Name: A3D3 All-hands meeting
Start: 2024-10-14T08:00:00-04:00
End: 2024-10-14T21:35:00-04:00
Location: Convergence Center @ Purdue University

A3D3 All-hands meeting

Monday, October 14, 2024 - 8:00 AM

Monday, October 14, 2024

8:00 AM Registration
Registration
8:00 AM - 9:00 AM
Room: Innovation Room
9:00 AM Morning Plenary: PI talks
PI talks
9:00 AM - 10:00 AM
Room: Innovation Room
Contributions

9:00 AM Welcome - Seungbin Park Yulei Zhang (University of Washington (US))

9:10 AM Efficient Multi-modal LLM on the Edge - Song Han (MIT)

9:35 AM Distribution Shifts in Graph Machine Learning and Graph Domain Adaptation - Pan Li
10:00 AM Morning Plenary: Trainee talks
Trainee talks
10:00 AM - 11:15 AM
Room: Innovation Room
Contributions

10:00 AM Trainee-led: Overview of MMA - Will Benoit

10:15 AM Trainee-led: Overview of Neuro - Seungbin Park

10:30 AM Trainee-led: Overview of HEP - Yuan-Tang Chou (University of Washington (US))

10:45 AM Trainee-led: Overview of HAC - Siqi Miao

11:00 AM NSF HDR ML Anomaly Detection Challenge - Advaith Anand (University of Washington (US))
11:15 AM Coffee
Coffee
11:15 AM - 11:45 AM
Room: Innovation Room
11:45 AM Morning Parallels: How to Apply to Jobs (Faculty, Postdoc, Industry)
How to Apply to Jobs (Faculty, Postdoc, Industry)
11:45 AM - 12:45 PM
Room: Innovation Room
Contributions

11:45 AM Dr. Zhijian Liu - Zhijian Liu (Massachusetts Institute of Technology)

11:50 AM Dr. Julia Gonski - Julia Lynne Gonski (SLAC National Accelerator Laboratory (US))

11:55 AM Dr. Dylan Rankin - Dylan Sheldon Rankin (University of Pennsylvania (US))

12:00 PM Panel Discusion - Dylan Sheldon Rankin (University of Pennsylvania (US)) Julia Lynne Gonski (SLAC National Accelerator Laboratory (US)) Zhijian Liu (Massachusetts Institute of Technology)
12:45 PM Lunch
Lunch
12:45 PM - 1:45 PM
Room: Innovation Room
1:45 PM Afternoon Plenary: Postbac Talks
Postbac Talks
1:45 PM - 2:15 PM
Room: Innovation Room
Contributions

1:45 PM Postbac research experience: light from black holes and other multi-messenger astronomy - Kira Nolan

1:55 PM Attention at Silicon Speed: Towards Efficient Transformers on FPGAs - Rian Flynn (Purdue University (US))

2:05 PM Postbac Research Experience: Multimodal Classification of Astronomical Transients - Alexandra Junell Brown
2:15 PM Afternoon Plenary: Trainee-led Talks: Part 1
Trainee-led Talks: Part 1
2:15 PM - 3:10 PM
Room: Innovation Room
Contributions

2:15 PM Implementation of `traccc` as-a-service - Miles Cochran-Branson (University of Washington (US))

2:28 PM Channel tagging: identifying events with supernova pointing information - Janina Dorin Hakenmueller (Duke University)

2:41 PM Combining Neural Networks for IceCube DeepCore Reconstruction and Classification Tasks - Josh Peterson

2:54 PM Real-time Anomaly Detection at the CMS L1 Trigger - Melissa Quinnan (Univ. of California San Diego (US))
3:10 PM Coffee
Coffee
3:10 PM - 3:30 PM
Room: Innovation Room
3:30 PM Afternoon Plenary: Trainee-led Talks: Part II
Trainee-led Talks: Part II
3:30 PM - 4:30 PM
Room: Innovation Room
Contributions

3:30 PM Neural Architecture Codesign for Fast Physics Applications - Jason Weitz (UCSD)

3:43 PM ConNAS4ML: Gradient-Based NAS Framework with Hardware Constraints for hls4ml - ChiJui Chen

3:56 PM Autoencoders for Anomaly Detection and Output Reduction on the Edge (AADORE) - Alexander Yue

4:09 PM Closed-loop Brain Stimulation in NHPs using hardware-accelerated Machine Learning - Rajeev Botadra
4:30 PM Afternoon Plenary: Trainee-led Town Hall (with PIs)
Trainee-led Town Hall (with PIs)
4:30 PM - 5:15 PM
Room: Innovation Room
5:15 PM Poster Session / Reception
5:15 PM - 7:42 PM
Room: Main hall
Contributions

5:15 PM Decoding multi-limb trajectories of naturalistic running from calcium imaging using deep learning - Seungbin Park

5:20 PM Noise or Astrophysical: Developing Machine Learning Classifiers for Characterizing Gravitational Wave Events - Seiya Tsukamoto

5:25 PM Detecting the Invisible: Electron Hit Localization in High-Resolution TEM Images Using Deep Learning on FPGAs - Derrick Appiah Osei

5:30 PM Estimating the Hubble Constant by combining posteriors from Multi-Messenger Kilonovae Observations - Megan Averill

5:35 PM Real-time compression of CMS detector data with machine learning - Mariel Peczak

5:40 PM Multi-messenger Astronomy Observations Via Alert Stream Filtering - Kira Nolan (California Institute of Technology)

5:45 PM Improving Sensitivity to Neutron Star Gravitational Wave Events using the Qp Transform - Emma de Bruin

5:50 PM Model Logging for FPGA Deployment - Julian Goddy

5:55 PM Accelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models - Mostafa Cham (iHARP)

6:00 PM Early-time Classification of Astronomical Transients - Alexandra Junell Brown

6:05 PM Finding Binary Black Hole Mergers - Steven Henderson (UMN - Twin Cities)

6:10 PM Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment - Jared Burleson (University of Illinois at Urbana-Champaign)

6:15 PM Smart Pixels: A Machine Learning Approach Towards Data Reduction in Next-Generation Particle Detectors - David Jiang (Univ. Illinois at Urbana Champaign (US))

6:28 PM Foundation Model for Real-Time Model Selection and Fitting - Cymberly Tsai