Incoming Assistant Professor
Computer Science Department
University of Illinois at Urbana-Champaign
Email: jiaxuan@cs.stanford.edu
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I am currently an Adjunct Assistant Professor at UIUC CS, and a Senior Research Scientist at NVIDIA. I will join UIUC CS as a tenure-track Assistant Professor in 2024 Fall.
I received my Ph.D. and M.S. degrees from Department of Computer Science, Stanford University, advised by Prof. Jure Leskovec.
I was supported by JPMC PhD Fellowship and Baidu Scholarship during my PhD.
My research leads to Kumo AI, where I built the first graph learning predictive system for relational databases as a core founding member from 2021 to 2023.
In the past, I have developed data-driven methods to study our interconnected world. I am broadly interested in deep learning for graphs, relational data, and databases. I am also excited about knowledge-augmented LLMs and multi-modal foundation models.
You may also check out a summary of my past research:
- Core graph/relational learning methods: Learning from graphs [NeurIPS 2018b/2019b/2020a, ICML 2019, AAAI 2021]; Generating & optimizing graphs [ICML 2018, NeurIPS 2018a/2019a]
- Democratize graph learning: Software and systems that make graph learning accessible to researchers and practitioners [GraphGym, PyG, Kumo AI]
- Graph-inspired machine learning: Neural architecture design [ICML 2020], multi-task learning [ICLR 2022], deep learning with missing data [NeurIPS 2020b].
- Interdisciplinary applications: crop yield prediction [AAAI 2017], drug discovery [NeurIPS 2018a], recommender systems [WWW 2019], financial transactions [KDD 2022], relational database [Kumo AI]
Ultimately, my research lab aims at building AGI in the digital world.
- AI Agent: Exploring methodologies to enable AI to utilize tools and optimize itself based on those tools.
- ML System: Strategies to enhance the inference and training of Large Language Models (LLMs) & Foundation Models, and facilitate their deployment and application.
- Empowering AI with Relational Data: Investigating the utilization of AI to analyze and comprehend the interconnected digital world.
- AI and Beyond: Delving into how AI research can profoundly reshape the future of scientific research and the broader human society.
- We warmly welcome students to propose new directions and insights. Together, we will strive towards the goal of realizing AGI in the Digital World.
Prospective student
- PhD openings: I am looking for multiple self-motivated PhD students starting in 2024 Fall (application occurs in Dec 2023). Students with strong ML or ML system background are preferred (e.g., could you explain data, model, tensor, pipeline, and FSDP parallelism?). If you are a prospective PhD student, I highly suggest reaching out to me early so that we can both check if there is a fit between us.
- Intern openings: I am also looking for self-motivated remote intern students starting at any time. Similarly, students with strong ML or ML system background are preferred. I would prefer that you are interested in working with me as a PhD student in the long run.
- Email format: "Interested in {position, e.g., Ph.D.} at {expected time, e.g., Fall 2024}”):
- CV: Include your background, experiences, and future research interests.
- Research: Summarize any projects, publications, and open-source software you have done.
- Why Us?: Any research work from me that you are interested in. Mention any new topics (could be fully unrelated to my past research) that you would like to explore.
- Google Form: You may also register your information on this Google Form. I will make sure to check out your information during the application process.