News & EventsDepartment Events & Announcements
Events
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Nov9
EVENT DETAILS
lessMonday / Distinguished Lecture Series
November 9 / 12:00 PM
Hybrid / Mudd 3514Speaker
Mohit Bansal, UNC Chapel HillTalk Title
AI Challenges: Trustworthy Collaboration, World Discovery, and Long-Horizon MemoryAbstract
In this talk, I will discuss 3 major pitfalls and challenges of current state-of-the-art AI agents, and present potential solutions for: (1) Teaching agents to be trustworthy and reliable collaborators based on: social/pragmatic multi-agent interactions via speaker-listener confidence calibration, learning to balance positive and negative persuasion, and multi-agent AI safety through the lens of compositional attacks, theory-of-mind, and belief-steering; (2) Discovering and improving skills/world models needed for efficient, robust action and collaboration based on: discovering reusable programmatic skills (including for multi-agent setups), weakness-driven adaptive data/environment generation for skill improvement, and structured world model discovery and inference; (3) Planning of long-horizon memory for multi-step reasoning and generation over continuously evolving, conflicting, and scattered information. We will cover diverse domains (math, commonsense, coding, tool-use, computer use, embodied actions, etc.), modalities (text, images, videos, audio, gaze, layouts, etc.), and real-world applications (early medical diagnosis and classroom education engagement).Biography
Dr. Mohit Bansal is the John R. & Louise S. Parker Distinguished Professor, Director of the MURGe-Lab (UNC-AI Group), and Core AI Lead of the ENGAGE NSF-AI Institute in the Computer Science department at UNC Chapel Hill. He received his PhD from UC Berkeley and his BTech from IIT Kanpur. His research expertise is in multimodal generative models, reasoning and planning agents, faithful language generation, and interpretable, efficient, and generalizable deep learning. He is an ACL and AAAI Fellow and recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), IIT Kanpur Young Alumnus Award, DARPA Director's Fellowship, NSF CAREER Award, Google Focused Research Award, Microsoft Investigator Fellowship, Army Young Investigator Award (YIP), DARPA Young Faculty Award (YFA), and outstanding paper awards at ACL, CVPR, EACL, COLING, CoNLL, and TMLR. He has been a keynote speaker for the IEEE/CVF WACV 2027, IEEE MLSP 2026, ECAI 2025, ACM-CODS 2025, AACL-IJCNLP 2023, CoNLL 2023, and INLG 2022 conferences. His service includes EMNLP Program Co-Chair, Associate Editor-in-Chief for TPAMI, CoNLL Program Co-Chair, ACL Executive Committee, ACM Doctoral Dissertation Award Committee, ACL Doctoral Dissertation Award Co-Organizer, ACL Mentorship Program Co-Founder, and Associate Editor for ACM AI Letters, TACL, CL, IEEE/ACM TASLP, and CSL journals. Webpage: https://www.cs.unc.edu/~mbansal/Research Interest/Area: AI, NLP, CV, ML
TIME Monday, November 9, 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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Nov13
EVENT DETAILSmore info
lessReinforcement learning has become a standard tool for post-training language models and teaching them to reason, follow instructions, and align with human preferences. Nevertheless, the ingredients that produce this reasoning capability are still only partly understood: reward models, the construction of verifiers, and long chains of thought optimized end to end against sparse reward signals.
This workshop investigates formal characterizations of reasoning and the role of reinforcement learning in promoting reasoning capability. Does post-training introduce new capabilities, or does it sharpen a distribution the base model already contains? How should credit be assigned across tokens when the only feedback is whether the final answer was correct? What happens when the verifier is imperfect, the reward is subject to hacking, or the task admits answers that are difficult to verify? Potential topics include:
RL from human feedback, preference optimization, and reward modeling
RL with verifiable rewards; automated verification for mathematics, code, and formal reasoning
Credit assignment and exploration over long reasoning traces
Theoretical accounts of post-training (e.g., sample complexity and generalization)
Reward hacking, specification gaming, and the robustness of learned verifiers
Multi-turn, agentic, and tool-using settings; RL for language-model agents
Evaluation of reasoning; data and benchmarks for reasoningThe one-day event consists of invited talks and a poster session, and aims to foster connections among researchers in reinforcement learning, natural language processing, learning theory, optimization, statistics, and related areas.
TIME Friday, November 13, 2026 at 9:15 AM - 5:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Bella Barrios marbella.barrios@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Nov16
EVENT DETAILS
lessRegular registration for Winter quarter begins
TIME Monday, November 16, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Nov17
EVENT DETAILS
lessTuesday / CS Seminar
November 17 / 12:00 PM
Hybrid / Mudd 3514Speaker
Nathan Nichols,Tableau/SalesforceTalk Title
AI Safety from the Inside: Notes from 20 Years of Building AIAbstract
Nate Nichols has spent two decades building and shipping AI products, from machine-generated content research during his CS PhD, to Chief Scientist at an NLG startup, to VP of Product at Salesforce. Lately he’s been asking a different question: what happens as these systems get more capable than the people building them expect? In this informal talk, he’ll share how his thinking on AI safety has changed, what the field’s open problems look like from a practitioner’s seat, and why people entering AI right now are unusually well placed to shape how it goes. Bring questions.Biography
As VP of Product Management at Tableau/Salesforce, Nate owns the Agentic Analytics strategy and works with product development teams across Tableau to define and communicate that vision. Nate was previously Chief Scientist at Narrative Science (acquired by Tableau in 2021) and has over 40 patents related to text generation and rhetorical modeling. He earned his Ph.D. in Artificial Intelligence from Northwestern University in 2010, with a thesis titled “Machine-generated Content.” Since 2025, Nate has become passionate about AI safety, leading public workshops in Chicago and hosting Kitchen Table, a weekly podcast and YouTube show dedicated to engaging parents and the public on AI safety topics.Research Interest/Area: AI safety
TIME Tuesday, November 17, 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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Nov19
EVENT DETAILS
lesstba
TIME Thursday, November 19, 2026 at 9:00 AM - 11:00 AM
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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Nov20
EVENT DETAILS
lessDissertation, PhD Final Exam, and change of grade forms due to TGS for Fall PhD candidates
TIME Friday, November 20, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Nov25
EVENT DETAILS
lessThanksgiving Vacation begins at 6:00 PM (No classes, University offices closed)
TIME Wednesday, November 25, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Nov30
EVENT DETAILS
lessFall Classes Resume
TIME Monday, November 30, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec4
EVENT DETAILS
lessTBA
TIME Friday, December 4, 2026 at 12:00 PM - 2:00 PM
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Dec5
EVENT DETAILS
lessFall classes end
TIME Saturday, December 5, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec7
EVENT DETAILS
lessFall Exams begin
TIME Monday, December 7, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec9
EVENT DETAILS
lessInformation systems have become the cornerstone of how decisions are made in nearly every domain of human activity, constituting the infrastructure through which knowledge is accessed and acted upon. At their core, an information system is any integrated combination of hardware, software, databases, networks, and human expertise organized to collect, process, store, and analyze information in support of operations and decision-making. The usability of such systems in practice is governed by two properties that have historically stood in tension: accuracy and fluency. Symbolic, query-based systems offer high accuracy but require specialized query languages or code to interact with effectively, while large language model (LLM)-based systems are fluent and accessible through natural language but remain prone to hallucination and factual error. The goal of this dissertation is twofold: to build information systems that answer questions and generate reports from data both accurately and fluently, and to generate training data that improves the accuracy and fluency of such systems, in both cases by using structured knowledge and rules as a source of ground truth for language generation.
To this end, this dissertation introduces a neurosymbolic framework, grounded in knowledge representations of data and analytics, that first produces language outputs that are accurate but not necessarily fluent, and then uses these outputs to condition LLM generation, yielding final outputs that are both accurate and fluent. These outputs can serve two roles: as the final product delivered to a user, or as training data for the models that later serve users. This framework is first demonstrated through Satyrn, a report generation system in which domain-independent report blueprints produce accurate but coarse information that is then used to condition LLMs, jointly producing reports that are more factual, fluent, and coherent than those generated by existing neural methods. The framework is then applied to training data generation through RingSQL, a platform that combines these knowledge representations with LLM-based paraphrasing to produce text-to-SQL training data that is as diverse and fluent as LLM-generated data while remaining more accurate and faithful to the underlying query. Data generated with RingSQL provides a stronger training signal than data produced with prior LLM-based methods, template-based methods, and even human-annotated data. Finally, this dissertation introduces methods for grounding the generation of reasoning traces in verifiable, rule-based evaluation, and uses these traces in a mixed-policy reinforcement learning setting, offering an additional path toward improving the accuracy of LLM-based information systems. Together, these contributions demonstrate that structured knowledge and language are complementary, and that unifying them can reconcile the accuracy-fluency trade-off in information systems.TIME Wednesday, December 9, 2026 at 6:00 PM - 8:00 PM
CONTACT Jensen Smith jensen.smith@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Dec12
EVENT DETAILS
lessFall exams end
TIME Saturday, December 12, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec12
EVENT DETAILSmore info
lessThe ceremony will take place on Saturday, December 12 in Pick-Staiger Concert Hall, 50 Arts Circle Drive.
TIME Saturday, December 12, 2026 at 4:00 PM - 6:00 PM
LOCATION Pick-Staiger Concert Hall map it
CONTACT Andi Joppie andi.joppie@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Dec24
EVENT DETAILS
lessWinter Recess Starts - University Closed Through January 1st, 2027
TIME Thursday, December 24, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar