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Fast Machine Learning for Science Workshop 2022

from Monday 3 October 2022 (09:00) to Thursday 6 October 2022 (12:30)
Southern Methodist University

        : Sessions
    /     : Talks
        : Breaks
3 Oct 2022
4 Oct 2022
5 Oct 2022
6 Oct 2022
AM
09:00 Welcome - Alessio Deiana (CERN) Allison Mccarn Deiana (Southern Methodist University (US))  
09:05 Machine Learning for Science - Pan Li (Purdue University)  
09:50 Efficient Computer Architectures - Sophia Shao (UC Berkeley)  
10:30 --- Coffee ---
11:00 Applications and Opportunities at the Large Hadron Collider - Maximilian J Swiatlowski (TRIUMF (CA))  
11:45 Application and Opportunities in Nuclear Physics - Dr Jin Huang (Brookhaven National Lab)  
09:00 Applications and Opportunities for Machine Controls - Verena Kain (CERN)  
09:45 Applications and Opportunities in Fusion Systems - Chris Hansen (University of Washington)  
10:30 --- Coffee ---
11:00 Autonomous experiments in scanning probe microscopy: opportunities for rapid inference and decision making - Rama Vasudevan (ORNL)  
11:30 Toward Real Time Decision-Making in Material Science Experiments - Mitra Taheri (JHU)  
09:00 Beyond CMOS - Jennifer Hasler (Georgia Tech)  
09:45 Applications and Opportunities in Neutrino and DM experiments - Prof. Jonathan Asaadi (University of Texas at Arlington) Jonathan Asaadi (University of Texas at Arlington (US))  
10:30 --- Coffee ---
11:00 Benchmarking Fast ML systems - Vijay Reddi (Harvard University)  
11:45 The need for low latency with billion-year old signals - Prof. Matthew Graham (California Institute of Technology) Matthew Graham (Caltech)  
09:00 hls4ml tutorial - Sioni Paris Summers (CERN) Elham E Khoda (University of Washington (US))  
11:00 hls4ml community session  
PM
12:30 --- Lunch ---
13:45
Contributed Talks (until 15:15)
13:45 Designing intelligent DAQ systems for radiation instrumentation with hls4ml - Prof. Audrey Corbeil Therrien (Université de Sherbrooke)  
14:00 Design and first test results of a reconfigurable autoencoder on an ASIC for data compression at the HL-LHC - Danny Noonan (Fermi National Accelerator Lab. (US))  
14:05 Increasing the LHC Computational Power by integrating GPUs as a service - William Patrick Mccormack (Massachusetts Inst. of Technology (US)) Yongbin Feng (Fermi National Accelerator Lab. (US))  
14:10 Exa.TrkX inference as-a-service - Yongbin Feng (Fermi National Accelerator Lab. (US))  
14:15 Quantized ONNX (QONNX) - Jovan Mitrevski (Fermi National Accelerator Lab. (US))  
14:30 Accelerating JEDI-net for jet tagging on FPGAs - Zhiqiang Que (Imperial College London)  
14:45 Implementation of a pattern recognition neural network for live reconstruction using AI processors - Patrick Schwaebig  
15:00 FKeras: A Fault Tolerance Library for DNNs - Olivia Weng  
15:15 --- Coffee ---
15:45
Contributed Talks (until 17:15)
15:45 Neural network accelerator for quantum control - Giuseppe Di Guglielmo (Fermilab)  
16:00 End-to-End Vertex Finding for the CMS Level-1 Trigger - Benjamin Radburn-Smith (Imperial College (GB)) Christopher Edward Brown (Imperial College (GB))  
16:15 Resource Efficient and Low Latency GNN-based Particle Tracking on FPGA - Shi-Yu Huang Bo-Cheng Lai  
16:30 Neural Signal Compression System for a Seizure-Predicting Brain Implant in CMOS 28nm - William Lemaire  
16:45 Exploring FPGA in-storage computing for Supernova Burst detection in LArTPCs - Benjamin Hawks (Fermi National Accelerator Lab)  
17:00 Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder - Surojit Saha (Institute of Astronomy, National Tsing Hua University, Taiwan)  
18:00
Reception (until 20:00)
12:00 Some current challenges in materials measurements: an industrial perspective - Roger Proksch (Oxford Instruments)  
12:30 --- Lunch ---
13:30
Contributed Talks (until 15:15)
13:30 Demonstration of Machine Learning-assisted real-time noise regression in LIGO - Muhammed Saleem Cholayil (University of Minnesota)  
13:45 Rapid Fitting of Band-Excitation Piezoresponse Force Microscopy Using Physics Constrained Unsupervised Neural Networks - Alibek Kaliyev (Lehigh University)  
14:00 Data Driven Weather Forecasting with Rudimentary Observables - Luke Fairbanks (ucsd)  
14:05 Low-latency Noise Subtraction of Gravitational Wave Data by DeepClean - Chia-Jui Chou (National Yang Ming Chiao Tung University)  
14:10 Harnessing ultrafast ML for new algorithms at the CMS L1 trigger - Daniel Diaz (Univ. of California San Diego (US))  
14:15 Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors - Micol Rigatti (Fermi National Accelerator Lab. (US))  
14:30 Application of deep learning to instability tracking using high-speed video cameras in magnetic confinement fusion - Mr Yumou Wei (Columbia University)  
14:45 Fast recurrent neural networks on FPGAs with hls4ml - Elham E Khoda (University of Washington (US))  
15:00 Deep Neural Network Algorithms in the CMS Level-1 Trigger - Anthony Vizcaino Aportela (Univ. of California San Diego (US))  
15:15 --- Coffee ---
15:45
Contributed Talks (until 17:15)
15:45 Extremely Noisy 4D-TEM Strain Mapping Using Cycle Consistent Spatial Transforming Autoencoders - Shuyu Qin  
16:00 Quantized Distilled Autoencoder Model on FPGA for Real-Time Crystal Structure Detection in 4D Scanning Transmission Electron Microscopy - Ryan Forelli (Lehigh University)  
16:15 Deployment of ML in changing environments - Christopher Edward Brown (Imperial College (GB)) Benjamin Radburn-Smith (Imperial College (GB)) Marco Barbone  
16:30 Low-latency Calorimetry Clustering at the LHC with SPVCNN - Alexander Joseph Schuy (University of Washington (US))  
16:45 CryoAI – Prototyping cryogenic chips for machine learning at 22nm - Mr Manuel Valentin (Northwestern University)  
17:00 Next Generation Coprocessors as a service - Dylan Sheldon Rankin (Massachusetts Inst. of Technology (US))  
18:00
Banquet (until 21:00)
12:30 --- Lunch ---
13:30
Contributed Talks (until 15:15)
13:30 A Deep Learning Approach to Particle Identification for the AMS Electromagnetic Calorimeter - Raheem Hashmani (Middle East Technical University (TR))  
13:45 A Normalized Autoencoder for LHC triggers - Luigi Favaro  
14:00 Interaction Network Autoencoder in the Level-1 Trigger - Sukanya Krishna (Univ. of California San Diego (US))  
14:05 Large CNN for HLS4ML and Deepcalo - Lin-Chi Yang Yan-Lun Huang ChiJui Chen  
14:10 End-to-end acceleration of machine learning in gravitational wave physics - Alec Gunny (Massachusetts Institute of Technology)  
14:15 FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning - Jules Muhizi (Fermilab/Harvard University)  
14:30 Robust anomaly detection using NuRD - Abhijith Gandrakota (Fermi National Accelerator Lab. (US))  
14:45 Quantized Neural Networks on FPGAs using HAWQ-V3 and hl4ml - Javier Ignacio Campos (Fermi National Accelerator Lab. (US))  
15:00 Implementing Deep Neural Network Algorithms inside the CMS Level-1 Trigger - Duc Hoang (MIT)  
15:15 --- Coffee ---
15:45
Contributed Talks (until 17:15)
15:45 Real-time image processing for high-resolution imaging detectors - Georgia Karagiorgi  
16:00 Semi-supervised Graph Neural Networks for Pileup Noise Removal - Shikun Liu  
16:05 A Machine Learning Software Infrastructure for Gravitational Wave Signal Discovery - Ethan Marx (MIT)  
16:10 Online-compatible Unsupervised Non-resonant Anomaly Detection - Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))  
16:15 In-System Parameter Update and I/O Capture for Machine Learning IP Cores - Brett McMillian (Crossfield Technology LLC)  
16:30 Detection for Core-collapse Supernova and Fast Data Preprocessing - Andy Chen (National Yang Ming Chiao Tung University (NYCU))  
16:35 EMD Neural Network Loss for ECON-T ASIC Autoencoder - Rohan Shenoy (Univ. of California San Diego (US))  
16:40 A novel ML-based method of primary vertex reconstruction in high pile-up condition - Haoran Zhao (University of Washington (US))  
16:45 In-Pixel AI: From Algorithm to Accelerator - Priyanka Dilip (Stanford University / Fermilab)  
17:00 Autonomous real-time science-driven follow-up of survey transients - Niharika Sravan  
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