By: Will Benoit, Cristina Andrade, Tyler Barna, Michael Davis, and Andrew Toivonen
April 24, 2025

The 2025 Transients from Space conference featured the novel efforts of A3D3 members working in the domain of multi-messenger astronomy (MMA). Below, members of Dr. Coughlin’s group at the University of Minnesota share their experiences attending this conference.

Pictured above are the attendees of the Transients from Space conference from the University of Minnesota. From left to right: Cristina Andrade, Michael Davis, Dr. Michael Coughlin, Will Benoit, Tyler Barna, and Andrew Toivonen

Will Benoit: The Transients from Space (TFS) workshop, which centered around time-domain astronomy and multi-messenger astrophysics, was well-organized and well-attended. Over the course of the three-day meeting, there were presentations from all corners of the astronomical transients community, as well as ample time for smaller group and individual discussions. Thanks to the flexibility of the workshop organizers, I had the opportunity to present the ml4gw library that our group has developed under the auspices of the MMA pillar of A3D3. This library contains the infrastructure we’ve developed to support our machine learning algorithms in gravitational-wave physics, and the hope was to communicate some of the lessons we’ve learned over the course of development. In a similar vein, I also presented on the broader technical and sociological hurdles we’ve faced in getting our algorithms to the stage of production deployment. It was informative to hear the thoughts and ideas of scientists from a domain with different challenges and requirements than my own, and I hope that the discussions we had will spur focused development on real-time machine learning tools for this area.

Will Benoit presents on “Deploying Machine Learning in Time-Domain Astronomy”

Cristina Andrade: Attending the Transients from Space conference at STScI (Space Telescope Science Institute) was a productive and energizing experience. My work spans gravitational waves, compact mergers and the development of transient detection metrics for the Vera C. Rubin Observatory. So, having a diverse array of transient scientists in one place made for incredibly productive conversations. The “Target of Opportunity” breakout session was a highlight, offering a rare chance to move the needle on how we coordinate large-scale, rapid-response observations across facilities in anticipation of upcoming missions. I also appreciated the thoughtful discussion in the machine learning session, especially around standardizing tools to streamline detection and around resource prioritization across missions. I had the chance to share updates and get targeted feedback from Rubin leadership on the LSST transient metrics I’m developing. I connected with teams from several international observatories affiliated with the GRANDMA collaboration (a telescope network), which I help to lead operational efforts for. The conference not only deepened existing relationships between my group and the wider transient field but also clarified technical and logistical challenges that weare actively working to address.

Tyler Barna: TFS was a very productive workshop; in just three days, it provided an overview of the current state of space-based observation and detailed the community’s plans for missions launching in the coming decade. Transients represent a unique challenge in astronomy – they generally occur without warning and fade from view on varying timescales. There is still much science to be done with early-time observations of essentially all classes of transients. Kilonovae, my field of study, occur at time scales even shorter than many other transients, on the order of days. As our estimates for their occurrence rates have developed, we have come to understand them to be relatively rare events, so any opportunity to observe them must be seized upon. Many of the missions discussed at TFS, such as the Nancy Grace Roman Space Telescope, have specifications that will be incredibly valuable for observing kilonovae. I hope the discussions at TFS surrounding collaboration in managing ToO (Target of Opportunity) and DDT (Director’s Discretionary Time) resources bear fruit so the community can maximize observation of rare events like kilonovae.

Michael Davis: The Transients from Space workshop at the Space Telescope Science Institute was a great opportunity to learn about the latest developments in time-domain and multi-messenger astronomy. Over three days, I was introduced to current and future space-based transient surveys, the challenges of rapid follow-up observations, and the role of upcoming missions like the Nancy Grace Roman Space Telescope. It was especially valuable to hear discussions on how to optimize Target of Opportunity strategies for rare and short-lived events. Beyond the talks, I enjoyed meeting others in the transient astronomy community and discussing shared challenges in data analysis and follow-up coordination.

Andrew Toivonen: As someone who works at the intersection of gravitational-wave searches and multi-messenger follow-up efforts, transient detection is a key aspect of my research. It was great to discuss with astronomers what is needed to make efficient and effective follow-up decisions in a rapidly evolving field. For example, while presenting my poster, I had an astronomer interested in GW (gravitational wave) follow-up approach me to comment on how such multi-messenger data products would be useful to their field. I also thought the machine learning discussions were positive. Driven by Will’s talk about ML4GW and a shared ML+GW library, there was discussion on whether a similar thing could be done for transient science. The main concern was the difficulty that many ML algorithms have with light curves, and the abundance and variety of training data that would be needed. This workshop has built momentum towards collaboration between different groups with common goals. 

By: Mark Neubauer
November 27, 2024

The 3rd annual conference of the NSF HDR Ecosystem was held on the campus of the University of Illinois Urbana-Champaign from the 9th to the 12th of September 2024

Attendees of the 2024 HDR Ecosystem Conference at the University of Illinois Urbana-Champaign

Venue

Conference Welcome and Overview

Dean Rashid Bashir of the Illinois Grainger College of Engineering kickoffs of conference with a welcome and vision
A3D3 Institute Director Shih-Chieh Hsu (U. Washington) describes the Institute’s Accomplishments, Activities, Plans

Keynote

Vipin Kumar (Minnesota) delivers the Keynote talk on Knowledge-Guided Machine Learning: A New Framework for Accelerating Scientific Discovery and Addressing Global Environmental Challenges

Evening Public Lecture

Suresh Venkatasubramanian (Brown University) delivers a Free and Open Public Lecture on AI Policy toward Making AI Safe, Effective and Trustworthy

Transdisciplinary and Cross-Cutting Research Breakouts

Participants joined discussion breakout sessions to discuss topics including LLMs / Foundation Models for Research, Responsible AI / Ethical AI, Knowledge-Guided ML / Physics-Informed Neural Networks, Continuous ML and Human-in-the-Loop Decision Making, Future ML challenges, Challenges and Opportunities for HDR Institutes and NAIRR Integration, and Interdisciplinary Careers

Lightning Talks and Poster Session

Participants give a 1-minute poster pitch talk on their topic before moving to the poster room to chat with others about their posters

Official Launch of the HDR Machine Learning Challenge!

A3D3 Deputy Director Phil Harris talks to the audience about the inaugural NSF HDR ML Challenge

Tour of the National Petascale Computing Facility

Brett Bode (National Center for Supercomputing Applications) gives conference attendees a tour of the computing facilities at Illinois. The DeltaAI supercomputer (center) was launched a month after the tour.

By: Kira Nolan
March 4, 2025

A new record for the largest gathering of astronomers was set this January, as around 3,700 people traveled to the 245th American Astronomical Society (AAS) meeting in National Harbor, Maryland. These biannual AAS meetings bring together scientists, engineers, educators, students and advocates from every corner of astronomy. I was able to attend this expansive conference for the first time, and will share my experience in this blog post.

1 – Everything, everywhere, all at once

Plenary talks ranged from how to crash a rocket into an asteroid (DART mission), to radio astronomy at the South Pole (South Pole Telescope), to planet accretion and best practices in research mentorship. At any given time, simultaneous sessions covered topics ranging from planetary science to cosmology. Even as a postbac, I tend to focus my attention on papers and work that I think will be directly helpful for my own projects. I intentionally chose to attend a mix of sessions that were either very relevant or completely disconnected from my work. Just like how A3D3 allows scientists to look outside of their domains, AAS is a great opportunity for young astronomers like myself to get a crash course on the bigger picture of work happening across the field. 

2 – Astronomy is big and small

Paradoxically for such a large conference, AAS makes the astronomy world feel small. My experience highlighted just how many connections the postbac has allowed me to make in the field, over a year of virtual collaboration with different groups and travel to conferences. I connected with people ranging from a graduate student I met the first week of my postbac to a professor I exchanged emails with regarding a research question. Browsing the conference exposition hall, I got to talk to the developer of software for the Fermi Gamma-Ray Space Telescope that I worked with. This exposure to the field is an invaluable part of the postbac experience.

3 – Presenting a poster

I presented a poster on my work automating the multi-messenger follow-up for binary black hole mergers. This was my first time presenting a poster outside of the A3D3 community, and the experience taught me lessons that I will be able to use for future poster sessions. As I talked with people ranging from undergraduate students to senior professors, from all different fields within astronomy, I got practice explaining my work.

4 – What about machine learning (ML)?

The AAS has recently established a task force focused on the rise of artificial intelligence (AI) in the field, and at this meeting, there were around ten different sessions explicitly dedicated to topics around ML in astronomy. These included discussions around developing astronomy datasets for machine learning challenges and using AI for advanced statistical inference. Some examples of work include efforts towards physics-informed AI for astronomy and high-dimensional inference for astronomical image reconstruction. While ML has long been applied to astronomy datasets, astronomers are faced with growing data streams and are interested in accessing the cutting edge of machine learning to most efficiently utilize those data for exciting discoveries.

By: Katrine Kompanets
March 4, 2025

The Conference for Undergraduate Women and Gender Minorities in Physics (CU*iP) is a unique annual event that takes place at multiple locations across the country simultaneously. This January, A3D3 was represented by members attending multiple locations of the conference.

Pictured from left to right: Megan Averill, Katrine Kompanets, Emma de Bruin, and Yiwen Chen attend CU*iP at Michigan Tech.

Students from the University of Minnesota attended CU*iP at Michigan Tech, where they got to explore the high-end laboratories there and network with fellow undergraduates. Yiwen Chen and Megan Averill presented “Distinguishing Astrophysical Signals from Noise: Machine Learning for Gravitational Waves Detection,” Emma DeBruin presented “Improving Sensitivity to Neutron Star Gravitational Wave Events using the Qp Transform,” and Katrine Kompanets shared her research on “Improving Sensitivity of Gravitational Wave Event Detections Using Machine Learning”. It was an amazing experience to meet students from all over the country and share experiences and research with each other!

At the University of California, Berkeley, students at the CU*iP career fair enjoyed learning about the A3D3 Postbaccalaureate Research Fellowship and the many different research areas and synergies under the A3D3 umbrella. Quite a few students expressed enthusiasm for applying to the postbac program, and a few even said it was already their top choice after graduation!

A3D3 members operate a booth at the UC Berkeley CU*iP career fair.

By: Cristina Andrade and Emma de Bruin
February 20, 2025

The Mid-American Regional Astrophysics Conference (MARAC) took place this past December at the University of Kansas (KU). Two students, who work on topics in multi-messenger astronomy in Dr. Michael Coughlin’s group at the University of Minnesota, share their experiences attending this conference.

Pictured above are Kat Kompanets, Cristina Andrade, and Emma de Bruin, at MARAC.

Cristina Andrade writes:

Attending the Mid-American Regional Astrophysics Conference (MARAC) was a rewarding experience that helped to develop both professional and personal skill sets. One of the highlights was collaborating with my research group. We had the opportunity to work together to practice elevator pitches for our research, review our posters, and prepare short presentations. This teamwork made it easier to identify areas for improvement and helped us better communicate our research.

This conference provided an occasion to present my poster on kilonova detectability with the Vera C. Rubin Observatory and gave me a chance to practice, reflect and improve my science communication skills with those outside of my research group. Feedback from peers and faculty was especially helpful in learning how to share my research effectively and to reduce my use of jargon.

I also had the chance to network with faculty from other universities, primarily the University of Kansas. These conversations provided insight into the variety of paths available in academia, along with valuable feedback on graduate applications and practical advice on networking strategies.

A key moment for me was hearing the keynote speaker, Stuartt Corder, explain how organizations like the NSF, NASA, and the DOD work both independently and together. Our conversations provided insights that gave me a better understanding of how to navigate the field and plan for my career based on my interests. It was also a wonderful opportunity to get feedback from someone who works in close proximity to the Rubin Observatory.

Throughout this event, we had the opportunity to travel independently, which has always been a positive experience for me. Travel like this builds my confidence and has helped prepare me to handle career experiences in a variety of environments.

Emma de Bruin adds: 

Attending MARAC was a great way to develop professional skills like networking and presenting research in a concise and engaging manner. Practicing my elevator pitch with faculty at KU was very helpful; I hadn’t realized how used to talking to gravitational wave (GW) people I am, and I was able to learn how to communicate my work with people outside of my discipline effectively. The advice given on aspects of applications like writing a personal statement and a CV was also useful as I am applying to graduate school. After the talk, I found myself modifying both documents to include the advice mentioned.

University of Minnesota hosts ZTF summer school

ZTF Summer School 2024

The Coughlin group at the University of Minnesota hosted the 2024 Edition of the ZTF Summer School, focusing on AI and Machine Learning. A3D3 members from the University of Minnesota, Caltech and MIT, along with external speakers from Space Telescope Science Institute and NASA Goddard, came to attend and give lectures on machine learning topics. There were 40 attendees in person, representing over 30 universities from 10 different countries, as well as more than 50 attendees online. Topics covered included supervised and unsupervised learning, simulation-based inference and anomaly detection, focused on multi-messenger astrophysics data sets.

By Angela Tran
May 2024

A3D3 recently engaged in one of the largest U.S. national physics conferences—the American Physical Society’s April meeting in Sacramento. A variety of A3D3 students presented their talks on institute-related activities.

A3D3 member Ethan Marx from The Massachusetts Institute of Technology brought his passion for data’s potential to bring new discoveries. His APS talk was about applying machine learning (ML) techniques to search for gravitational waves in LIGO-Virgo-Kagra (LVK) data. He says, “Specifically, we validated our algorithm on archival data from the third LVK observing run. The end goal of this work is to deploy this algorithm in real-time in order to issue alerts for electromagnetic astronomers to follow-up. This work is a direct implementation of the institute’s aim for real time processing of large datasets.” In his research, he enjoys the challenge of applying new cutting edge statistical/ML techniques. After being part of A3D3 for about three years, he says he likes that the Institute emphasizes the similarities between seemingly distinct fields.

A3D3 member Will Benoit from The University of Minnesota contributed his enjoyment of how his field probes the behavior of the most extreme universe matter. His APS talk was about demonstrating the production-readiness of a custom AI algorithm for the real-time detection of gravitational waves. He says, “Gravitational waves are one of the messengers in multi-messenger astronomy, and the ability to detect them more quickly will allow other instruments to follow-up more quickly.” In his two and a half years with A3D3, he has especially appreciated the interdisciplinary collaboration with fellow students working on similar technical problems, and seeing how results from different fields can apply to one another.A3D3 member Jared Burleson from The University of Illinois at Urbana-Champaign works in Experimental High Energy Particle Physics to answer the big questions about the fundamental laws of the universe. His APS talk was about the use of machine learning and artificial intelligence for track reconstruction for upgrades to the Large Hadron Collider, which he says “will see a drastic increase in the amount of real-time data gathered. My work aims to utilize AI for processing large data in real time with a focus on discovery in high energy physics.” After about a year with A3D3, he says, “I really enjoy being able to connect with other people in my field and outside my field who are interested in data-driven computing solutions to problems.”

Links to some of the talks presented at APS by A3D3 members are listed below.

  • D14.1 Jared Burleson, Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs with detector segmentation and regional processing.
  • G13.1 Will Benoit, A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
  • D03.2 Ethan Marx, A search for binary mergers in archival LIGO data using aframe, a machine learning detection pipeline
  • DD03 Yuan-Tang Chou, NSF HDR ML Anomaly Detection Challenge
  • DD03 Haoran Zhao, Graph Neural Network-based Track finding as a Service with ACTS

By: Yuan-Tang Chou

Grace Williams

April 2, 2024

The 22nd International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2024) is an exciting workshop held every 18 months. It brings together computational experts from around the world who work in diverse fields of physics, like particle and astrophysics, to discuss advanced technology with the potential to enhance research in physics.. 

This year’s theme is “Foundation Models for Physics—Nexus of Computation and Physics through Embracing the Era of Foundation Models.” Our researchers, Vladimir Loncar and Javier Duarte, chaired two of the exciting sessions. 

Below, we highlight some of the many of the exciting contributions from A3D3 members that were presented during the week-long workshop.

Monday

On Monday afternoon, Zihan Zhao (A3D3 Grad student) presented his work on “Self-supervised Learning for Jet Tagging,”  demonstrating the potential of self-supervised learning to utilize real, unlabeled data for more efficient and accurate jet classification. 

Zihan Zhao presenting at ACAT 2024

Tuesday

Researcher Javier Duarte and steering board member Nhan Tran (Fermilab Wilson Fellow) chaired Tracks Two: Data Analysis – Algorithms and Tools which discussed advanced ML algorithms for physics object reconstruction in the particle experiment. 

Nhan also gave a plenary on Tuesday about “AI and Microelectronics for Science.”

Wahid Bhimji, A3D3 steering board, presented “Fair Universe: HiggsML Uncertainty Challenge” which brings FAIRness to the ML community, and also convened the plenary on Thursday. 

Pic: Wahid Bhimji Convened plenary 

In the afternoon session at the “Towards the Construction of Foundational Models at the LHC” talk given by Phil Harris (A3D3 deputy director), he presented recent work that proposed novel Self-Supervised Learning Strategies. The method constructs a space that preserves discrimination power and reduces the impact of systematic uncertainties. 

Xiangyang Ju (LBNL computing system engineer, A3D3 affiliated member) talked about a novel idea to use a large language model to perform particle tracking in his “Leveraging Language Models for Particle Reconstruction.”   

Xiangyang Ju presents on the use of large language models in particle tracking

Thursday

On Thursday morning, Aobo Li (UCSD Professor) gave a plenary talk about “Detecting Rare Events Using Artificial Intelligence.” He proposed a unified multimodal foundation model for all rare event search experiments.

 “This could forge the experiments like a union,” Aobo said.

Aobo Li gives his presentation on “Detecting Rare Events Using Artificial Intelligence”

In the afternoon, Yuan-Tang Chou (A3D3 Postdoc) presented “ACTS as a Service” and demonstrated how to utilize heterogeneous computing resources better to accelerate track reconstruction using the Triton GPU Inferences server.

“We’re proposing this idea to better utilized heterogeneous computing systems to accelerate the track reconstructions in ACTS. I think people are quite interested in how this can actually apply in the current production framework.”

-Yuan-Tang Chou

By: Deep Chatterjee

December 27, 2023

New Orleans, LA – The 37th Conference on Neural Information Processing Systems was held in New Orleans between Dec 10 – 16, 2023. The Machine Learning and the Physical Sciences Workshop at NeurIPS brought together researchers pursuing applications of Machine Learning techniques in Physics, and developing new techniques based on physical concepts like conversation laws and symmetries. There was a total of 250 accepted papers for the workshop poster session – both in-person and remote. While most papers were related to Physics and Astronomy, there were several papers on applications in medical sciences, material science, and earth sciences. A3D3 had a prominent presence at the workshop, with a contributed talk, 4 papers from A3D3 members, and 2 papers from A3D3 Steering Board committee members’ team. 

Elham E Khoda from UW Seattle presented on the implementation of Transformers on FPGA using HLS4ML, for low-latency applications like L1 triggers at the LHC and ATLAS, and searches for anomalous signals in gravitational-wave data. 

Elham Khoda presents a contributed talk on the implementation of transformers in HLS4ML

Deep Chatterjee from MIT presented a poster on optimizing likelihood-free inference by marginalizing nuisance parameters using self-supervision. [paper link] 

Niharika Sravan from Drexel University presented Pythia – a reinforcement learning model that maximizes the search for kilonovae in the presence of several contaminant objects from the Zwicky Transient Facility. [paper link] 

Anni Li from UCSD gave a poster on Induced Generative Adversarial Particle Transformers on the use of induced particle-attention blocks to surpass existing particle simulation generative models. [paper link] 

Deep Chatterjee (left) and Alex Gagliano (right) present posters.

There were two posters presented from A3D3 steering board members. Ashley Villar (along with Alex Gagliano) gave a poster on convolutional variational autoencoder to estimate the redshift, stellar mass, and star-formation rates of galaxies from multi-band imaging data. [paper link] Nhan Tran (along with C. Xu) have a poster on a Proximal Policy Optimization (PPO) algorithm to uniform proton beam intensity for the Mu2e experiment at Fermilab. [paper link] 

By: Katya Govorkova and Yuan-Tang Chou
November 27, 2023

The “AI and the Uncertainty Challenge in Fundamental Physics” workshop was a dynamic experience filled with experts from different areas of applied AI in science. Experts from fundamental science, computer science, and statistics exchange ideas on how to incorporate uncertainty in AI. The workshop took place at the Sorbonne Center for Artificial Intelligence (SCAI) in Paris and at Institut Pascal Université Paris-Saclay in Orsay, France.

Researcher Mark Neubauer (Faculty at the University of Illinois at Urbana-Champaign, A3D3 co-PI) led the discussion in the Uncertainty Quantification session on Tuesday and emphasized the idea of explainable AI.

The session on Wednesday afternoon was specifically dedicated to the FAIR Universe ML challenge, which addresses uncertainties in physics through AI. A3D3, represented by Yuan-Tang Chou (A3D3 Postdoc at the University of Washington) and Katya Govorkova (A3D3 Postdoc at the Massachusetts Institute of Technology), shared valuable insights from organizing an anomaly detection data challenge, emphasizing the necessity of robust frameworks and collaboration. Lessons learned from Katya’s experiences underscored the crucial role challenges play in advancing the field. She also underlined what can be improved in future challenges. 

Katya Govorkova presenting Exploring Data Challenges and Leveraging Codabench: A Practical Journey with unsupervised New Physics detection at 40 MHz.

Yuan-Tang highlights another ML challenge example for NSF HDR institute beyond particle physics. The challenge utilized the Neuroscience dataset provided by Prof. Dadarlat’s lab ( Purdue University, A3D3), which tried to decode limb trajectories from neuro activity with ML.

Yuan-Tang presenting ML challenge using Neuroscience dataset

The Codabench platform, noted for its versatility, was highlighted as instrumental in organizing public challenges. The public showed a great interest in hosting and participating in challenges. Challenges were recognized as bridges connecting different disciplines, particularly physics and computer science. The event highlighted those challenges.

The participants provided many great ideas and suggestions during the one-week workshop. Elham E Khoda (Postdoc at the University of Washington, A3D3) gave an excellent summary on the last day to discuss the lesson learned during the week and the next step to improve the HiggsML Uncertainty Challenge. “We definitely want not only people outside the particle physics to join the ML Challenge,” Elham said. “We also encourage participation outside of the domain who can think differently and come up with innovative ideas.”

By: Rajeev Bhavin Botadra

November 22, 2023

Researchers Javier M. Duarte (Faculty at the University of California San Diego, A3D3), Luke Song (Graduate student at the Ohio State University, Imageonics), and Rajeev B. Botadra (Graduate student at University of Washington, A3D3) represented the National Science Foundation (NSF) Harnessing the Data Revolution (HDR) programs at the 2023 National Diversity in STEM (NDiSTEM) conference hosted in Portland, Oregon.

The SACNAS NDiSTEM conference is held annually to provide a platform for underrepresented groups in STEM to connect with peers and mentors and explore opportunities within academia and industry. By participating in this event the team aims to promote the programs under the HDR initiative while furthering the broader NSF effort of diversity in STEM.

The representatives presented opportunities across all five institutions under the HDR grant,  emphasizing the different scientific applications studied under each branch as they aligned with students’ interests. They also shared their experiences and career journeys, advising students unsure of the next step in their careers and making connections for future collaborations.

“The SACNAS NDiSTEM Conference is by far the largest gathering of its kind in the country,” Prof. Duarte said. “It’s a unique opportunity to reach potential trainees that we may not find at other conferences. Everyone is very open about sharing their cultures and identities because they recognize that it’s not separate from their science.”

The team set up a simple Pokémon classification demo at the booth using a webcam, a Pynq-Z2 FPGA board, and a monitor for display output. Using a simple quantized ResNet model fine-tuned on an open-source pokemon dataset, the Pynq-Z2 classified Pokémon in front of the camera and transmitted the labeled output to the external monitor. Rajeev commented, “the demo was very helpful in drawing people’s attention amongst dozens of other booths and breaking the ice towards a longer conversation about our research.”

“We got to interact with so many people and they were so excited to find out about the HDR  research opportunities,” Prof. Duarte said. “We hope to come back every year!”

By: Xiangyang Ju (Computing System Engineer, LBNL)

November 13, 2023

In the vibrant Mile High City, members and affiliates of the A3D3 institute converged with a gathering of over 100 scientists, engineers, and educators. Their mission: to fortify the Harnessing the Data Revolution (HDR) ecosystem. This annual HDR-wide conference, now in its second year, aims not only to strengthen the HDR ecosystem but also to extend its reach to other related NSF-supported initiatives, fostering collaboration in our collective endeavors.

The HDR initiative, funded by the National Science Foundation (NSF), is a nationwide effort that commenced in 2016. It seeks to enable new avenues of data-driven discovery, addressing fundamental questions at the forefront of science and engineering. Within the HDR ecosystem, the A3D3 institute spearheads a paradigm shift by deploying real-time artificial intelligence on a grand scale to advance scientific knowledge and expedite the process of discovery.

The conference had four primary objectives for its participants:

  1. Foster community-building, forging stronger ties among HDR entities and the broader data-intensive research communities.
  2. Facilitate cross-learning by building on successes, best practices, and innovative products.
  3. Provide a platform for reflection on the achievements and future goals of each HDR entity.
  4. Identify overarching challenges in data-intensive research, not only among HDR entities but also beyond. The conference aimed to foster new collaborations and explore future opportunities.

To meet these goals, the conference featured a diverse range of activities, including keynote presentations, discussion panels, and “unconference” sessions. Notable activities included a pitch session and subsequent asset mapping for the winning pitches, in which the A3D3 Institute made substantial contributions.

In summary, A3D3 played a pivotal role in the conference, leading the reflection session titled “Humans in the Equation: Stories of Collaboration.” The session featured a success story narrated by Javier Duarte from UC San Diego, highlighting the Postbaccalaureate program organized by the A3D institute. This program aims to enhance access to scientific careers for post-baccalaureates interested in gaining insights into scientific research and exploring various scientific domains. Professor Duarte’s presentation was followed by cross-community discussions on “Equitable, Diverse, and Inclusive Training, Education, and Outreach Opportunities in the HDR Ecosystem,” co-led by Professor Mark Neubauer.

During the same session, A3D3 members engaged in developmental reflections, examining the context of the ecocycle planning phases and potential obstacles. The primary purpose of this activity was to identify impediments and opportunities for progress.

The six posters given by A3D3 members covered a wide range of scientific frontiers. 

  • Brian Healy: the “Machine Learning Classification of Time-varying Astrophysical Sources” 
  • Seungbin Park: “Decoding multi-limb running trajectories from two-photon calcium imaging using deep learning.”
  • Ben Carlson: “Module for evaluation of machine learning algorithms in FPGA hardware for high energy physics.”
  • Mark Neubauer: “A3D3 community engagement, education and outreach”
  • Javier Duarte: “hls4ml: Open-source codesign of machine learning algorithms on FPGA for scientific discovery.”
  • Phil Harris: “Real-time Gravitational Wave Alerts using AI.”

Figures: left: Brian Healy. Middle: Seungbin Park, Right: Ben Carlson

A3D3 took the lead in the topical session addressing equity, diversity, and inclusion (EDI) within the realm of training, education, and outreach. The session served as a platform for discussing the promotion of EDI in workforce development within the HDR ecosystem. Key topics of discussion included ensuring fair trainee selection processes, establishing, maintaining, and enhancing specialized training programs, creating specific forums, and extending program impact through education and outreach. During the session, A3D3 members shared their best practices with other institutes within the HDR ecosystem, covering areas such as equitable trainee recruitment, outreach event organization, and the establishment of post-baccalaureate programs.

A separate session led by A3D3 delved into the realm of machine learning challenges. Professor Philip Harris, representing MIT, delivered a comprehensive presentation titled “Machine Learning Challenges, FAIR, and Reproducible Machine Learning Workflows.” In this talk, Philip introduced the latest technical developments in the Codabench platform, designed to host diverse challenges and facilitate automated metric calculations. Many other HDR institutes expressed keen interest in using this platform for publishing machine learning challenges. The discussions extended into an ideation exploration session, during which Philip Harris advocated for the inception of an Anomaly Detection challenge spanning all HDR institutes—a pan-HDR “Grand Challenge.”

As the annual HDR conference concluded, it left attendees inspired by new ideas and fresh possibilities. Looking ahead, A3D3 will take the helm next year, hosting the conference at UIUC in Urbana-Champaign.

By: Patrick McCormack (Postdoc MIT, A3D3)

October 30, 2023

At the workshop, seven A3D3 trainees gave presentations on their work.  Leading off, Farouk Mokhtar of UCSD and Santosh Parajuli of UIUC presented their work on implementing machine learning models for the LHC experiments CMS and ATLAS, respectively.  Though working on independent efforts, both use graph neural networks to efficiently and scalably reconstruct particles. Alongside the first smatterings of autumn leaves, an international assortment of more than 150 Physicists, Computer Scientists, Engineers (and more) descended upon Imperial College London (ICL) this past week.  There they enjoyed the crisp weather and the fourth iteration of the Fast Machine Learning for Science (FastML) Workshop, which ran from September 25-28.

The FastML workshop series was born in 2019 as a small and informal workshop focused on High Energy Physics (HEP), but it has since grown to include participants from diverse fields, such as medicine, astrophysics, and statistics.  Unsurprisingly, a workshop centered on this multidisciplinary approach to accelerated machine learning drew the participation of several members of A3D3.  And just as A3D3 revolves around mutual support and cross-disciplinary efforts, participants in the workshop were intrigued to see how the same techniques and algorithms were found in diverse applications across different disciplines.

“The workshop brings together researchers from very different specialties who do not typically have a chance to come together and exchange ideas,” said Fermilab’s Nhan Tran, one of the original FastML organizers.  “Despite this, it was so refreshing to see many amazing talks and enthusiastic discussion from all the workshop participants willing to get out of their comfort zones and expand their research.  I really appreciate that spirit and it makes the workshop series very unique and fun.”

Emphasizing the increased scope of the workshop series, Fermilab’s Kevin Pedro said that he attended the workshop “to learn about new cutting-edge computational techniques that are accelerating ML throughout many scientific fields.”

At the workshop, seven A3D3 trainees gave presentations on their work.  Leading off, Farouk Mokhtar of UCSD and Santosh Parajuli of UIUC presented their work on implementing machine learning models for the LHC experiments CMS and ATLAS, respectively.  Though working on independent efforts, both use graph neural networks to efficiently and scalably reconstruct particles.

Jeffrey Krupa presents his work on developing a Sparse Point Voxel CNNSantosh Parajuli presents his work on implementing graph neural networks for Event Filter Tracking in ATLAS.  In keeping with the “Fast” theme of the workshop, he is developing a VHDL implementation of his algorithm to run on FPGAs. for machine-learning-based clustering in hadronic calorimeters.

Next up, Patrick McCormack and Jeffrey Krupa, both of MIT, gave talks about two projects that they both work on.  One is a CMS effort to implement GPU acceleration for ML-methods via an Inference as a Service scheme, and the other is an implementation of a Sparse Point Voxel CNN (SPVCNN) for determining clusters of energy in hadron calorimeters for LHC experiments.  According to Krupa, “the SPVCNN algorithm is a first-time use of HCAL depth segmentation in clustering for CMS, and it removes the latency associated with HCAL clustering from reconstruction workflows.”

Jeffrey Krupa presents his work on developing a Sparse Point Voxel CNN for machine-learning-based clustering in hadronic calorimeters.

The last A3D3 talk from the workshop’s first day was given by Duc Hoang, also from MIT, who presented his work on algorithms for the CMS Layer-1 Trigger. These algorithms must be able to produce inferences at 40 MHz, such that one must balance algorithmic complexity with speed.  Thanks to the efforts of Duc and his collaborators, the algorithms that they have implemented on FPGAs for both bottom quark and tau lepton identification will increase the efficiency of the CMS trigger system for rare processes with these particles.

During the second day of the workshop, the focus shifted away from the LHC.  The gravitational waves side of A3D3 was represented by Katya Govorkova and Eric Moreno of MIT.  Katya presented her work on the development of Gravitational Wave Anomalous Knowledge (GWAK), a method for tagging gravitational waves from anomalous sources.  This algorithm is related to the Quasi-Anomalous Knowledge (QUAK) technique that was developed by A3D3 members for application in LHC contexts.  The GWAK algorithm has since evolved and can be used in real time to help distinguish between truly anomalous gravitational waves and meaningless detector glitches.

Katya Govorkova presents “Gravitation Wave Anomalous Knowledge”, or GWAK.  As she was giving the first talk about gravitational waves at the workshop, she covered the basic physics behind the LIGO experiment.

Eric’s talk focused on the Machine Learning for Gravitational Waves (ML4GW) package, which is a suite of tools enabling real-time machine learning for gravitational wave (GW) experiments, such as LIGO.  These tools have accelerated GW-detection, parameter estimation for events, noise regression, and anomaly detection via GWAK.

The workshop’s concluding remarks were given by A3D3’s deputy director and MIT professor Phil Harris.  He exhorted the audience with this playful paradox: “In order to go fast, we have to go slow.  By which I mean that designing an algorithm or workflow for the fastest possible performance takes time and careful consideration.”  In his 20 minute talk, he pointed out many of the similarities and common tools being used across disciplines, such as sparsification and quantization of neural networks, hardware-based acceleration using GPUs and FPGAs, and deep learning architectures such as transformers and graph neural networks.  A complete summary of the topics covered in the workshop would be far too long for this article, but the workshop’s timetable, along with links to most presentations can be found here.

“I really enjoyed that the workshop has a very diverse group of participants and talks that I found very inspiring,” said Professor Mia Liu from Purdue reflecting on the workshop.  “I am learning and thinking of new ways of accelerating science by developing appropriate algorithms and learning methods, in addition to my current research in real-time ML in low latency and high throughput systems.  For example, robust learning methods for embedding of scientific data, that can account for the variance due to the nature of the physical object and the measurement methods etc, is challenging but crucial for broader and long lasting impact of ML on scientific discoveries.”

The workshop also included tutorials on state-of-the-art deployment techniques, such as the hls4ml package for creating firmware implementations of machine learning algorithms, Intel AI Suite for deploying algorithms on Intel FPGAs, and the use of Intelligence Processing Units (IPUs) from Graphcore.  There were also informal tours of several of the labs at ICL.

On a lighter note, some workshop participants found time to explore the sights and sounds of London.  A3D3 members Eric and Duc also put together a public lecture from rapper Lupe Fiasco (Wasalu Jaco), who is currently a visiting professor at MIT.  He discussed some of his work with Google on creating TextFX, which is a large language model for exploring relationships between words and generating phonetically, syntactically, or semantically linked phrases.  They also managed to bring in Irving Finkel of the British Museum, who discussed the history of the game of Ur, which is a shared love with Lupe, Eric, and Duc.

Duc, Eric, Irving Finkel, and Lupe Fiasco discussed the game of Ur in a panel after Lupe’s public lecture on the relationship between rap and large language models.

The workshop proved to be a valuable experience for A3D3 attendees, and I suspect that future iterations will be well attended by our members.  “Attending the workshop was an incredible experience for me,” said Santosh Parajuli.  “The FastML workshop provided a unique platform to learn from experts, exchange ideas, and explore the latest advancements in different fields.  Additionally, I had a chance to share our exciting work on using advanced technology and machine learning to improve how we track particles in high-energy physics, which could help us make big discoveries in the future!”