Eric Larson, Ph.D.


 A headshot of Eric Larson, a member of the Lyle School of Engineering Faculty.

Eric Larson, Ph.D.

Associate Professor, Computer Science

Office Location: Caruth Hall 451

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  • Ph.D. in Electrical and Computer Engineering, University of Washington, Seattle, WA
  • Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK
  • Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK


Dr. Eric Larson is an associate professor of computer science at SMU Lyle, with a courtesy appointment in the department of Operations Research and Engineering Management. His research explores the interdisciplinary relationship of machine learning and signal/image processing with the fields of security, health, education, human-machine teaming, and ubiquitous computing—where he has secured over $9 million dollars in federal and corporate funding, including NSF, NIH, IES, DOE, ONR, USAFA, and others.

He is a fellow of the Hunt Institute for Engineering Humanity, member of the Darwin Deason Institute for Cybersecurity, member of the SMU AT&T Center for Virtualization, and Member of the SMU Academy of Distinguished Teachers. Dr. Larson has published one textbook and disseminated his research in over 80 peer-reviewed conference and journal papers, garnering more than 5,500 citations and 8 best paper awards nominations. He received his Ph.D. from the University of Washington where he was an Intel Science and Technology fellow. At UW, he was co-advised by MacArthur Genius Fellow Shwetak Patel and IEEE Fellow Les Atlas. He also has an MS in Image and Signal Processing from Oklahoma State University, where he was advised by Damon Chandler.

Honors and Awards

  • Associate Editor for the Journal of IMWUT (2018 to present)
  •  Platinum Best Paper IITSEC (2020)
  • Altshuler Distinguished Teaching Award (2023)



  • Machine Learning and Deep Learning
  • Privacy and Security
  • Applied Machine Learning in Health and Education
  • Human Machine Teaming and Biometrics

Recent Publications 

  • Wang, Z. Wu, J. Dai, T. Morgan, A. Garbens, H. Kominsky, J. Gahan, and E.C. Larson (2023). Evaluating Robotic Partial Nephrectomy Surgeons with Fully Convolutional Segmentation and Multitask Attention Networks. Journal of Robotic Surgery (JORS), 2023. Doi: 10.1007/s11701-023-01657-0. (Impact Factor: 2.48).
  • Fouzani, B. Gnade, and E. C. Larson, (2023). CMOS-Based Rotational Spectroscopy: Massive Spectral Fingerprint Generation for Enabling Automated Molecular Detection. Cell: Heliyon. HELIYON-D-22-25421R2. DOI: 10.2139/ssrn.4273800. (Impact Factor: 3.78, H-5 Index: 75).
  • Wu, G. Alford, S. Stothoff, O. Pensado, and E. C. Larson (2023). Exploring Convolutional Neural Networks for Predicting Sentinel-C Backscatter between Image Acquisitions. IEEE Transactions on Geoscience and Remote Sensing. 10.1109/TGRS.2023.3283217. (Impact Factor: 8.15, H-5 Index: 113).
  • Ding, Y. Fang, T. Han, and E.C. Larson (2022). An Approach for Combining Multimodal Fusion and Neural Architecture Search Applied to Knowledge Tracing. Journal of Applied Intelligence (APIN), International Journal of Research on Intelligent Systems. Springer Publishing. Pgs. 1-12. (Impact Factor: 5.1, H-5 Index: 65).
  • Viswanath, J. Hoffman, X. Ding, E.C. Larson, Edward Wang (2022). Towards Ubiquitous SpO2 Sensing on Unmodified Smartphones: Deep Learning Applied to a Varied Fractional Inspired Oxygen (FiO2) Study. NPJ Digital Medicine. Springer Nature. Pgs 1-10. (Impact Factor: 11.65, H-5 Index: 72).

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