I am an assistant professor of teaching in the electrical and computer engineering department at UC Davis. I am broadly interested in algorithmic aspects of data science and on learning and reasoning under constraints on computational resources like memory, precision, and network communication. I am particularly interested in exploiting randomness to develop algorithms that have formal guarantees of correctness while lending themselves to realization in highly-parallel and noisy hardware.

Prior to joining UC Davis, I was a postdoctoral scholar in the Redwood Center for Theoretical Neuroscience at UC Berkeley. I received my PhD in Computer Science from UC San Diego in September 2023, where I was supervised by Sanjoy Dasgupta and Tajana Rosing. I received my Bachelor of Science, with high honors, in Agricultural Economics from the University of California, Berkeley in 2013 and previously worked as a Senior Research Analyst at Brown University for Justine Hastings, Jesse Shapiro and Nate Baum-Snow.

Reading Group on Randomness, Algorithms, and ML Hardware

I am organizing a paper reading group for UC Davis students on randomness and its use in designing efficient approximation algorithms for tasks in machine learning (esp. linear algebra). The goal of the reading group is threefold: (1) develop an understanding of randomized methods and the mathematical analysis thereof, (2) build skills for reading research papers, and (3) think about research projects extending from reading topics. If this is of interest to you please contact me over email.

TA Positions

If you are a student interested in serving as a TA for a class I am teaching, please submit an application through the process managed by the ECE department. I cannot accept TA assignment requests made over email and will not respond to emails asking for TA positions.

PTA Requests

I cannot grant permission to add (PTA) or drop (PTD) requests. Please contact CoE or ECE advising for PTA/PTD related enquiries.

Publications

For a complete list of publications please see my GoogleScholar profile.

Selected Publications

• Christopher J. Kymn, Sonia Mazelet, Anthony Thomas, Denis Kleyko, E. Paxon Frady, Friedrich T. Sommer, and Bruno A. Olshausen. “Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps.” Advances in Neural Information Processing Systems (NeurIPS), 2024 (to appear).
• Denis Kleyko, Christopher J. Kymn, Anthony Thomas, Bruno A. Olshausen, Friedrich T. Sommer, E. Paxon Frady. “Principled Neuromorphic Reservoir Computing.” Nature Communications (to appear).
• Arman Khachiyan, Anthony Thomas, Huye Zhou, Gordon H Hanson, Alex Cloninger, Tajana Rosing, and Amit Khandelwal. “Using Neural Networks to Predict Micro-Spatial Economic Growth” American Economic Review: Insights, vol. 4, np. 4, pp 491-506, 2022.
• Anthony Thomas, Sanjoy Dasgupta, and Tajana Rosing. “A Theoretical Perspective on Hyperdimensional Computing” Journal of Artificial Intelligence Research, vol. 72, pp. 215-249, 2021.
• Anthony Thomas and Arun Kumar. “A comparative evaluation of systems for scalable linear algebra-based analytics” Proceedings of the VLDB Endowment, vol. 11, no. 13, pp. 2168-2182, 2018, VLDB Endowment.

Additional Publications

• Anthony Thomas, Sanjoy Dasgupta, Tara Javidi, and Tajana Rosing. “A Formal Perspective on Learning with Vector Symbolic Architectures” Under submission, 2023.

• Anthony Thomas, Behnam Khaleghi, Gopi Krishna Jha, Sanjoy Dasgupta, Nageen Himayat, Ravi Iyer, Nilesh Jain, and Tajana Rosing. “Streaming Encoding Algorithms for Scalable Hyperdimensional Computing.” arXiv preprint arXiv:2209.09868, 2022.

• Justin Morris, Hin Wai Lui, Kenneth Stewart, Behnam Khaleghi, Anthony Thomas, Thiago Marback, Baris Aksanli, Emre Neftci, and Tajana Rosing. “HyperSpike: HyperDimensional computing for more efficient and robust spiking neural networks.” In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 664-669. IEEE, 2022.

• Alireza Amirshai, Anthony Thomas, Amir Aminifar, Tajana Rosing, and David Atienza. “M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities.” IEEE Journal of Biomedical and Health Informatics (2022).

• Namiko Matsumoto, Anthony Thomas, Tara Javidi, and Tajana Rosing. “Hyperdimensional Computing and Spectral Learning.” Association for Computing Machinery CogArch21 (2021).

• Fatemeh Asgarinejad, Anthony Thomas, and Tajana Rosing. “Detection of epileptic seizures from surface EEG using hyperdimensional computing.” In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 536-540. IEEE, 2020.

• Behnam Khaleghi, Sahand Salamat, Anthony Thomas, Fatemeh Asgarinejad, Yeseong Kim, and Tajana Rosing. “Shearer: highly-efficient hyperdimensional computing by software-hardware enabled multifold approximation.” In Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, pp. 241-246. 2020.

• Anthony Thomas, Amir Aminifar, and David Atienza. “Noise-resilient and interpretable epileptic seizure detection” 2020 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5, 2020, IEEE.

• Justin Morris, Mohsen Imani, Samuel Bosch, Anthony Thomas, Helen Shu, and Tajana Rosing. “CompHD: Efficient hyperdimensional computing using model compression.” In 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), pp. 1-6. IEEE, 2019.

• Anthony Thomas, Yunhui Guo, Yeseong Kim, Baris Aksanli, Arun Kumar, Tajana Rosing. “Hierarchical and distributed machine learning inference beyond the edge” 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), pp. 18-23, 2019