CV
Alexey Yermakov
Summary
PhD student in Electrical & Computer Engineering at the University of Washington, advised by Dr. J. Nathan Kutz.
Education
- PhD, Electrical & Computer EngineeringPresentUniversity of Washington - SeattleGPA: 3.98/4.00
- MS, Applied Mathematics2025-06University of Washington - SeattleGPA: 3.98/4.00
- BS, Applied Mathematics2023-05University of Colorado - BoulderGPA: 4.00/4.00
- BS, Computer Science2023-05University of Colorado - BoulderGPA: 4.00/4.00
Internships
- Research Intern, Climate Modeling2026-06 -AI2On the Climate Modeling team at AI2, working on the ACE family of models.
- Software Engineering Intern2023-06 - 2023-09NASA/Caltech Jet Propulsion LaboratorySpearheaded the development of a VxWorks 7 operating system abstraction layer (OSAL) in C for the Mars Sample Return (MSR) mission.
- Ported the open-source bsdiff and bspatch software to VxWorks 7 to reduce bandwidth usage during the MSR mission
- Software Engineering Intern2022-06 - 2022-09Lockheed MartinImplemented low-level software to communicate between a star tracker (ST) and a command & data handling (C&DH) board using the MIL-STD-1553 protocol.
- Implemented functional verification tests (FVTs) to ensure written code was following logical requirements
- Software Engineering Intern2021-06 - 2021-09Lockheed MartinImplemented functional requirements for flight software built on top of NASA's Core Flight Executive (cFE) framework in the VxWorks operating system.
- Wrote low-level code for satellite to ground radio communication for a technology demonstration which required testing code on real hardware
- Software Engineering Intern2020-06 - 2020-09WorkivaDeveloped new visual and functional features for an internal search engine.
- Added pagination capabilities and the TF-IDF statistic for page ranking
- Prevented Cross Site Scripting (XSS) attacks and enhanced visual appeal of the site
Publications
- Learning the Koopman Operator using Attention Free Transformers2026
- CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models2026
- The Seismic Wavefield Common Task Framework2026
- T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders2026
- Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms2025
- Latent Nonlinear Wave Dynamics in Image Datasets and Autoencoder Reconstructions2025
- Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms2025Championing Open-source DEvelopment in ML Workshop, ICMLWyder, Goldfeder, Yermakov, et al. (2025).
- A Transformer-Based Deep Learning Approach to Anomaly Detection of High-Bandwidth Multivariate Time-Series Satellite Communications2025
Presentations
- The Seismic Wavefield Common Task Framework (Poster)2026ICLRUSA
- Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms (Poster)2025NeurIPSUSA
- Latent Nonlinear Wave Dynamics in Image Datasets and Autoencoder Reconstructions (Poster)2025ML and the Physical Sciences Workshop, NeurIPSUSA
- Common Task Framework for a Critical Evaluation of Scientific Machine Learning Algorithms (Poster)2025CODEML@ICML25USA
- A Transformer-Based Deep Learning Approach to Anomaly Detection of High-Bandwidth Multivariate Time-Series Satellite Communications2025SpaceOps25USATalk
- Transformer-Based Anomaly Detection for DSN Time Series2025D34 Technologist Seminar, JPLPasadena, CA, USATalk
- Imaging Three-Dimensional Surface Waves Using a Synthetic Schlieren Method (Poster)2022Discovery Learning Apprenticeship SymposiumBoulder, CO, USA
- Imaging Three-Dimensional Surface Waves Using a Synthetic Schlieren Method2022SIAM-FRAMSCUSATalk
References
- J. Nathan Kutz, ProfessorApplied Mathematics and Electrical & Computer Engineering, University of Washington. kutz@uw.edu