Wei Wang is a professor of computer science, and has served as the department chair since July 1, 2025. She holds the Leonard Kleinrock Term Chair in Computer Science and has a joint faculty appointment in the Department of Computational Medicine, which is affiliated with both the UCLA David Geffen School of Medicine and UCLA Samueli.

At UCLA, Wang directs the Scalable Analytics Institute, which develops tools and techniques to analyze large datasets. Her research interests include big data analytics, data mining, machine learning, natural language processing, bioinformatics and computational biology, computational medicine and AI for science. She is also a member of UCLA’s Jonsson Comprehensive Cancer Center, the Institute for Quantitative and Computational Biology, and the Bioinformatics Interdepartmental Ph.D. program.

Wang is a fellow of both the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). She has won numerous awards and has been recognized for her leadership in the data analytics field by both the IEEE International Conference on Data Mining and the ACM Special Interest Group on Knowledge Discovery and Data Mining, where she currently serves as chair. As of 2025, she has advised more than 45 graduate students and many postdoctoral researchers.

Prior to joining the UCLA Samueli faculty in 2012, Wang was a computer science professor at the University of North Carolina at Chapel Hill. She earned her doctorate in computer science from UCLA Samueli after completing her master’s in systems science at Binghamton University in New York. Before joining UNC at Chapel Hill, Wang worked as a research staff member at IBM’s Thomas J. Watson Research Center.

“We are at an unprecedented moment in the AI revolution. It’s a very exciting time. Working with Dean Park and the rest of the leadership at UCLA, I want to think about how we can best embrace the AI wave — not only for faculty and students already in AI, but for everyone.”

Q: What are some of the main research projects that you are focusing on this academic year?
A: One of the exciting areas for us is agentic AI. You’ve probably heard this term in many settings, but at UCLA we have a large project with many faculty and students involved in building AI agents in a systematic way. We want to create what we call a generalized agent arena, which is basically an ecosystem for developing agents across many different application scenarios.

A couple of weeks ago, we sent several papers to top machine learning venues, and we’re continuing to build in that direction. That’s something we’re very excited about right now.

Another effort we’re equally excited about is our collaboration with faculty in the Mathematics Department on AI for mathematical reasoning. We want to build systems that can assist human mathematicians in proving theorems and making discoveries. You may have seen news about work in this space, for example, the collaboration between DeepMind and Mathematics Professor Terence Tao. He is also part of our team, and I’m the lead PI. Our goal is to support much more advanced mathematics areas like analysis, number theory, and partial differential equations. These problems are far beyond competition style questions, but they also have a much broader impact across STEM.

Q: How do you work with undergraduate and graduate students on these research projects?
A: We engage a lot of students. In my lab, we have about 20 Ph.D. students, as well as many graduate and undergraduate students. They’re very self-motivated and often self-organized into teams. I encourage that structure, and I meet regularly with each team to discuss their research agendas. For the math project, we recruit both undergraduate and graduate students not only as researchers, but also as users and evaluators. They help us understand how well the system can assist with proving theorems, doing derivations, and enabling discovery. We’ve already seen a lot of interest from the student population. I receive many emails from students asking whether they can join.

Q: How will your research be translated into new technologies?
A: All research goes through different development phases. Some of the tools I mentioned started just last year, so they’re still very much in the research stage. But other projects are moving closer to practice. For example, we have a collaboration with materials scientists called Meta Scientist. The idea is to build an AI ecosystem platform to help engineers design materials. Today, a design engineer needs deep knowledge across materials science and mechanical engineering. They come up with an initial design and run dynamic simulations to see whether it has the desired properties (often it doesn’t). Then a human designer must revise the design and rerun the simulations. It can last months or even years. What we hope is that AI can play the role of both the reasoning and the simulation. It can propose an initial design, suggest improvements and lower the barrier for the human designer. Ultimately, we want an end-to-end system where someone can describe what they need in natural language and receive a blueprint that can go directly to a 3D printer. We already have a prototype of this, and that’s our long-term goal.

Q: How could private funding through donor gifts enable you to further your research at UCLA?
A: Private support from donors plays a critical role in advancing our research by enabling us to involve more students in our work and by providing us with flexible funding to pursue ambitious projects. With donor support, we can experiment with groundbreaking ideas, take calculated risks, and push the boundaries of innovation.

In the past few years, especially under the leadership of Dean Park, UCLA has gained substantially more support from industry. In my lab, we’ve been fortunate to receive support from Amazon, Google, NEC and Optum Labs, the research arm of UnitedHealth. This enables our students to work on frontier research and collaborate very actively with industry groups. For instance, I have four students supported by Amazon Fellowships. Each of them collaborates with a different team at Amazon. After completing summer internships there, when they come back, they incorporate that work into their Ph.D. dissertations. They get incredible exposure to what’s happening on the industrial side of AI, and it really strengthens their research.

Q: As the newly appointed Department Chair in Computer Science, could you discuss your key priorities and goals for the department moving forward? How does your department cultivate connections between students and faculty?
A: We are at an unprecedented moment in the AI revolution. It’s a very exciting time. Working with Dean Park and the rest of the leadership at UCLA, I want to think about how we can best embrace the AI wave — not only for faculty and students already in AI, but for everyone. AI is penetrating every field. So, we need to ask: how do we prepare our students and faculty for this upcoming revolution? Part of that is asking ourselves how do we revise our curriculum, so students learn the newest and best technologies? It also means helping people who are not currently in AI find ways to integrate AI into their research and education in a way that feels rewarding and even adventurous.

We’re also exploring how AI connects with other disciplines, such as within the semiconductor hub, chip design, aerospace, and health. The Computer Science Department has many faculty in this area, so we feel a responsibility to help UCLA make this transition and create a better environment for the whole campus.

Q: With the rapid growth of heterogeneous and unstructured data, what new directions in AI discovery excite you most for solving real-world societal and scientific challenges?
A: We’re moving forward with foundation models, agentic AI frameworks, and what we call physical AI. Robotics is one example where systems interact directly with the physical world but there are many others. In the past, AI largely existed in virtual spaces. In the next phase, agentic AI combined with robotics will be much more integrated into the physical environment. We already have faculty focusing on building physical AI we believe our research can really make it happen.