NIMH VIRTUAL WORKSHOP:
SOLVING COMPUTATIONAL CHALLENGES IN GENOMICS AND NEUROSCIENCE VIA PARALLEL & QUANTUM COMPUTING
March 28, 2018
9:00 am – 1:00 pm EST
Goal of the workshop
This virtual workshop aims to highlight core computational problems faced by genetics and the subdomains of neuroscience that parallel or quantum computing can address. By bringing together experts in quantum and parallel computing with experts in genetics and neuroscience, we hope to start a dialogue between academic and industry partners working in this area with the focus on algorithm optimization and development. This virtual workshop will be the forum and the nexus to find convergence between cross-disciplinary fields that are operating mostly independently – 1) genomics and neuroscience, and 2) AI/machine learning and 3) quantum computing. The goal is to identify key avenues for computation optimization via parallel and quantum algorithms. This workshop will facilitate the use of state-of-art computational technologies for addressing core bottlenecks in genomics and neuroscience.
This workshop will cover the following topics with 5 minutes break following each topic discussion:
- Opening Remarks (10 min)
- Topic 1: Computational Challenges in Genetics and Neuroscience (1.5 hour)
- Topic 2: AI, machine learning and parallel computing (45 min)
- Topic 3: Quantum Algorithms for Accelerated Computation: Opportunities and Challenges (1 hour)
- Roundtable Discussion & Summary (30 mins)
*NOTE: Some speakers are yet to be confirmed and/or subject to change.
9:00 – 9:10 am:Opening Remarks – Thomas Lehner, Geetha Senthil, Susan
Wright, National Institute of Mental Health, Office of Genomics Research Coordination
Chairs: Alan Anticevic, Ph.D., Yale University and Alan Aspuru-Guzik, Ph.D., Harvard University
Topic 1: Computational Challenges in Genetics and Neuroscience
This session is to highlight where computational challenges/bottlenecks exist at the level of scaling (data and computational features) and computational speedup.
9:10 – 9:25 am: Presentation 1: Genetics and functional genomics
Michael McConnell, Ph.D., University of Virginia, Michael Gandal, M.D., Ph.D., University of California, Los Angeles
9:25 – 9:40 am: Presentation 2: Neurophysiology (processing data, extracting, analysis)
Potential speakers: Mike Halassa, M.D., Ph.D., Massachusetts Institute of Technology
9:40 – 9:55 am: Presentation 3: Neuroimaging
Potential speakers: Alan Anticevic, Ph.D., Yale University, Stephen Smith, Oxford
9:55 – 10:10 am: Presentation 4: Quantitative deep phenotypic analysis
Potential speakers: Andrey Rzhetsky, Ph.D., University of Chicago, Justin Baker, M.D., Ph.D., Massachusetts General Hospital, Jukka-Pekka Onnela, M.Sc., Ph.D., Harvard University
10:10 – 10:25 am: Presentation 5: Computational modeling
Suggested topic: Spiking and neural models and ion channel modelling – spiking network simulation
Speakers: John Murray, Ph.D., Yale University, Michael Hines, Ph.D., Yale University
10:25 – 10:30 am: Break
Topic 2: AI, machine deep learning and parallel computing
This session is to discuss application of state-of-the-art classical parallel computing algorithm applications for machine learning, simulation, & optimization of analysis with ‘big’ data.
10:30 – 10:45 am: Presentation 1: Overview of machine learning via classical and parallel computing technologies
Potential speakers: Guillermo Sapiro, M.Sc., Ph.D., Duke University
10:45 – 11:00 am: Presentation 2: Deep Learning for AI applications – e.g. DeepMind
Potential speakers: Tim Lillicrap, Ph.D., DeepMind
11:00 – 11:15 am: Presentation 3: Parallel processing & GPUs
Suggested topic: Nvidia parallel processing & GPU capabilities for efficient high-performance applications
Potential speakers: Alan will reach out to his contact at Nvidia
11:15 – 11:20 am: Break
Chairs: Aram Harrow, Ph.D., Massachusetts Institute of Technology, and John Murray, Ph.D.,
Topic 3: Quantum Algorithms for Accelerated Computation: Opportunities and Challenges
This session will discuss the current state of quantum hardware and algorithms. What kind of advantages (either in terms of speed or solution quality) can be obtained by using quantum machine learning? How close are existing or proposed near-term hardware platforms to being able to implement these algorithms?
11:20 – 11:35 am: Presentation 1: Overview and primer: what is quantum computing good for?
Potential speakers: Alán Aspuru-Guzik, Ph.D., Harvard University
11:35 – 11:50 am: Presentation 2: Status and Prospects for Quantum Hardware
Potential speaker: Nicole Barberis, IBM
11:50 am – 12:05 pm: Presentation 3: Promising Quantum Computing Algorithms on the
Potential speakers: Ashley Montanaro, Ph.D., University of Bristol
12:05 – 12:20 pm: Presentation 4: Quantum Machine Learning and Optimization
Seth Lloyd, Massachusetts Institute of Technology
12:20 – 12:30 pm: Break
12:30 – 12:50 pm: Roundtable Discussion & Summary
Moderators: Stefan Bekiranov, University of Virginia & John Murray, Yale University
- What are the immediate avenues for computation optimization via parallel computing?
- Which problems are suitable for parallel vs. quantum computing?
- What are the distinct challenges facing parallel vs quantum computing platforms?
- Which are the most impactful avenues for quantum algorithm development from the standpoint of neuroscience and genomics?
- Opportunities for public private partnership?
12:50 – 1:00 pm: Summary/Closing Remarks
Potential speakers: Alán Aspuru-Guzik, Harvard University, Alan Anticevic, Yale University
1:00 pm: Adjourn