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Fast Machine Learning for Science Workshop 2023

from Monday 25 September 2023 (08:30) to Thursday 28 September 2023 (18:00)
Imperial College London (Blackett Laboratory, Lecture Theatre 1)

        : Sessions
    /     : Talks
        : Breaks
25 Sept 2023
26 Sept 2023
27 Sept 2023
28 Sept 2023
AM
08:30 Registration  
09:30
Invited Talks (until 10:30)
09:30 Workshop Opening - Alex Tapper (Imperial College London) Sioni Paris Summers (CERN)  
09:45 Fast Machine Learning at the Large Hadron Collider experiments - Thea Aarrestad (ETH Zurich (CH))  
10:30 --- Break ---
11:00
Invited Talks (until 12:30)
11:00 Fast Machine Learning at European XFEL - Steve Aplin (EuXFEL)  
11:45 Bridging AI and biomedicine: Towards AI-driven scientific discoveries - Maria Brbic  
09:00
Invited Talks (until 10:30)
09:00 Fast Machine Learning for accelerator control - Karin Rathsman (European Spallation Source)  
09:45 Fast Machine Learning for laser wakefield acceleration - Matt Streeter  
10:30 --- Break ---
11:00
Invited Talks (until 12:30)
11:00 Fast ML for fusion simulation, optimization, and control - Jonathan Citrin  
11:45 Machine Learning in Exoplanet Characterisation - Kai Hou Yip Ingo Waldmann (UCL)  
09:00
Invited Talks (until 10:30)
09:00 Co-Design for Efficient & Adaptive ML - Yaman Umuroglu  
09:45 In network ML: Inference at the Speed of Data - Noa Zilberman  
10:30 --- Group Photo ---
10:45 --- Break ---
11:00
Invited Talks (until 12:30)
11:00 Need for Speed: How to harness the power of Large Language Models - Tobias Becker (Maxeler Technologies)  
11:45 Deep Learning for Fast MR Imaging and Analysis - Chen Qin  
09:00 Coprocessors/SONIC Developers Meeting - Kevin Pedro (Fermi National Accelerator Lab. (US)) William Patrick Mccormack (Massachusetts Inst. of Technology (US))   (Blackett Laboratory, Lecture Theatre 3)
09:00 QONNX Developers Meeting - Nhan Tran Jovan Mitrevski (Fermi National Accelerator Lab. (US)) Yaman Umuroglu   (Blackett Laboratory, Lecture Theatre 2)
10:00
Tutorial - Javier Campos (until 12:00) (Blackett Laboratory, Lecture Theatre 2)
PM
12:30 --- Lunch ---
13:30
Contributed Talks (until 15:00)
13:30 Smart embedded DAQ systems for radiation instrumentation – Testbench and latest results - Prof. Audrey Corbeil Therrien (Université de Sherbrooke)  
13:45 Scalable neural network models and terascale datasets for particle flow reconstruction - Farouk Mokhtar (Univ. of California San Diego (US))  
14:00 Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs - Santosh Parajuli (Univ. Illinois at Urbana Champaign (US))  
14:15 smartpixels: on-pixel featurization for single layer silicon tracking - Rachel Kovach-Fuentes (University of Chicago)  
14:30 Portable Acceleration of CMS Mini-AOD Production with Coprocessors as a Service - William Patrick Mccormack (Massachusetts Inst. of Technology (US))  
14:45 Accelerating Hadronic Calorimetry with Sparse Point-Voxel Convolutional Neural Networks - Jeffrey Krupa (Massachusetts Institute of Technology)  
15:00 --- Break ---
15:30
Contributed Talks (until 16:45)
15:30 Convolutional Neural Networks for Real-Time Processing of ATLAS Liquid-Argon Calorimeter Signals - Anne-Sophie Berthold (Technische Universitaet Dresden (DE))  
15:45 Fast ML inference in FPGAs for the Level-1 Scouting system at CMS - Thomas Owen James (CERN)  
16:00 Yggdrasil Conifer: Latency and resource-aware decision trees for faster FPGA inference at the LHC - Andrew George Oliver  
16:15 fwXmachina part 1: Classification with boosted decision trees on FPGA for L1 trigger - Tae Min Hong (University of Pittsburgh (US))  
16:30 Realtime Anomaly Detection in the CMS Experiment Global Trigger Test Crate - Chang Sun (ETH Zurich (CH))  
16:45 --- Break ---
17:00
Contributed Talks (until 18:30)
17:00 fwXmachina part 3: Anomaly detection with decision tree autoencoder on FPGA for L1 trigger - Stephen Roche (Saint Louis University)  
17:05 Fast muon identification algorithm on FPGAs for the Phase II level 0 trigger of the ATLAS experiment - Graziella Russo (Sapienza Universita e INFN, Roma I (IT))  
17:10 Neuromorphic Computing for On-Sensor Data Filtering on Smart-Pixels - Shruti R Kulkarni (Oak Ridge National Laboratory)  
17:15 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))  
17:20 Harnessing charged particle tracks in the Phase-2 CMS Level-1 Trigger with ultrafast Machine Learning - Christopher Edward Brown (Imperial College (GB))  
17:25 B-tagging and Tau reconstruction in the Level-1 Trigger with real-time Machine Learning - Duc Minh Hoang (MIT)  
17:30 Machine Learning based Data Compression on FPGA with HLS4ML - Pratik Jawahar (University of Manchester (UK - ATLAS))  
17:35 Using NVIDIA Triton Server for Inference-as-a-Service at Fermilab - Claire Savard (University of Colorado Boulder (US))  
17:40 Jets as sets or graphs: Fast jet classification on FPGAs for efficient triggering at the HL-LHC - Denis-Patrick Odagiu (ETH Zurich (CH))  
17:45 Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation - Mr Ryan Liu (University of California, Berkeley)  
17:50 Fast b-tagging at the high-level trigger of the ATLAS experiment - Stefano Franchellucci (Universite de Geneve (CH))  
17:55 BDT for tau identification in the ATLAS Level-1 trigger - David Reikher (Tel Aviv University (IL))  
18:00 A Convolutional Neural Network for topological fast selection algorithms in FPGAs for the HL-LHC upgrade of the CMS experiment - Maciej Mikolaj Glowacki (University of Bristol (GB))  
18:05 Low Energy LArTPC Signal Detection using Anomaly Detection - Jovan Mitrevski (Fermi National Accelerator Lab. (US))  
18:10 Graph Neural Networks on FPGAs with HLS4ML - Jan-Frederik Schulte (Purdue University (US))  
18:15 Optimizing Sparse Neural Architectures for Low-Latency Anomaly Detection - Luke McDermott (UC San Diego & Modern Intelligence)  
18:20 Accomodating Transformer in the FastML era of HEP-EX - Sitian Qian (Peking University (CN))  
19:00
Social event (until 21:00)
12:30 --- Lunch ---
13:30
Contributed Talks (until 15:30)
13:30 Deep Spectral Networks: Enhancing Orbit Propagation and Determination in Astrodynamics - Sabin Anton (Imperial College London, Department of Aeronautics, Postgraduate Student)  
13:45 Machine Learning Explorations in GRB Studies: From Classification to Extended Emission Identification - Keneth Stiven Garcia Cifuentes (Universidad Nacional Autónoma de México)  
14:00 GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders - Katya Govorkova (Massachusetts Inst. of Technology (US))  
14:15 Tools and Results for Real-Time Deep Learning in Gravitational-Wave Physics - Eric Anton Moreno (Massachusetts Institute of Technology (US))  
14:30 Real Time End to End Supernova Pointing - Maira Khan  
14:45 Edge AI for accelerator controls: beam loss deblending - Jovan Mitrevski (Fermi National Accelerator Lab. (US))  
15:00 Adaptive Machine Learning for Quench Prediction - Maira Khan  
15:15 Real-Time Instability Tracking with Deep Learning on FPGAs in Magnetic Confinement Fusion Devices - Ryan Forelli (Lehigh University)  
15:30 --- Break ---
16:00
Contributed Talks (until 17:40)
16:00 SAMBA: A Trainable Segmentation Web-App with Deep-Learning Powered Labelling - Ronan Docherty (Imperial College London)  
16:15 Real-time Fitting and Materials Characterization in Band- Excitation Piezoresponse Force Microscopy - Veronica Obute (Drexel)  
16:30 Real-Time Machine Learning in Materials Microscopy and Spectroscopy - Prof. Joshua Agar (Drexel)  
16:45 A hybrid data-driven and data assimilation operational model for long term spatiotemporal forecasting: Global and regional PM2.5 forecasting - Dr Fangxin Fang (Imperial College London)  
17:00 Towards lightweight transformer-based models with multimodal data for low-latency surgical applications - Miguel Xochicale (University College London)  
17:15 Approximating Many-Electron Wave Functions using Neural Networks - Matthew Foulkes (Imperial College London)  
17:30 Crystallization Learning with Delaunay Triangulation - Guosheng Yin (Department of Mathematics Imperial College London) Prof. Guosheng Yin (Department of Mathematics Imperial College London)  
17:35 Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer - Zehui Li  
19:00 Public lecture: Algorithms and Flow: Lupe Fiasco’s Creative Use of LLMs - Lupe Fiasco   (Blackett Lab. Lecture Theatre 1)
12:30 --- Lunch ---
13:30
Contributed Talks (until 15:30)
13:30 Hardware-aware pruning of real-time neural networks with hls4ml Optimization API - Benjamin Ramhorst (Imperial College London)  
13:45 Efficient Quantization of Deep Learning Models for Hardware Acceleration - Cheng ZHANG (Imperial College London) Mr Jianyi Cheng (University of Cambridge)  
14:00 High Granularity Quantization​ for Ultra-Fast ML Applications on FPGAs​ - Chang Sun (ETH Zurich (CH))  
14:15 EventDetector: A Python Package for Time Series Event Detection - Dr Menouar Azib (Akkodis)  
14:30 FKeras: A Sensitivity Analysis Tool for Edge Neural Networks - Olivia Weng  
14:45 Reconfigurable Fused and Branched CNN Accelerator - Dr Marko Andjelkovic (IHP - Leibniz-Institut für innovative Mikroelektronik) Dr Markus Ulbricht (IHP - Leibniz-Institut für innovative Mikroelektronik) Mr Rizwan Tariq Syed (IHP - Leibniz-Institut für innovative Mikroelektronik) Prof. Milos Krstic (IHP - Leibniz-Institut für innovative Mikroelektronik)  
15:00 PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference - Marta Andronic (Imperial College London)  
15:15 Exploring medical applications of fast ML with a novel FPGA firmware framework - Freddie Renyard (University of Bristol)  
15:30 --- Break ---
16:00
Contributed Talks (until 17:30)
16:00 Running Converged HPC & AI Workloads on the Groq AI Inference Accelerator - Dr Tobias Becker (Maxeler Technologies)  
16:15 Optimizing for Imperfections in Analog Neural Computations on BrainScaleS-2 - Hendrik Borras (Heidelberg University) Eric Kern (Heidelberg University)  
16:30 Implementing Machine Learning Methods on QICK Hardware for Qubit Readout - Javier Campos  
16:45 Post-training ReLU Sparsification for Faster CNN Inference on FPGA Streaming Accelerators - Mr Zhewen Yu (Imperial College London) Mr Krish Agrawal (Imperial College London) Mr Alexander Montgomerie-Corcoran (Imperial College London)  
17:00 ATHEENA: A Toolflow for Hardware Early-Exit Network Automation - Benjamin Biggs  
17:15 Simplifying Time-Series Recognition: Automated Feature Extraction and Modern Classification - Jan Zavadil (Department of Mathematics, FNSPE, Czech Technical University in Prague.)  
17:20 Universal approximation theorem and error bounds for quantum neural networks and quantum reservoirs - Dr Lukas Gonon (Imperial College London)  
17:25 AI Upscaling with Super Resolution CNNs on FPGAs and ASICs - Ryan Forelli (Lehigh University)  
17:30 Workshop Summary - Philip Coleman Harris (Massachusetts Inst. of Technology (US))  
19:00
Social event (until 23:00)
12:00 --- Lunch ---
13:00 Intel® FPGA AI Suite and AI Tensor Blocks: Empowering Real-time, Low-Latency, and Low-Power Deep Learning Inference with Intel FPGAs - Jahanzeb Ahmad Suleyman Demirsoy   (Blackett Laboratory, Lecture Theatre 2)
15:00 AI for Simulation: Transforming Traditional HPC with Graphcore IPUs - Alexander Titterton (Graphcore)   (Blackett Laboratory, Lecture Theatre 2)
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