At Weinberg College, artificial intelligence isn't housed in one department — it's a lens that linguists, astrophysicists, statisticians, humanists, and mathematicians are all turning on the same fundamental questions: how we think, discover, communicate, and understand our world. Across the College, researchers are asking what AI reveals — and what it demands of us — from cognition to the cosmos.
Mark Hersam and Indira Raman co-led a brain-inspired chip that flags irregular heartbeats using roughly 10,000 times fewer computing operations than standard AI methods.
Kra co-authored the Leiden Declaration, calling on mathematicians and institutions to safeguard the integrity of the discipline as AI reshapes how math is practiced.
Two cross-departmental initiatives bringing an AI lens to the humanities and cognitive sciences at Weinberg College.
AI in the Mind Sciences
The AIMS Initiative uses AI and machine learning to fuel research and teaching in linguistics, neurobiology, philosophy, and psychology — not just applying better tools to old questions about the mind, but opening up entirely new ones.
Launching fall 2026, the HumAIn Initiative brings historians, philosophers, and literary scholars into direct conversation with AI's fastest-moving questions — through public talks, a book club, conferences, and new curriculum on what it means to be human alongside intelligent machines.
The humanities are tasked with centrally examining what it means to be human, and this is the domain of the biggest questions AI is forcing everyone to address.”
Sylvester Johnson, Chair and Professor of Black Studies
Faculty Spotlights
Sylvester Johnson
Chair, Professor of Black Studies
Johnson studies how intelligent machines are reshaping human identity, power, and belief. He previously led a team of humanists and technologists at Northwestern to develop an early machine-learning system for historical text analysis, and he now directs Future Humans, Human Futures, a project on the ethical governance of emerging technology.
Professor of Linguistics, Director of the Cognitive Science Program
Goldrick uses behavioral experiments and computational models to understand how the mind produces and processes language. He directs the Cognitive Science Program and its AI in the Mind Sciences (AIMS) Initiative.
Liang is an economic theorist whose research examines how AI and machine learning reshape incentives, information, and economic outcomes — from algorithmic fairness to delegating decisions to AI systems. She is a recipient of the National Science Foundation's CAREER Award.
Assistant Professor of Statistics and Data Science
A machine learning researcher, Stadie focuses on reinforcement learning — teaching AI systems new behaviors from feedback, with applications ranging from robotics and physical planning to large language models. Before joining Northwestern, he was a research scientist at OpenAI.
I just want to solve hard problems. People feel like they’re getting personal leverage from technological advancements. This requires researching what problems can be solved using AI to sort through noise, while leaving humans as the principal architects.”
Bradly Stadie, Assistant Professor of Statistics and Data Science
Course Offerings on AI
Curious what AI looks like in a Weinberg College classroom? Explore a sampling of courses from across the disciplines.
ECON 101-7-3
What happens when computers can think?
HIST 300
When did humans first try to build a thinking machine?
AMER_ST 310
Why do we keep building the very machines we're afraid of?
ASTRON 105-8-2
How is artificial intelligence reshaping science, culture, and the way we think?
STAT 461
What does it take for an AI to act, not just answer?
PHIL 101-7-23
Will big data usher in a new age of prosperity, or erode the values that hold us together?
COG_SCI 207
What is a mind, and can a computer help us understand one?
ENGL 305
Where does AI help your writing — and where should you take back control?
ECON 313
What exactly are algorithms learning, and what should that mean for how we regulate them?
STAT 415
How do you teach a computer to make predictions from data or know when to trust them?
MATH 362
How does a computer find the hidden structure in a tangled network?
Course Spotlight
In this Political Science College Seminar, first-year students use AI to test Machiavelli's political theory against his own diplomatic letters — a research project once reserved for advanced scholars.
In Ricardo Court's Machiavelli's Ends and Means (POLI_SCI 101-7), students don't just read The Prince and The Discourses — they put Machiavelli's theories to the test against his actual diplomatic correspondence, using an AI platform to search a large archive of Renaissance letters for supporting or contradicting evidence. Working like historians rather than students reciting a text, they form a research question, revise it as new evidence complicates their thinking, and build an argument over the course of the term — the kind of open-ended, iterative inquiry once reserved for multi-term senior theses. In this learning environment, AI is a tool for building intellectual agency.
Beyond Weinberg College
Weinberg College researchers are also helping lead AI work that lives outside the College itself.
A National AI Research Institute funded by NSF and the Simons Foundation, SkAI pairs Northwestern astrophysicists with AI researchers to make sense of an unprecedented flood of sky-survey data — building the models needed to find what next-generation telescopes will see.
By advancing research and training the next generation of leaders, Northwestern is ensuring that it remains a global powerhouse across all disciplines as they transform in the age of AI.”
Vicky Kalogera, Daniel I. Linzer Distinguished University Professor of Physics & Astronomy; Director, CIERA & SkAI Institute
AI Resources for Students, Faculty, and Staff
Using AI in Coursework
Weinberg College's principles and guidelines on academic integrity and artificial intelligence — for students and instructors navigating AI use in coursework.
What does "progress" really mean — and is AI its next chapter, or something else entirely? Grounded in Steven Pinker's Enlightenment Now, this seminar traces the sources of economic and scientific progress across centuries, then turns that same lens on artificial intelligence. Discussion topics evolve with student interest, but the question at the center stays the same: what happens when computers can think — or seem to?
Artificial intelligence is not new — long before the term was coined in 1955, engineers and inventors sought to mechanize human thought and behavior. This course traces the chronological arc of AI from eighteenth-century automatons to today's large language models, examining how changing conceptions of human intelligence and creativity shaped its development, and engaging in debates over the problems and possibilities of machine sentience.
This course traces a century of American anxiety about robots — from early cinema's clanking "mechanical men" to today's fights over generative AI — through fiction by Asimov and Philip K. Dick, films like The Iron Giant, and the legal battles over self-driving cars and AI-made art. By the end, the question isn't just what we fear about robots, but what that fear reveals about us.
Exploring AI: Shaping the Future in Science, Society & Thinking
About the Course
This seminar introduces first-year students to the foundations of AI — how machines learn, where they're already changing industries and daily life, and the ethical questions that come with that power. No technical background required, just curiosity about what it means to think and live in an AI-shaped world.
What does it take to build an AI system that doesn't just answer a question, but actually plans, acts, and learns from what happens next? This advanced course explores modern approaches to building autonomous AI agents and agentic systems, covering agent architectures, planning and reasoning, reinforcement learning, multi-agent systems, and tool use and memory. Students combine conceptual study with hands-on projects, culminating in a final project implementing an autonomous agent system for a real-world application.
Through smartphones, social media, and large language models, our everyday lives generate digital traces that governments, companies, and scientists now use to make decisions that shape our lives. This seminar weighs the promise of algorithmic decision-making — better health, education, and efficiency — against its risks to fairness, autonomy, and self-determination.
This course introduces the foundational ideas of artificial intelligence and cognitive science — knowledge representation, analogy, reasoning, language understanding, and even emotion and consciousness — through hands-on work with AI software. No programming experience needed: the goal is to think like a cognitive scientist, not to code like one.
Generative AI can draft, brainstorm, and revise — but knowing when to lean on it and when to override it is a skill of its own. This course teaches students to use Gen AI critically and responsibly across the writing process, while examining its ethical pitfalls and its limits on accuracy and credibility. Students finish by researching how Gen AI is already reshaping communication in the career field they're headed toward — from business to healthcare to journalism.
As algorithms increasingly shape hiring, lending, and policy decisions, what exactly are they learning from — and what should that mean for how we regulate them? This course builds a theoretical toolkit for thinking through the stakes of predictive data use, moving from how individuals learn from information, to how people respond strategically to algorithms, to the broader market and policy consequences of a data-driven world.
This course introduces the core methods of machine learning, from regression and classification to clustering, with an emphasis on the statistical reasoning behind why these techniques work and how to evaluate them rigorously.
This course dives into the linear algebra underneath modern machine learning — matrix factorizations, singular value decomposition, and principal component analysis — then extends those same tools to graphs, using spectral methods and graph Laplacians to uncover patterns and clusters in connected data. Next offered Winter 2027.
