Please join the Pathobiology Graduate Program for the final examination of Titus Maina for the degree of Doctor of Philosophy. The candidate will present himself for examination on the dissertation entitled “Deciphering HPV16 Genetic Variation and the Cervical Immune Microenvironment in HIV-Associated Dysplasia and Cancer”.

Join the Data Science Institute and Cogut Institute for a panel with experts on what threatens, makes, and maintains trust in data and research:

What factors are affecting public and scholarly trust in academic research and data and why does it matter? What can universities do to maintain the integrity of research and data? Panelists will consider these and other questions from various period, disciplinary, and institutional perspectives.

Please register in advance for this event.

This event is open to the public only with advance registration. Registration or Brown IDs will be checked at the door.

Panel Moderator:

Dr. Holly Case

Professor of History, Professor of Humanities, Deputy Director of the Data Science Institute

Holly Case is professor of History focusing on the history of modern Europe who has written two books and a number of academic and general-interest articles on a variety of themes relating to politics, literature, diplomacy, science, war, and the history of ideas. She now teaches a course on the History of Artificial Intelligence (HIST 1956S) and together with Eliezer Upfal in Computer Science has initiated an interdisciplinary reading group on AI across multiple fields at Brown (Computer Science, History, Neuroscience, History of Art, Applied Math, Philosophy, Modern Culture and Media, and Data Science).

Panelists: 

Juliane Blyth

Senior Director, Research Integrity, Brown Division of Research

Jules is the Senior Director of the Office of Research Integrity at Brown University, where she oversees the institution’s research compliance programs, including the Human Research Protection Program, Animal Research Compliance, Research Integrity, Conflict of Interest, Export Control Compliance, and Research Ethics Education. Prior to joining Brown, she worked in research compliance at Partners Healthcare in Boston and at Johns Hopkins University in Baltimore.

Dr. Prudence Carter

Sarah and Joseph Jr. Dowling Professor of Sociology; Peltz Ruttenberg Family Director of the Center for the Study of Race and Ethnicity in America (CSREA), Brown University

Prudence Carter’s research explores the enduring inequalities in education and society, with a particular focus on their root causes and potential solutions. Her work examines how race, ethnicity, class, and gender shape academic achievement and mobility disparities both in the United States and globally.

Melissa Cherney

CEO, Rhode Island Community Food Bank

Melissa Cherney has been a leader and advocate in hunger relief for nearly two decades. She is the new chief executive officer of the Rhode Island Community Food Bank. Cherney served first as president and then as chief executive officer of the Great Plains Food Bank, North Dakota’s largest hunger relief organization and its only food bank. There, she led initiatives to expand food access, mobilize legislators, advocate for policy change, and strengthen community partnerships on behalf of the more than 70,000 people served through 200 distribution sites each month.

Frank Donnelly

Head of GIS & Data Services, Brown University Library; Coordinator of GeoData@SciLi.

Frank is a a geospatial information professional whose expertise lies at the intersection between academic librarianship and geographic research. He plays multiple roles in his work as a librarian, instructor, and researcher. As a librarian, he maintains an online repository of data, develops value-added data resources, creates tutorials, manages a GIS lab, and works with students and faculty to help them find, process, organize, and interpret information and data. As an instructor, he hosst introductory workshops in GIS and guest lecture in a variety of courses, primarily on using census data for research. As a researcher, he applies these skills and knowledge to produce both peer-reviewed scholarship and practitioner-based reports and information. His academic background is rooted in the disciplines of geography and history, with an emphasis on urban and regional geography and GIS.

Dr. Diana Freed 

Assistant Professor of Computer Science and Data Science, Brown University

Diana Freed’s research interests are in human-computer interaction (HCI), computer security, privacy, inclusive design, technology policy, and digital health. She works on designing, building, and evaluating sociotechnical systems in the context of youth interpersonal relationships, intimate partner violence, and caregiving systems. She also develops resources to improve digital literacy to enable individuals to make informed choices regarding technology use and to improve understanding of digital risks and harms. She uses qualitative and computational social science methods to develop new tools, technologies, and theories to detect and mitigate digital harms and inequities, facilitate safety, and inform policy.

The use and misuse of AI in university admissions

AI is transforming higher education, including the admissions process. Using a novel dataset of nearly 20,000 applications to a competitive graduate program, we study how applicants use large language models–often violating stated policies–and how this impacts admissions outcomes. We then revisit the debate over standardized test scores. While test scores are often treated as a key signal of academic ability, we show that AI-extracted signals from application materials largely subsume their informational value, rendering the marginal benefit of test scores negligible in our setting. This result reflects both the power of AI to extract rich, decision-relevant information from unstructured materials and a common statistical error in the literature, which conflates predictions with decisions.

Sharad Goel is a Professor of Public Policy at Harvard in the Kennedy School of Government. He looks at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary social issues. He is particularly interested in the use (and misuse) of AI for education. His recent work has also considered empirical tests for discrimination (policing, outcome tests, disparate impact), the design of equitable algorithms (theory, label bias, guidance, language models, healthcare, automated speech recognition), access to education (undermatching, admissions), and democratic governance (swing voting, polling errors, voter fraud, political polarization). He founded, and now co-directs, the Computational Policy Lab.

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This seminar and discussion will be mediated by Emilia Huerta-Sanchez (CCMB, EEOB)

The Data Matters Seminar Series is hosted by the Data Science Institute, covering why data matters across the physical, biological, computational, and social sciences. Data Matters is intended to stimulate conversations and collaboration by bringing multiple perspectives to challenging data-driven problems.

Join the Brown Center for Computational Molecular Biology for our seminar with Dr. Sumaiya Iqbal (Bioinformatics and Machine Learning, Broad Institute of MIT and Harvard), on April 8, 2025:

“ Connecting the Dots in Biology at Scale in the Age of AI”

Abstract:

We live in the era of big data and AI: the biomedical community now has access to millions of predicted and experimental protein structures, alongside clinical genomics data (genetic, disease-associated variants), functional genomics data (genome-edited synthetic mutations), and clinical/health data. Investigating clinical and functional data in the context of protein conformations, dynamics, and interactions helps generate hypotheses and design biophysical and therapeutic interventions—computationally and/or experimentally. However, this also demands seamless integration of data across genomics, transcriptomics, proteomics, and structural biology—which remains challenging. My lab develops methods and tools to bridge gaps across complex multi-omics data using data science, statistics, and ML/AI.

In this talk, I will dive deep into the background, rationale, and scalable applications of the Broad Institute Genomics 2 Proteins (G2P) portal (https://g2p.broadinstitute.org/), a human proteome-wide resource and discovery tool for linking genetic screening outputs to protein sequences and structures. I will then highlight computational methods we developed at the intersection of multiple omics for biologically actionable hypothesis generation using integrated data from the G2P portal and beyond, including: (1) interpretation of the molecular effects of genetic mutations, (2) structure–function analyses of base-editor tiling mutagenesis data, (3) prediction of ion channel variant function, and (4) discovery of novel drug targets in fusion-driven pediatric oncology.

Bio:

Sumaiya Iqbal is a senior group leader in the Ladders to Cures (L2C) Accelerator at the Broad Institute of MIT and Harvard–an initiative to accelerate advances towards treatments and cures for patients with rare genetic diseases. She is also an associate member of the Cancer Data Sciences, Dana-Farber/Harvard Cancer Center (DF/HCC). She developed the Broad Institute Genomics 2 Proteins (G2P) portal, a human proteome-wide discovery platform for linking genetic screening outputs to protein sequences and structures.

Iqbal is a computer scientist by training but a life science researcher by determination. At the Broad, she leads the bioinformatics and machine learning group aimed at connecting genomics to proteins and mechanism using data sciences, statistics, and ML/AI. The focus of the Iqbal lab is: building bioinformatics resources to bridge the gap across complex multi-omics data types, developing methods to unveil the molecular effect of genetic/synthetic mutations on protein structure-function relationships, and building ML/AI-driven innovative tools for new target and small-molecule hit discovery. Iqbal lab works across all disease areas, with a special focus on rare genetic diseases and pediatric cancer.

Iqbal started her lab at the Broad Institute in 2023, after two years as a postdoc at the Analytic and Translational Genetics Unit of MGH, Harvard Medical School, and the Stanley Center for Psychiatric Research, and three years as a research scientist at the Center for Development of Therapeutics at the Broad. Iqbal has earned multiple awards including the Broad Institute SPARC award, the Merkin Institute award for Transformative Technologies in Healthcare, and the BroadIgnite award. She is also a recipient of Broad Institute Staff Scientists Distinction Award in Scientific Collaboration and serves as the chair for the Broad Institute Machine Learning for Drug Discovery symposium.

Iqbal holds a Ph.D. in computer science from the LSU New Orleans, and a M.Sc. and B.Sc. in computer science from the Bangladesh University of Engineering and Technology.

Professor Diane Mathis from the Harvard Medical School will present “Thymic mimetic cells:  tolerogenic masqueraders”. This lecture is part of the 2026 Pathobiology Spring Seminar Series.

Presenter: Alex Diaz-Papkovich, Center for Computational Molecular Biology

“Dimensionality reduction, visualization, and clustering of genomic data”

Genomic data, such as genotype or RNA sequencing data, are high dimensional. One of the critical early steps in analysis is to carry out dimensionality reduction, often for visualization, exploratory analysis, or downstream tasks such as clustering. This workshop will give an overview of common methods such as principal components analysis (PCA) and uniform manifold approximation and projection (UMAP) with a focus on human genetic data and discussion of single-cell data applications. Code and interactive notebooks will be provided; while some knowledge of R, Python, or genomics is ideal, this workshop will also benefit those curious about or new to the field.

Level: Some experience

Required Prior Knowledge: Assumes experience with Python and/or R

DSCoV (Data Science, Computation, and Visualization) workshops are lunchtime introductions to basic data science and programming skills and tools, offered by and for Brown staff, faculty, and students (with occasional presenters from outside Brown). The workshops are interactive, so bring a laptop. All are welcome.

Workshops will be held virtually on Zoom this semester: https://brown.zoom.us/j/96158269691

Please join the Pathobiology Graduate Program for the final examination of Melanie Ortiz Alvarez de la Campa for the degree of Doctor of Philosophy. The candidate will present themself for examination on the dissertation entitled “Early life stress accelerates murine microbiome maturation and contributes to persistent anxiety”.

Please join the Pathobiology Graduate Program for the final examination of Noe Mercado for the degree of Doctor of Philosophy. The candidate will present himself for examination on the dissertation entitled “Investigating the role of cytomegalovirus in glioblastoma tumor growth and progression”.

Mariel Finucane, Ph.D.,
Director of Statistical Methods at Blue Cross Blue Shield of MA

Talk Title: Statistical Significance Makes our Mission: Impossible

Abstract: It’s an old trick among statisticians: when we can’t answer the question that our collaborators are substantively interested in, we answer a question that sounds like that question. We hope they won’t notice the bait and switch. That, in a nutshell, is the story of statistical significance in the field of social policy evaluation. People want some assurance that the policy being studied moved the needle for the individuals or communities it serves. Statistical significance and p-values sound like they can provide that assurance. They can’t, and when we pretend they can (which we still do horrifyingly often!), we can be making a large-magnitude – not just semantic – mistake. In this talk, we provide examples of how statistical significance is an obstacle to achieving our mission, and we describe how Bayesian methods using evidence-based priors can give us what we thought we were getting with statistical significance, and more.

Lunch will be provided*

Associate Professor Aleeza Gerstein from the University of Manitoba will present “Human fungal pathogens and recurrent infections through the lens of evolution”. This lecture is part of the 2026 Pathobiology Spring Seminar Series.

Yong Chen, Ph.D.,
Professor of Biostatistics and Senior Scholar
Center for Clinical Epidemiology & Biostatistics
University of Pennsylvania, Perelman School of Medicine

Talk Title: Principled AI

Abstract: We are entering an era where artificial intelligence is transforming science, medicine, and society, yet the enduring principles of statistics remain central to ensuring validity, interpretability, and trust. This talk reflects on core statistical foundations, including error control and calibration, optimality and decision-theoretic framing, reproducibility and robustness, sampling, prior/empirical Bayes, modern statistical/mathematical computation, and the likelihood principle. And we will examine their continued impact in the age of AI. Through examples drawn from several clinical studies, the discussion will show how principled statistical thinking safeguards against bias while enabling innovation. These case studies illustrate that statisticians are not bystanders but essential architects of reliable evidence generation and decision-making in an AI-driven world.

Lunch will be provided*

Brown University Center for Gerontology & Healthcare Research is pleased to announce our 37th Annual Katz Lecture:

Workforce Challenges in the Care of Older Adults

Speaker: David C. Grabowski, PhD

The US long-term care system has experienced long-standing staffing shortfalls. During this talk, Dr. Grabowski will provide an overview of how staff matter in the production of high-quality care and why the long-standing shortfall has persisted. In particular, he will discuss his research on different long-term care workforce strategies including the use of foreign-born workers. Finally, he will discuss systematic changes that the US might adopt to meet the workforce demand of an aging population.

David C. Grabowski, PhD, is a professor of health care policy in the Department of Health Care Policy at Harvard Medical School. His research examines the economics of aging with a particular interest in the areas of long-term care and post-acute care. He is a member of the National Academy of Medicine. He has published nearly 300 peer-reviewed articles, and his work has appeared in leading economics, health policy, and medical journals. He has testified to Congress on seven separate occasions. From 2017 through 2023, Dr. Grabowski was a member of the Medicare Payment Advisory Commission (MedPAC), which is an independent agency established to advise the U.S. Congress on issues affecting the Medicare program. He has also served on several CMS technical expert panels. During the pandemic, he served on the CMS Nursing Home Coronavirus Commission.