Me
Ruochen Wang 王若宸
CS PhD Student @ UCLA

Abstract

I am a final-year CS PhD at UCLA, Founder and Lead of TurningPoint AI Research, and affiliated with Google Research / DeepMind. I was the 1st-author recepient of the Outstanding Paper Award at ICLR. I obtained my B.S. in Computer Science and Statistics from University of Michigan, with the highest distinction. I study Multimodal Foundational Models, and am into Venture Capitals.

Education

[01/2022 - 12/2024] Ph.D. in Computer Science, University of California, Los Angeles
[01/2020 - 01/2022] M.S. in Computer Science, University of California, Los Angeles
[09/2015 - 08/2019] B.S. in Computer Science, University of Michigan, Ann Arbor
[09/2016 - 08/2019] B.S. in Statistics (2nd degree), University of Michigan, Ann Arbor
[09/2013 - 07/2015] B.S. in Finance Honors Program (transferred), Shanghai University of Finance and Economics

Experiences (Selected)

[08/2023 - Present ] Founder & Lead at TurningPoint AI Research Group
[06/2022 - Present ] Student Researcher at Google
[05/2019 - 09/2019] Research Intern at Microsoft Research Asia

Outreach

  • [Work] I am open to industrial opportunities (Corporate Research Team, VC, Startups) focus on building and commericializing next generation AIGC technologies. If you are in VC/Startup business or leading a corporate research team, I'd be delighted to have a chat with you.
  • [TurningPoint AI Hiring]: TPAI is a long-term multi-lab research collaboration dedicated to exploring key technologies in building reliable multimodal foundational agents. We are a compact and hardcore team comprised of 4 senior advisors and multiple project leads. Checkout our website for research, team construction and hiring!

Publications and Manuscripts

MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?
Xirui Li*, Hengguang Zhou*, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Cho-Jui Hsie, (*Equal Contribution)
Arxiv, 2024
[Paper] [Project Website] [Code]
TurningPoint AI
Large Language Models are Interpretable Learners
Ruochen Wang, Si Si, Felix Yu, Dorothea Wiesmann, Cho-Jui Hsieh, Inderjit Dhillon
Arxiv, 2024
[Paper] [Code]
Google
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise
Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Boqing Gong, Cho-Jui Hsieh, Minhao Cheng
Arxiv, 2024
[Paper]
TurningPoint AI
Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?
Kuei-Chun Kao, Ruochen Wang, Cho-Jui Hsieh
EMNLP, 2024
[Project Website]
Understanding the Impact of Negative Prompts: When and How Do They Take Effect?
Yuanhao Ban, Ruochen Wang, Tianyi Zhou, Minhao Cheng, Boqing Gong, Cho-Jui Hsieh
ECCV, 2024
[Paper]
TurningPoint AI
MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion
Sen Li, Ruochen Wang, Cho-Jui Hsieh, Minhao Cheng, Tianyi Zhou
Arxiv, 2024
[Paper] [Project Website] [Code]
TurningPoint AI
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers
Xirui Li, Ruochen Wang, Minhao Cheng, Tianyi Zhou, Cho-Jui Hsieh
EMNLP, 2024
[Paper] [Project Website] [Code]
TurningPoint AI
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
Ruochen Wang*, Sohyun An*, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, Cho-Jui Hsieh (*Equal Contribution)
ICML, 2024
[Paper] [Project Website] [Code]
TurningPoint AI
On Discrete Prompt Optimization for Diffusion Models
Ruochen Wang, Ting Liu, Cho-jui Hsieh, Boqing Gong
ICML, 2024
[Paper] [Project Website] [Code]
Google
Ameliorate Spurious Correlations in Dataset Condensation
Justin Cui, Ruochen Wang, Yuanhao Xiong, Cho-jui Hsieh
ICML, 2024
[Paper]
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
Justin Cui, Ruochen Wang, Si Si, Cho-jui Hsieh
ICML, 2023
[Paper]
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Yuanhao Xiong*, Ruochen Wang*, Minhao Cheng, Felix Yu, Cho-Jui Hsieh, (*Equal Contribution)
CVPR, 2023
[Paper]
DC-BENCH: Dataset Condensation Benchmark
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
NeurIPS, 2022
[Paper] [Code] [Project Website]
Efficient Non-Parametric Optimizer Search for Diverse Tasks
Ruochen Wang, Yuanhao Xiong, Minghao Cheng, Cho-Jui Hsieh
NeurIPS, 2022
[Paper] [Code]
Generalizing Few-Shot NAS with Gradient Matching
Shoukang Hu*, Ruochen Wang*, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng, (*Equal Contribution)
ICLR, 2022
[Paper] [Code]
Learning to Schedule Learning Rate with Graph Neural Networks
Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh
ICLR, 2022
[Paper]
RANK-NOSH: Efficient Predictor-Based NAS via Non-Uniform Successive Halving
Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh
ICCV, 2021
[Paper] [Code]
Rethinking Architecture Selection in Differentiable NAS
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh
ICLR, 2021
[Paper] [Code]
Outstanding Paper Award (1/8)
DrNAS: Dirichlet Neural Architecture Search
Xiangning Chen*, Ruochen Wang*, Minhao Cheng*, Xiaocheng Tang, Cho-Jui Hsieh, (*Equal Contribution)
ICLR, 2021
[Paper] [Code]

Awards (Selected)

  • Outstanding Graduate Student (1 per department) - UCLA CS Department, 05/2022.
  • Outstanding Paper Award (1/8) - ICLR, 04/2021.
  • Award of Excellence - Microsoft Research Asia, 09/2019.
  • Highest Distinction Graduate - The University of Michigan, 08/2019.
  • Fung’s Excellence Scholarship, UC Berkeley Graduate Admission Committee, 03/2019.
  • Outstanding Intern Award, SenseTime, 01/2019.
  • James B. Angell Scholar, The University of Michigan, 2017-2019.
  • Shanghai Scholarship, 2014
  • Renmin Scholarship - First Prize, 2014

    • Teaching

  • Teaching Assistant, UCLA CS 180: Algorithms & Complexity Winter 2023
  • Teaching Assistant, UCLA CS 247: Advanced Data Mining Spring 2023

    • Academic Services

    Organizer: 1st Dataset Distillation Workshop at CVPR 2024
    PC Member/Reviewer: ICML 2021~, NeurIPS 2021~, ICLR 2022~, CVPR 2023~, ICCV 2023~, AAAI 2024, ECV 2023, TMLR, NeuroComputing, e.t.c.

    Miscellaneous

    Reading and Martial Arts

  • I have a bit of a chaotic reading habit, jumping between around 10 books at the same time. Topics include but are not limited to history, philosophy, business, science and engineering.
  • I practice Katori Shintō-ryū (main branch), trained directly by current Shihan Dai (師範代).

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