Workshop
Sat Jul 23 06:00 AM -- 03:00 PM (PDT) @ Room 309
AI for Science
Machine learning (ML) has revolutionized a wide array of scientific disciplines, including chemistry, biology, physics, material science, neuroscience, earth science, cosmology, electronics, mechanical science. It has solved scientific challenges that were never solved before, e.g., predicting 3D protein structure, imaging black holes, automating drug discovery, and so on. Despite this promise, several critical gaps stifle algorithmic and scientific innovation in AI for Science: (1) Under-explored theoretical analysis, (2) Unrealistic methodological assumptions or directions, (3) Overlooked scientific questions, (4) Limited exploration at the intersections of multiple disciplines, (5) Science of science, (6) Responsible use and development of AI for science. However, very little work has been done to bridge these gaps, mainly because of the missing link between distinct scientific communities. While many workshops focus on AI for specific scientific disciplines, they are all concerned with the methodological advances within a single discipline (e.g., biology) and are thus unable to examine the crucial questions mentioned above. This workshop will fulfill this unmet need and facilitate community building; with hundreds of ML researchers beginning projects in this area, the workshop will bring them together to consolidate the fast growing area of AI for Science into a recognized field.
| Opening Remarks | |
| Frank Noe (Talk) | |
| Rafael Gomez-Bombarelli (Talk) | |
| Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks, Chuanbo Hua (Contributed Talk) | |
| Daphne Koller (Talk) | |
| Poster Session (Poster) | |
| Animashree Anandkumar (Talk) | |
| Learning to solve PDE constraint inverse problem using Graph Network, Qingqing Zhao (Contributed Talk) | |
| Anthony Gitter (Talk) | |
| Jiequn Han (Talk) | |
| Understanding the evolution of tumours using hybrid deep generative models, Tom Ouellette (Contributed Talk) | |
| Carla P. Gomes (Talk) | |
| A Density Functional Recommendation Approach for Accurate Predictions of Vertical Spin Splitting of Transition Metal Complexes, Chenru Duan (Contributed Talk) | |
| Max Tegmark (Talk) | |
| Closing Remarks | |
| $O(N^2)$ Universal Antisymmetry in Fermionic Neural Networks (Poster) | |
| Centralized vs Individual Models for Decision Making in Interconnected Infrastructure (Poster) | |
| Removing parasitic elements from Quantum Optical Coherence Tomography data with Convolutional Neural Networks (Poster) | |
| Mesh-Independent Operator Learning for Partial Differential Equations (Poster) | |
| Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers (Poster) | |
| Curvature-informed multi-task learning for graph networks (Poster) | |
| Unsupervised Discovery of Inertial-Fusion-Relevant Plasma Physics using Differentiable Kinetic Simulations (Poster) | |
| Weakly Supervised Inversion of Multi-physics Data for Geophysical Properties (Poster) | |
| Path Integral Stochastic Optimal Control for Sampling Transition Paths (Poster) | |
| Improving Subgraph Representation Learning via Multi-View Augmentation (Poster) | |
| Reinforced Genetic Algorithm for Structure-based Drug Design (Poster) | |
| Understanding the evolution of tumours using hybrid deep generative models (Oral) | |
| Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks (Oral) | |
| Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks (Poster) | |
| A Density Functional Recommendation Approach for Accurate Predictions of Vertical Spin Splitting of Transition Metal Complexes (Poster) | |
| One-Shot Transfer Learning of Physics-Informed Neural Networks (Poster) | |
| Transform Once: Efficient Operator Learning in Frequency Domain (Poster) | |
| Multiresolution Matrix Factorization and Wavelet Networks on Graphs (Poster) | |
| Carla P. Gomes (Talk) | |
| The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning (Poster) | |
| Intelligent Digital Twins can Accelerate Scientific Discovery and Control Complex Multi-Physics Processes (Poster) | |
| Understanding the evolution of tumours using hybrid deep generative models (Poster) | |
| Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization (Poster) | |
| Variational Inference for Soil Biogeochemical Models (Poster) | |
| Neural Basis Functions for Accelerating Solutions to high Mach Euler Equations (Poster) | |
| MAgNet: Mesh Agnostic Neural PDE Solver (Poster) | |
| DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks (Poster) | |
| Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks (Poster) | |
| Pre-training Graph Neural Networks for Molecular Representations: Retrospect and Prospect (Poster) | |
| Provable Concept Learning for Interpretable Predictions Using Variational Autoencoders (Poster) | |
| Learning to Solve PDE-constrained Inverse Problems with Graph Networks (Poster) | |
| Predicting generalization with degrees of freedom in neural networks (Poster) | |
| No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit (Poster) | |
| PowerGraph: Using neural networks and principal components to determine multivariate statistical power trade-offs (Poster) | |
| LAST: Latent Space Assisted Adaptive Sampling for Protein Trajectories (Poster) | |
| Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks (Poster) | |
| Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges (Poster) | |
| Sample Efficiency Matters: Benchmarking Molecular Optimization (Poster) | |
| Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction (Poster) | |
| Unifying physical systems’ inductive biases in neural ODE using dynamics constraints (Poster) | |
| From Kepler to Newton: Explainable AI for Science Discovery (Poster) | |
| How Much of the Chemical Space Has Been Explored? Selecting the Right Exploration Measure for Drug Discovery (Poster) | |
| Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors (Poster) | |
| Learning to Solve PDE-constrained Inverse Problems with Graph Networks (Oral) | |
| Multiresolution Equivariant Graph Variational Autoencoder (Poster) | |
| Target-aware Molecular Graph Generation (Poster) | |
| Featurizations Matter: A Multiview Contrastive Learning Approach to Molecular Pretraining (Poster) | |
| Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control? (Poster) | |
| Evaluating Self-Supervised Learned Molecular Graphs (Poster) | |
| On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods (Poster) | |
| Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks (Poster) | |
| LinkBERT: Language Model Pretraining with Document Link Knowledge (Poster) | |
| MultiScale MeshGraphNets (Poster) | |
| An Optical Pulse Stacking Environment and Reinforcement Learning Benchmarks (Poster) | |
| A Density Functional Recommendation Approach for Accurate Predictions of Vertical Spin Splitting of Transition Metal Complexes (Oral) | |
| Deep Learning and Symbolic Regression for Discovering Parametric Equations (Poster) | |
| Bias in the Benchmark: Systematic experimental errors in bioactivity databases confound multi-task and meta-learning algorithms (Poster) | |
| GAUCHE: A Library for Gaussian Processes in Chemistry (Poster) | |