Yuanqi Du @YuanqiD Twitter profile

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Yuanqi Du

3 days

🧵1/7 Introducing an “Encyclopedia” of Molecular Design with Machine Learning @NatMachIntell ! Collaboration with @arian_jamasb *, @JeffGuo__ *, @TianfanFu , @charlieharris01 , @yingheng_wang , @chenru_duan , @pl219_Cambridge , @pschwller , and Tom Blundell.

Machine learning-aided generative molecular design

Nature Machine Intelligence - Data-driven generative methods have the potential to greatly facilitate molecular design tasks for drug design.

www.nature.com

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Yuanqi Du

@YuanqiD

2 years

Machine learning for molecule design is a fast-growing field with massive literature, to the best of our knowledge, we are the first to **comprehensively** review this field, the preprint is now available at Arxiv .

MolGenSurvey: A Systematic Survey in Machine Learning Models for...

Molecule design is a fundamental problem in molecular science and has critical applications in a variety of areas, such as drug discovery, material science, etc. However, due to the large...

arxiv.org

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Yuanqi Du

@YuanqiD

2 months

🧵1/6 Introducing Diffusion Constrained Samplers 🥳🥳🥳 Interested in optimization problems where (partial) constraints are unknown (protein/molecule design)? We show diffusion models learn it implicitly and optimize feasible solutions thru sampling!

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Yuanqi Du

@YuanqiD

2 years

If you are working on or interested in graph generation, you may want to check out our recent survey about deep graph generation with methods and applications, available in Arxiv now .

A Survey on Deep Graph Generation: Methods and Applications

Graphs are ubiquitous in encoding relational information of real-world objects in many domains. Graph generation, whose purpose is to generate new graphs from a distribution similar to the...

arxiv.org

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Yuanqi Du

@YuanqiD

1 year

(1/3) I am thrilled to announce that @AI_for_Science workshop is back with #NeurIPS2023 ! This year we put together several new programs with a new theme "from theory to practice", including a panel discussion to align the expectation between academia and funding agencies.

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Yuanqi Du

@YuanqiD

2 years

Happy to announce our new initiative AI4Science101. We wrote a series of documents to encourage knowledge sharing and collection in AI for Science from both the view of AI and Science researchers to motivate them to learn, join and work on AI for Science.

AI for Science: The Way Forward

With the rapid development of AI, people have started to apply AI methods to almost every field, from natural language processing to…

link.medium.com

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Yuanqi Du

@YuanqiD

1 year

(1/n) After a month of "on hold" on arXiv, I am excited to share our latest work on unlocking the potential of ML for materials discovery! ML has been successfully applied to modeling molecular structures, esp. biomolecules.

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Yuanqi Du

@YuanqiD

10 months

Last year, we started AI4Science101 () which aims to bridge AI and Science community with a series of blogs designed for beginners. After a year, we have 12 blogs and many more are coming soon! We hope you enjoy reading the blog and consider joining us!

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Yuanqi Du

@YuanqiD

1 year

2022 was a year of new and exciting ventures for me: 1. I stepped out of the ML industry and joined a startup led by a group of physicists, chemists, etc. 2. I launched AI4Science101, a blog series aimed at building a new knowledge system for AI for Science. (1/n)

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Yuanqi Du

@YuanqiD

2 years

Prof. David Baker talking about RFDiffusion at @AI_for_Science workshop happening now!

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Yuanqi Du

@YuanqiD

9 months

Finally accepted @NeurIPSConf ! Cannot wait to discuss molecular dynamics, transition path sampling and stochastic optimal control in the coming December! #NeurIPS2023

Lars Holdijk

@HoldijkLars

2 years

Excited to share our latest work at the intersection of machine learning and computational chemistry; Path Integral Stochastic Optimal Control for Sampling Transition Paths between molecular conformations. With @YuanqiD *, @priyankjaini , Ferry Hooft, @BerndEnsing and @wellingmax

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Yuanqi Du

@YuanqiD

2 years

Hero #NeurIPS2022 @AI_for_Science organizers! @TianfanFu @cwcoley @WenhaoGao1 me @wellingmax @chenru_duan @hcwww_ (from left to right) Shout out to the other organizers as well! @DaisyYDing @AnimaAnandkumar Yoshua Bengio, Carla Gomes, Aviv Regev and @marinkazitnik

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Yuanqi Du

@YuanqiD

1 year

One of the coolest topics to listen at Hawaii (ICML 2023) this summer: structured probabilistic inference & generative modeling! This field has been rapidly growing and can’t wait to follow the new frontier!

Dinghuai Zhang 张鼎怀

@zdhnarsil

1 year

Our #ICML2023 workshop proposal "Structured Probabilistic Inference & Generative Modeling" has been accepted 🎉. We can't wait to engage in insightful discussions with experts in probabilistic ML and other areas at the beautiful Hawaii 🌴🏖️. Check🔍:

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Yuanqi Du

@YuanqiD

5 months

Revisited Prof. Weinan E’s opinion paper “The Dawning of a New Era in Applied Mathematics”! The history of applied math is very inspiring and “generalize to/align with” the development of comp. tool (numerical, CS, AI) in Science. Highly recommended!

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Yuanqi Du

@YuanqiD

1 year

Don’t miss the deadline for the Structured Probabilistic Inference and Generative Modeling workshop at ICML 2023 (May 26th)! We are calling submissions covering all aspects of probabilistic machine learning! Website: Submission:

ICML 2023 Workshop SPIGM

Welcome to the OpenReview homepage for ICML 2023 Workshop SPIGM

openreview.net

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Yuanqi Du

@YuanqiD

11 months

I will be traveling to #ICML2023 in Hawaii next week! I will present two papers, main conf track (Flexible Diffusion) and oral presentation @TAGinDS workshop (LEFTNet). Happy to chat about Geometric DL, Generative Models and AI for Science! DM me if you'd like to chat!

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Yuanqi Du

@YuanqiD

11 months

AI for Science is a fast-growing and promising field for both AI and Science community. We started building this community several years ago and are super excited to share the first paper by the community effort, lead by @marinkazitnik , co-lead by @hcwww_ *, @TianfanFu * and me!

Eric Topol

@EricTopol

11 months

How can #AI transform science? Let us count the ways A brilliant review @Nature @marinkazitnik @TianfanFu @YuanqiD and colleagues @AI_for_Science #ScienceTwitter

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Yuanqi Du

@YuanqiD

3 months

✈️✈️✈️I am visiting Georgia Tech tmr and next Monday! I am flattered to give a talk at the Applied and Comp Math seminar. I will be sharing some of my work and thoughts on "Accelerating Molecular Discovery with ML: A Geometric, Sampling and Optimization Perspective"!

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Yuanqi Du

@YuanqiD

6 months

Happy holidays, graph and geometric deep learning community! Do not forget that if you like to become one of the @LogConference organizers in 2024, you still have time to sign up for the application form . We will review them on January!

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Yuanqi Du

@YuanqiD

5 months

We tried this idea about two years ago. The interesting observation is that our architecture and modality are sensitive to different part of information thus make them “complement” each other. Glad this idea has been picked up. Joint work with @Zhu_Yanqiao

Improving Molecular Pretraining with Complementary Featurizations

Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug...

arxiv.org

Günter Klambauer

@gklambauer

5 months

Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry Three chemical modalities are contrasted against each other and used for property prediction. Unfortunately, only evaluated on the MoleculeNet benchmarks

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Yuanqi Du

@YuanqiD

2 months

Our new work on generative models for chemical reactions: much faster inference with flow matching (OT path) training scheme, better leveraging our knowledge about the problem is a key to solve science problems! Check out the paper if you are interested!

Chenru Duan

@chenru_duan

2 months

New paper alert: React-OT: Optimal Transport for Generating Transition State in Chemical Reactions (). React-OT formulates TS search as a transport problem, approaching chemical accuracy while taking only 0.5 seconds in inference on a single GPU. #compchem

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Yuanqi Du

@YuanqiD

3 months

🥳🥳🥳 The AI for Science slack channel has almost reached 1,000 users, super excited about this growth! Welcome anyone interested in AI for Science to join, chat and post any related events, resources, or hiring info! @AI_for_Science

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Yuanqi Du

@YuanqiD

7 months

The third year of @AI_for_Science workshop @NeurIPSConf , covering more diverse areas, featuring more speakers from science communities, @OpenCatalyst competitions, and a panel from funding agencies about the future of AI for Science! Join us on December 16th!

AI for Science

@AI_for_Science

7 months

🎉 Join us at NeurIPS 2023 AI for Science Workshop on 12/16: 7 speakers on cutting-edge AI research across fields🧠 Future-focused panel with funding agencies 💼 Open Catalyst Challenge announcement 🏆

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Yuanqi Du

@YuanqiD

5 months

I find this encouraging in my life, I was not trained enough in mathematics and physics in early years so I have a hard time catching up (slowly). I find it so relieving to admit things I don’t know and I’m learning. It doesn’t frighten me to ask question and understand why.

Prof. Feynman

@ProfFeynman

5 months

Don't get frightened by not knowing things. I have approximate answers, and possible beliefs, and different degrees of certainty about different things, but I'm not absolutely sure of anything. There are many things I don't know anything about. It doesn't frighten me.

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Yuanqi Du

@YuanqiD

6 months

Delighted that our paper is the highlight of the December issue @NatComputSci !

Nature Computational Science

@NatComputSci

6 months

📢Our December issue is now live! Highlights include an approach to identify transition state structures in chemical reactions, a denoising method for fluorescence images, and an approach to identify stable surface reconstructions of complex materials. 👉

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Yuanqi Du

@YuanqiD

2 years

Hanging out with @LogConference heroes #NeurIPS2022 Looking forward to seeing you all again in another week @LogConference (please register if you haven’t!) @Zhu_Yanqiao me @GabriCorso @dereklim_lzh @andreeadeac22 @HannesStaerk (from left to right)

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Yuanqi Du

@YuanqiD

7 months

I will be in NYC Dec 3-6th, traveling to NeurIPS Dec 10-17th, and staying around Seattle till January. Let me know if anyone would like to chat at any point if we come across! 🌟🌟🌟 #NeurIPS2023 I am happy to chat anything about AI for Science and Science for/of AI/Science.

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Yuanqi Du

@YuanqiD

5 months

Many people asked me why AI for Science and I answered with several arguments and beliefs in this blog. Some interesting observations and trends in 2023 and hope the momentum continues for 2024!

AI for Science

@AI_for_Science

5 months

🚀 Exciting News! Our blog “AI for Science in 2023: A Community Primer” is now live! In this blog, we delve into how AI intersects with various scientific fields - from Chemistry, Biology, Physics, Computer/Math. Science, Neuroscience to Earth Science.

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Yuanqi Du

@YuanqiD

2 years

Very excited to announce the first conference dedicated to machine learning on graphs, together with @HannesStaerk @dereklim_lzh @chaitjo @andreeadeac22 @DutaIulia @Josh_d_robinson , big thanks to all the organizers and advisors!

Learning on Graphs Conference 2024

@LogConference

2 years

Here it is: the first Learning on Graphs Conference! 🎊 We think this new venue will be valuable for the Graph/Geometric Machine Learning community. What makes it so important+unique? See our blog post! 1/6

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Yuanqi Du

@YuanqiD

9 months

One more week to submit your work to @AI_for_Science workshop at @NeurIPSConf ! Do not miss this opportunity to attend one of the best annual events about AI for Science! We are also soliciting education-related papers to lower the barrier to entering the field! #NeurIPS2023

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Yuanqi Du

@YuanqiD

1 month

When people are talking about AF3 and equivariance, it is a great time to advertise our @AI_for_Science workshop #icml2024 , we feature the theme "Scaling in AI for Science", and there would be lots of fun discussing those questions! Info at

AI for Science: Scaling in AI for Scientific Discovery

ai4sciencecommunity.github.io

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Yuanqi Du

@YuanqiD

2 years

Check out our new paper using diffusion model for structure-based drug design! Diffusion models are particularly powerful with inpainting and could be suitable for many drug design scenarios. Play with our demo if you are interested!

Arne Schneuing

@rneschneuing

2 years

Happy to share our new paper: Structure-based Drug Design with Equivariant Diffusion Models A demo is available on Google Colab: 🧵1/6

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Yuanqi Du

@YuanqiD

6 months

Met so many old and new friends! See you next time! #NeurIPS2023

AI for Science

@AI_for_Science

6 months

Thanks for all the speakers, organizers, authors, reviewers, area chairs to make the AI for Science workshop a great success! Looking forward to meeting you in the coming year!

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Yuanqi Du

@YuanqiD

2 years

Finally on Arxiv! Amazing collaboration with @HoldijkLars , @priyankjaini , Ferry Hooft, @BerndEnsing , @wellingmax while visiting @AmlabUva !

Lars Holdijk

@HoldijkLars

2 years

Excited to share our latest work at the intersection of machine learning and computational chemistry; Path Integral Stochastic Optimal Control for Sampling Transition Paths between molecular conformations. With @YuanqiD *, @priyankjaini , Ferry Hooft, @BerndEnsing and @wellingmax

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Yuanqi Du

@YuanqiD

4 months

Excellent weather, magnificent view and inspiring talks! #LOGNYC

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Yuanqi Du

@YuanqiD

3 months

🥳🥳🥳 I wrote a short blog post on some of my personal experiences and thoughts on organizing academic events and community building (esp. in AI and ML)

About academic event organization and community building in AI and ML

Aim and Scope

medium.com

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Yuanqi Du

@YuanqiD

9 months

Now accepted at @NeurIPSConf ! Feel free to play with our code to adapt state-of-the-art ML models to your materials problems or develop your new models with diverse materials datasets! #NeurIPS2023

Yuanqi Du

@YuanqiD

1 year

(1/n) After a month of "on hold" on arXiv, I am excited to share our latest work on unlocking the potential of ML for materials discovery! ML has been successfully applied to modeling molecular structures, esp. biomolecules.

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Yuanqi Du

@YuanqiD

3 months

Super excited about this topic and see how scaling plays a role in AI for Science (alone from scaling that was long studied in science)! We also put together a highly diverse program to share ideas/lessons across different fields!

AI for Science

@AI_for_Science

3 months

🥳🥳🥳 We are excited to share that AI for Science workshop will be held again with @icmlconf 2024, Vienna! This time, we focus on scaling in AI for Science (as a new dimension to theory, methodology and discovery)! Tentative schedules can be found:

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Yuanqi Du

@YuanqiD

6 months

I really enjoyed reading this discussion from prestigious ML researchers about **now** and **future** of ML research, particularly @andrewgwils 's view on discovering scientific theory, highly recommended, a wonderful holiday read! @AI_for_Science

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Yuanqi Du

@YuanqiD

6 months

Happy new year, my friends on twitter! 2023 has been a challenging yet awarding year for me: both in work and life: (1) going through early PhD crisis - find out what interests me the most and what are the essentials to support them, (2) putting consistent effort in education

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Yuanqi Du

@YuanqiD

11 months

Had so much fun and inspiration from #ICML2023 , see you next time, old and new friends!

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Yuanqi Du

@YuanqiD

1 year

A nice introduction for molecular dynamics (I found it very helpful for myself when working with Yanze)! This is also under the AI4Science101 initiative, more blogs are coming out soon!

AI for Science 101

ai4science101.github.io

Valence Labs

@valence_ai

1 year

In our latest community blog, Yanze Wang and @YuanqiD provide an introductory overview of molecular dynamics simulations. If you're interested in learning more, you can read the full blog here:

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Yuanqi Du

@YuanqiD

1 year

Interested in reviewing and learning the frontiers of probabilistic inference and generative models? Sign up this form for ICML 2023 workshop Structured Probabilistic Inference and Generative Modeling! #ICML2023

SPIGM 2023 (co-located with ICML) Reviewer Sign-up Form

Workshop webpage: https://spigmworkshop.github.io/

docs.google.com

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Yuanqi Du

@YuanqiD

5 months

Super excited to kick off this seminar by @StefanoErmon on diffusion models and applications in science! This seminar series will be open to all, live-streamed and recorded! Zoom link:

CUAISci

@CUAISci

5 months

We are excited to announce the AI for Science seminar series! The seminar will feature both pioneers and Schmidt Futures AI for Science postdocs on advances and challenges at the frontier of AI for scientific discovery. We hope you’ll join us!

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Yuanqi Du

@YuanqiD

1 year

Great resource to beginners!

Chaitanya K. Joshi

@chaitjo

1 year

❓New to Geometric GNNs, GDL, PyTorch Geometric, etc.? Want to understand how theory/equations connect to real code? Try this practical notebook before diving into this exciting area! **Geometric GNNs 101**

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Yuanqi Du

@YuanqiD

1 month

Life update: I will stay in Seattle this summer, happy to chat about science if anyone around is interested! (I am also down to attend CVPR for the first time!) I am also visiting Cali near the end of May (Bay area and LA), reach out if you like to catch up at some point!

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Yuanqi Du

@YuanqiD

1 year

Come and join us if you are interested in a new perspective to build expressive and efficient equivariant GNNs! Very excited!

Hannes Stärk

@HannesStaerk

1 year

This Monday, we discuss a new 3D GNN framework with the authors Weitao Du, @YuanqiD , and @limei69990587 : Come discuss with the authors what is new about this one more 3D GNN Join us at 11am EDT / 5pm CEST on Zoom:

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Yuanqi Du

@YuanqiD

7 months

Sign up for the second annual Learning on Graph conference! Follow the most recent progress in the field and enjoy the big party of graph machine learning community! 🔥🔥🔥 @LogConference

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Yuanqi Du

@YuanqiD

11 months

Presenting Flexible Diffusion Model today 2pm at Exhibit Hall 1 Poster #425 . Looking forward to meeting you all!

Yuanqi Du

@YuanqiD

11 months

I will be traveling to #ICML2023 in Hawaii next week! I will present two papers, main conf track (Flexible Diffusion) and oral presentation @TAGinDS workshop (LEFTNet). Happy to chat about Geometric DL, Generative Models and AI for Science! DM me if you'd like to chat!

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Yuanqi Du

@YuanqiD

2 years

With @bmorphism and @Abel0828 , we are about to organize a @LogConference local meetup around NYC area, please vote for the time period that you like!

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Yuanqi Du

@YuanqiD

3 months

Glad to have @mmbronstein to talk about GNNs in our next week seminar! Welcome to join us online!

CUAISci

@CUAISci

3 months

We are excited to announce that this Friday, March 22nd Dr. Michael Bronstein will be joining us for a Physical Perspective on Graph Neural Networks. Hope you'll join us! Zoom:

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Yuanqi Du

@YuanqiD

6 months

We use the advances in geometric deep learning and generative models to accelerate the “searching” of transition state in chemical reactions with high efficiency and accuracy! Great work with @chenru_duan @KulikGroup !

Nature Computational Science

@NatComputSci

6 months

📢 @chenru_duan , @KulikGroup , @YuanqiD and colleagues introduce a diffusion model that generates chemical reactions in 3D with all desired symmetries preserved. 👉

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Yuanqi Du

@YuanqiD

1 year

Join the meeting group if you are interested! Previous machine learning potential work often considers predicting force/energy to drive the simulation, while we tackle another challenging problem to sample transition paths between two states (e.g. modeling protein folding)!

Hannes Stärk

@HannesStaerk

1 year

Tomorrow @HoldijkLars presents his paper "Path Integral Stochastic Optimal Control for Sampling Transition Paths" () I think the SOC ideas might have even more applicability in this field! Join on Zoom at 11am EDT / 5pm CET:

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Yuanqi Du

@YuanqiD

2 years

New Preprint! We believe the “optimal” forward process of diffusion models should be learned. Inspired by symplectic structure and Riemannian metric, we build the **first** learnable forward process of diffusion models with theoretical guarantees!

DeepAI

@DeepAI

2 years

A Flexible Diffusion Model by Weitao Du et al. including @YuanqiD #ComputerScience #Learning

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Yuanqi Du

@YuanqiD

4 months

I knew @ZimingLiu11 more than three years ago when I just entered this field. He has been a great researcher, mentor and friend for me. He re-ignited my curiosity in physics and discovery. If you are looking for someone who works on AI + physics (science), reach out to him!

Ziming Liu

@ZimingLiu11

4 months

This fall, I’ll be on job market looking for postdoc and faculty positions in US! My research interests span in AI + physics (science). If there’re opportunities to present in your school, institute, group, seminar, workshop etc., I really appreciate it! 🥹

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Yuanqi Du

@YuanqiD

10 months

Education is the foundation of AI for Science. We are calling people who are interested in joining us and discussing the future this December at NeurIPS! If you have any questions or suggestions regarding the submission format for this track, please let us know!

AI for Science

@AI_for_Science

10 months

We are opening a new track for Education @AI_for_Science workshop this year #NeurIPS2023 . Education has been and will continue to be one of the largest gaps in AI for Science. We are calling for contributions of educational resources in flexible formats!

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Yuanqi Du

@YuanqiD

1 year

@AI_for_Science workshop talks are freely accessible now!

NeurIPS Conference

@NeurIPSConf

1 year

You can now watch the recorded material from #NeurIPS2022 online without registration at:

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Yuanqi Du

@YuanqiD

11 months

A very nice and long review (w/ perspective) about AI for (Physical) Science! Education and community building are indispensable yet challenging parts in @AI_for_Science . This paper could serve as a great educational resource for people who are interested in this promising area!

Shuiwang Ji

@ShuiwangJi

11 months

Excited to share our latest survey paper: "Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems"! 🚀 ArXiv: .

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Yuanqi Du

@YuanqiD

29 days

Incredible growing interest in @AI_for_Science this year at ICML! This already surpassed what we had last time at ICML!

AI for Science

@AI_for_Science

29 days

We have received ~150 high quality submissions, we have ~200 reviewers and ~50 ACs waiting to bid and read your exiting work! Abstract due time AoE 23rd end of day which is less than 24 hours from now!

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Yuanqi Du

@YuanqiD

2 years

Will attend #NeurIPS2022 in-person next week! Can’t wait to meet new and old friends (many we haven’t met due to COVID)! If you would like to chat anything with me (research, community, etc.), feel free to message! I will also be around @AI_for_Science workshop on December 2nd.

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Yuanqi Du

@YuanqiD

1 year

We designed an object-aware equivariant diffusion model tailored for transition state generation in chemical reaction! Check more details in the preprint if you are interested!

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Yuanqi Du

@YuanqiD

11 months

It was a wonderful chat with Jaanak brothers about my experience and perspective about AI for Science research. If you are interested in how I got into the field, found my direction and my thoughts about the present and future of the field, check out this podcast!

STARTS Podcast

@STARTS_Podcast

11 months

We greatly enjoyed speaking with @YuanqiD about research at the intersection of biology and computer science, the importance of multidisciplinary research, and the PhD journey. Please feel free to listen below! :)

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Yuanqi Du

@YuanqiD

9 months

Will be around Boston this weekend and early next week, please reach out if you like to chat!

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Yuanqi Du

@YuanqiD

2 years

Just curious, anyone interested in a @LogConference local meetup around New York City area? Hit like and comment if you do!

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Yuanqi Du

@YuanqiD

1 year

(3/3) We are also extremely honored to partner with @OpenCatalyst and have the competition announcement as part of our workshop schedule! For more details, please check our website: We are looking forward to seeing you all back in New Orleans this year!

AI for Scientific Discovery: From Theory to Practice

ai4sciencecommunity.github.io

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Yuanqi Du

@YuanqiD

8 months

A great memory visiting friends and chatting about everything related to AI, Physics, Mechanistic Interpretability, Science of Science, etc. and (more interestingly) the smiling pattern is similar to the "God fathers of Deep Learning"!

Ziming Liu

@ZimingLiu11

8 months

If I ever grew taller, these two photos will be more similar 😝 @ke_li_2021 @YuanqiD Look up to role models @geoffreyhinton , Yoshua Bengio, @ylecun

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Yuanqi Du

@YuanqiD

7 months

Looking forward to seeing you all tomorrow! I am most proud of our ever-growing local meetup program this year!

Learning on Graphs Conference 2024

@LogConference

7 months

LoG is happening tomorrow! Highlights of the program: 🎤Exciting keynotes from @jure , @andreasloudaros , Stefanie Jegelka, @KyleCranmer , @ktschuett 🌟 12 orals 💻 Tutorials on scalability & recommendation 🤗 poster sessions & networking Join now via

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Yuanqi Du

@YuanqiD

1 year

Call for reviewers and area chairs @AI_for_Science #NeurIPS2023 ! Please sign up if you are interested!

AI for Science

@AI_for_Science

1 year

We received around 200 submissions last year, responding to the high demand, we are openly recruiting new reviewers () and (new this year!) area chairs ()! Please sign up the form if you are interested!

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Yuanqi Du

@YuanqiD

27 days

🔥🔥🔥 In response to many requests, we have extended the deadline for **Structured Probabilistic Inference and Generative Modeling** workshop @icmlconf 2024 two days to May 27th AoE. We also relax the submission page limit from 4 to 4-8 pages.

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Yuanqi Du

@YuanqiD

10 months

As many conf. submission/rebuttal deadlines are close, we just decided to extend our ddl for extra 7 days. Take this opportunity to prepare your submission to the best conf. in graph machine learning/geometric deep learning with high-quality reviews and engaged discussions!

Learning on Graphs Conference 2024

@LogConference

10 months

The abstract submission deadline of the #LoG conference is extended to August 18th AoE! Known for high-quality reviews :) There is a nonarchival 5-page track as well. Thanks for the feedback about the gap between abstract and paper submission deadlines!

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Yuanqi Du

@YuanqiD

6 months

Yes, these have also been shown in the benchmark PMO developed by @WenhaoGao1 and @TianfanFu ! Part of my reasoning about this is that I felt deep generative model-based methods have not yet fully used all available data. (So I'm not surprised CO methods work much better).

Chaitanya K. Joshi

@chaitjo

6 months

Very thought provoking: "...we have shown that genetic algorithms are very strong baselines for molecular generation tasks, performing at least as well as many more complicated methods..." @austinjtripp @jmhernandez233

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Yuanqi Du

@YuanqiD

4 months

Molecules inherently have multiple modalities and it’s interesting to note despite they may have almost exact same information in the eyes of chemists, neural architectures treat them differently. It could be a great testbed to understand inductive biases.

JCIM & JCTC Journals

@JCIM_JCTC

4 months

Molecular Contrastive Pretraining with Collaborative Featurizations @Zhu_Yanqiao @YuanqiD #JCIM Vol64 Issue4 #MachineLearning #DeepLearning

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Yuanqi Du

@YuanqiD

1 month

Very cool work and impressive results on using diffusion models for optimization in scientific problems!

Adeesh Kolluru

@AdeeshKolluru

1 month

🚨 New work at the intersection of Diffusion x Catalysis x Optimization accepted at #ICML2024 🎉! Introducing AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion ⚛️ Paper - Code - 1/n

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Yuanqi Du

@YuanqiD

1 year

Amazing team and co-organizers: @wellingmax Yoshua Bengio, @marinkazitnik , Carla P. Gomes, @jure , @mariabrbic , @WenhaoGao1 , @KexinHuang5 , @ZimingLiu11 , @rociomer3 , @MilesCranmer , @Shengchao_Liu , @LijingWang8 , Daisy Yi Ding, @chenru_duan , Ada Fang, @hcwww_ , @TianfanFu !

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Yuanqi Du

@YuanqiD

1 year

LoG 2022 has been a success with recognitions from our community. In LoG 2023, we will (1) continue the reviewer award mechanism to explore the best way for our community to grow, (2) have decentralized local meetups to encourage and form local communities.

Learning on Graphs Conference 2024

@LogConference

1 year

Welcome back! After a very successful first edition in 2022, we are thrilled to announce the second venue of the Learning on Graphs Conference! 📆27 - 30 November 2023 💻Virtual & free-to-attend 🤗Stronger emphasis on local meetups around the world

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Yuanqi Du

@YuanqiD

2 years

I will be at NeurIPS the whole week, let me know if you like to discuss anything related to #AI4Science , geometric deep learning, deep generative models! Don’t miss our workshop on December 2nd!

AI for Science

@AI_for_Science

2 years

#NeurIPS2022 is taking off today! Looking forward to meeting you all on Friday, Room 388-390 for the #AI4Science workshop. We have a stellar lineup of speakers covering a wide range of AI for science topics, along with 5 contributed talks and amazing posters!

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Yuanqi Du

@YuanqiD

1 month

📢📢📢 5 days before submission deadline to the structured probabilistic inference and generative modeling workshop @icmlconf 2024! Looking forward to your submissions!

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Yuanqi Du

@YuanqiD

7 months

This is a dream meetup!

Antonio Longa

@AntonioLonga94

7 months

@LogConference italian meetup, the karaoke night is going on... 🎤🎉🥳

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Yuanqi Du

@YuanqiD

8 months

Second LoG conference is around the corner 😉

Learning on Graphs Conference 2024

@LogConference

8 months

LoG 2023 is less than one month away, and we're super excited to hear from our keynote speakers, @loukasa_tweet @jure @KyleCranmer and Stefanie Jagelka!

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Yuanqi Du

@YuanqiD

2 months

The current review process is totally biased by perspective of the **assigned** reviewer. Some like SOTA performance, some like cool application, some like lengthy theory and proof, some like simple/some like complicated method, etc.

Lenka Zdeborova

@zdeborova

3 months

As an ML community, if we want a healthy review process, we need to educate the reviewers. As area chairs, now is the time to do so. There are many papers with sound ideas that got low scores based on wrong reasons. See below what I say to some of the reviewers in those cases:

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Yuanqi Du

@YuanqiD

1 year

We are committed to building a home for graph machine learning and putting incentive into our peer review process! If you would like to support us, please consider sponsor us!

Learning on Graphs Conference 2024

@LogConference

1 year

We are actively looking for sponsors for LoG'23 🤗95% of the sponsorship goes back to the community towards paying our top reviewers -- we think this initiative helped elevate the review quality in graph ML last year based on positive community feedback!

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Yuanqi Du

@YuanqiD

1 month

Very well-deserved, congrats @wellingmax and @dpkingma !

ICLR 2024

@iclr_conf

1 month

And... the first-ever ICLR test-of-time award goes to "Auto-Encoding Variational Bayes" by Kingma & Welling. Runner-up: "Intriguing properties of neural networks", Szegedy et al. The awards will be presented at 2:15pm tomorrow; there will be retrospective talks. Please attend!

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Yuanqi Du

@YuanqiD

2 months

🥳🥳🥳 We are recruiting new reviewers for the Structured Prob. Inference and Generative Modeling workshop @icmlconf 2024! Sign up to follow the frontiers of prob. inference, sampling, decision-making, uncertainty, optimization and beyond with structures!

SPIGM Workshop 2024 (co-located with ICML) Reviewer Sign-up Form

Workshop webpage:https://spigmworkshop2024.github.io/ Each reviewer will receive no more than 2 papers.

docs.google.com

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Yuanqi Du

@YuanqiD

1 year

It’s been a unique and rewarding experience to organize @LogConference , check out our proceedings and preface on PMLR!

Learning on Graphs Conference 2024

@LogConference

1 year

Final proceedings from the inaugural edition of the Learning on Graphs Conference are now available on PMLR! Our sincere gratitude to the program committee, authors, supporters, and research community for making LoG possible 🤗 📖:

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Yuanqi Du

@YuanqiD

2 years

Don’t miss out this opportunity if you haven’t signed up! We are calling for good reviewers in the area of Machine Learning and Graphs/Geometry!

Learning on Graphs Conference 2024

@LogConference

2 years

The new LoG conference is looking for more reviewers! We have a special emphasis on review quality via high monetary rewards, a more focused conference topic, and low reviewer load (max 3 papers). But for this we need your help! Sign up here:

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Yuanqi Du

@YuanqiD

2 months

This is actually a very interesting application to use diffusion models to solve inverse problems (decipher crystal structures from XRD patterns).

DP Technology

@TheDPTechnology

3 months

Introducing #XtalNet by @guolin_ke and team: E2E crystal structure prediction from PXRD data with contrastive learning and #diffusion based conditional crystal structure generation. paper: dataset： #AI4Sci #GenAI #Crystallography

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Yuanqi Du

@YuanqiD

1 year

(2/3) As always, we aim to improve diversity and bring more "non-NeurIPS regulars" to attend NeurIPS! We open a new Education track to solicit collected education resources (of flexible format) to improve the knowledge collection in AI for Science.

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Yuanqi Du

@YuanqiD

3 months

@miniapeur @Pseudomanifold @mmbronstein @crisbodnar @ninamiolane @gin_bianconi @naturecomputes @jo_brandstetter @HajijMustafa @rballeba @lorgiusti @ClaBat9 @hansmriess We should add @maurice_weiler for sure as well! And potentially many other more junior researchers :-)

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Yuanqi Du

@YuanqiD

1 year

@miniapeur It feels quite true. Do you suspect any reason behind that? Is it because I’m early days there was no clear boundary between fields as today and people do science more driven by curiosity? Interesting to retrospect this and inspire our next-generation education.

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Yuanqi Du

@YuanqiD

11 months

We are looking forward to your submissions!

Hannes Stärk

@HannesStaerk

11 months

There are 12 days left if you want to submit your paper to the @LogConference ! August 11th is the deadline. If you have exciting work related to learning on Geometries, Graphs, 3D objects ... submit it here: Please share with friends and family :)

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Yuanqi Du

@YuanqiD

15 days

@GrigoryBartosh Nice work! We also had a work quite a while ago to learn forward process of diffusion models you might be interested!

A Flexible Diffusion Model

Diffusion (score-based) generative models have been widely used for modeling various types of complex data, including images, audios, and point clouds. Recently, the deep connection between...

arxiv.org

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Yuanqi Du

@YuanqiD

6 months

There are many good interpretations and discussions already. My personal take on this is that AI not only provides tools for one particular science field but the problems across fields can be understood together and cross-field discussion can be brought up again!

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Yuanqi Du

@YuanqiD

2 years

Graphein is an amazing library for network analysis on biomolecular structure and interaction network. I’m so happy to be part of it and working with @arian_jamasb (and the team) is so joyful! Highly recommended if you are working on this area!

Arian Jamasb

@arian_jamasb

2 years

Thrilled our paper “Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks” has been accepted at #NeurIPS2022 Check out the code (and give us a 🌟) here:

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Yuanqi Du

@YuanqiD

13 days

☀️Hey twitter friends, I am really into scientific history these days and I am looking for good resources/persons to understand how science gets developed and organized into disciplines and how computing started playing crucial roles. Would also love to hear your opinions!

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Yuanqi Du

@YuanqiD

2 years

It’s definitely one of my favorite parts @LogConference to see all of the growing local communities!

Ingo Scholtes

@ingo_S

2 years

Thanks to all the speakers of our @LogConference local meetup @Uni_WUE . Discussions are now thriving in our poster session!

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Yuanqi Du

@YuanqiD

2 years

Very excited for this! After a year, we would like to look back the success and failure in AI for Science and pave the way for the future of AI for Science research! Looking forward to receiving your amazing submission and meeting you @NeurIPSConf !

AI for Science

@AI_for_Science

2 years

The third AI for Science workshop is coming in-person/hybrid again with @NeurIPSConf , New Orleans Dec 2022. This time, we focus on the progress and promises of AI for Science and aim to discuss what has been the key to the success of AI for Science and what’s next?

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Yuanqi Du

@YuanqiD

1 year

Last year we had a great batch of interesting papers published in LoG with high review quality! Join us either by an author or reviewer!

Hannes Stärk

@HannesStaerk

1 year

With the @LogConference abstract deadline approaching, there are still a few days left to help out and get a stab at our best reviewer rewards! (money) You can select how many papers to review and bid on papers at our conference! Sign up here:

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Yuanqi Du

@YuanqiD

1 year

See you in summer Hawaii!

ICML Conference

@icmlconf

1 year

#ICML2023 Workshops have been posted:

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Yuanqi Du

@YuanqiD

2 months

@chaitjo @dom_beaini Indeed nice work! You may also be interested in these two works And

Neural scaling of deep chemical models

Nature Machine Intelligence - Deep learning methods in natural language processing generally become more effective with larger datasets and bigger networks. But it is not evident whether the same...

www.nature.com

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Yuanqi Du

@YuanqiD

22 days

Very inspiring story, everyone gets confused and uncertain about what comes ahead, but the process to figure them out is the most valuable part. When you are put in a position, “someone” must have seen something in you that you may have not realized yet. You should trust yourself

Tao Long (he/him)

@taolongg

23 days

some notes with this milestone: last year at this time, i was battling imposter syndrome and considering quitting PhD: i thought it’s a FAULT to go to a phd straight from undergrad without doing any postbacc RA or work experiences. i felt like i was nothing but my degrees… (1/n)