News & EventsDepartment Events & Announcements
Events
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May22
EVENT DETAILS
lessAdvanced Persistent Threats (APTs) have emerged as one of the most consequential categories of cyberattacks, causing widespread damage to enterprise infrastructure, critical systems, and national security. In response, the research community has made substantial progress in APT detection and defense, especially through the development of provenance-based intrusion detection systems (PIDS). Despite these advances, significant gaps persist between academic research and operational practice. First, complex graph-learning-based detectors incur high computational overhead, excessive detection latency, and degraded performance under the bursty, irregular workloads common in production environments. Second, the absence of systematic, scalable methods for generating realistic APT attack scenarios limits the thoroughness with which defense systems can be stress-tested. Lastly, the field continues to be constrained by the scarcity of comprehensive, realistic, and up-to-date benchmark datasets for APT intrusion detection research. This dissertation addresses these three operational gaps through ML and AI, particularly generative AI. It defines, analyzes, and proposes solutions for: (1) efficiency challenges in provenance-based intrusion detection; (2) the absence of systematic, scalable methods for generating realistic, causality-preserving APT attack scenarios for rigorous red-team evaluation; and (3) the scarcity of comprehensive, realistic, and up-to-date benchmark datasets for APT intrusion detection.
TIME Friday, May 22, 2026 at 11:00 AM - 1:00 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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May22
EVENT DETAILS
lessStructured schematic images—such as diagrams, maps, and puzzles—convey meaning through discrete visual elements and spatial relations. Although modern vision-language models offer strong semantic priors, they often struggle with fine-grained structure, precise relational grounding, and long-horizon state tracking. I propose a neuro-symbolic approach to schematic visual understanding centered on explicit, grounded intermediate representations.
The work builds on cognitive science accounts in which visual understanding proceeds from primitives and objects to qualitative spatial relations and task-relevant structures. It extends CogSketch, a cognitive visual-understanding system that represents scenes using glyphs and qualitative relations and links them to analogical reasoning systems such as SME, MAC/FAC, and SAGE. CogSketch plus analogy has been used in both cognitive modeling and deployed systems where the input is digital ink. This prospectus addresses the challenge of starting with images of structured semantic materials. VLMs are used as components in a representation-building pipeline that produces visual elements and spatial relations for downstream symbolic and analogical reasoning. Planned experiments to test these ideas include visual-to-formal encoding for puzzle solving, planning, and theory of mind reasoning. My claim is that explicit grounded representations offer a more interpretable, data-efficient, and reliable basis for advanced reasoning than direct end-to-end vision-language methods alone.
TIME Friday, May 22, 2026 at 3:00 PM - 5:00 PM
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May26
EVENT DETAILS
lessWednesday / CS Seminar
May 26 / 12:00 PM
Hybrid / Mudd 3514Speaker
Amil Dravid, UC BerkeleyTalk Title
Deep Learning as a Natural Science: From Neurons to Scaling LawsAbstract
"As neural networks grow larger, they increasingly resemble complex systems with systematic behavior rather than isolated engineered artifacts. This is reflected in neural scaling laws, which show that performance follows a precise power law as a function of model size, data, and compute. Yet, we still lack a principled scientific understanding of how these models organize information internally.
In this talk, I will describe an approach to studying neural networks as scientific objects, drawing on ideas from neuroscience and statistical physics. I will focus on neurons that respond to similar inputs across independently trained models of the same modality — for example, neurons that fire for similar words across language models or similar objects and textures across vision models. We find that the population of shared neurons grows as a power law with model size, but represents a shrinking fraction of the overall population. We account for this trend with a simple theoretical model, which also predicts a polarization effect: as models grow, shared neurons become increasingly selective for specific concepts, such as code or visual textures, while the remaining neurons become less selective and encode multiple features. We then test this prediction in large-scale language and vision models. I will conclude with a case study showing how neuron selectivity can guide targeted data selection to improve training. Together, these results suggest that scaling laws are not restricted to external model behavior; they also describe regularities in how neural networks are organized internally."
Biography
Amil Dravid is a third-year PhD student in computer science at UC Berkeley, advised by Alyosha Efros. His research develops tools for understanding the internal structure of deep learning systems, with the goal of informing the design of more effective algorithms and models. He previously earned his BS in Computer Science from Northwestern University. His research is supported by the U.S. Department of Energy Computational Science Graduate Fellowship.Research Areas: Deep learning, computer vision, natural language processing
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Zoom Link
Panopto LinkTIME Tuesday, May 26, 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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May26
EVENT DETAILS
lessTraditional database management systems (DBMSs) rely on data-dependent observability, where the optimizer utilizes selectivities and intermediate cardinalities to select efficient execution plans. As privacy concerns increase and regulatory requirements are enforced, privacy-preserving DBMSs lose access to this information. While secure query execution becomes feasible, it often incurs high computational costs. An additional challenge arises in production environments. In multi-tenant cloud platforms, when a tenant experiences a slow query, developers would typically re-execute the query on the original data to diagnose performance regressions. However, confidentiality requirements prevent this approach, leading to genuine but unreproducible performance regressions. This thesis proposes that database metadata can serve as a substrate to address both challenges. From the perspective of DBMS builders, public schema constraints and protocol-visible information can substitute for the private statistics used by conventional optimizers. From the perspective of DBMS operators, differentially private releases of physical metadata can reproduce execution behavior on substitute datasets.
My prior work develops three systems based on this principle. Alchemy derives a circuit-aware cost optimizer for oblivious SQL using public schema constraints. HAMMER extends the principle beyond a single privacy primitive, routing public operators to plaintext, slot-wise arithmetic to fully homomorphic encryption (FHE) on GPUs, and control-flow-heavy operators to secure multi-party computation (MPC). ScanTwin generates differentially private sketches of Parquet footers, allowing scan-level performance regressions to be reproduced on synthetic data without accessing tenant records. Building on these results, the proposed thesis extends ScanTwin to PerfTwin, releasing operator-level differentially private sketches for additional operators to enable reproducing full-pipeline performance regressions. Overall, the goal is to demonstrate that, despite privacy constraints, the database stack can be efficiently built and reliably operated from safe metadata alone.
TIME Tuesday, May 26, 2026 at 1:00 PM - 3:00 PM
LOCATION Mudd 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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May26
EVENT DETAILS
lessCSPAC and CSSI are excited to announce our upcoming series of events on public speaking! All are welcome and encouraged to participate. See below for details:
Lightning talk competition @ May 26, 3:30-5 PM [sign up to present]
Present a 3-minute lightning talk about your research to a panel of judges. Winners will be awarded at the department awards ceremony on May 27. Snacks and refreshments will be provided. While we encourage all students to present, you are also welcome to join to watch and support your peers!
Students at any stage in their program are highly encouraged to participate! This is a great opportunity to practice an essential research skill, share your research with others, and receive constructive feedback from supportive faculty!
Questions? Email melissac@u.northwestern.edu.
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What is CSPAC?We are the CS PhD Advisory Council. We are a PhD student-led organization, and our mandate is to interface between PhD students and faculty on academic issues. We want to advocate for PhD students in the department, so if there is some way we can support you, please come talk to us. We welcome PhD students to our weekly meetings on Wednesday 9:30am-10:30am in Mudd 3501 and on zoom. We also welcome anonymous concerns/feedback at any time via this form. Anyone in the community can reach us at cspac@u.northwestern.edu.
TIME Tuesday, May 26, 2026 at 3:30 PM - 5:00 PM
CONTACT Melissa Chen melissac@u.northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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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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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 Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)