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