Eric Qu

PhD Student at UC Berkeley

CV

ericqu [at] {berkeley [dot] edu, meta [dot] com}

🖋 About Me

I am a second-year Computer Science PhD Student at University of California, Berkeley, Berkeley AI Research Lab (BAIR), advised by Aditi Krishnapriyan. I am also a visiting researcher at Meta Fundamental AI Research (FAIR), FAIR Chemistry Group, advised by Brandon Wood and Zachary Ulissi. I received my B.Sc. with distinction in Data Science from Duke Kunshan University and Duke University, where I was mentored by Dongmian Zou and Kai Zhang. I also worked as a research intern at Microsoft Research Asia, Shanghai AI/ML Group led by Dongsheng Li.

My research interest mainly falls on Geometric Deep Learning and AI for Science. Recently, I have been particularly interested in exploring how to scale up machine learning models to tackle complex challenges in scientific domains. More broadly, I am interested in combining ideas from mathematics with machine learning, and using machine learning to solve interdisciplinary problems.

🔥 News

• [Sep 2024] Our new EScAIP model is accepted by NeurIPS 2024! 🎉 Stay tuned for more details.
• [Sep 2024] Joined as a visiting researcher at Meta FAIR!
• [Jan 2024] Our work "CNN Kernels Can Be the Best Shapelets" is accepted by ICLR 2024! 🎉
• [Aug 2023] Starting my PhD at UC Berkeley!
• [May 2023] Graduated from Duke Kunshan University with distinction! 🎓
[May 2023] Duke Kunshan University reported my undergrad story (in Chinese) here
[Jan 2023] Our work "Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer" is accepted (Spotlight) by ICLR'23! 🎉
[Oct 2022] Our work "Lorentz Direct Concatenation for Stable Training in Hyperbolic Neural Networks" is accepted (Poster) by NuerIPS'22 NeurReps Workshop! 🎉
[May 2022] Joined as a research intern in Microsoft Research Asia. 👨‍💼
[Jan 2022] Our work "Lorentzian Fully Hyperbolic Generative Adversarial Network" is now public on arXiv.
[Aug 2021] Times Higher Education (Duke Kunshan University) reported our work in the news here.
[Apr 2021] Our work "Quantifying Nanoparticle Assembly States in a Polymer Matrix through Deep Learning" is accepted by Macromolecules!
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📝 Publications

The Importance of Being Scalable: Efficient Architectures for Neural Network Potentials

NeurIPS 2024 Poster

Eric Qu, Aditi Krishnapriyan

Coming Soon!

CNN Kernels Can Be the Best Shapelets

ICLR 2024 Poster

Eric Qu, Yansen Wang, Xufang Luo, Wenqiang He, Kan Ren, Dongsheng Li

Paper Code

Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer

ICLR 2023 Spotlight

Eric Qu, Xufang Luo, Dongsheng Li

Paper Code Slides Poster

Hyperbolic Convolution via Kernel Point Aggregation

arXiv:2306.08862 (Submitted to LOG)

Eric Qu, Dongmian Zou

Paper Code Poster

Autoencoding Hyperbolic Representation for Adversarial Generation

Transactions on Machine Learning Research (2024)

Eric Qu, Dongmian Zou

Paper Code Slides

Lorentz Direct Concatenation for Stable Training in Hyperbolic Neural Networks

NeurIPS 2023, NeurReps Workshop

Eric Qu, Dongmian Zou

Paper Poster

Quantifying Nanoparticle Assembly States in a Polymer Matrix through Deep Learning

Macromolecules, 54 (7), 3034-3040, (2021)

Eric Qu, Andrew Matthew Jimenez, Sanat K. Kumar, Kai Zhang

Paper Code Dataset

In-situ AFM tracking of Nanoparticle Diffusion in Semicrystalline Polymers

ACS Macro Lett. 2022, 11, 6, 818–824, (2022)

Kamlesh Bornani, Nico Mendez, Abdullah S. Altorbaq, Alejandro Muller, Max Yueqian Lin, Eric Qu, Kai Zhang, Sanat K. Kumar, Linda S. Schadler

Paper Code

💬 Talks

• Stable Hyperbolic Neural Networks for Graph Generation and Classification - Tokyo, Japan, ICIAM 2023 Mathematics of Geometric Deep Learning Minisymposium

• Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer - Kigali, Rwanda, ICLR 2023 Spotlight Presentation, Oral 6 Track 5: Applications & Deep Learning

🏆 Awards and Honors

• [2024.5] NERSC GenAI for Science CFP Award (2500 GPU hrs) - U.S. Department of Energy
• [2023.10] Open Catalyst Challenge 3rd Place - NeurIPS 2023 Competitions
• [2023.5] Graduation with Distinction (Top 5%) - Duke Kunshan University
• [2023.5] Graduation with Latin Honors cum laude - Duke Kunshan University
• [2023.5] Zu Chongzhi Math Signature Work Award - Duke Kunshan Zu Chongzhi Math Center
• [2023.5] Conference Travel Grant (ICLR 2023) - Duke Kunshan University
[2022.11] Conference Travel Grant (NeurIPS 2022) - Duke Kunshan University
[2022.6, 2020.6] Summer Research Scholar - Duke Kunshan University
[Fall 2019, Fall 2020, Sring 2021, Fall 2021, Spring 2022] Dean's List - Duke Kunshan University
[2020.4] Honorable Mention - Mathematical Contest In Modeling
[2019.3] Top 5% (Top 2 China Region) - Canadian Computing Competition Senior Division
[2016, 2017] First Prize (Top 20 in Shandong Province) - China National Olympiad in Informatics Senior Group
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📖 Educations

• 2023.8 - present  PhD Student in Computer Science, University of California, Berkeley

• 2019.8 - 2023.5   Dual Degree Undergraduate in Data Science, Duke Kunshan University / Duke University

🏢 Work Experiences

• 2024.9 - Present  Meta Research, Fundamental AI Research

• 2022.5 - 2022.9  Microsoft Research, Shanghai AI/ML Group

• 2023.1 - 2023.6  Microsoft Research, Shanghai AI/ML Group

🏫 Services

• Reviewer/PC Member: NeurIPS {2023, 2024}, ICLR {2024, 2025}, ICML 2024, AAAI 2025, ACML 2024

• Journal Reviewer: TNNLS