News & EventsDepartment Events & Announcements
Events
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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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Jun1
EVENT DETAILS
lessEmbodied AI promises agents that perceive, reason, and act in the physical world, yet realizing such agents demands more than algorithmic novelty. Progress at the frontier increasingly depends on the co-design of three tightly coupled layers: the methods that learn behavior, the simulation infrastructure that supplies the data and experience to learn from, and the systems that bridge learned policies to physical hardware. This dissertation argues that the next generation of embodied-AI researchers must be full-stack roboticists, fluent across all three layers, and presents my previous works that together trace this thesis.
TIME Monday, June 1, 2026 at 10:30 AM - 11:30 AM
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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Jun3
EVENT DETAILS
lessModern hardware is parallel and is increasingly relying on parallel programming to achieve
both performance and energy efficiency. However, today’s parallel programming places a disproportionate burden on developers, requiring them to design and fine-tune parallel execution strategies across diverse hardware platforms and varying program inputs. I postulate that optimizing compilers can relieve developers from this burden by automatically reasoning about parallel execution strategies while also enforcing correctness properties. Achieving high parallel performance across modern hardware requires selecting an effective parallel execution plan, including how and where parallelism should be expressed and implemented. To automate this selection process, I introduce the Parallel-Semantics Program Dependence Graph (PS-PDG), a compiler abstraction
that captures developer-encoded parallel semantics alongside compiler-derived analysis. PS-PDG
enables compilers to override suboptimal execution plans with superior alternatives. While static
execution-plan selection is effective for regular workloads, irregular and input-sensitive applica
tions require runtime adaptation of parallel granularity. To address this challenge, I introduce the
Heartbeat Compiler (HBC), a compilation system that automatically translates high-level fork
join parallelism into binaries capable of dynamically controlling task granularity through heartbeat
scheduling to eliminate the need for manual task-size tuning. Beyond performance, parallel C++
applications also face severe memory-safety risks arising from operations on data collections, in
cluding iterator invalidation. These bugs are difficult for existing tools to detect precisely and
become even harder to diagnose under nondeterministic thread interleavings. To address this chal
lenge, I introduce Ledger, a data collection-oriented static analyzer that provides high-precision
detection of invalidation vulnerabilities in programs that operate on complex data collections.TIME Wednesday, June 3, 2026 at 2:00 PM - 4:00 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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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)
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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)