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Wednesday, October 20
Monday, October 25

#1

Scalable Deep Learning for Large Scale Scientific Machine Learning: Challenges and Opportunities

#2

Integrated Data - Driving the Future of NASA Missions and Technology

#3

How Do We Know if Data Science is "For Good"?

#4

Discovery and Action Through Geospatial Visualization and Urban Analytics

#5

Opening: Intro to Morning

#6

What Do We Mean by “AI Ethics”?: Conceptual Tools and Examples for a Common Future

#7

Deep Learning-Based Blood Glucose Predictors In Type 1 Diabetes

#8

How to Teach AI Multiple Languages?

#9

Welcome: Intro to Afternoon

#10

Random-Walk Based Graph Representation Learning Revisited

#11

Explainable Deep Learning Approaches to Predict Development of Brain Metastases in Patients with Lung Cancer Using Electronic Health Records

#12

Automatic Machine Learning with AutoGluon - Algorithms, Domains, Applications

#13

Scalable and Sustainable AI Acceleration for Everyone: Hashing Algorithms Train Billion-parameter AI Models on a Commodity CPU faster than Hardware Accelerators

#14

Multi-Task Deep Learning Framework for Sales Forecasting and Product Recommendation

#15

Multi-Task Learning for Demand Prediction Through a Hyper-Network

#16

ML for Energy Transition

#17

ShiftAddNet: A Hardware-Inspired Deep Network

#18

Neural Architecture Search for Inversion

#19

Democratizing Deep Learning with Commodity Hardware: How to Train Large Deep Learning Models on CPU Efficiently with Sparsity

#20

How to Deal with Volume and Velocity Associated with Hundreds of Terabytes (and Beyond) of Genomics Data?

#21

Break

#22

Lunch + Virtual Booths

#23

Break

#24

Closing Remarks

#25

Emu: Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads

#26

Co-Manifold Learning

#27

Using Visual Feature Space as a Pivot Across Languages

#28

Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints

#29

Generalized Zero-Shot Learning via Normalizing Flows

#30

Parallel RRT Algorithm for Robotic Motion Planning

#31

Posters and Networking

#32

Data4Good & Responsible AI-Automated Data Collection: How Do You Ensure Data4good?

#33

Opening: Pre-Recorded Poster Presentations

#34

Opening: Pre-Recorded Sponsor Presentations

#35

Break

#36

Break

#37

MLPrE – A Tool for Preprocessing Data and Conducting Exploratory Data Analysis Prior to Machine Learning Model Construction

#38

Diabetes Management in Underserved Communities: Data-Driven Insights from Continuous Glucose Monitoring

#39

Closing Remarks

#40

Outdoor Networking Reception with Sponsors

#41

Conference Registration, Breakfast, Networking

#42

Welcome

#43

Lunch + Networking

#44

Afternoon Break and Networking

#45

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

#46

MaGNET: Uniform Sampling from Deep Generative Network Manifolds without Retraining

#47

Magnified Convolutional Enrichment Representation Model

#48

PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication

#49

Quantification of Myxococcus Xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Images

#50

Opening: Pre-Recorded Poster Presentations