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Artificial Intelligence Lab: Data, Models, and Decisions

This course is an introduction to the basic concepts of artificial intelligence (AI) as is practiced in the 2020s. At the end of this course you will be able to build AI systems that you can train to solve a variety of problems.

Graph Neural Networks

Graph Neural Networks (GNNs) are information processing architectures for signals supported on graphs. They have been developed and are presented in this course as generalizations of the convolutional neural networks (CNNs) that are used to process signals in time and space.

Introduction to Deep Learning & Applications

This course covers the fundamentals of deep learning and the basics of deep neural networks, including different network architectures and optimization algorithms for training these networks, as well as applications to computer vision, robotics, and sequence modeling.

Jailbreaking Large Language Models

This tutorial provides an overview of recent advances in jailbreaking attacks, defenses, and evaluation methods, with particular emphasis on applications in robotics and agentic systems.

Linear Control System Theory

This undergraduate-level course focuses on single-input single-output linear time-invariant control systems emphasizing frequency-domain methods. Topics include modeling of feedback control systems, transient and steady-state behavior, Laplace transforms, stability, root locus, frequency response, Bode plots, Nyquist plots, Nichols plots, PID control, and loop shaping.

Planning & Learning in Robotics

This course covers optimal control fundamentals and their application to motion planning and decision making in robotics. Topics include Markov decision processes (MDPs), dynamic programming, search-based and sampling-based motion planning, value and policy iteration, linear quadratic regulation (LQR), and model-free reinforcement learning.

Robot Proving Grounds

An introduction to robotics algorithms for mapping, localization, planning, and control, as well as a realistic testbed to try before deploying the algorithms on a real robot. The PyBullet physics simulator is used to demonstrate these algorithms on ground wheeled robots in simulation.

Safety in Generative AI

The rapid advancement of generative AI has brought remarkable innovations, from creating realistic images to generating human-like text. However, this power comes with significant responsibility. AI safety is crucial to ensure these systems are used ethically, fairly, and without unintended harm. Without proper safety measures, generative AI can spread misinformation, reinforce biases, or be exploited for malicious purposes. By prioritizing AI safety, researchers and developers can build trust, create guidelines for responsible use, and mitigate risks before they affect society on a large scale.

More coming soon...

Introducing K-12 Students to Data Science and AI

In collaboration with middle and high school teachers and undergraduate interns, TILOS develops activities to introduce K-12 students to data science, programming, artificial intelligence, and optimization. Training is available to help teachers learn to run these activities and incorporate into curricula. Contact tilos@ucsd.edu to bring an activity to your classroom or event.

See the Lab 3.0 website for additional resources.

Community Partners

• Sweetwater Union High School District
• San Diego Union School District
• Scouting America (San Diego-Imperial Council)
• Heights Philadelphia
• George Washington High School

Welcome to our video interview series, where we spotlight TILOS faculty and student researchers, as well as conversations with visiting researchers from academia and industry. In these candid conversations, you'll hear firsthand about their innovative research, they challenges they face, and the journeys that led them toward careers in AI and optimization research.

Why Topology Matters for Data Science and AI with Gunnar Carlsson

Topology offers a way to organize complex data by looking for shape, structure, and relationships. Yusu Wang, Ph.D., UC San Diego, and Gunnar E. Carlsson, Ph.D., Stanford University, discuss how ideas from algebraic topology move from pure mathematics into data science, machine learning, and AI. Carlsson explains why topological data analysis helps identify meaningful groups in large data sets, how examples and accessible tools can make abstract math useful, and why interpretability matters as AI systems grow more complex. The conversation connects collaboration across fields, customer-focused entrepreneurship, and the need to train mathematicians and AI researchers to help formulate real-world problems. Their exchange shows how mathematical thinking can make powerful technologies more understandable, usable, and accountable.

How Machine Learning Improves Algorithms with Ellen Vitercik

Hard optimization problems often look impossible through worst-case analysis, but real-world problems can contain structure that helps algorithms work faster. Ellen Vitercik, Ph.D., of Stanford University explains how machine learning can improve algorithm design for NP-hard optimization problems while preserving the formal guarantees that make solvers useful. She discusses beyond worst-case analysis, problem-specific heuristics, and the gap between tools that perform well in practice and methods that prove optimality. Vitercik also describes research on LLM reasoning using data structure tasks, where answers can be checked programmatically and failures reveal when models rely on pattern matching rather than true generalization. Her work helps clarify how AI may support stronger algorithms, more useful benchmarks, and more reliable reasoning systems.

Learning Beyond the Data: Adam Klivans on Distribution Shift and the Future of AI

Trustworthy machine learning requires models that still work when real-world data changes, and Adam Klivans, Ph.D., Director of the Institute for Foundations of Machine Learning (IFML), emphasizes learning under “distribution shift” as a major barrier to relying on models to predict disease across different patient populations. IFML focuses on foundational algorithms and mathematical techniques that push generative AI forward, including better methods for training and inference in deep learning and advances in diffusion approaches. Klivans highlights robustness and safety as core priorities, asking how to trust a model trained in one setting when it is applied in another. IFML connects these foundations to use-inspired domains such as imaging, protein engineering and biologics, and AI for mathematical discovery.

Big Data, Better Answers: Optimization at Scale with Courtney Paquette

Large-scale optimization and machine learning shape modern data science, and Courtney Paquette, Ph.D., McGill University, studies how to design and analyze algorithms for large-scale optimization problems motivated by applications and data science. Paquette draws on probability, complexity theory, and convex and non-smooth optimization, and examines scaling limits of stochastic algorithms. Speaking with Saura Naderi, UC San Diego, Paquette describes an unconventional path from finance to pure mathematics and explains how persistence and comfort with uncertainty support long-term research. She highlights the challenge of building missing foundations while advancing through graduate training, and she connects that experience to the realities of doing original work. Paquette also reflects on rapid progress in machine learning and frames AI systems as tools that can be used thoughtfully.

Artificial Intelligence and Security: A Conversation with Yaron Singer

Yaron Singer, Vice President of AI and Security at Cisco, co-founded a company specializing in artificial intelligence solutions, which was acquired by Cisco in 2024. They developed a firewall for artificial intelligence, a tool designed to protect AI from making critical mistakes. No matter how sophisticated AI is, errors can still happen, and these errors can have far-reaching consequences. The product is designed to detect and fix such mistakes. This technology was developed long before ChatGPT and its competitors burst onto the scene, making it the hottest industry in tech investment. Join Singer as he sits down with TILOS Foundations team member Mikhail (Misha) Belkin to discuss his work and the continued effort to make artificial intelligence secure.

Machine Learning, Fairness and AI: A Conversation with Adam Kalai

OpenAI researcher Adam Kalai sits down with TILOS Foundations team member Mikhail (Misha) Belkin to discuss his work in machine learning, algorithmic fairness, and artificial intelligence. Kalai has contributed research in areas like fairness in AI models, word embeddings, and human-AI collaboration. He has worked at Microsoft Research and has published influential papers on bias in machine learning models. His work has helped shape discussions on ethical AI and the development of more equitable AI systems.

Building Fast and Reliable Machine Learning Systems with Yian Ma

TILOS Foundations team member Yian Ma talks about his research using scalable inference methods for credible machine learning. This involves designing Bayesian inference methods to quantify uncertainty in the predictions of complex models; understanding computational and statistical guarantees of inference algorithms; and leveraging these scalable algorithms to learn from time series data and perform sequential decision making tasks.

Machine Learning and Mathematics with Tristan Brugère

Tristan Brugère, a Ph.D. student in the Halıcıoğlu Data Science Institute at UC San Diego, discusses his research at The Institute for Learning-enabled Optimization at Scale. Specifically, how he is working on optimal transport and neural networks on graph generative models with applications to chip design.

Using AI to Build Better Wireless Networks with Tara Javidi

TILOS Neworks team member Tara Javidi, an Electrical and Computer Engineering professor at UC San Diego's Jacobs School of Engineering, discusses her research in artificial intelligence and large-scale wireless networks.

The Future of Robotics with CJ Taylor

When not teaching at the University of Pennsylvania, CJ Taylor is part of the Robotics team working at The Institute for Learning-enabled Optimization at Scale. He talks with Saura Naderi about his upbringing, his early interest in data science, and his current position. He also talks about his involvement in projects that benefit the community.

The AI for Workforce program provides current and future workers with key skills in artificial intelligence. Community colleges use more than 500 hours of AI content and professional training for faculty to develop AI certificates, augment existing courses, and launch full AI associate degree programs.

The Summer Institute is a week-long workshop run by the San Diego Supercomputer Center (SDSC) at UC San Diego. Aimed at researchers in academia and industry who have problems that cannot typically be solved using local computing resources, the workshop covers various topics in High Performance Computing and Data Science.

TILOS supports the development of course modules by National University for their Certificate in AI and Machine Learning program, as well as Bachelor of Science and advanced degrees in Data Science.

Launched in 2018 as part of the Electronics Resurgence Initiative, OpenROAD's mission is to democratize integrated circuit design and boost both hardware and electronic design automation innovation at scale. The project is led by TILOS member Andrew Kahng at UC San Diego's VLSI CAD Laboratory.