AllTutorialsCourses

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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.

Community Partners

• Sweetwater Union High School District (SUHSD)
  • Chula Vista Middle School
  • Mar Vista Middle School
  • Southwest Middle School
  • Hilltop Middle School
• San Diego Union School District (SDUSD)
  • Urban Discovery Academy Charter
  • Bell Middle School
  • Morse High School
• Scouting America (San Diego-Imperial Council)
• Heights Philadelphia
• George Washington High School (Philadelphia)

Welcome to our video interview series, where we spotlight TILOS faculty and student researchers, as well as conversations with visiting researchers from 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.

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.