Join us for a Brown2026 lecture on how access to information in the digital age can affect civic participation.

Join the Data Science Institute for our next Data & Donuts session featuring Serena Booth, Assistant Professor of Computer Science at Brown University, where she studies how people design, understand, and evaluate AI systems. Her research focuses on human-AI and human-robot interaction, particularly how users specify goals and behaviors for machine learning systems. She also examines the societal impacts of AI, including how these technologies affect workers and consumers.
The Data & Donuts series offers an informal space for students, faculty, and staff to connect over short talks by Brown faculty and data scientists on topics and campus resources related to data science. These roundtable-style sessions are open to all members of the Brown community with an interest in data science.

Join the Brown Center for Computational Molecular Biology for our seminar with Dr. Yang Lu (UW Madison), on March 11, 2025:

Advancing Trustworthy and Data-Driven Hypothesis Generation in Biomedical Research

Abstract: Rapid developments in high-throughput sequencing have enabled biologists to collect large volumes of multi-omics data with unprecedented resolution. However, interpretation of such an increasing amount of heterogeneous biological data becomes highly nontrivial. In my talk, I will present a data-driven research paradigm to discover testable hypotheses directly from biological data in an interpretable and trustworthy fashion. In particular, the talk will focus on three recent works that address key aspects of biomedical research: analyzing data, generating and prioritizing hypotheses:

(1) An interpretation method that generates testable biological hypotheses from machine learning (ML) models. Specifically, I developed an uncertainty-aware method to identify from single-cell RNA-seq data a combinatorial gene set signature to characterize the single-cell type. This method pioneers efforts to streamline existing single-cell analysis pipelines through a unified framework for easy interpretation.

(2) A statistical method that prioritizes non-additive interactions from any ML models. The prioritized interactions, treated as hypotheses, are rigorously controlled for statistical errors without relying on p-values. This method was the first to demonstrate to the community that higher-order interpretations of ML models can be achieved with confidence guarantees.

(3) A critical reevaluation of problematic statistical estimation of the Basic Alignment Search Tool (BLAST), a cornerstone tool used in daily biomedical analysis over the past 30 years. We have introduced an alternative method to address this issue, ensuring that it does not yield inflated estimates of significance. Our study has the potential to influence and reshape numerous conclusions drawn by researchers.

Speaker Bio: 

Yang Lu is an assistant professor at the University of Wisconsin-Madison. He was an assistant professor at University of Waterloo. Prior to that, he was a postdoctoral researcher in Prof. William Noble’s group at the University of Washington.He obtained his Ph.D. in Computational Biology and Bioinformatics under the supervision of Prof. Fengzhu Sun from University of Southern California.

Before moving to the United States, he received M.S. and B.S. degrees in Computer Science and Engineering from Shanghai Jiao Tong University. Yang Lu’s research focuses on developing machine learning and statistical methods for genomics and proteomics data analysis. He is particularly interested in developing interpretation methods to find scientifically interesting and statistically confident hypotheses from complex biological data.

Affiliations:

(Primary) Department of Biomedical Engineering, UW-Madison

Department of Biostatistics & Medical Informatics, UW-Madison

Join the Data Science Institute and the Brown Quantum Initiative for a conversation with Celia Merzbacher, the Executive Director Quantum Economic Development Consortium at Semiconductor Research International (the largest semiconductor manufacturer consortium in the US). 

Student Lunch (1:30pm):

Celia will host a lunch with students and answer questions about quantum and computational career pathways.

Talk (3:00pm):

Celia will discuss quantum computation and semiconductor research, describing administration priorities and funding at agencies like NSF and DOE, as well as the legislation working its way through Congress to expand the National Quantum Initiative.

–

Speaker Bio:

Dr. Celia Merzbacher is Executive Director of the Quantum Economic Development Consortium (QED-C), a global, industry-driven consortium managed by SRI International that aims to grow the quantum ecosystem. Dr. Merzbacher has more than two decades of experience as a leader of large multidisciplinary partnerships and programs at the intersection of government, industry, and academia. She was named a member of the Quantum 100 as part of the International Year of Quantum for her dedication and contributions. She has testified to Congress twice at hearings on the National Quantum Initiative and is a member of the GAO Expert Network that advises Congress. Merzbacher is a member of the U.S. delegation to the NATO Transatlantic Quantum Community and the G7 Quantum Working Group and, in partnership with UK Quantum, co-leads the UK-US Quantum Industry Exchange program. Celia is the host of “Been There, Done That”, a podcast where founders and executives from companies in the quantum sector share their experiences and lessons learned.

Previously, Dr. Merzbacher was Assistant Director for Technology R&D in the White House Office of Science and Technology Policy and Executive Director of the President’s Council of Advisors on Science and Technology. She is a Fellow of the AAAS and serves on the National Academies of Science, Engineering, and Medicine (NASEM) Standing Committee on Horizon Scanning and was Chair of the NASEM National Materials and Manufacturing Board. Dr. Merzbacher began her career as a materials scientist at the U.S. Naval Research Laboratory in Washington D.C., where her research led to six patents and more than 50 technical publications. She holds a BS degree in geological sciences from Brown University and MS and PhD degrees in geochemistry and mineralogy from The Pennsylvania State University.

 

RSVP to Student Lunch with Celia Merzbacher (1:30pm)

RSVP to afternoon talk below:

Join the Brown Center for Computational Molecular Biology for our seminar with Dr. Pei Wang (Mt. Sinai), on February 25, 2025:

Pan-cancer proteogenomics characterization of tumor immunity

Join the Data Science Institute for a Data Science Industry Talk with DSI alum, Annie Phan ’21:

Designing AI That Survives the Real World: Lessons from Enterprise Practice and the AI Maturity Mandate

This hands-on session connects the “messy middle” of enterprise AI to the realities of data science work for career professionals and emerging leaders in the Brown Data Science ScM program. Drawing on case studies from Diligent, Fanatics Collectibles, and earlier consulting work, I’ll walk through what actually makes AI initiatives succeed or stall—data foundations, stakeholder alignment, translation muscle, and operating models, not just model performance. We’ll reverse‑engineer several real enterprise AI patterns from my upcoming book AI Maturity Mandate, then map those patterns onto participants’ project contexts: clarifying problem statements, scoping minimal viable data products, defining success metrics, and stress‑testing risks and failure modes.

Speaker Bio:
Annie Phan is a data and AI leader and Staff AI Solution Architect at Diligent, where she designs and deploys secure, enterprise-scale AI systems across go-to-market, legal, and finance functions. Previously, she led major AI-driven transformation initiatives at Fanatics Collectibles and served as a Data & AI expert at McKinsey & Company. A Forbes Business Council member and published thought leader, she has written on data strategy and AI maturity for outlets such as Forbes and Analytics Insight, served as a judge for programs including the Globee Awards, Business Intelligence Group, AI Forge, Hackathon Raptors, and Stevie Awards, and spoken at industry‑wide events alongside leaders from top AI and technology companies, including the Velric Hiring & Leadership Summit and Brown’s Data Science Institute. Annie holds a Master’s in Data Science and a Bachelor’s in Economics from Brown University.

–

This event is hosted in-person and on Zoom. Brown students and staff may attend in-person or online; community members external to Brown should attend online. 

This event will bring together panelists from DEEPS, Sociology, and Data Science:
Dr. Chris Horvat
Dr. Jung Eun Lee
Dr. Kim Cobb
Dr. Karianne Bergen
to explore how data-driven approaches inform climate and environmental research, highlighting the range of climate modeling and analytical work happening across Brown.

We will begin with a moderated panel in the DSI open space, followed by small-group roundtable discussions and Q+A in breakout rooms – with food from Kabob and Curry!

We hope this event offers insight into how data and computation advance climate modeling, the tools and methods behind groundbreaking work, the intersections between data science and geoscience, and opportunities for students to get involved in climate research!

The CCMB Graduate Research Symposium is an opportunity for Center for Computational Molecular Biology (CCMB) graduate students to learn about their peers’ research and to practice sharing their work with colleagues. Senior PhD students will give 15-minute presentations on their work.

This event will give new graduate students exposure to the type of work their colleagues do and foster collaboration and connection between our graduate students.

 

Spring 2026 Presenters:

 

Ananya Pavuluri

Advisors: Erica Larschan, Ritambhara Singh

“Let’s talk about sex: investigating differences between males and females across diverse species.”

 

Ria Vinod

Advisor: Lorin Crawford

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.

Light refreshments will be provided. 

 

South American Archaeology, at Scale?: Using Vision Foundation Models to Map Sites en masse in GeoPACHA-AI

Abstract: Archaeologists excel at documenting the material dimensions of social life at local and regional scales but we often struggle to generate national and continental-sale datasets with continuous, systematic coverage. Collecting such data is important for at least three reasons: 1) macro-regional datasets provide context for understanding the variation that we observe at local and regional scales; 2) synthetic databases stitched together out of regional projects often amplify sampling biases; and 3) past peoples themselves frequently understood and acted in their worlds through large scale networks, including empires, circuits of interregional trade, and long-distance seasonal migration. In this presentation, I discuss the development and deployment of one solution to this problem: GeoPACHA-AI (Geospatial Platform for Andean Culture, History, and Archaeology), an international collaborative project that develops and harnesses Vision Transformer-based AI models to conduct continental-scale archaeological imagery survey. GeoPACHA’s project area covers nearly the entirety of the central Andes, from northern Ecuador through southern Chile, and we seek to enable new perspectives on interregional social networks, the broad impacts of imperial expansions, and long-term responses to climate change.

Parker VanValkenburgh’s research and publications employ archaeological methods to address anthropological research questions, with a particular focus on the long-term impacts of colonialism and imperialism on Indigenous people and environments in the Peruvian Andes. Through the study of diverse materials and media––including architecture, ceramics, environmental datasets, and archival documents––he seeks to understand how relationships between people, institutions, and environments are transformed in the course of imperial histories, as well as how the strategies of survival and resilience that communities develop to deal with empires are passed down and reworked across generations. In the course of doing so, he strives to generate approaches that are widely applicable to the study of empire(s) beyond the Andean region and which contribute to interdisciplinary understanding of imperial legacies in the modern world. In this work, he draws amply on digital methodologies, including the tools of geographic information systems (GIS), to map and analyze social, political, and environmental change in space and time. He also applies a critical lens to the study of digital media and methodologies, asking not just how these techniques facilitate archaeological scholarship, but how digital mediation transforms the ways we work with collaborators, research subjects, students, and public audiences.

__

This seminar and discussion will be mediated by Karianne Bergen (DSI, DEEPS)

*Spring 2026 workshops will be held virtually (https://brown.zoom.us/j/96158269691)*

Presenter: Ref Bari, Masters student in Physics and PI for T-REX

“The data science behind imaging black holes with T-REX: Time-Resolving Explorer Satellite”

 

In 2019, the Event Horizon Telescope (EHT) directly imaged a black hole for the first time, heralding a new era in astronomy. The T-REX (Time-Resolving Explorer Satellite) mission extend the EHT to space, achieving sub-ISCO temporal resolution and enabling the first time-resolved movies of Sgr A*’s accretion flow at 86 GHz.

This workshop will cover the data science aspect of T-REX: imaging a black hole generates petabytes of data and involves sophisticated fourier transforms, solving inverse problems, and data compression.

This talk will be of interest to physicists and data scientists alike.

Level: Some experience

–

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.

These workshops will be held virtually on Zoom: https://brown.zoom.us/j/96158269691

Are you interested in the Data Fluency undergraduate certificate?

Join the Data Science Institute (DSI) on Thursday, January 29th at 12:00pm for an informational session with the certificate’s Director of Undergraduate Studies (DUS), DSI academic staff, and current certificate students. Come learn about the certificate requirements, important deadlines, and more! A light lunch will be provided. The session will be recorded for students who can’t attend.

The Brown Center for Computational Molecular Biology invites Dr. Rajiv McCoy (Johns Hopkins) to speaker in our seminar series on January 28, 2025.

Human genome evolution within and across generations

Rajiv McCoy is an Associate Professor in the Department of Biology, with a secondary appointment in the Department of Genetic Medicine, at Johns Hopkins University. He received his PhD in Biology from Stanford University and completed postdoctoral training at the University of Washington and Princeton University before joining Johns Hopkins in 2018. His lab applies computational and statistical approaches to large-scale human genetic datasets. Research accomplishments include contributing to analysis of the first complete human genome, development of functional genomic resources for globally diverse populations, and discovery and characterization of common genetic variants associated with aneuploidy—the leading cause of human pregnancy loss. The McCoy lab is supported by funding from the NIH, NSF, Lalor Foundation, and American Society of Hematology and is actively engaged in teaching and mentoring in computational genetics at both the graduate and undergraduate levels.

The mission of the Center for Computational Molecular Biology is to promote the development, implementation, and application of analytical and computational methods to foundational questions in the biological and medical sciences. The research programs of the core faculty in the CCMB lie foundationally at the intersection of computer science, evolutionary biology, mathematics, and molecular and cell biology. 

Please join us for our upcoming virtual information session for admitted students of Brown’s online Master’s in Data Science. Attendees will gain further insight into the program structure, student resources and next steps.

Join the Data Science Institute as we recognize the recipients of our inaugural Early Career Breakthrough Research Awards and Seed Grant Awards.

We look forward to celebrating the outstanding the outstanding research at Brown that leverages data science to propel research and innovation across disciplines. 

This celebration will recognize each award recipient. Refreshments will be provided. All are welcome to attend. 

This event can also be viewed over Zoom: https://brown.zoom.us/j/95629661736

 

2026 Early Career Breakthrough Research Award Recipients:

• In the Biological Sciences: Ying Ma, Edens Family Assistant Professor of Healthcare Communications and Technology and Assistant Professor of Biostatistics
• In the Physical Sciences & Engineering: Loukas Gouskos, Christopher M. Barter Assistant Professor of Physics
• In the Humanities & Social Sciences: Kim Fernandes, Assistant Professor of Anthropology

2026 Data Science Seed Grant Recipients:

• Qualitative Content Analysis Using R and Large Language Models (Jacy Weems, Emmanuelle Belanger)

• Creating the Trypanosoma brucei digital cell (Laura Smithson, Ana Sofia Cepeda-Diaz, and Chris de Graffenried)

• Leveraging Rhode Island Linked Administrative Data to Understand the Effects of Paid Leave on Children’s Health and Educational Outcomes (Margot Jackson, Jiwan Lee, Michael Silverstein and Lauren Schlichting)

• Developing an Oxford Nanopore Whole-Genome Sequencing and Analysis Pipeline for Plasmodium falciparum Drug Resistance Surveillance (Abebe Fola)

• How Does the Dissolution of the US Department of Education Impact the Education Ecosystem? (Lindsey Kaler, Cameron Arnzen, Niamh Stull and Susan Moffit)

• The Jewish Studies Citation Network (Michael Satlow)

• Bloom: A Smart Plant System Integrating Sensor-Driven Data Analytics to Promote Urban Sustainability and Human-Plant Interaction (Jenny Zhu, Savvas Koushiappas, Ayaan Jamal, Bruno Rodriguez-Mendez, Ryan Pang)

• iPREDiCT: A toolkit for cancer recurrence prediction using integrated genomics and clinical data (Ece Uzun, Jeremy Warner, Seema Nagpal and Samuel Rubinstein)

• Data Science Collaborative for Real-World Evidence in Cancer Therapeutics (Rebecca Hubbard)

• From Engagement to Outcomes: Predicting and Improving Student Career Readiness at Brown (Viktor Gavrielov, Amanda Khoo)

• Brown Efficient AI Compression Network (BEACON) (Richard Gaitskell, Gaetano Barone, Ian Dell’Antonio, Matt LeBlanc, Jonathan Pober and Jennifer Roloff)

• Machine Learning Conference on Justice in Education, Healthcare, and Public Policy (Kenya Andrews)

Are you considering continuing your education at Brown through the Fifth-Year Master’s program? Join us for this information session to learn how you can extend your Brown experience and earn a master’s in data science in just one additional year.
During the session, admissions staff and program leadership will share insights on:

• The structure and benefits of the data science fifth-year master’s program
• Application requirements and tips for submitting a strong application
• Resources and support available to master’s students at Brown

This session is designed to answer your questions—your participation will guide the conversation. We look forward to helping you explore the next step in your academic journey at Brown.

Yule definitely want to attend this event.

Join us for the DSI Winter Wonderland Social, where we’ll be spreading cheer, serving treats, and snow-stalling your finals stress with fun activities and cozy vibes.

It’s the perfect chance to take a study break, mingle, and get your jingle on before the semester wraps up.

When: Thursday, December 11
Time: 4:00 – 6:00 PM
Where: Data Science Institute, 164 Angell St, Floor 3
What: Food, fun, and lots of festive energy

We’ll be sleighing the season together, don’t flake out on us.

Join the Data Fellows Program for presentations from our Fall 2025 cohort on their projects this semester!

The Data Science Fellows program offers a unique opportunity for students to collaborate with a participating faculty member to infuse data science tools and practice in existing undergraduate courses. Undergraduates interested in data science and collaborative work with a faculty member have the opportunity to enroll in the Data Science Fellows course (DATA 1150, offered each fall semester). This course will prepare the Data Science Fellows to serve as consultants for faculty wishing to enhance data science curricula at Brown. This program is offered in collaboration with the Sheridan Center for Teaching and Learning. Students learn a core set of data science practices, active learning pedagogies, and collaborative communication skills.

See past projects on the Data Fellows website

Join the Data Science Institute for our next Data & Donuts session featuring Professor Björn Sandstede, Alumni-Alumnae University Professor of Applied Mathematics at Brown University. Professor Sandstede is a leading scholar in applied dynamical systems whose research connects mathematical theory with data-driven modeling across fields such as computational biology, pattern formation, and nonlinear dynamics. His work advances our understanding of complex systems while bridging rigorous analysis with modern data science.

The Data & Donuts series offers an informal space for students, faculty, and staff to connect over short talks by Brown faculty and data scientists on topics and campus resources related to data science. These roundtable-style sessions are open to all members of the Brown community with an interest in data science.

Data Science Institute Industry Talk

In this fireside chat, Professor Lipman shares his experience working as a Data Scientist and Tech Lead Manager at Google. Peter will summarize his contributions at Google, where he managed cross-functional analytics-focused teams, and discuss his views on the skills needed to succeed in big tech. 

Peter J. Lipman, PhD, recently joined Brown’s faculty as an Associate Professor of the Practice of Biostatistics at the School of Public Health. He holds a BA in Mathematics from Johns Hopkins University and a PhD in Biostatistics from Harvard University. Peter joins us after spending over a decade at Google; his work largely focused on labelled data generation for machine learning models in both Ads (2013-2022) and the Play Store (2022-2025). 

“Statistical and Computational Advances for Detecting Nonlinear Contributions to Complex Traits”

Advisors: Lorin Crawford, Ph.D. and Daniel Weinreich, Ph.D.

Statistical genetics has improved our understanding of how genetic variation shapes human complex traits and diseases. Growing biobanks, improved sequencing technologies, and efforts of scientific collaboration have enabled the field to make progress in estimating heritability, detecting single-variant associations, and elucidating polygenic architectures. Genome-wide association studies (GWAS), rare variant tests, and models for gene-by-gene (GxG) and gene-by-environment (GxE) interactions have established a foundation for translating the insights from statistical genetics to clinical risk prediction and drug target discovery.

Despite ever-increasing scale and availability of data, many questions about the genetic architecture of complex traits remain open. Traits may be driven by thousands of variants with small effects, while linkage disequilibrium patterns, pleiotropy, epistasis, and heterogeneity in environmental exposures obscure genetic inference. GWAS signals frequently are located in non-coding regions, complicating biological interpretation, and predictive models lose accuracy across ancestries likely due to differing linkage disequilibrium and environments. Many methodological challenges remain in order to better understand nonlinear contributions to phenotypic variation, disease susceptibility, and frailty.

The present dissertation addresses these challenges by extending the marginal interaction framework to multivariate analysis and time-to-event traits, and by providing a framework for integration of multi-omic information into genetic association studies. Chapter 1 introduces a multivariate marginal epistasis test (mvMAPIT) that leverages genetic correlations between traits to detect genetic variants involved in GxG interactions. This method is implemented as a multivariate linear mixed model and improves power for detecting marginal epistasis in multivariate data. Chapter 2 presents the Cox proportional hazards gene-by-environment interaction test (CphGxE). This model enables the detection of GxE interactions in time-to-event traits by partitioning the heritable variance of frailty into genetic, environmental, and GxE interaction components. Chapter 3 proposes the Sparse Marginal Epistasis (SME) test. This approach enables the integration of functional data as biological priors into genome-wide association studies of epistasis, achieving substantial improvements in power and scalability of the marginal interaction framework to biobank-scale data.

Together, these methods present computational and statistical advances by providing scalable and interpretable approaches for studying nonlinear genetic architectures in complex traits.

Andriy Norets, Ph.D.,
Professor of Economics
Department of Economics at Brown University

 

Talk Title: Bayesian Nonparametric Model for Nonseparable Instrumental Variable Regression (joint work with Simone Martinalli)

Abstract: We propose a flexible Bayesian model for estimation of nonseparable instrumental variable regressions with a univariate endogenous covariate and a univariate unobservable responsible for endogeneity.
Our model uses recently developed nonparametric priors for conditional distributions based on covariate dependent mixtures that deliver optimal adaptive posterior contraction rates in settings with discrete and continuous variables under possible smoothness and sparsity. We develop posterior concentration results and a Markov chain Monte Carlo algorithm for the model. The proposed methodology provides a simple parametric baseline model that can be gradually extended to more flexible parametric models and ultimately a nonparametric one by increasing the number of mixture components or setting a prior on it. Thus, robustness to nonlinearities, nonseparabilty, heteroskedasticity and other deviations from the baseline can be gradually introduced in a computationally tractable way. We illustrate the model performance in simulations and applications.

Stephen Bach, Ph.D.,
DSI Campus Advisory Board Co-Chair, Eliot Horowitz Assistant Professor of Computer Science at Brown University

 

Talk Title: A Data-Centric Approach to AI Adaptation and Alignment

Abstract: Training generative AI is not a one-step process. In the case of large language models (LLMs), self-supervision is often followed by supervised and reinforcement learning stages to improve instruction following, safety, and other desirable qualities. This multi-stage process that has emerged in the last 3 years has led to huge leaps in model capabilities. It has also led to new challenges and risks. In this talk, I will overview some of our group’s work to identify and address such challenges by focusing on the training data used at different stages. First, I will discuss the problem of adapting LLMs to new, specialized domains and the role that synthetic, i.e. LLM-generated, training data can play. Then, I will share some of our work showing how mismatches in training data at different stages can lead to safety alignment risks. In one case, LLMs with inadequate safety training can be more likely to respond to harmful queries when presented in languages with less abundant data like Swahili or Scots Gaelic. In another case, LLMs trained to reason about solving math problems can then deploy those same reasoning skills to reason out of their own safety guardrails. Together, these findings highlight the importance of careful training data management at all stages of AI development.

Presenter: Alice Paul, Assistant Professor of Biostatistics

“Intro to Simple Data Analysis in R”

This workshop will start by introducing how to load data into R and conduct a short exploratory analysis, including generating summary statistics, creating summary tables, and generating simple plots. Additionally, we will practice implementing some common statistical tests.

Level: Beginner

Required Prior Knowledge: Introductory statistics

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.

These workshops can be attended in person (Carney Innovation Hub, 164 Angell St, Floor 4) or on Zoom: https://brown.zoom.us/j/96158269691

Food will not be served; please bring your own lunch.

Presenter: Paul Xu, Center for Computation and Visualization

“Hyper-Modern Python Project Development with uv”

Blazing fast and feature-rich, uv has emerged as the go-to tool for modern python development. We will cover how to use uv to manage Python dependencies and build/publish Python packages

Level: Beginner

Required Prior Knowledge: Basic python proficiency

___

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.

These workshops can be attended in person (Carney Innovation Hub, 164 Angell St, Floor 4) or on Zoom: https://brown.zoom.us/j/96158269691

Food will not be served; please bring your own lunch.

Join the Brown Center for Computational Molecular Biology for our seminar with Dr. Weihan Li, Assistant Professor of Molecular Biology, Cell Biology and Biochemistry at Brown, on November 12, 2025:

Imaging a single mRNA molecule throughout its life cycle

Abstract:

The Li Lab seeks a molecular understanding of how cells control the spatial organization of gene expression. We use advanced single-molecule imaging techniques to study the precise localization of RNAs and proteins. For instance, in yeast, approximately 20% of all the mRNAs are targeted to the surfaces of the endoplasmic reticulum and mitochondria, where they are translated to support organelle biogenesis and maintenance. Our lab aims to uncover the mechanisms that regulate the spatial organization of gene expression, and determine the consequences of its dysregulation.

Presenter: Meredith Mendola, Center for Technological Responsibility, Re-imagination, and Redesign

“Accessibytes: An A11y Toolkit”

This workshop is designed to equip you with the practical tools and knowledge needed to build more inclusive digital content. We’ll demystify digital accessibility by moving beyond abstract concepts and focusing on concrete, actionable steps you can take in your daily work, from creating presentations and documents to designing applications. You’ll learn to identify common accessibility barriers and apply simple, effective techniques to ensure your work is usable by everyone. This is a toolkit for anyone who designs, creates, or codes—no prior accessibility knowledge required.

Level: Beginner

Required Prior Knowledge: None

This workshop will be held primarily virtually. Our regular meeting location (Carney Innovation Hub) will be used as a viewing location for those who would like to attend in person. 

___

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.

These workshops can be attended in person (Carney Innovation Hub, 164 Angell St, Floor 4) or on Zoom: https://brown.zoom.us/j/96158269691

Food will not be served; please bring your own lunch.

Join DSI, CCMB, and CNTR for a casual Q&A session with current Brown PhD students about applying to grad school!

This event is for students at Brown who are or may be applying to graduate school to ask questions of current grad students from DSI / CCMB / CNTR about the process of applying and the experience of grad school! If you are applying to PhD programs this year or are thinking about applying in the future, come have your questions answered and hear about our grad students’ experiences with applying and starting graduate school.

We will have a handful of current PhD students from Computational Biology, Computer Science, and other computational fields represented at DSI gathered to answer questions and chat with curious undergrads and masters students.

If you are a current PhD student and would like to participate as one of our grad student panelists, please reach out to aspensc@brown.edu to confirm.

All Brown students are welcome to attend. We will provide a limited amount of refreshments for this event.

Probabilistic Characterization of Transcriptomic States in Single-Cell Data

Advisor: Lorin Crawford, PhD

Understanding how cells adopt and transition between transcriptional states is central to deciphering tissue organization, disease progression, and therapeutic response. Single-cell RNA sequencing (scRNA-seq) provides a powerful platform for characterizing this heterogeneity at unprecedented resolution. However, current computational methods for identifying and interpreting cell states face several major challenges. They often rely on clustering heuristics that assume a fixed number of states, separate the inference of states from the discovery of their defining gene markers, and fail to account for how cellular state composition varies across biological contexts such as time, perturbation, or patient background. These limitations hinder the interpretability and actionability of inferred states, constraining their potential as biomarkers in precision medicine.

This dissertation develops a family of scalable, interpretable, and probabilistically grounded models for the unsupervised characterization of transcriptomic cell states in single-cell data. The proposed framework addresses three key methodological needs: (i) the joint inference of cell states and their associated gene markers, (ii) the relaxation of assumptions regarding the number of latent states, and (iii) the probabilistic modeling of state persistence and context-dependent variation over biological time. The models build on Bayesian nonparametric principles—particularly the Dirichlet and dependent Dirichlet process mixture frameworks—and employ sparse priors to identify gene-level markers that distinguish cell states within high-dimensional expression space. Variational inference algorithms are derived to enable scalable estimation on datasets containing millions of cells.

In the first part of the thesis, a sparse nonparametric Bayesian clustering model is
developed that infers both the number of cell states and their characteristic gene expression patterns directly from expression data, without requiring prior cluster specification. The second part introduces NCLUSION, a scalable variational implementation that jointly performs clustering and marker selection, providing statistically robust and biologically interpretable results. The final part presents i-NCLUSION, a hierarchical extension that integrates experimental structure via a directed acyclic graph representation, allowing for dynamic modeling of cell state preferences across related contexts such as time points or patient samples. Applied to large-scale datasets—including an 11-million-cell mouse developmental time series and longitudinal profiles of human breast milk—these methods
outperform structure-agnostic baselines and uncover biologically consistent patterns of cellular adaptation.

Collectively, this work contributes a unified probabilistic framework for defining, interpreting, and contextualizing cell states as actionable biomarkers. By combining statistical rigor, scalability, and biological interpretability, the models developed here lay the foundation for more transparent and clinically relevant analyses of single-cell data, advancing the broader goals of precision medicine and systems-level understanding of cellular behavior.

Presenter: Xiran Liu, Postdoctoral Researcher, Center for Computational Molecular Biology (CCMB)

Uncovering Latent Structures in Data: An Introduction to Topic Modeling and Its Applications Beyond NLP

___

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.

These workshops can be attended in person (Carney Innovation Hub, 164 Angell St, Floor 4) or on Zoom: https://brown.zoom.us/j/96158269691

Food will not be served; please bring your own lunch.

Join the Data Science Institute for our next Data & Donuts session featuring Professor Ellie Pavlick, Assistant Professor of Computer Science and Linguistics at Brown University and Research Scientist at Google DeepMind. Professor Pavlick leads the LUNAR Lab, where her research explores how humans and AI systems understand language, concepts, and reasoning, bridging computational modeling with insights from cognitive science, neuroscience, and philosophy.

The Data & Donuts series offers an informal space for students, faculty, and staff to connect over short talks by Brown faculty and data scientists on topics and campus resources related to data science. These roundtable-style sessions are open to all members of the Brown community with an interest in data science.

 

Data & Donuts

Join the DSI at 4:00 pm on Thursday, October 30 for Data & Donuts!

Ellie Pavlick

Ellie Pavlick is the Briger Family Distinguished Associate Professor of Computer Science, an Associate Professor of Cognitive and Psychological Sciences, and a Research Scientist at Google Deepmind. She leads the Language Understanding and Representation (LUNAR) Lab, which seeks to understand how language “works” and to build computational models which can understand language the way that humans do. Her lab’s projects focus on language broadly construed, and often includes the study of capacities more general than language, including conceptual representations, reasoning, learning, and generalization. Her lab is interested in understanding how humans acheive these things, how computational models (especially large language models and similar types of “black box” AI systems) achieve these things, and what insights can be gained from comparing the two. They often collaborate with researchers outside of computer science, including cognitive science, neuroscience, and philosophy.

The format of this series allows colleagues to connect informally with students and will feature short talks by Brown faculty and data scientists on research or campus resources related to data science.

These talks are open to students, faculty, and staff of all levels with an interest in data science and are set up as a community roundtable to engage everyone.

Donuts will be served!

Ting Ye, Ph.D.,
Assistant Professor of Biostatistics
School of Public Health at University of Washington

 

Talk Title: From Estimands to Robust Inference of Treatment Effects in Master Protocol Trials

Abstract: A master protocol trial is an innovative clinical trial design that uses a single overarching protocol to evaluate multiple treatments, diseases, or disease subtypes, where participants are often randomized to different subsets of treatment arms based on individual characteristics, enrollment timing, and treatment availability. While offering increased flexibility, this constrained and non-uniform treatment assignment poses inferential challenges, with two fundamental ones being the precise definition of treatment effects and robust, efficient inference on these effects. Such challenges arise primarily because some commonly used analysis approaches may target estimands defined on populations inadvertently depending on randomization ratios or trial operational format, thereby undermining interpretability. This article, for the first time, presents a formal framework for constructing a clinically meaningful estimand with precise specification of the population of interest. Specifically, the proposed entire concurrently eligible (ECE) population not only preserves the integrity of randomized comparisons but also remains invariant to both the randomization ratio and trial operational format. Then, we develop weighting and post-stratification methods to estimate treatment effects under the same minimal assumptions used in traditional randomized trials. We also consider model-assisted covariate adjustment to fully unlock the efficiency potential of master protocol trials while maintaining robustness against model misspecification. The SIMPLIFY trial, a master protocol assessing continuation versus discontinuation of two common therapies in cystic fibrosis, is utilized to further highlight the practical significance of this research. All analyses are conducted using the R package RobinCID.

 

Data & Donuts

Join the DSI at 4:00 pm on Thursday, October 23 for Data & Donuts!

Dr. Jamie Trost

Dr. Jamie Trost is a lecturer of statistics whose roots lie in cognitive psychology. While pursuing her doctoral degree studying attention and perception at the University of Notre Dame, Jamie became heavily interested in the Scholarship of Teaching and Learning (SoTL), particularly in regard to teaching quantitative statistics and methodologies. At Brown, Jamie leads courses in statistics at both undergraduate and graduate levels and will serve as a statistical consultant to graduate students in the CoPsy department. 

The format of this series allows faculty to connect informally with students and will feature short talks by Brown faculty and data scientists on research or campus resources related to data science.

These talks are open to students, faculty, and staff of all levels with an interest in data science and are set up as a community roundtable to engage everyone.

Donuts will be served!

Join the Nelson Center for Entrepreneurship for a Founder Talk with Yeshimabeit “Yeshi” Milner ’12, founder of Data for Black Lives.

Learn how Yeshimabeit took her love for data to make change in the lives of Black people. Data For Black Lives is a nonprofit organization and a movement of activists, organizers, and scientists committed to the mission of using data to create concrete and measurable change in the lives of Black people.

This event is held in collaboration with the Data Science Institute, Brown Center for Students of Color, and the Center for the Study of Race and Ethnicity in America.

Join the Brown Center for Computational Molecular Biology for our seminar with Dr. Sendurai Mani on October 22, 2025:

Reactivation of Embryonic EMT Programs to Unlock Cancer Stem Cell Traits, Metastasis, and Resistance

Abstract:
Epithelial–mesenchymal transition (EMT) is a key developmental process that cancer cells often hijack to promote progression and evade therapy. Reactivation of EMT programs allows cancer cells to gain stem cell–like traits that facilitate metastasis and contribute to therapy resistance. In this talk, I will present evidence and demonstrate how reactivating embryonic EMT circuits underpins gaining stemness traits, cellular plasticity, enabling tumor cells to switch between epithelial and mesenchymal states. Additionally, I will explore the mechanistic connections between EMT and cancer stem cells and how these interrelated processes contribute to tumor progression. Finally, I will discuss ways to target EMT–stemness pathways to overcome metastasis and the development of resistance to treatments.

Sendurai A. Mani is a Professor in the Department of Pathology and Laboratory Medicine at Brown University. He is also the Associate Director of Translational Oncology at Brown University Legorreta Cancer Center. Dr. Mani earned a Ph.D. from The Indian Institute of Science, Bangalore, India and then did postdoctoral work with Dr. Robert A. Weinberg at the Whitehead Institute/Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA. He then joined the University of Texas MD Anderson Cancer Center, Houston, Texas as an Assistant Professor in December of 2007 and has been promoted to Associate Professor with tenure in 2013. In late 2022, Dr. Mani joined Brown University Legorreta Cancer Center as a professor and Associate Director of Translational Oncology. Dr. Mani has received numerous prizes and awards for his research, including a Jimmy V foundation’s V-Scholar Award and The American Cancer Society Research Scholar award. Dr. Mani’s original finding demonstrating the cancer cells acquire stem cell properties by activating latent embryonic epithelial-mesenchymal transition (EMT) program provided the foundation and explanation for the presence of plasticity within the tumor as well as the development of resistance to various treatments.

The mission of the Center for Computational Molecular Biology is to promote the development, implementation, and application of analytical and computational methods to foundational questions in the biological and medical sciences. The research programs of the core faculty in the CCMB lie foundationally at the intersection of computer science, evolutionary biology, mathematics, and molecular and cell biology. 

The Data Science Institute is excited to bring together our affiliated faculty community to meet and mingle at our annual Affiliated Faculty Mixer! 

Please join us for an evening with colleagues from across campus to make connections and get involved with the Data Science Institute.

If you are a DSI affiliated faculty member, please let us know that you’re coming in advance!

Join the Data Science Online Master’s Program for an engaging conversation with David Firrincieli, Adjunct Faculty Member and AI Engineer in Deloitte’s Government & Public Services practice, where he currently consults full-time for a large federal health agency on their data infrastructure and AI capabilities. David will share strategic insights on how federal agencies architect, deploy, and manage enterprise data systems, exploring the intersection of technical implementation, regulatory compliance, and cross-agency collaboration. This session will examine how data infrastructure decisions shape government capabilities and public outcomes.

Register to attend this online webinar.

Presenter: Frank Donnelly, Head of GIS and Data Services and the coordinator of GeoData@SciLi.

“Data Rescue and Screenscraping with Python”

Learn about the Data Rescue Project, an international volunteer effort led by librarians, archivists, non-profits, and data scientists who are downloading and preserving US federal government datasets. Discover how you can screen scrape and automate data downloads with Python.

Level: Beginner

Required Prior Knowledge: The first half of the session will be a presentation. Some basic programming experience is helpful if you would like to do the hands-on exercises in the second half.

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.

These workshops can be attended in person (Carney Innovation Hub, 164 Angell St, Floor 4) or on Zoom: https://brown.zoom.us/j/96158269691

Food will not be served; please bring your own lunch.

The CCMB Day is an opportunity for Center for Computational Molecular Biology (CCMB) graduate students to learn about their peers’ research and to practice sharing their work with colleagues. Senior PhD students will give 15-minute presentations on their work.

We hope this event will give new graduate students exposure to the type of work their colleagues do and foster collaboration and connection between our graduate students.

 

Fall 2025 Presenters:

Name: Cecile Meier-Scherling

Advisor: Jeff Bailey and Lorin Crawford

Presentation Title: Predicting Drug Resistance: Computational Modeling of Artemisinin Resistance in Africa

 

Name: Jazeps Medina-Tretmanis

Advisor: Emilia Huerta-Sanchez

Presentation Title: TBA

 

Name: Tuan Pham

Advisor: Ritambhara Singh

Presentation Title: TBA

Name: Leah Darwin

Advisor: David Rand

Presentation Title: Selective response of mitochondrial and nuclear genomes to an OXPHOS inhibitor in experimental populations of Drosophila