News & EventsDepartment Events & Announcements
Events
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May4
EVENT DETAILS
lessMonday / CS Seminar
May 4 / 12:00 PM
Hybrid / Mudd 3514Speaker
Moon DuchinTalk Title
TBAAbstract
TBABiography
TBA---
Zoom: TBA
Panopto: TBATIME Monday, May 4, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May4
EVENT DETAILS
lessThis dissertation develops and applies computational methods to three problems at the frontier of empirical economics, united by a common objective: to operationalize abstract economic concepts using modern machine learning tools, to uncover new empirical knowledge through those measurements, and to evaluate the conditions under which such tools can be trusted.
The first essay asks what makes a scientific paper novel, and whether novelty of different kinds matters differently for impact. Treating novelty as a multidimensional construct rather than a single attribute, the essay leverages Large Language Models (LLMs) and embedding models to measure novelty directly from the full text of scientific papers. It finds that specific dimensions of novelty, defined by a paper's distinctiveness from its intellectual neighbors versus its engagement with the current frontier, predict fundamentally different outcomes. This finding reveals a strategic tension in knowledge production that citation-based measures of impact cannot capture. The second essay asks why strategic behavior systematically departs from game-theoretic predictions in complex environments. Drawing on a massive dataset of hundreds of millions of chess games, it develops an interpretable measure of when a position is genuinely difficult for a boundedly rational agent, and uncovers a new stylized fact: complexity arises endogenously more often among higher-skilled players. A structural model reveals that this pattern reflects experts' ability to sustain complexity, rather than a preference for risk, a finding with broader implications for understanding how people create and exploit strategic complexity. The third essay asks how much we should trust a predictive model when it is applied outside the context in which it was estimated. It formalizes this out-of-domain prediction problem, derives coverage-guaranteed forecast intervals for transfer error, and finds suggestive evidence that the in-domain superiority of black-box algorithms does not reliably generalize across domains, a cautionary note with direct relevance for the use of machine learning in situations requiring generalizability.
Together, these essays argue that the value of computational tools in economics lies not only in their raw predictive power, but more importantly in their capacity, when disciplined by economic theory, to render previously unobservable quantities measurable and to rigorously evaluate the limits of predictive models.
TIME Monday, May 4, 2026 at 1:30 PM - 3:30 PM
LOCATION Mudd 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May4
EVENT DETAILS
lessGenerative models have made it possible to synthesize convincing content across language, vision, speech, and even biological sequences. This progress has opened powerful avenues for privacy-preserving data sharing, scientific discovery, and creative work, but it has also introduced new risks: misuse for deception, copyright infringement, leakage of sensitive training data, and the potential design of harmful biological agents. This dissertation presents a unified body of work on generating synthetic content responsibly and detecting and constraining it when misused.
The dissertation contributes five complementary frameworks. FakeDB synthesizes relational databases that preserve semantic constraints and statistical properties, enabling secure information exchange in place of sensitive real data. GEM steers multimodal generation toward user-specified objectives through prompt tuning and discriminative feedback, and is paired with an efficient finetuning strategy that makes high-quality synthesis more scalable. O3 addresses unintended information retention in large language models by supporting continual unlearning through orthogonal adapters and out-of-distribution detection. CADD, a context-based audio deepfake detector, leverages transcripts and contextual metadata such as news and social media signals to identify fabricated speech, particularly in public statements where the cost of deception is high. Finally, SafePro extends these ideas beyond conventional modalities to protein language models, introducing a multi-objective alignment framework that jointly optimizes structural foldability while steering generation away from toxicity and virulence.
Together, these contributions form a coherent agenda for promoting authenticity, privacy, safety, and trust in the generative AI era, and they suggest concrete paths forward for deploying generative systems in domains where the stakes of getting it wrong are highest.TIME Monday, May 4, 2026 at 5:00 PM - 7:00 PM
LOCATION 3001, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May6
EVENT DETAILS
lessSpeaker: Chenhao Zhang
Logistics: Wednesday May 6th 12-1pm in Mudd 3514. Lunch will be provided.
Title: Barriers to Counterfactual Credit Attribution for Autoregressive Models
Abstract: Generative AI disrupts the practice of giving credit to work that came before. Ideally, a generative model would give credit to any work on which its output depends in a significant way. In this talk, I will present my recent joint work with Aloni Cohen from the University of Chicago on the theory of credit attribution of generative models. We consider the proper credit attribution notion counterfactual credit attribution (CCA)---a relaxation of differential privacy---recently introduced by Livni, Moran, Nissim, and Pabbaraju (NeurIPS 2024) for classification models. We initiate the study of CCA autoregressive generative (AR) models and uncover barriers to two natural approaches to obtaining them. First, an AR model’s next-token predictor satisfying CCA does not automatically imply that the model itself satisfies CCA. Second, adding non-excessive credit to an existing non-credit-attribution AR model to satisfy CCA is computationally hard.
Bio: Chenhao Zhang is a final-year Computer Science PhD student working with Jason Hartline and Christos Dimoulas. His primary research lies in the intersection of computer science and law using various tools spanning from the theory of algorithms to programming languages.
Note: This talk is open to everyone, including faculty members as well as friends outside the CS community. Critical feedback from those working on practical aspects of LLM are particularly welcomed (regardless of being constructive or having fundamental disagreement on the limitation of theoretical research).
TIME Wednesday, May 6, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May13
EVENT DETAILS
lessWednesday / CS Seminar
May 13 / 12:00 PM
Hybrid / Mudd 3514Speaker: Zhiru Zhang, Cornell University
Title: Hypothesizing Autonomous Accelerator Design
Abstract: We are living through a fundamental shift in computing, where performance and efficiency gains increasingly come from specialized accelerators tailored to "hot" domains like AI. Yet as accelerator-centric computing proliferates, it continues to build atop a longstanding disconnect between the way we design these systems and the way we program them. This divide slows hardware innovation, complicates the software stack, and makes accelerators far harder to evolve than the rapidly changing applications they are meant to serve. While increasingly capable AI agents can help alleviate some of these challenges, many key pieces are still missing to truly close this loop. In this talk, I will share lessons learned from our recent work on (1) workload mapping for emerging accelerator architectures, (2) abstractions that help unify accelerator design and programming, and (3) agentic approaches to compiler construction. I will discuss how these directions may collectively move us closer to a future of more autonomous accelerator design.
Bio: Zhiru Zhang is a Professor in the School of ECE at Cornell University. His current research investigates new algorithms, design methodologies, and automation tools for heterogeneous computing. Dr. Zhang is an IEEE Fellow and has been honored with the Intel Outstanding Researcher Award, AWS AI Amazon Research Award, Facebook Research Award, Google Faculty Research Award, DAC Under-40 Innovators Award, DARPA Young Faculty Award, IEEE CEDA Ernest S. Kuh Early Career Award, and NSF CAREER Award. He has also received 10+ best paper awards from premier conferences and journals in computer systems and EDA. Prior to joining Cornell, he co-founded AutoESL, a high-level synthesis start-up later acquired by Xilinx (now part of AMD). AutoESL's HLS tool evolved into Vivado HLS (now Vitis HLS), which is widely used for designing FPGA-based hardware accelerators.
TIME Wednesday, May 13, 2026 at 12:00 PM - 1:00 PM
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May14
EVENT DETAILS
lessWednesday / CS Distinguished Lecture
May 14 / 4:00 PM
Hybrid / Ford ITW 1350Speaker
Prabhakar RaghavanTalk Title:
Can AI assist in Mathematics and Computer Science research?Abstract:
We share our experience using LLMs to obtain new results in mathematics and computer science. We begin with an illustrative example from load-balancing in planet-scale cloud systems, outlining the abilities and limitations of LLMs. Next, we describe our experience with AlphaEvolve, an evolutionary language model from Google DeepMind, to establish new results in the approximability of the Traveling Salesman Problem (TSP), and MAX-CUT problem. We also derive new bounds for several Ramsey numbers. Our methodology entails evolving fleets of Python programs that generate proof chunks to yield these results, and to accelerate proof verification by up to 10,000x. We suggest that our results on inapproximability and Ramsey theory could not have been discovered by hand, and conclude with reflections on the state and promise of AI in mathematics and CS research.Biography:
Prabhakar Raghavan is the Chief Technologist at Google, where he has held several senior roles since joining in 2012, including Senior Vice President with oversight of Search, Maps, Advertising, Gemini and Payments, and before that, responsibility for Gmail, Google Drive, Calendar and Google Docs. Previously, he led Yahoo! Labs and served as CTO at Verity, Inc following over a decade at IBM Research. He co-authored the textbooks Randomized Algorithms and Introduction to Information Retrieval. Raghavan received a PhD from Berkeley and a Dottore ad honorem from the University of Bologna, and is a member of the National Academy of Engineering.---
ZoomTIME Thursday, May 14, 2026 at 4:00 PM - 5:00 PM
LOCATION ITW 1350, Ford Motor Company Engineering Design Center map it
CONTACT Jensen Smith jensen.smith@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May18
EVENT DETAILS
lessMonday / Student Seminar
May 18 / 12:00 PM
Mudd 3514Speaker: TBA
Title: TBA
Abstract: TBA
Bio: TBA
TIME Monday, May 18, 2026 at 12:00 PM - 1:00 PM
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May20
EVENT DETAILS
lessWednesday / CS Distinguished Lecture
May 20 / 12:00 PM
Hybrid / Mudd 3514Speaker
Julio OttinoTalk Title
TBAAbstract
TBABiography
TBA---
Zoom: TBA
Panopto: TBATIME Wednesday, May 20, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May27
EVENT DETAILS
lessCome celebrate with us at the annual end of year department awards on Wednesday May 27th. Details to come.
TIME Wednesday, May 27, 2026 at 3:00 PM - 5:00 PM
LOCATION TGS Commons, 2122 Sheridan Road map it
CONTACT Wynante Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)