen. His research focuses on artificial intelligence, medical imaging, computational physiology, and image-guided therapy, with the goal to develop solutions to enable next generation treatments. He is currently an AIMBE Fellow. He and his team have been honored of multiple awards, in particular the Siemens Inventor of the Year 2020, the MICCAI Young Scientist (2011, 2013, and 2018), and the NJ Thomas Alva Edison Patent Award (2015 and 2019).(Based on document published on 19 January 2022).  https://www.tmansi.net/

Scott McClain, Ph.D., brings 33 years of experience in toxicology, biotechnology, and product safety risk assessment. His academic background is environmental and molecular toxicology with a focus in quantitative risk assessment. He received his advanced degrees from Miami University of OH.  Career industry experience ranges across immunology combined with cellular genetics, disease model research and analytics/statistics. More recently, the focus has been on regulatory safety data communications and analytic process development with global regulatory agencies.  He currently serves as strategic advisor to the Health and Life Science practice at SAS Institute, Inc.

Annie Qu, Ph.D., Annie Qu is Chancellor’s Professor, Department of Statistics, University of California, Irvine. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms for personalized medicine, text mining, recommender systems, medical imaging data, and network data analyses for complex heterogeneous data. The newly developed methods can extract essential and relevant information from large volumes of intensively collected data, such as mobile health data. Her research impacts many fields, including biomedical studies, genomic research, public health research, social and political sciences. Before joining UC Irvine, Dr. Qu was a Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded the Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC and was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association, and the American Association for the Advancement of Science. She is also a recipient of IMS Medallion Award and Lecturer in 2024. She serves as Journal of the American Statistical Association Theory and Methods Co-Editor from 2023 to 2025 and as IMS Program Secretary from 2021 to 2027.

Qu Lab website: https://faculty.sites.uci.edu/qulab/ 

Aaditya Ramdas is an Associate Professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University, as well as a visiting academic at Amazon Research. His work has been recognized by the Sloan fellowship, the IMS Peter Hall Early Career Prize, the inaugural COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, faculty research awards from Google and Adobe, and discussion papers at JASA and JRSSB. 

Malaikannan Sankarasubbu, Ph.D., is the Chief Technology Officer at Saama Technologies, where he leads initiatives to accelerate clinical trials through advanced artificial intelligence and analytics. With over 15 years in software product development, Malaikannan is dedicated to “bringing certainty to uncertainty” in AI research and life sciences, helping streamline clinical trials for faster, more reliable outcomes. As a seasoned entrepreneur, he has founded companies and built high-performance teams that drive innovation in healthcare.

His research spans Natural Language Understanding, uncertainty bounds, and Explainable AI, contributing to transparent and effective AI applications in precision medicine. With numerous peer-reviewed publications in major conferences, [available on Google Scholar](https://scholar.google.com/citations?user=znWv6tUAAAAJ&hl=en), Malaikannan is a thought leader advancing AI’s role in life sciences, bringing new insights and efficiency to clinical research.

Suchi Saria, Ph.D. is the founder and president of Bayesian Health, the most scientifically advanced clinical AI platform company that augments care teams by bringing together state of the AI/ML technology combined with responsible AI best practices to dramatically improve quality while saving clinicians’ time. She is also an AI Professor at Johns Hopkins where she holds the John C. Malone endowed chair and is the Director of AI and health lab.

Dr. Saria’s work in AI over the last two decades has led to foundational advances in the technology, best practices around translation, and AI policy. She has written several seminal papers in AI/ML around issues of learning robust models, detecting drifts, monitoring and learning from messy real-world datasets. Her applied research has built on these technical advances to develop novel next generation diagnostic and treatment planning tools that use AI/ML to individualize care. Her work has been funded by leading organizations including the NSF, DARPA, FDA, NIH and CDC and she regularly serves as a scientific advisor to leading Fortune 500 companies.

Dr. Saria completed her PhD in AI at Stanford, her BSc in Physics, Computer Science and Statistics at Mount Holyoke. She’s a Sloan Research Fellow, named by IEEE to “AI’s 10 to Watch”. Modern Healthcare’s Top 25 Innovators, World Technology Forum’ Technology Pioneer, and her work was recognized as one of TIME’s Best Inventions in 2023. Her work with Bayesian has also received FDA Breakthrough Designation. She is a co-founder and on the board of directors for Coalition of Health AI (CHAI) and serves on the National Academy of Medicine AI Code of Conduct.