Hi, I'm Jocelyn

EDUCATION
‍PhD in Computer Science (expected 2024), Drexel University
     Thesis (proposed): "Learning from the Brain: bridging the gap between biological and machine intelligence"
     Candidacy: “Sight Unseen: Generative Models and Robust Computer Vision”
     Advisor: Dr. Edward Kim
MS in Computer Science (2021), Drexel University
BS in Neuroscience (2019), Villanova University
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SELECT PUBLICATIONS
J. Rego, Y. Watkins, G. T. Kenyon, E. Kim, M. Teti. "A novel model of the primary visual cortex using biologically plausible sparse coding", Applications of Machine Learning. SPIE: Applications of Machine Learning, 2023.
S. V. Dibbo, S. Mansingh, J. Rego, G. T. Kenyon, J. Moore, M. Teti. "How can neuroscience help us build more robust deep neural networks?", ICML 2nd Workshop on New Frontiers in Adversarial Machine Learning, 2023.
S. Nesbit, A. O’Brien, J. Rego, G. Parpart, C. Gonzalez, G. T. Kenyon, E. Kim, T. C. Stewart and Y. Watkins. “Think Fast: Time Control in Varying Paradigms of Spiking Neural Networks”, International Conference on Neuromorphic Systems (ICONS) 2022.
G. Parpart, C. Gonzalez, T. C. Stewart, E. Kim, J. Rego, A. O’Brien, S. Nesbit, G. T. Kenyon and Y. Watkins. “Dictionary Learning with Accumulator Neurons”, International Conference on Neuromorphic Systems (ICONS) 2022.
E. Kim, M. Daniali, J. Rego, G.T. Kenyon. “The Selectivity and Competition of the Mind’s Eye in Visual Perception“, arXiv preprint arXiv: 2011.11167, 2021.
J. Carter, J. Rego, D. Schwartz, V. Bhandawat, E. Kim, “Learning Spiking Neural Network Models of Drosophila Olfaction“, International Conference on Neuromorphic Systems (ICONS) 2020.
E. Kim, J. Rego, Y. Watkins, G. T. Kenyon. “Modeling Biological Immunity to Adversarial Examples”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
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