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Mon Dec 13 05:00 AM -- 01:55 PM (PST)
AI for Science: Mind the Gaps
Payal Chandak · Yuanqi Du · Tianfan Fu · Wenhao Gao · Kexin Huang · Shengchao Liu · Ziming Liu · Gabriel Spadon · Max Tegmark · Hanchen Wang · Adrian Weller · Max Welling · Marinka Zitnik

Workshop Home Page

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) Unrealistic methodological assumptions or directions, (2) Overlooked scientific questions, (3) Limited exploration at the intersections of multiple disciplines, (4) Science of science, (5) 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 Remark
AI X Science (Invited Talks)
AI X Mathematics (Invited Talks)
Live Panel (Discussion Panel)
AI X Chemistry (Invited Talks)
Discovering Dynamical Parameters by Interpreting Echo State Networks (Poster)
AI X Molecule (Invited Talks)
Scientific Argument with Supervised Learning (Poster)
AI X Cosmology (Invited Talks)
Apertures in Agriculture Seeking Attention (Poster)
Uncovering motif interactions from convolutional-attention networks for genomics (Poster)
AI X Discovery (Invited Talks)
AI X Neuroscience (Invited Talks)
Awards and Closing Remark (Closing Remark)
Single Reference Frequency Loss for Multi-frequency Wavefield Representation using Physics-Informed Neural Networks (Poster)
Novel fuzzy approach to Antimicrobial Peptide Activity Prediction: A tale of limited and imbalanced data that models won’t hear (Poster)
AI as statistical methods for imperfect theories (Poster)
3D Pre-training improves GNNs for Molecular Property Prediction (Poster)
GraphGT: Machine Learning Datasets for Graph Generation and Transformation (Poster)
Linear Transformations in Autoencoder Latent Space Predict Time Translations in Active Matter System (Poster)
Generative Neural Network Based Non-Convex Optimization Using Policy Gradients with an Application to Electromagnetic Design (Poster)
Distributed Deep Learning for Persistent Monitoring of agricultural Fields (Poster)
Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography (Poster)
Towards trustworthy explanations with gradient-based attribution methods (Poster)
From Convolutions towards Spikes: The Environmental Metric that the Community currently Misses (Poster)
A Fresh Look at De Novo Molecular Design Benchmarks (Poster)
Identification of Enzymatic Active Sites with Unsupervised Language Modelling (Poster)
Learning to Simulate Unseen Physical Systems with Graph Neural Networks (Poster)
Fragment-Based Sequential Translation for Molecular Optimization (Poster)
$\textit{Ab Initio}$ Discovery of Biological Knowledge from scRNA-Seq Data Using Machine Learning (Poster)
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach (Poster)
Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science (Poster)
Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study (Poster)
Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery (Poster)
Scalable Bayesian Optimization Accelerates Process Optimization of Penicillin Production (Poster)
Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process (Poster)
A Search Engine for Discovery of Scientific Challenges and Directions (Poster)
Bringing Atomistic Deep Learning to Prime Time (Poster)
This Earthquake Doesn't Exist (Poster)
Multi-task Learning with Domain Knowledge for Molecular Property Prediction (Poster)
High-Dimensional Discrete Bayesian Optimization with Self-Supervised Representation Learning for Data-Efficient Materials Exploration (Poster)
Drug Re-positioning via Text Augmented Knowledge Graph Embeddings (Poster)
Multi-modal Self-supervised Pre-training for Large-scale Genome Data (Poster)
Physical Benchmarking for AI-generated Cosmic Web (Poster)
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data (Poster)
Towards Brain-to-Text Generation: Neural Decoding with Pre-trained Encoder-Decoder Models (Poster)
Traversing Geodesics to Grow Biological Structures (Poster)
Bayesian Optimal Experimental Design for Simulator Models of Cognition (Poster)
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks (Poster)
Improving Hit-finding: Multilabel Neural Architecture with DEL (Poster)
Semi-supervised Graph Neural Network for Particle-level Noise Removal (Poster)
Human-in-the-loop for a Disconnection Aware Retrosynthesis (Poster)
On the feasibility of small-data learning in simulation-driven engineering tasks with known mechanisms and effective data representations (Poster)
AI Methods for Designing Energy-Efficient Buildings in Urban Environments (Poster)
Scalable Geometric Deep Learning on Molecular Graphs (Poster)
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning (Poster)
Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks (Poster)
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning (Poster)
Neuroprospecting with DeepRL agents (Poster)
Multiple Sequential Learning Tasks Represented in Recurrent Neural Networks (Poster)
Adaptive Pseudo-labeling for Quantum Calculations (Poster)
Regression modeling on DNA encoded libraries (Poster)