Wednesday / CS Seminar
January 15th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Sainyam Galhotra, Cornell University

Talk Title
Context-aware Responsible Data Science

Abstract
"Data-based systems are increasingly used in applications that have far-reaching consequences and long-lasting societal impact. However, the development process remains highly specialized, tedious, and unscalable. This produces a manually fine-tuned rigid solution that works only for one specific problem in one specific context. The system fails to adapt to the changing world and severely limits the full utilization of valuable data.

So, how can you avert this fate for your systems?

In this talk, I present my vision of context-aware systems that enable even non-expert users to develop correct, explainable, and equitable data-science pipelines. To achieve this, I will focus on i) re-thinking the design of data science pipelines, and ii) the importance of causal inference for trustworthy data analysis. I will present a data discovery framework that automatically identifies useful data on behalf of end-users for various tasks. Lastly, I will discuss my proposal of leveraging counterfactual reasoning and causal inference to quantify the impact of an input on the outcome. These topics are the pieces of the puzzle that come together to create the Data Scientists' holy grail - an easily deployable, scalable, and robust system that you can trust even as everything around it evolves."

Biography
Sainyam Galhotra is an Assistant Professor in Computer Science at Cornell University and a field member for Computer Science, Statistics and Data Science. Previously, he was a Computing Innovation Fellow pursuing postdoctoral research at the University of Chicago. He received his Ph.D. from the University of Massachusetts Amherst under the supervision of Prof. Barna Saha (currently at UC San Diego). The goal of his research is to lay the foundation of responsible data science, that enable efficient development and deployment of trustworthy data analytics applications. His research has combined techniques from Data Management, Probabilistic Methods, Causal Inference, Machine Learning, and Software Engineering. His research has been published in top-tier Data Management (SIGMOD, VLDB, PODS, & ICDE), AI (NeurIPS, AAAI & AIES) and Software Engineering (FSE) conferences. He is a recipient of the Best Paper Award in FSE 2017 and Most Reproducible Paper Award in both SIGMOD 2017 and 2018, and Best Artifact Paper Honorable Mention Award in SIGMOD 2023. He was recognized as a Data Science rising star, a DAAD AInet Fellow, and as the first recipient of the Krithi Ramamritham Award at UMass for contribution to database research.

Research/Interest Areas
Data Management
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Zoom: https://northwestern.zoom.us/j/92345079181?pwd=4K3AzzUtPHxoMnaB97EZqRXJ5s4vva.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=07420843-9285-40df-81c0-b26001574825
DEI Minute: Diversity tinyurl.com/cspac-dei-minute

Title: Inferring thermodynamic limits on non-equilibrium membrane morphologies

Members of the NITMB community are invited to join us for Research-In-Progress meetings, an informal venue for members of the NITMB to discuss ongoing and/or planned research.

Jordon Shivers is a Schmidt AI in Science Postdoctoral Fellow at the University of Chicago, where he is jointly mentored by Suri Vaikuntanathan and Aaron Dinner. Shivers is broadly interested in the physics and engineering of active soft and biological matter, and tackles problems in this area using a combination of computer simulations, theoretical soft matter physics, and machine learning techniques, often in collaboration with experimental groups.

Learn more about Jordon Shivers’ research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Wednesdays at 3pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.

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AI may soon become a key driver of geopolitical competition, with countries vying for technological supremacy and economic dominance. The potential of AI technologies to revolutionize industries, enhance military capabilities and shape societal norms has far-reaching implications for the disruption of traditional geopolitical balances, particularly as AI development outpaces the ability of policymakers to establish comprehensive governance frameworks.

What are the geopolitical risks and opportunities associated with AI development? What strategies are being developed to prevent the misuse of AI? How can states promote responsible and ethical AI development to shape the future of AI in a way that benefits humanity?

Join us for our winter quarter Buffett Symposium on AI and Geopolitics convening leading strategists, researchers and policymakers to discuss the transformative opportunities and profound challenges that AI poses in geopolitics. These leaders will offer insights on the increasing influence of AI technologies on global power dynamics, national security, economic development, international relations and more.

Co-sponsored by the Northwestern Security & AI Lab (NSAIL) and University College Cork.

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Dear ChBE Department and affiliates,

Please join us next Thursday, January 9th at 9:30am in Tech L361 for student seminars by AJ Deberghes and Spencer Hong.

Fanglin Che will present a seminar titled, "Advancing Sustainability: Physics-Informed and Interpretable Machine Learning in Catalyst Design".

Bagels and coffee will be provided at 9:30am, and the seminar will start at 9:40am. Please plan to arrive on time to grab a bagel and mingle!

*Please note that there will be no Zoom option for seminars this year.

The Technology & Social Behavior PhD program is excited to welcome Dr. Tal August of University of Illinois at Urbana-Champaign to campus on Thursday, January 16th. Tal will give a talk, titled AI for Science Communication: Adapting to Different Stakeholders.

The talk is in-person, live, only in the Center for Human-Computer Interaction + Design. Light lunch fare will be served.

Abstract: Communicating complex scientific ideas to the public is critical for an equitable, informed society, but doing so without misleading or overwhelming people is challenging. As large language models become more capable at summarizing and simplifying scientific text, we have a unique opportunity to use these models to make science more accessible. In this talk I will share my group’s research developing language tools and systems to help communicate science to more people. I will highlight two key communication strategies—based on our previous work—focused on different levels of language: explaining new findings from scientific papers and defining individual scientific terms. For both, I will discuss novel techniques we developed for adjusting generated language to fit the needs of different audiences and methods for modeling an individual reader’s background. I will close by discussing how these techniques generalize to other knowledge intensive communication tasks (e.g., legal and educational settings) and the opportunities of developing new techniques for these settings.

Bio: Tal August is an assistant professor at the University of Illinois at Urbana-Champaign. He studies how to adapt language to different audiences, with a focus on knowledge intensive domains like science, health and legal communication. Tal conducts empirical analyses to study how changes in language will affect different audiences, and he builds intelligent reading and writing systems for augmenting our language in new ways. The long term goal of Tal’s research is to improve our communication with—and understanding of—one another through technology. Tal August previously was a Young Investigator at the Allen Institute for AI. Tal received his PhD at the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Katharina Reinecke and Noah Smith.

Sara Solla is a Professor of Physics and Physiology at Northwestern University. Solla’s research interests lie in the application of statistical mechanics to the analysis of complex systems. Her research has led her to the study of neural networks, which are theoretical models that incorporate "fuzzy logic" and are thought to be in some aspects analogous to the way the human brain stores and processes information. She has used spin-glass models (originally developed to explain magnetism in amorphous materials) to describe associative memory, worked on a statistical description of supervised learning, investigated the emergence of generalization abilities in adaptive systems, and studied the dynamics of incremental learning algorithms.

Learn more about Sara Solla's research

The NSF-Simons National Institute for Theory and Mathematics in Biology Seminar Series aims to bring together a mix of mathematicians and biologists to foster discussion and collaboration between the two fields. The seminar series will take place on Fridays from 10am - 11am at the NITMB offices in the John Hancock Center in downtown Chicago. There will be both an in-person and virtual component.

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Leveraging multi-study, multi-outcome data to improve external validity and efficiency of clinical trials for managing schizophrenia

Caleb H. Miles, Assistant Professor of Biostatistics, Columbia University Mailman School of Public Health

Abstract: As data sources have become more plentiful and readily accessible, the practice of data fusion has become increasingly ubiquitous. However, when the focus is on a causal effect on a particular outcome, a major limitation is that this outcome may not be available in all data sources. In fact, different randomized experiments or observational studies of a common exposure will often focus on potentially related, yet distinct outcomes. One such example is the Database of Cognitive Training and Remediation Studies (DoCTRS), which consists of several randomized trials of the effect of cognitive remediation therapy on various outcomes among patients with schizophrenia. We develop causally principled methodology for fusing data sets when multiple outcomes are observed across studies, which leverages outcomes of secondary interest as informative proxies for the missing outcome of primary interest, thereby maximizing power and efficiency by making full use of the available data. As this methodology relies on a key transportability assumption, we also develop methods to assess the degree of sensitivity to violations of this assumption. We apply this methodology to data from the DoCTRS trials to make improved causal inferences about the effectiveness of cognitive remediation therapy on cognition among patients with schizophrenia.

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Are you looking to speed up your coding workflow? GitHub Copilot, an AI-powered coding assistant, can enhance your productivity by helping you write code faster and more efficiently. In this workshop, you’ll get an introduction to GitHub Copilot, including best practices and important considerations for using it effectively in your projects. We’ll also demonstrate how to use GitHub Copilot in VS Code and RStudio.

Prerequisites: Prior to the workshop, participants will receive instructions for obtaining access to GitHub Copilot. While access is not required to attend, having it will ensure you get the most out of the session.

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Abstract: For centuries, researchers have sought out ways to connect disparate areas of knowledge. With the advent of Artificial Intelligence (AI), we can now rigorously explore relationships that span across distinct areas – such as, mechanics and biology, or science and art – to deepen our understanding, to accelerate innovation, and to drive scientific discovery. However, many existing AI methods have limitations when it comes to physical intuition, and often hallucinate. To address these challenges, we present research that blurs the boundary between physics-based and data-driven modeling through a series of physics-inspired multimodal graph-based generative AI models, set forth in a hierarchical multi-agent mixture-of-experts framework. The design of these models follows a biologically inspired approach where we re-use neural structures and dynamically arrange them in different patterns and utility, implementing a manifestation of the universality-diversity-principle that forms a powerful principle in bioinspired materials. This new generation of models is applied to the analysis and design of materials, specifically to mimic and improve upon biological materials. Applied specifically to protein engineering, the talk will cover case studies covering distinct scales, from silk, to collagen, to biomineralized materials, as well as applications to medicine, food and agriculture where materials design is critical to achieve performance targets.

Bio: Markus J. Buehler is the McAfee Professor of Engineering at MIT. Professor Buehler pursues new modeling, design and manufacturing approaches for advanced bio-inspired materials that offer greater resilience and a wide range of controllable properties from the nano- to the macroscale. He received many distinguished awards, including the Feynman Prize, the ASME Drucker Medal, the J.R. Rice Medal, and many others. Buehler is a member of the National Academy of Engineering.

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Members of the NITMB community are invited to join us for Research-In-Progress meetings, an informal venue for members of the NITMB to discuss ongoing and/or planned research. Efe Gökmen is an NITMB Fellow. Gökmen’s expertise lies at the crossroads of machine learning, statistical physics, and information theory. Learn more about Efe Gökmen’s research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Wednesdays at 3pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.

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Are you looking to teach a class where your students need access to GPUs? Do you need more GPUs than are available on Quest to train new AI models? Do you have specific hardware needs or are you hoping to deploy a science gateway? ACCESS-CI and NAIRR (National AI Research Resource) are two federally-funded computing resources available to support these and other computing needs. This workshop will introduce you to these platforms and cover how to set up accounts and request resources. This workshop will not be recorded.

Prerequisites: It may be helpful to have an account on the ACCESS-CI platform. You can sign up for an account on the ACCESS CI Registration Page if interested.

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Abstract:

Despite decades of research, signalized intersections remain a major urban bottleneck and traffic signal timing in practice is suboptimal. Signal timing algorithms must address two challenges: performance under uncertainty in future demand and turning proportions, and real-time computation. One possible approach is max-pressure signal timing. By modeling the traffic network as a Markov decision process, max-pressure control is mathematically proven to maximize throughput under uncertainty using Lyapunov drift. Nevertheless, the control itself is easy to compute with the technical difficulty relegated to the mathematical analysis of throughput properties. Recent work on max-pressure signal timing has integrated some practicalities of traffic signal timing into the mathematical control and analyses, such as cyclical phase selection, pedestrian phases, signal coordination, transit signal priority, and limited deployment. Moreover, simulation results comparing max-pressure control against current signal timings in Hennepin County corridors suggest significant improvements from using max-pressure control. This seminar will introduce max-pressure control and then present recent work on bridging the mathematical theory with the practice of signal timing towards implementation on public roads.

Bio:

Michael W. Levin is an Associate Professor in the Department of Civil, Environmental, and Geo- Engineering at the University of Minnesota. He received a B.S. degree in Computer Science and a Ph.D. degree in Civil Engineering from The University of Texas at Austin in 2013 and 2017, respectively. Dr. Levin is a member of the Network Modeling Committee (AEP40) of the Transportation Research Board and is on the editorial board of Transportation Research Part B: Methodological. His work has been published in top journals including Transportation Science, Transportation Research Part C: Emerging Technologies, and IEEE Transactions on Intelligent Transportation Systems and has received several awards, including the 2019 Ryuichi Kitamura Award and the 2016 Milton Pikarsky Award from the Council of University Transportation Centers. His research focuses on traffic flow and network modeling of connected autonomous vehicles and intelligent transportation systems.

The Role of AI in Scientific Discovery: Opportunities and Limitations

Xiangliang Zhang, Leonard C. Bettex Collegiate Professor of Computer Science, University of Notre Dame

Abstract: Artificial Intelligence (AI) is reshaping the landscape of scientific discovery, enabling breakthroughs across diverse fields. However, when these AI tools are applied to scientific problems, gaps and mismatches often arise. The inherent uncertainty in scientific phenomena, coupled with issues like data quality, biases, and interpretability, poses significant challenges. This talk will discuss the transformative potential of AI in scientific discovery, focusing on its applications in predictive modeling, generative tasks, optimization strategies, and literature analysis. Examples will include AI models ranging from traditional neural networks to large language models (LLMs). At the same time, their limitations will be critically examined, calling for collaboration between the AI and scientific communities to address these challenges and unlock AI’s full potential in advancing scientific discovery.

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Friday / CS Seminar
January 24th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Meena Jagadeesan, UC Berkeley

Talk Title
Steering Machine Learning Ecosystems of Interacting Agents

Abstract

"Modern machine learning models—such as LLMs and recommender systems—interact with humans, companies, and other models in a broader ecosystem. However, these multi-agent interactions often induce unintended ecosystem-level outcomes such as clickbait in classical content recommendation ecosystems, and more recently, safety violations and market concentration in nascent LLM ecosystems.

In this talk, I discuss my research on characterizing and steering ecosystem-level outcomes. I take an economic and statistical perspective on ML ecosystems, tracing outcomes back to the incentives of interacting agents and to the ML pipeline for training models. First, in LLM ecosystems, we show how analyzing a single model in isolation fails to capture ecosystem-level performance trends: for example, training a model with more resources can counterintuitively hurt ecosystem-level performance. To help steer ecosystem-level outcomes, we develop technical tools to assess how proposed policy interventions affect market entry, safety compliance, and user welfare. Then, turning to content recommendation ecosystems, we characterize a feedback loop between the recommender system and content creators, which shapes the diversity and quality of the content supply. Finally, I present a broader vision of ML ecosystems where multi-agent interactions are steered towards the desired algorithmic, market, and societal outcomes."

Biography
Meena Jagadeesan is a 5th year PhD student in Computer Science at UC Berkeley, where she is advised by Michael I. Jordan and Jacob Steinhardt. Her research investigates multi-agent interactions in machine learning ecosystems from an economic and statistical perspective. She has received an Open Philanthropy AI Fellowship and a Paul and Daisy Soros Fellowship.

Research/Interest Areas
Artificial Intelligence, Machine Learning, Economics and Computation
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Zoom: https://northwestern.zoom.us/j/92665903240?pwd=YGOjSzB0Pxk4oDvRsVBbA0auFIvH0p.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=408ccd84-8bf4-409a-b995-b261015654c8
DEI Minute: tinyurl.com/cspac-dei-minute

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Tensor Time Series: Factor Modeling and Deep Neural Networks

Yuefeng Han, Assistant Professor, Department of Applied and Computational Mathematics and Statistics, University of Notre Dame

Abstract: The analysis of tensors (multi-dimensional arrays) has become a vital area in modern statistics and data science, driven by advancements in scientific research and data collection. High-dimensional tensor data arise in diverse applications such as economics, genetics, microbiome studies, brain imaging, and hyperspectral imaging. These tensors are often high-dimensional and high-order, yet key information typically resides in reduced-dimensional subspaces governed by structural properties. This talk explores novel methodologies and theories for tensor time series analysis.

The presentation consists of two parts. The first part introduces a factor modeling framework for high-dimensional tensor time series, leveraging a structure similar to CP tensor decomposition. We propose a computationally efficient estimation procedure incorporating a warm-start initialization and an iterative simultaneous orthogonalization scheme. The algorithm achieves $\epsilon$-accuracy within $\log\log(1/\epsilon)$ iterations. Additionally, we establish inferential results, demonstrating consistency and asymptotic normality under relaxed assumptions. The second part integrates tensor factor models with deep neural networks. Specifically, a Tucker-type low-rank tensor structure is employed as a tensor-augmentation module in neural networks. Extensive experiments demonstrate the integration of this module into transformers and temporal neural networks for tensor time series prediction and tensor-on-tensor regression. The results highlight significant performance improvements, underscoring its potential for advancing time series forecasting.

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Would you like to simplify your machine learning code and minimize repetitive tasks? Scikit-Learn's pipelines can help you organize and streamline your data processing and model training, as well as make your code cleaner and easier to manage. In this workshop, we will cover why and how to use pipelines in your machine learning code.

Prerequisites: Participants should be familiar with Python at the level of the Python Fundamentals Bootcamp, another introductory Python workshop, or be a self-taught Python coder. Basic familiarity with machine learning and Scikit-Learn is required.

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

"Using Computational Approaches to Understand the Social and Structural Drivers of Health"

By Michelle Birkett, Associate Professor of Medical Social Sciences (Determinants of Health) and Preventive Medicine and IPR Associate

This event is part of the Fay Lomax Cook Winter 2025 Colloquium Series, where our researchers from around the University share their latest policy-relevant research.

Please note all colloquia this quarter will be held in-person only.

In recent years generative artificial intelligence (GAI) tools have increased in availability and popularity at universities, but not everyone knows how to use them for better library search results. In this 60-minute hybrid session we will demonstrate how to use Microsoft Copilot, a free GAI tool for all NU students and faculty, to improve searches with the gold standard of reliable content, library databases. This session is geared toward participants with little-to-no familiarity with generative artificial intelligence, and will also address the basics of what generative artificial intelligence is, how it works, its risks, and limitations. Students can learn about NU’s Copilot accounts here.

This workshop is presented by Tracy Coyne, Distance Learning and Professional Studies Librarian; Frank Sweis, User Experience Librarian; and Jeannette Moss, User Education Librarian.

A Northwestern Zoom Account is required to access this session.

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

AI for Nature: From Science to Impact

Tanya Berger-Wolf, Professor, Computer Science and Engineering and Director, Translational Data Analytics Institute, The Ohio State University

Abstract: Computation has fundamentally changed the way we study nature. New data collection technologies, such as GPS, high-definition cameras, autonomous vehicles under water, on the ground, and in the air, genotyping, acoustic sensors, and crowdsourcing, are generating data about life on the planet that are orders of magnitude richer than any previously collected. Yet, our ability to extract insight from these data lags substantially behind our ability to collect it.

The need for understanding is more urgent ever and the challenges are great. We are in the middle of the 6th extinction, losing the planet's biodiversity at an unprecedented rate and scale. In many cases, we do not even have the basic numbers of what species we are losing, which impacts our ability to understand biodiversity loss drivers, predict the impact on ecosystems, and implement policy.

The talk will discuss how AI can turn these data into high resolution information source about living organisms, enabling scientific inquiry, conservation, and policy decisions. It will introduce a new field of science, imageomics, and present a vision and examples of AI as a trustworthy partner both in science and biodiversity conservation, discussing opportunities and challenges.

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute

Drawing on the Northwestern Engineering whole-brain philosophy and leadership in cognitive science, the Master of Science in Artificial Intelligence program would like to invite you to learn more at our upcoming webinar.

Take this opportunity to join Dr. Kristian Hammond, Professor of Computer Science and director of the MSAI program, as he discusses the complexities of this field, and how this newly offered program at Northwestern Engineering will prepare students for a career in artificial intelligence. At the end of the presentation, we will offer an open Q&A where you will be able to have your specific questions answered. You are also welcome to email your questions to us ahead of the session (msai@northwestern.edu).

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Curious about how AI can transform your text data analysis? Large Language Models (LLMs), like those behind ChatGPT, offer powerful ways to extract information from text, such as identifying key individuals, finding information about events, or selecting sections of documents. Learn how LLMs can assist with information extraction, and how they compare with other approaches such as rule-based methods, regular expressions, and pre-trained entity recognition models.

Prerequisites: Open to anyone working with or interested in text data, this workshop will provide hands-on examples in Python, though the concepts apply across various programming languages. While prior experience in natural language processing is helpful, it’s not required to participate.

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Monday / CS Seminar
January 13th / 12:00 PM
Hybrid / Mudd 3514

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DEI Minute: tinyurl.com/cspac-dei-minute