News & EventsDepartment Events & Announcements
Events
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May27
EVENT DETAILS
lessDear CS Community:
We are excited to invite you to our End of Year Awards Celebration! Join us on Wednesday, May 27th in TGS Commons at 3pm to help recognize the amazing dedication and hard work our students, faculty, and staff have done this year. Light refreshments and snacks will be served.
Please RSVP no later than May 25th by following this link: Computer Science End of Year Awards Celebration
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)
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May27
EVENT DETAILS
lessWhen viewers read a data visualization, they are translating visual marks into quantitative judgments that are systematically imperfect. Decades of graphical perception research have documented these errors, but most findings take the form of ordinal rankings or categorical taxonomies. While helpful, these descriptions do not make quantitative predictions: they cannot predict how large a viewer's error will be for a specific chart design, dataset, and task, nor can they generalize to combinations not yet tested experimentally.
This dissertation develops computational models that make quantitative predictions about how viewers interpret data visualizations. Four studies model different aspects of visualization perception with increasing generality. First, I develop a formal model of y-axis truncation in bar charts that defines task-dependent conditions under which truncation preserves or distorts data structure, replacing heuristics with formally grounded, computable design guidance. Second, I test whether deep neural network features trained on natural images can serve as computational proxies for human similarity judgments of visualizations. Third, I apply signal detection theory (SDT) to visual lineup analysis, demonstrating that SDT provides a richer computational model of lineup perception than accuracy-based approaches by separating viewer sensitivity from decision criterion. Finally, I propose visual decoding operators --- composable perceptual primitives, each with estimable bias and variance --- and provide an existence proof that operators characterized on PDF and CDF charts compose to predict scatterplot mean-estimation performance with no parameters fit to the target data.
Together, these studies demonstrate that computational models of visualization perception are both feasible and productive: they predict quantities that ordinal rankings cannot, expose mechanisms that holistic accuracy measures obscure, and generalize across chart types and tasks.TIME Wednesday, May 27, 2026 at 3:00 PM - 5:00 PM
LOCATION Mudd 3501, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Jensen Smith jensen.smith@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May28
EVENT DETAILS
lessJoin us for free bagels and coffee followed by an informal discussion hosted by CSPAC and CSSI.
TIME Thursday, May 28, 2026 at 9:00 AM - 11:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May29
EVENT DETAILS
lessPrivate set operators are functions over private inputs that multiple parties can jointly evaluate while revealing only the prescribed output. Two such operators are intersection and join-and-compute, realized respectively by private set intersection (PSI), outputting the intersection of two sets, and private join and compute (PJC), outputting aggregates such as cardinality, sum, and inner product over matching records. Classical PSI and PJC target one-shot two-party settings where each party holds its full input. Real deployments rarely fit this model: servers maintain persistent datasets reused across many clients, and inputs are often split across multiple data owners. Existing protocols fall short: they lack cross-execution consistency, require per-execution server reprocessing, or incur substantial overhead for distributed inputs.
This thesis develops efficient and provably secure protocols for private set operators in practical client-server settings, through three schemes together with new cryptographic primitives:
(1) Inspired by password-checkup applications, we study client-output PSI in which the server publishes a one-time, linear-size encoding of its set, after which each client executes PSI with the server at cost linear only in its own set, with simulation-based security against malicious adversaries. A key ingredient is an efficient oblivious verifiable unpredictable function (OVUF).
(2) We introduce committed vector oblivious linear evaluation (C-VOLE), which generates VOLE correlations on a pre-committed vector and serves as a unifying tool for zero-knowledge proofs of committed values and actively secure multi-party computation. Built on a tailored LPN-based commitment, our matching C-VOLE protocols exploit the commitment structure to minimize the cost of binding the committed vector to the VOLE correlation, and efficiently instantiate a maliciously secure server-output PSI protocol.
(3) Beyond intersection, we study computation over matching records from distributed datasets, motivated by applications such as privacy-preserving ad conversion measurement. We propose the first efficient approximate PJC protocol with communication sublinear in the input size. Its core is a new adaptation of the Alon-Matias-Szegedy (AMS) sketch, redesigned for efficient evaluation under fully homomorphic encryption via structured randomness.
TIME Friday, May 29, 2026 at 10:00 AM - 12:00 PM
LOCATION Mudd 3501, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Jensen Smith jensen.smith@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May29
EVENT DETAILS
lessWhile the ubiquitous adoption of algorithms in decision-making and content generation greatly improves societal efficiency, it has also raised various regulatory concerns, including antitrust, nondiscrimination, and intellectual property protection.
This dissertation investigates techniques from three different computer science research areas to support the regulation of algorithms: First, based on ideas from online learning theory, we propose a framework for the regulation of algorithmic collusion by auditing from data. Second, by adapting principles from information flow control with dynamic policies, we design a type system to reason about iteration (probabilistic) independence to support the regulation of the fairness of classification algorithms. Third, we study a proper data attribution notion informed by data privacy concepts for the regulation of credit attribution of generative models. These results demonstrate that the foundations of the regulation of algorithms can benefit from techniques from these distinct areas of computer science research and point to future research directions.
TIME Friday, May 29, 2026 at 1:00 PM - 3:00 PM
LOCATION Mudd 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Jensen Smith jensen.smith@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Jun5
EVENT DETAILS
lessThere is growing momentum—from industry, government, and academia—to use AI for automating cybersecurity tasks. Yet practitioners remain skeptical: while 87% of security leaders expect AI to enhance their roles, only 9% believe it will replace significant parts of them. This gap stems from two fundamental barriers: limited capability and lack of trust. In this talk, I present my research on addressing these barriers through explainable AI. I first introduce StateMask, a method that automatically identifies critical decision steps in AI agent trajectories, enabling security professionals to understand why an AI-generated patch succeeded or failed. A user study with 41 experienced developers shows that 89% find our explanations aligned with their reasoning. I then present GPO, which leverages these explanations to synthesize high-quality training data without expensive expert annotation, thereby improving model capability. GPO-trained open-source models achieve performance competitive with leading commercial models on vulnerability patching, and its extension, EntroPO, ranks 1st on SWE-Bench Lite among all open-weight models. I conclude by discussing future directions toward building AI systems that are robust to imperfect data, trusted by security professionals, and capable of tackling real-world cybersecurity challenges.
TIME Friday, June 5, 2026 at 9:00 AM - 11:00 AM
CONTACT Jensen Smith jensen.smith@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)