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https://www.nist.gov/itl/math/acmd-seminar-explainable-models-data-driven-optimization

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# ACMD Seminar: Explainable Models via Data-Driven Optimization

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Howard Heaton, Ph.D.*Senior Optimization Engineer, Edge & Node / Creator of Typal Research & Typal Academy*

Tuesday, March 7, 2023, 3:00-4:00 PM ET (1:00-2:00 PM MT)

**A video of this talk will be made available to NIST staff in the Math channel on NISTube, which is accessible from the NIST internal home page. It will be taken down from NISTube after 12 months at which point it can be requested by emailing the ACMD Seminar Chair.**

**Abstract:**Flexible, human-interpretable machine learning models are gaining interest as applications increasingly require explainable artificial intelligence. This talk overviews recent developments in the "learn to optimize" (L2O) methodology wherein model inferences are defined to be solutions to parameterized optimization problems. The idea is to have domain experts create intuitive optimization models that include *both* analytic and parameterized terms. This fusion merges data-driven modeling with strong analytic guarantees (*e.g.* inferences satisfying linear systems of constraints). We will cover the key tools needed to design and implement L2O models along with numerical examples.

**Bio:**Howard is a Senior Optimization Engineer at Edge & Node and the creator of Typal Research and Typal Academy. Prior to joining Edge & Node, he was an Assistant Adjunct Professor in the Department of Mathematics at UCLA. He received a PhD in mathematics from UCLA in 2021, where he worked under the guidance of Wotao Yin and Stanley Osher. In his spare time, he creates online educational content for undergraduate optimization and analysis and conducts research at the intersection of optimization and deep learning. In particular, he is interested in developing optimization algorithms and solving inverse problems by using optimization models that have tunable parameters.

**Host:**Zach Grey

**Note**: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)

**Note**: Visitors from outside NIST must contact Lochi Orr (301) 975-3800; at least 24 hours in advance.

Mathematics and statistics

## Contacts

                -  Zachary Grey  zachary.grey@nist.gov      (303) 497-5920   
                -  Lochi Orr  lochi.orr@nist.gov      (410) 598-6986   

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