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A3D3 all-hands: High-Throughput AI Methods and Infrastructure Workshop

10–14 Jul 2023
University of Washington
US/Pacific timezone

Contribution List

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  1. 1. Workshop Introduction & Overview
    Melissa Quinnan (Univ. of California San Diego (US))
    10/07/2023, 08:30
  2. 15. Maria Makin Group (Purdue)
    Megan Hope Lipton, Seungbin Park
    10/07/2023, 09:00
  3. 14. Amy Orsborn Group (UW)
    Lauren Peterson (University of Washington), Si Jia Li (UW Bioengineering)
    10/07/2023, 09:25
  4. 13. Eli Shlizerman Group (UW)
    JINGYUAN LI
    10/07/2023, 09:50
  5. 16. Michael Coughlin Group (UMN)
    Brian Healy
    10/07/2023, 10:30
  6. 17. Matthew Graham Group (Caltech)
    Prof. Matthew Graham (California Institute of Technology)
    10/07/2023, 10:55
  7. 18. Erik Katsavounidis Group (MIT)
    Alec Gunny, Eric Anton Moreno (Massachusetts Institute of Technology (US)), Ethan Marx (MIT)
    10/07/2023, 11:20
  8. 20. Kael Hanson Group (Wisconsin)
    Josh Peterson
    10/07/2023, 11:45
  9. 23. Philip Harris Group (MIT, CMS)
    William Patrick Mccormack (Massachusetts Inst. of Technology (US))
    10/07/2023, 13:30
  10. 22. Miaoyuan Liu Group (Purdue, CMS)
    Dmitry Kondratyev (Purdue University (US)), Jan-Frederik Schulte (Purdue University (US)), Miaoyuan Liu (Purdue University (US))
    10/07/2023, 13:55
  11. 21. Javier Duarte Group (UCSD, CMS)
    Daniel Diaz (Univ. of California San Diego (US)), Javier Mauricio Duarte (Univ. of California San Diego (US)), Melissa Quinnan (Univ. of California San Diego (US)), Russell Denilson Marroquin Solares (Univ. of California San Diego (US))
    10/07/2023, 14:20
  12. 24. Shih-Chieh Hsu Group (UW, ATLAS)
    Elham E Khoda (University of Washington (US))
    10/07/2023, 14:45
  13. 25. Mark Neubauer Group (UIUC, ATLAS)
    Dewen Zhong (Univ. Illinois at Urbana Champaign (US))
    10/07/2023, 15:10
  14. 71. Kate Scholberg Group (Duke)
    Janina Hakenmüller (Duke University)
    10/07/2023, 15:35

    Janina's flight got delayed. So, she will present later in the afternoon

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  15. 26. Scott Hauck Group (UW)
    Xiaohan Liu
    10/07/2023, 16:20
  16. 27. Pan Li Group (Georgia Tech)
    Shikun Liu
    10/07/2023, 16:45
  17. 28. Deming Chen Group (UIUC)
    Jialiang Zhang
    10/07/2023, 17:10
  18. 31. Song Han Group (MIT)
    Dr Wei-Chen Wang (MIT)
    10/07/2023, 17:35
  19. 70. A3D3 Equity & Career Activities
    Janina Hakenmüller (Duke University)
    10/07/2023, 19:00

    The Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute, funded by the National Science Foundation (NSF), under the Harnessing the Data Revolution (HDR) program, is a multi-disciplinary and geographically distributed entity with the primary mission to lead a paradigm shift in the application of real-time artificial intelligence (AI) at scale to advance scientific knowledge and...

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  20. 36. Accelerating CNNs on FPGAs for Particle Energy Reconstruction
    Alexander Joseph Schuy (University of Washington (US))
    10/07/2023, 19:00

    Given the recent advances of machine learning techniques, the Large Hadron Collider (LHC) at CERN is incorporating deep learning (DL) models, such as DeepCalo, to enhance the quality of data analysis of particle experiments. However, the need for in-time inference to keep up with data generation rates, as well as the dynamics of the experiments, require that the data processing feature short...

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  21. 37. Accelerating Hadronic Calorimetry with Sparse Point-Voxel Convolutional Neural Networks
    Alexander Joseph Schuy (University of Washington (US))
    10/07/2023, 19:00

    In this study, we demonstrate the potential of sparse point-voxel convolutional neural networks (SPVCNN) for hadronic calorimetry tasks using HCAL and HGCAL datasets. By employing a modified object condensation loss, we train the network to group cell deposits into clusters while filtering out noise. We show that SPVCNN performs comparably to generic topological cluster-based methods in both...

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  22. 56. Analog-Domain Implementation of Neural Networks for Energy-Efficient High Energy Physics Applications
    Dewen Zhong (Univ. Illinois at Urbana Champaign (US))
    10/07/2023, 19:00

    Efficient computational strategies are paramount for devices in resource-limited settings, particularly within high-energy physics experiments. To address this, we propose research primarily focused on improved energy efficiency and reduced latency inherent to AI algorithms implemented with analog circuits such as memristive crossbar arrays that perform in-memory matrix-vector multiply...

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  23. 68. Benchmarking HLS4ML vs. SystemVerilog
    Waiz Khan
    10/07/2023, 19:00

    High-level synthesis (HLS) offers the promise of simpler and easier hardware development, but at a cost. We consider the application of high-level synthesis to machine learning applications, seeking to quantify the resource and performance costs of this technique within the widely used HLS4ML framework. By creating carefully optimized SystemVerilog versions of identical HLS4ML designs, we...

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  24. 53. Data for Low-Latency Electromagnetic Training
    Abigail Gray
    10/07/2023, 19:00

    The current and upcoming Gravitational Wave (GW) observing runs by LIGO/Virgo/KAGRA detectors will result in significantly more detections than previous runs. Preparation to follow up associated electromagnetic signals promptly and accurately from binary neutron star (BNS) and neutron star black hole (NSBH) detections now depends heavily on real-time ML implementation at the detectors. The...

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  25. 32. Decoding Upsampled Limb Trajectories of a Running Mouse from 2-Photon Calcium Imaging Using a Recurrent Neural Network Encoder-Decoder
    Seungbin Park
    10/07/2023, 19:00

    Neural decoding is a critical task for understanding the function of the brain and providing solutions for neurological injury and disease. Two-photon calcium imaging has been a promising recording technique to observe a large population of neurons; however, decoding from two-photon calcium images is challenging because of the indirect and nonlinear representation of neural activity, low...

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  26. 45. Denoising Autoencoder for LArTPC Detectors
    Van Tha Bik Lian (Duke University)
    10/07/2023, 19:00

    We present a denoising autoencoder for extracting low-energy signals in Liquid Argon Time Projection Chamber (LArTPC) detectors. In particular, we are interested in neutrinos originating from core-collapse supernova events, and the detection of these neutrinos can help improve our knowledge of the physics of core-collapse supernova events [1]. Additionally, if we can detect them fast enough,...

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  27. 51. Developments in Digital Optical Module Waveform Processing for the IceCube Neutrino Observatory
    Josh Peterson
    10/07/2023, 19:00

    The IceCube Neutrino Observatory is a neutrino telescope located at the South Pole designed to detect Cherenkov radiation produced when neutrinos interact with the ice. It consists of 86 strings of digital optical modules, each containing a photomultiplier tube, embedded deep in the Antarctic ice. Each photon that encounters a photomultiplier tube produces a voltage waveform, and the photon...

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  28. 30. Graph Neural Network Triggers for 𝜏 -> 3𝜇 Events at the HL-LHC
    Benjamin Simon (Purdue University (US))
    10/07/2023, 19:00

    A graph neutral network (GNN) was constructed to identify charged lepton flavor violating decays of a tau particle into three muons in proton-proton collisions recorded with the CMS detector of the Large Hadron Collider. The muons from this decay are expected to have very low momentum, making them hard to detect in the high pileup environment expected at the high luminosity LHC (HL-LHC). We...

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  29. 66. Graph Neural Network-based particle tracking as a Service
    Elham E Khoda (University of Washington (US))
    10/07/2023, 19:00

    Recent studies on the ITk data showed that the Graph Neural Network (GNN) -based track finding can provide not only satisfied track efficiency but also reasonable track resolutions. However, the GNN-based track finding is computationally slow in CPUs, demanding the usage of coprocessors like GPUs to speed up the inference time. The large graph size, normally 300k nodes and 1M edges,...

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  30. 81. GWAK: Gravitational-Wave Anomalous Knowledge
    Eric Anton Moreno (Massachusetts Institute of Technology (US))
    10/07/2023, 19:00

    Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission.
    However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and other stochastic sources. Therefore the development...

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  31. 54. Hyperparameter Tuning for Semi-Supervised Graph Neural Network for Pileup Mitigation
    Jack Rodgers
    10/07/2023, 19:00

    In the Large Hadron Collider (LHC) at CERN, protons collide more than a million times per second. Pileup, which are interactions in the same or nearby proton bunch crossings in the accelerator, can be thought of as noise which affects many reconstructed physics variables such as the Jet Mass, Jet PT, and missing transverse momentum. This noise also results in worse resolution and as a...

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  32. 67. Interaction Networks for Anomaly Detection at the CMS Level-1 Trigger
    Andrew Skivington (University of California-San Diego, Duarte Lab)
    10/07/2023, 19:00

    At the LHC proton bunches are collided at a rate of 40MHz. The Compact Muon Superconducting Solenoid (CMS) detector’s Level-1 (L1) trigger system is responsible for reducing this data rate to about 100kHz so that approximately 1% of these events can be saved for offline physics analyses. The task is to develop algorithms to determine what data to keep and what to discard. Traditionally,...

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  33. 69. Jet Tagging Algorithm for Long-Lived Particles at the CMS Level-1 Trigger
    Anthony Vizcaino Aportela (Univ. of California San Diego (US))
    10/07/2023, 19:00

    This poster presents an exploration into the realm of Beyond the Standard Model (BSM) Long-Lived Particles (LLPs) with a focus on the integration and development of a jet-tagging algorithm for the Compact Muon Solenoid (CMS) experiment's Level 1 (L1) Trigger system, in the context of the forthcoming upgrade to the High Luminosity Large Hadron Collider (HL-LHC). In spite of the challenges posed...

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  34. 12. Lyapunov-Guided Embedding for Hyperparameter Selection in Recurrent Neural Networks
    YANG ZHENG
    10/07/2023, 19:00

    Recurrent Neural Networks (RNN) are ubiquitous computing systems for sequences and multivariate time series data. While several robust architectures of RNN are known, it is unclear how to relate RNN initialization, architecture, and other hyperparameters with accuracy for a given task. In this work, we propose to treat RNN as dynamical systems and to correlate hyperparameters with accuracy...

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  35. 65. Machine learning evaluation in the Global Event Processor FPGA for the ATLAS Phase 2 Level 0 trigger upgrade
    Elham E Khoda (University of Washington (US)), Zhixing "Ethan" Jiang (University of Washington (US))
    10/07/2023, 19:00

    During the next update of the High-Luminosity Large Hadron Collider (HL-LHC) of ATLAS, a new global trigger subsystem will be installed into the L0 Trigger. New and improved hardware and algorithms will be deployed during the upgrade to increase the performance of the trigger system. The global trigger subsystem consists of various components, including the FPGA-based Global Event Processor...

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  36. 10. Machine learning for HEP simulations
    Raghav Kansal (Univ. of California San Diego (US))
    10/07/2023, 19:00

    Fast, accurate detector simulations are necessary to keep up with the data collected in the coming years in HEP. Due to their stochastic nature, ML-based generative models are natural opportunities for fast, differentiable simulations. We present two such graph- and attention-based models for generating LHC-like data using sparse and efficient point cloud representations, with state-of-the-art...

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  37. 33. MCUNetV1 & V2: On-Device Inference of Tiny Deep Learning on IoT Devices
    Dr Wei-Chen Wang (MIT)
    10/07/2023, 19:00

    Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers....

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  38. 34. MCUNetV3: On-Device Training Under 256KB Memory
    Dr Wei-Chen Wang (MIT)
    10/07/2023, 19:00

    On-device training enables the model to adapt to new data collected from the sensors by fine-tuning a pre-trained model. However, the training memory consumption is prohibitive for IoT devices that have tiny memory resources. We propose an algorithm-system co-design framework to make on-device training possible with only 256KB of memory. On-device training faces two unique challenges: (1) the...

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  39. 83. Multi-block RNN Autoencoders Enable Broadband ECoG Signal Reconstruction
    JINGYUAN LI
    10/07/2023, 19:00

    Neural dynamical models reconstruct neural data using
    dynamical systems. These models enable direct reconstruction and estimation of neural time-series data as well as estimation of neural latent states. Nonlinear neural dynamical models using recurrent neural networks in an encoder-decoder architecture have recently enabled accurate single-trial reconstructions of neural activity for...

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  40. 63. Multi-objective Bayesian Optimization for High-resolution Electron Ptychography
    Desheng Ma
    10/07/2023, 19:00

    Electron ptychography enables deep sub-angstrom spatial resolution of atomic structures by solving the inverse problem of electron scattering through the sample, provided the complete distribution of transmitted electrons enabled by a new generation of detectors for scanning transmission electron microscopy (STEM). However, in practice, ptychographic reconstructions are computationally...

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  41. 47. Parameter Estimation of Unmodeled Burst Gravitational Waves Using Likelihood-free Inference
    Deep Chatterjee
    10/07/2023, 19:00

    The observed events from the LIGO-Virgo-Kagra collaboration (LVK) have been modeled sources called compact binary coalescences (CBCs). Un-modeled transients, for example, from core-collapse supernovae or pulsar glitches remain undiscovered. In this work, we demonstrate the use of likelihood-free inference using normalizing flows for parameter estimation of un-modeled burst-type...

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  42. 52. Pointing to a supernova with the DUNE experiment
    Janina Hakenmüller (Duke University)
    10/07/2023, 19:00

    The detection of a supernova burst is a unique opportunity to derive insights on astro and particle physics especially neutrinos. Neutrinos are the first hint of a supernova occuring to arrive on Earth due to their very low interaction cross section. They can provide extremely valuable information on the direction of burst enabling to point optical detection systems there in a multi messenger...

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  43. 29. Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction
    Yichen Guo
    10/07/2023, 19:00

    Pulsed-laser deposition (PLD) is a powerful technique to grow complex oxides with controlled stoichiometry. To understand growth dynamics, it is common to leverage in situ spectroscopies such as reflection high energy electron diffraction (RHEED) to monitor surface crystallinity. Most commercial systems rely on video-rate cameras operating at 60-120 Hz that lack sufficient temporal resolution...

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  44. 43. Progress towards an improved particle-flow algorithm at CMS with machine learning
    Javier Mauricio Duarte (Univ. of California San Diego (US)), Michael Zhang
    10/07/2023, 19:00

    The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity. In recent years, the machine learned particle-flow (MLPF) algorithm, a...

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  45. 55. PyLog-HLS4ML Integration: Introducing higher level of automatic design in HLS4ML
    Jialiang Zhang
    10/07/2023, 19:00

    HLS4ML is an influential Python package that creates firmware implementations of machine learning algorithms uses high-level synthesis (HLS) technique. While most of the templates are hand-written in HLS4ML, we want to further automate this manual design process by introducing PyLog, an algorithm-centric Python-based FPGA programming and synthesis flow, into the current HLS4ML flow and...

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  46. 82. Real-Time AI for the Particle Flow and PUPPI Algorithm in the CMS Level-1 Trigger Upgrade
    Noah Paladino (Massachusetts Inst. of Technology (US))
    10/07/2023, 19:00

    The Particle Flow algorithm has proven highly effective in the offline reconstruction of events in the CMS detector. Combined with Pile-Up Per Particle Identification (PUPPI), the two algorithms provide the necessary basis for the construction of higher-level physics options, such as jets and taus. With the upcoming High Luminosity upgrade of the Large Hadron Collider (HL-LHC), implementing...

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  47. 64. Real-time Fitting and Materials Characterization in Band-Excitation Piezoresponse Force Microscopy
    Veronica Obute (Drexel University), Yael Passy
    10/07/2023, 19:00

    Increased development and utilization of multimodal scanning probe microscopy (SPM) and spectroscopy techniques have led to an orders-of-magnitude increase in the volume, velocity, and variety of collected data. While larger datasets have certain advantages, practical challenges arise from their increased complexity including the extraction and analysis of actionable scientific information. In...

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  48. 46. Searching Better, Faster: Detecting Binary Black Hole Mergers with Deep Learning Networks
    Ethan Marx (MIT)
    10/07/2023, 19:00

    As the global network of gravitational wave detectors grows in both size and sensitivity, the traditional matched filtering method for detecting signals from compact object mergers becomes computationally prohibitive. Machine learning algorithms are a compelling alternative approach to this problem due to their ability to shift the computational cost to the model training process, enabling...

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  49. 44. Self-Supervised Learning for Jet Tagging
    Zihan Zhao (Univ. of California San Diego (US))
    10/07/2023, 19:00

    Limited by the lack of truth labels on real data, fully supervised ML algorithms are constrained to training only with simulated samples. With self-supervised learning, we can leverage vast amounts of unlabeled real data to facilitate training. We investigate the application of [VICReg][1], a contrastive learning model, on a classification task: discriminating signal jets (e.g. $H \rightarrow...

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  50. 41. Sleep Spindle (LFADs) Project Abstract
    Xiaohan Liu
    10/07/2023, 19:00

    A specific type of Electroencephalography (EEG) signals, sleep spindle, is believed to contribute to neuronal plasticity and memory consolidation. In this project, we proposed a system that is based on ultra-low latency and power FPGA to detect and interact with the sleep spindles to further understand the mechanism behind the theory. The proposed system will have a programmed FPGA that...

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  51. 50. SpectroGW: A computer vision model for Binary Black Hole merger classification
    Arif Chu (University of Washington Seattle)
    10/07/2023, 19:00

    Spectrograms are frequently used to provide qualitative insights into the types of noise and signals present in audio data. Similarly, we can use them to gain insights from data such as real gravitational wave from gravitational wave detectors. Simply by eye, we can see characteristic chirp signals from gravitational waves due to the physics of the black holes' inspiral. Designing a novel...

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  52. 9. STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers
    Trung Le
    10/07/2023, 19:00

    Modeling neural population dynamics underlying noisy single-trial spiking activities is essential for relating neural observation and behavior. A recent non-recurrent method - Neural Data Transformers (NDT) - has shown great success in capturing neural dynamics with low inference latency without an explicit dynamical model. However, NDT focuses on modeling the temporal evolution of the...

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  53. 11. Structural Re-weighting Improves Graph Domain Adaptation
    Shikun Liu
    10/07/2023, 19:00

    In many real-world applications, graph-structured data used for training and testing have differences in distribution, such as in high energy physics (HEP) where simulation data used for training may not match real experiments. Graph domain adaptation (GDA) is a method used to address these differences. However, current GDA primarily works by aligning the distributions of node representations...

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  54. 42. Symmetry Informed Autoencoder for Domain Classification of BaTiO3 Brightfield Images
    Xinqiao Zhang
    10/07/2023, 19:00

    Ferroelectrics, characterized by spontaneous polarization and reversible switching, play a crucial role in various applications such as non-volatile FeRAM, ferro-TFET, and catalysis. However, the influence of environmental factors on ferroelectric domain dynamics remains poorly characterized. This work aims to investigate the impact of temperature and background gas on the domain mapping of...

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  55. 49. Testing the Supernova Pointing Resolution of DUNE with ICEBERG
    Joshua Queen
    10/07/2023, 19:00

    The Deep Underground Neutrino Experiment (DUNE), a 40 kt fiducial mass liquid argon time projection chamber (LArTPC), will be unique among supernova (SN) neutrino detectors due to its ability to measure the electron neutrino flavor component of a SN burst. Crucial to achieving a good pointing resolution is the ability to discriminate the directionality of primary electron tracks via a process...

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  56. 48. UNRAVELING GRAVITATIONAL RIPPLES: NEURAL NETWORK CLASSIFICATION
    Mr Daniel G Fredin (University of Washington), Mr Cole Welch (University of Washington)
    10/07/2023, 19:00

    The Laser Interferometer Gravitational Wave-Observatory (LIGO) has accumulated more than 4.5 petabytes (Pb) of data in its quest to detect gravitational waves. Furthermore, it is anticipated that the total data accrued will increase by approximately 0.8 petabytes per year. The processing and analysis of the extensive volume of data from LIGO necessitates a tremendous amount of computational...

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  57. 35. Using convex feature selection to improve offline feature decoding
    Lauren Peterson (University of Washington)
    10/07/2023, 19:00

    Brain-computer interfaces use the electrical activity of the brain to control an external device, but decoding complex neural signals requires large amounts of computational power and time. We use a novel convex optimization algorithm to do real-time feature selection based on relevance, sparsity, and smoothness. We demonstrate that the algorithm can reduce the feature set while maintaining...

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  58. 4. Internal A3D3 NSF Visit
    11/07/2023, 08:30

    please see https://indico.cern.ch/event/1263918/

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  59. 5. Internal A3D3 NSF site visit
    12/07/2023, 08:00

    please see https://indico.cern.ch/event/1263918/

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  60. 57. Affiliate Flash Talks: Xiangyang Ju
    Xiangyang Ju (Lawrence Berkeley National Lab. (US))
    12/07/2023, 14:00
  61. 60. Affiliate Flash Talks: Dylan Rankin
    Dylan Sheldon Rankin (University of Pennsylvania (US))
    12/07/2023, 14:05
  62. 58. Affiliate Flash Talks: Ben Carlson
    Ben Carlson (Westmont College)
    12/07/2023, 14:10
  63. 61. Affiliate Flash Talks: Bo-Cheng Lai
    Bo-Cheng Lai
    12/07/2023, 14:15
  64. 59. Affiliate Flash Talks: Ari Sravan
    Niharika Sravan
    12/07/2023, 14:20
  65. 72. Introduction & Overview
    Melissa Quinnan (Univ. of California San Diego (US))
    12/07/2023, 14:25

    hackathon is introduced and helpers are introduced

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  66. 75. Topic 1: Neuroscience mouse touch stimulus brain signals
    Megan Hope Lipton, Seungbin Park
    12/07/2023, 14:40
  67. 74. Topic 2: MMA telescope (ZTF) source classification
    Brian Healy
    12/07/2023, 15:00
  68. 73. Topic 3: HEP Trigger Anomaly Detection
    Elham E Khoda (University of Washington (US)), Melissa Quinnan (Univ. of California San Diego (US))
    12/07/2023, 15:20
  69. 76. Group Formation/Go to breakout rooms
    12/07/2023, 15:40

    Based on participant desires and group needs people are split into 3 projects

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  70. 85. Meet outside physics building to walk over to CENPA
    12/07/2023, 16:40
  71. 62. CENPA nuclear physics lab tour (Prof. Alejandro Garcia)
    Alejandro Garcia (University of Washington)
    12/07/2023, 17:00

    Lab tour - CENPA nuclear physics lab tour (Prof. Alejandro Garcia)

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  72. 78. HEP breakout session
    12/07/2023, 17:30
  73. 79. MMA breakout session
    12/07/2023, 17:30
  74. 80. Neuro breakout session
    12/07/2023, 17:30
  75. 86. Neuro Hackathon Presentation
    14/07/2023, 09:00
  76. 87. MMA Hackathon Presentation
    Brian Healy
    14/07/2023, 09:30
  77. 88. HEP Hackathon Presentation
    14/07/2023, 10:00
  78. 39. Poster Award Presentation
    Daniel Diaz (Univ. of California San Diego (US)), Melissa Quinnan (Univ. of California San Diego (US))
    14/07/2023, 11:00
  79. 6. Workshop Conclusion
    Melissa Quinnan (Univ. of California San Diego (US))
    14/07/2023, 11:30
  80. 84. Graph Neural Networks for Electron and Photon Reconstruction at CMS
    Simon Rothman (Massachusetts Inst. of Technology (US))

    The Compact Muon Solenoid (CMS) detector is one of two general-purpose detectors at
    the CERN LHC. Products of proton-proton collisions at a center of mass energy of 13 TeV are reconstructed in the CMS detector to probe the standard model of particle physics and to search for processes beyond the standard model. The development
    of precision algorithms for this reconstruction is therefore a...

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  81. 77. Helper Introductions

    people who have volunteered to help out with all hackathon groups introduce themselves and their areas of expertise

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  82. 38. Internal A3D3 discussion

    please see https://indico.cern.ch/event/1263918/

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  83. 19. Kate Scholberg Group (Duke)
    Janina Hakenmüller (Duke University)
  84. 3. Public Lecture
  85. 7. Public Lecture
  86. 2. Tips for an Effective Research Poster Presentation