Thursday / CS/ME Seminar
February 6th / 3:00 PM
Hybrid / Ford ITW

Speaker
Amira Abdel-Rahman, Massachusetts Institute of Technology (MIT)

Talk Title
Inverse Design of Material-Robot Systems

Abstract
"In human history, no innovation has progressed as rapidly as digital technologies. Yet, it wasn't until the 2000s that we began to feel this impact on the physical world. With the rise of the Internet of Things, our built environment is becoming smarter, more dynamic, and more complex. Each year, the line between physical and digital blurs further. However, examining the history of computer-aided design (CAD), engineering (CAE), and manufacturing (CAM) tools reveals few radical advancements since the invention of "Sketchpad" in 1963—the first interactive CAD system. We urgently need these radical advances to faithfully model and respond to our complex environment, and to enable us to imagine and design a new world where the physical and digital are indistinguishable.
To achieve that, we need novel physical and digital tools that can handle complexity and grow reliability. Just as digitizing analog signals revolutionized and scaled communication and computing, applying discretization and error correction principles to the physical world unlocks the ability of the precise placement of functional materials with embedded electrical and mechanical properties. These “Digital Material” structures introduce digital programmability to the physical realm, mirroring the transformative impact seen in digital technologies.
In my talk, I will introduce fully declarative and inverse workflows to design and build scalable Digital Material systems. Using these workflows the user can model and design systems that span scales (micro, meso, macro) and disciplines (electrical, mechanical, aerospace, architectural engineering) without being an expert in all — or any — of these fields.
Using principles of discretization, distributed computing, hierarchy, and error correction; the introduced workflows use domain knowledge as priors and universal design representations across all stages—simulation, optimization, fabrication, and control. These tools have been used to design a plethora of static and dynamic structures, ranging from bridges and shelters to aerospace structures, robots, and electronics."

Biography
Amira Abdel-Rahman is a PhD student at the Massachusetts Institute of Technology (MIT) Center for Bits and Atoms, where she develops inverse design workflows, empowered by physics-informed AI, to advance the design and fabrication of large-scale material-robot systems. She earned her Master in Design Studies (MDes) in Technology from Harvard Graduate School of Design and her Bachelor’s degree from the American University in Cairo. Her professional experiences include NVIDIA as part of the Simulation Technology Team, and Autodesk as part of the Generative Design Group, where she contributed to cutting-edge research at the intersection of computational design, AI, and simulation technologies.

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Zoom: https://northwestern.zoom.us/j/99856023678?pwd=IWSX91NYBlQ6VEwRtnVMr5FlDY5nxl.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=bd8a3dec-e2d0-4ae8-b5c4-b27401679ad9

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
February 10th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Sidhanth Mohanty

Talk Title
A quest for an algorithmic theory for high-dimensional statistical inference

Abstract

"When does a statistical inference problem admit an efficient algorithm?
There is an emergent body of research that studies this question by trying to understand the power and limitations of various algorithmic paradigms in solving statistical inference problems; for example, convex programming, Markov chain Monte Carlo (MCMC) algorithms, and message passing algorithms to name a few.

Of these, MCMC algorithms are easy to adapt to new inference problems and have shown strong performance in practice, which makes them promising as a universal algorithm for inference. However, provable guarantees for MCMC have been scarce, lacking even for simple stylized models of inference.

In this talk, I will survey some recent strides that I have made with my collaborators on achieving provable guarantees for MCMC in inference, and some new tools we introduced for analyzing the behavior of slow-mixing Markov chains."

Biography

"Sidhanth is broadly interested in theoretical computer science and probability theory, and his primary interests are on the algorithms and complexity of statistical inference, and spectral graph theory.

Sidhanth is currently a postdoctoral researcher at MIT, hosted by Sam Hopkins. Previously, he received his PhD in Computer Science at UC Berkeley in 2023 where he was advised by Prasad Raghavendra."

Research/Interest Areas
Theoretical computer science, algorithmic statistics, analysis of Markov chains, spectral graph theory, semidefinite programming, random matrix theory
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Zoom: https://northwestern.zoom.us/j/98400432043?pwd=t4PaTy1pIWxa9R0QrhxojIjKeJ8pho.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d9a67624-ddf1-4295-bb3a-b27b011768d7
Community Connections Topic: Lab counterculture

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

Speaker
Wenting Zhao

Talk Title
Reasoning in the Wild

Abstract
In this talk, I will discuss how to build natural language processing (NLP) systems that solve real-world problems requiring complex reasoning. I will address three key challenges. First, because real-world reasoning tasks often differ from the data used in pretraining, I will introduce WildChat, a dataset of reasoning questions collected from users, and demonstrate how training on it enhances language models’ reasoning abilities. Second, because supervision is often limited in practice, I will describe my approach to enabling models to perform multi-hop reasoning without direct supervision. Finally, since many real-world applications demand reasoning beyond natural language, I will introduce a language agent capable of acting on external feedback. I will conclude by outlining a vision for training the next generation of AI reasoning models.

Biography
Wenting Zhao is a Ph.D. candidate in Computer Science at Cornell University, advised by Claire Cardie and Sasha Rush. Her research focuses on the intersection of natural language processing and reasoning, where she develops techniques to effectively reason over real-world scenarios. Her work has been featured in The Washington Post and TechCrunch. She has co-organized several tutorials and workshops, including the VerifAI: AI Verification in the Wild workshop at ICLR 2025 and the Complex Reasoning in Natural Language tutorial at ACL 2023. In 2024, she was recognized as a rising star in Generative AI and was named Intern of the Year at the Allen Institute for AI in 2023.

Research/Interest Areas
natural language processing, AI, reasoning
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Zoom: https://northwestern.zoom.us/j/93876523769?pwd=T8R2mKPRz0mgkb1Ygjokp3OcRwAUnX.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=f01e4368-11b0-45f1-bdb6-b27b0117a5cd
Community Connections Topic: Building a community of care

Towards Data-efficient Training of Large Language Models (LLMs)

Baharan Mirzasoleiman, Assistant Professor, Computer Science Department, UCLA

Abstract: High quality data is crucial for training LLMs with superior performance. In this talk, I will present two theoretically-rigorous approaches to find smaller subsets of examples that can improve the performance and efficiency of training LLMs. First, I will present a one-shot data selection method for supervised fine-tuning of LLMs. Then, I'll talk about an iterative data selection strategy to pretrain or fine-tune LLMs on imbalanced mixtures of language data. I'll conclude by showing empirical results confirming that the above data selection strategies can effectively improve the performance of various LLMs during fine-tuning and pretraining.

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Friday / CS Seminar
February 13th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Kaitlyn Zhou, Stanford University

Talk Title
Broadening AI Utility Through Natural Language Interfaces

Abstract
In this talk, I will present the novel dynamics of human interaction with large language models (human-LM interaction), focusing on how these systems shape human decision-making, trust, and reliance. As the world seeks to integrate the innovations of foundation models into everyday work and life, my mission is to design human-centered natural language interfaces to augment human intelligence and democratize access to AI. My work pioneers key advancements in natural language processing and human-computer interaction by: 1) uncovering core algorithmic risks in current human-LM interactions, 2) articulating the factors that complicate human-AI interactions, and 3) proposing new human-LM interactions to serve the needs of a broader population.

Biography
Kaitlyn Zhou is PhD candidate in computer science at Stanford University, advised by Dan Jurafsky. Her contributions have been recognized at top-tier conferences in NLP and HCI. She has received awards such as MIT EECS Rising Star, Stanford Graduate Fellowship, and the College of Engineering Dean’s Medal, and her methods have been featured in high-profile news outlets like the New York Times and Wall Street Journal. Kaitlyn has long advocated for increased access, inclusion, and equity in higher education and was appointed by the Washington State Governor to serve on the University of Washington Board of Regents.

Research/Interest Areas
natural language processing, human-computer interactions
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Zoom: https://northwestern.zoom.us/j/98362439009?pwd=c89YlFRcBHSe1XH0PEl5rKu0HtuRpA.1
Panopto: https://northwestern.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=6719566d-cf13-45ec-a0f3-b27b0117b56e
Community Connections Topic: Black History Month

Monday / CS Seminar
February 17th / 12:00 PM
Hybrid / Mudd 3514

Speaker
Ainesh Bakshi

Talk Title
Understanding Quantum Systems via the Algorithmic Lens

Abstract
Quantum mechanics is one of our most profound and successful theoretical frameworks for understanding the physical world. It continues to drive remarkable technological and theoretical breakthroughs, spanning computing, coding theory, cryptography, material science, and chemistry. In this talk, I will describe how the algorithmic lens has been pivotal in rigorously analyzing such quantum systems and revealed deeper structural properties that were previously inaccessible through traditional approaches.

Biography
Ainesh Bakshi is a Postdoctoral Fellow jointly appointed in the Mathematics and Computer Science departments at MIT. Prior to that, he obtained his PhD in Computer Science at CMU. He is broadly interested in theoretical computer science and quantum information. His main research thread revolves around using the algorithmic toolkit, consisting of iterative methods and convex hierarchies, to understand large quantum systems. These results have gained significant attention recently, including two Quanta articles, two QIP Invited Plenaries, a QIP Best Student Paper, and being featured in Quanta Magazine’s “Biggest Breakthroughs in Computer Science 2024.” He is also interested in extending this algorithmic toolkit and applying it to problems arising in high-dimensional statistics, privacy, metric embeddings, and numerical linear algebra.

Research/Interest Areas
Theoretical Computer Science, Quantum Information
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Zoom: TBA
Panopto: TBA
Community Connections Topic: TBA

The Media and Design Studio and Language Resource Center are pleased to offer a hands-on workshop on the use of Microsoft Copilot. This workshop expands on examples presented in the fall quarter seminar series by allowing participants to directly interact with various recommended generative artificial intelligence ("Gen AI") tools.

Participants will learn how to use AI to develop lesson plans and materials and to create and shape various forms of multimedia for teaching use.

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Abstract:

Professional associations serve as catalysts for innovation, interdisciplinary collaboration, and professional growth. INFORMS, with its diverse scope spanning operations research, management science, analytics, AI, applied mathematics, and beyond, plays a vital role in advancing these fields and shaping real-world decision-making.

In this talk, Elena Gerstmann, Executive Director of INFORMS, will explore the organization’s impact—highlighting key initiatives, upcoming plans, and the ways INFORMS is driving progress across industries. She will also engage the Northwestern community in an interactive discussion, gathering insights on how we can collectively advance smarter decisions for a better world.

Bio:

Elena Gerstmann, PhD, FASAE, CAE (she/her), is the Executive Director of INFORMS, the leading organization for advancing the science and technology of decision-making to save lives, save money, and solve complex problems.

Before joining INFORMS, Elena was a Principal at Avenue M Group and previously served on the executive teams at the American Society of Mechanical Engineers (ASME) and the Institute of Electrical and Electronics Engineers (IEEE).

Elena holds a PhD in social psychology from Rutgers University. She currently serves on the Board of Directors for the Council of Engineering and Scientific Society Executives (CESSE) and is a past board member and Fellow of the American Society of Association Executives (ASAE).

Beyond her professional work, Elena and her wife are the proud parents of two young adults, one happy dog, and one ornery cat. They are also co-founders of SocialOffset, a nonprofit that helps travelers align their spending with their values.

From generating text and images to understanding problems and making decisions, artificial intelligence has prompted a wave of experimentation at work. Join us for an insightful conversation with Dr. Eric Horvitz, Chief Scientific Officer of Microsoft and Dr. David Autor, Rubinfeld Professor of Economics at MIT, contributing authors to the National Academies Report on Artificial Intelligence and the Future of Work. This timely discussion will delve into the evolving landscape of work, addressing critical issues such as creating new forms of valuable work and augmenting workers to changing workplace dynamics and labor. The public will gain valuable strategies for navigating workforce changes. Researchers will gain critical insights into AI development and work. And policymakers will understand the need for flexible responses. Don’t miss this chance to engage with thought leaders shaping the future of work and gain actionable insight to stay ahead in our rapidly changing world.

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Teaching, Playing, & Performing with AI

With an introduction from Dean E. Patrick Johnson and Associate Dean for Research Molly Losh and a moderated discussion led by Eliza Bent.

Presenters
Melissa Blanco Borelli. Associate Professor in Theatre and Performance Studies
Thomas DeFrantz, Professor in Theatre and Performance Studies
Duri Long, Assistant Professor in Communication Studies

CommConnections: The SoC symposium series established to cross disciplines and create partnerships within the school
Reception to follow

Register Now

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

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Community Connections Topic: TBA

Part of the 2025 University Practicum on Supporting Student Success, participants may attend any and all practicum events.

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

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Community Connections Topic: TBA

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

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Community Connections Topic: TBA

Title: Astrophysical Fluid Dynamics at Exascale

Speaker: James Stone, Institute for Advanced Study

Abstract: Most of the visible matter in the Universe is a plasma -- that is
a dilute gas of electrons, ions, and neutral particles -- interacting
with both magnetic and radiation fields. Studying the structure
and dynamics of astrophysical systems, from stars and planets, to
galaxies and the large-scale structure of the Universe itself,
usually requires numerical methods to solve the coupled equations
of compressible radiation magnetohydrodynamics (MHD). Robust
numerical algorithms for modeling astrophysical fluids, including
new methods for calculating radiation transport in relativistic
flows, will be discussed. Efficient implementation of these methods
on modern high-performance computing systems is crucial, and an
approach based on the Kokkos programming model that enables performance
portability will be described. Performance on a variety of
architectures of a new adaptive mesh refinement (AMR) astrophysical
MHD code will be given, including scaling on up to 65536 GPUs on
the OLCF Frontier exascale computer. Finally, a case study will
be presented that demonstrates some of the many new insights that
have come from applying computational methods to one particular problem:
how plasma accretes onto the black holes in the centers of galaxies.

Zoom: https://northwestern.zoom.us/j/95581369835

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

Speaker
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Talk Title
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Abstract
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Biography
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Research/Interest Areas
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Zoom: TBA
Panopto: TBA
DEI Minute: tinyurl.com/cspac-dei-minute

Speaker:

Morgan Frank, Assistant Professor, Department of Informatics and Networked Systems, University of Pittsburgh

Title:

AI, Complexity, and the Future of Work

Abstract:

Artificial Intelligence has evolved and now challenges our understanding of skills, careers, and the future of work. Using a variety of data on employment, occupations’ skill requirements, millions of resumes, and unemployment data from US states’ unemployment insurance offices, this talk will explore how workers’ skills shape their careers and how automation estimates fit into a framework for career adaptability and the economic resilience of labor markets. Work from this talk comes from a variety of publications in PNAS, Nature Communications, and Science Advances.

Speaker Bio:

Morgan Frank is an Assistant Professor at the School of Computing and Information at the University of Pittsburgh. Morgan is interested in the complexity of AI, the future of work, and the socio-economic consequences of technological change. While many studies focus on phenotypic labor trends, Morgan’s recent research examines how genotypic skill-level processes around AI impact individuals and society. Combining labor research with investigations into the nature of AI research and the social or societal implications of AI adoption, Morgan hopes to inform our understanding of AI’s impact. Morgan has a PhD from MIT’s Media Lab, was a postdoc at MIT IDSS and the IDE, and has a master’s degree in applied mathematics from the University of Vermont where he was a member of the Computational Story Lab.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/91407653122
Passcode: NICO25

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.

Related Info

The Media and Design Studio and Language Resource Center are pleased to offer a hands-on workshop on the use of Microsoft Copilot. This workshop expands on examples presented in the fall quarter seminar series by allowing participants to directly interact with various recommended generative artificial intelligence ("Gen AI") tools.

Participants will learn how to use AI to develop lesson plans and materials and to create and shape various forms of multimedia for teaching use.

Related Info

Register Now

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

Speaker
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Talk Title
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Abstract
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Biography
TBA

Research/Interest Areas
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Zoom: TBA
Panopto: TBA
DEI Minute: tinyurl.com/cspac-dei-minute

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

Speaker
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Biography
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Zoom: TBA
Panopto: TBA
DEI Minute: tinyurl.com/cspac-dei-minute

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

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Zoom: TBA
Panopto: TBA
DEI Minute: tinyurl.com/cspac-dei-minute

Speaker:

Neda Bagheri, Associate Professor, Department of Biology, University of Washington

Title:

Computational modeling of emergent spatiotemporal cell population dynamics

Abstract:

Computational models are essential tools that can be used to simultaneously explain and guide biological intuition. My lab employs agent-based modeling, machine learning, and dynamical systems to explain biological observations and interrogate multi-lateral regulatory networks that drive individual cellular decisions as well as cell population dynamics. We are interested in the inherent multiscale nature of biology, with a specific focus on system-level dynamics that emerge from interactions of simpler individual-level modules.

In this presentation, I introduce a multiscale agent-based model of a generic solid tumor microenvironment that integrates subcellular signaling and metabolism, cell-level decision processes, and dynamic vascular architecture and function. We use this modeling framework to understand decision processes among heterogeneous cell agents in changing microenvironments. The model is open-source and flexible/adaptable (it can characterize countless cell population dynamics!), but it is computationally costly to simulate and analyze at large scales. I highlight these challenges along with strategies to mitigate them, and showcase successes that derive from our model development process. I also describe how the model can be used to inform the design of experiments, interventions, and hypotheses that modulate population level responses.

Speaker Bio:

Neda Bagheri earned her doctorate in Electrical Engineering from the University of California in Santa Barbara. Her focus on control theory and dynamics piqued her interest in biology. After completing a postdoc in Biological Engineering at MIT, she joined the Chemical & Biological Engineering faculty at Northwestern University (2012). In 2019, she was recruited to both the University of Washington Seattle (where she holds a joint position in Biology and Chemical Engineering) and the Allen Institute for Cell Science.

In recognition for her research accomplishments and vision, Bagheri was awarded a National Science Foundation CAREER Award (2017) and a Senior Moulton Medal (2020). She was honored as a Distinguished Speaker for the Accelerated Discover Forum at IBM Research-Almaden (2018) as well as for the Mindlin Foundation (2019), and as the Plenary Speaker for the triennial International Federation of Automatic Control DYCOPS conference (2022). She serves on multiple science advisory and editorial boards, guiding the frontier of multidisciplinary research.

Location:

In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/97273424116
Passcode: NICO25

About the Speaker Series:

Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, data science and network science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.

Related Info

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.

Martin Falk is a postdoctoral scholar in the Murugan and Irvine groups at the University of Chicago. The goal of Martin Falk’s research is to create next-generation materials which can compute, morph, and continually adapt to their environments. Falk’s work draws on techniques from computer vision and active learning and developing AI agents capable of autonomous search and rigorous scientific discovery in novel material platforms.

Learn more about Martin Falk'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.

Related Info

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

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Talk Title
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Abstract
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Biography
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Research/Interest Areas
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Zoom: TBA
Panopto: TBA
DEI Minute: tinyurl.com/cspac-dei-minute