Yuanqi Du is a CS PhD student at Cornell University, working on Computational Sustainability with Prof. Carla Gomes. During the past, he spent time at AMLab at University of Amsterdam advised by Prof. Max Welling and DP Technology supervised by Dr. Linfeng Zhang. He is a passionate community builder, which he founded the series of AI for Science workshops, co-founded a new Learning on Graphs conference, led an initiative AI for Science 101 building knowledge systems for AI for Science. He received Bachelor’s degree in Computer Science from George Mason University in 2021. He is very fascinated by Sciences and interested in developing ML tools for scientific problems, especially for new knowledge discovery. He is actively collaborating with researchers in chemistry, biology, physics, and machine learning. He maintains a slack channel for AI for Science, feel free to join and say Hi to people here!
- Computational Sustainability
- Deep Generative Models
- Geometric Deep Learning
- Machine Learning for Discovery
- AI for Science (Biology/Chemistry/Physics/Materials/Pharma/etc)
News! Follow me @Twitter
- 9/22 Our Paper Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks has been accepted in NeurIPS 2022 check out our GitHub.
- 7/22 AI for Science: Progress and Promises will be held again in-person with NeurIPS 2022! Looking forward to your excellent submissions!
- 6/22 We are launching a new initiative AI for Science 101 that aims to encourage knowledge-sharing and collection in AI for Science, find out what we are doing and why we are doing and read more!
- 5/22 Our paper Equivariant Graph Neural Networks with Complete Local Frames has been accepted in ICML 2022!
- 3/22 We have released a comprehensive survey on molecule design, MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design, welcome any comments or feedback!
- 3/22 The sceond AI for Science workshop will be hosted together with ICML 2022 (hybrid), stay tuned for more information!
- 3/22 We have recenlty been working on a new initiative, Learning on Graphs conference along with a stellar list of advisory board members where we aim to advance graph machine learning as a community and emphasize on the review quality! Any thoughts are welcome!
- 12/21 Excited to announce that we (with Prof. Adji Bousso Dieng, Prof. Yoon Kim, Dr. Rianne van den berg, Prof. Yoshua Bengio) are hosting the second Deep Generative Models for Highly Structured Data workshop with ICLR 2022, welcome to join us (submission deadline has been extended to March 7)!
- 10/21 GraphGT, the first Machine Learning Datasets for Graph Generation and Transformation, has been accepted in NeurIPS 2021. Welcome feedback and dataset contribution!
- 7/21 I am proud to announce that the first AI for Science workshop will be held with NeurIPS 2021. We focus on the gaps that stifle advacenment of AI for Science this year. We invite a list of poineers in AI for Science for great talks and panels. We also introduce very exciting Attention Track, Mentorship, and Award programs. Welcome to submit your work and join us in December! Follow us on Twitter.