Internship, Purple Gaze Inc., May 2020 - July 2020
Contributed to the development of advanced eye-tracking software at Purple Gaze Inc., building AI-driven algorithms and real-time image processing pipelines for production deployment.
Research Position, Lawrence Berkeley National Laboratory, September 2020 - May 2025
Conducted research at the intersection of machine learning and high energy physics, developing novel computational methods for analyzing particle physics data.
Research Internship, Microsoft Research, May 2022 - August 2022
Conducted theoretical and computational research at Microsoft Research, advancing mathematical foundations of non-local field theory and its applications to optimization.
Internship, Bridgewater Associates, June 2023 - August 2023
Worked on systematic trading research and execution at Bridgewater Associates, developing probabilistic models and strategic frameworks to optimize investment decisions.
Yale University, 2020
Recommended citation: Desai, Krish. (2020). "Padé Approximants and the Anharmonic Oscillator." Yale University. MS Mathematics Thesis.
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Published in the proceedings of Conference on Neural Information Processing Systems (NeurIPS), ML4PS Track, 2021
Recommended citation: Desai, K., Nachman, B., & Thaler, J. (2021). Symmetry Discovery with Deep Learning. NeurIPS ML4PS 117 (2021)
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Published in Journal of Geometry, 2021
Recommended citation: Adelstein, I., Desai, K., Ji, A., & Zdeblick, G. (2022). Oblivious points on translation surfaces. Journal of Geometry, 113(1), 6.
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Published in Physical Review D, 2022
Recommended citation: Desai, K., Nachman, B., & Thaler, J. (2022). SymmetryGAN. Physical Review D, 105(9), 096031.
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Published in the proceedings of Conference on Neural Information Processing Systems (NeurIPS), ML4PS Track, 2022
Recommended citation: Desai, K., Nachman, B., & Thaler, J. (2022). Deconvolving Detector Effects for Distribution Moments. NeurIPS ML4PS, 43.
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Published in the proceedings of Conference on Neural Information Processing Systems (NeurIPS), ML4PS Track, 2024
Recommended citation: Zhu, H., Desai, K., Kuusela, M., Mikuni, V., Nachman, B., & Wasserman, L (2024) Multidimensional Deconvolution with Profiling. NeurIPS ML4PS (150).
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Published in the proceedings of Conference on Neural Information Processing Systems (NeurIPS), ML4PS Track, 2024
Recommended citation: Acosta, F. T., Chan, J., Desai, K., Mikuni, V., Nachman, B., & Pan, J. (2024) Neural Posterior Unfolding. NeurIPS ML4PS (177).
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Published in Physical Review D, 2024
Recommended citation: Desai, K., Nachman, B., & Thaler, J. (2024). Moment extraction using an unfolding protocol without binning. Physical Review D, 110(11), 116013.
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Preprint on arXiv, 2025
Recommended citation: Desai, K., Long, O., & Nachman, B. (2025). Unbinned Inference with Correlated Events. arXiv:2504.14072.
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University of California, Berkeley, 2025
Recommended citation: Desai, Krish. (2025). Machine Learning Methods for Cross Section Measurements. University of California, Berkeley. PhD Physics Dissertation.
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Associate Instructor, University of California, Berkeley, Fall 2020
Head Associate Instructor, University of California, Berkeley, Fall 2021
Introduction to electricity, magnetism, electromagnetic waves, optics, and modern physics. The course presents concepts and methodologies for understanding physical phenomena, and is particularly useful preparation for upper division study in biology and architecture.
Associate Instructor, University of California, Berkeley, Spring 2021
Associate Instructor, University of California, Berkeley, Summer 2021
Physics 7B is the second of the two physics classes required for chemistry and chemical engineering majors. This class covers a lot of material and focuses on three major physics concepts: thermodynamics, electricity, and magnetism. There are two, two-hour discussions each week and a total of five labs in the course that need to be completed. Homework is done on MasteringPhysics.
Associate Instructor, University of California, Berkeley, Fall 2022
Associate Instructor, University of California, Berkeley, Fall 2022
Associate Instructor, University of California, Berkeley, Fall 2023
Complex numbers, linear algebra, ordinary differential equations, Fourier series and transform methods, introduction to partial differential equations, introduction to tensors. Applications to physics will be emphasized. This course or an equivalent course required for physics major.
Associate Instructor, University of California, Berkeley, Summer 2023
Associate Instructor, University of California, Berkeley, Fall 2024
Introductory scientific programming in Python with examples from physics. Topics include: visualization, statistics and probability, regression, numerical integration, simulation, data modeling, function approximation, and algebraic systems. Recommended for freshman physics majors.
Associate Instructor, University of California, Berkeley, Fall 2024
Introduction to data science with applications to physics. Topics include: statistics and probability in physics, modeling of the physical systems and data, numerical integration and differentiation, function approximation. Recommended for freshmen intended to major in physics or engineering with emphasis on data science.
Associate Instructor, University of California, Berkeley, Summer 2024