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Jonathan Y. Zhou[zh] [Webpage] [CV]
MS Student, Computer Science [Concentration: Machine Learning]
College of Computing
Georgia Institute of Technology
Atlanta, GA 30332, USA


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"Cum enim mundi univerſi fabrica ſit perfectiſſima, atque a Creatore ſapientiſſimo abſoluta, nihil omnino in mundo contingit, in quo non maximi minimive ratio quæpiam eluceat".

— Leonhardus Eulerus

"For since most perfect is the whole fabric of the heavens, and by the most wise Creator developed, nothing in universal totality comes to pass for which some relation of minimums or maximums might not be elucidated".

— Leonhard Euler


About Me

I am a fourth-year combined masters and bachelor’s student (BSMS) in Computer Science at the Georgia Institute of Technology. I am broadly interested developing accessible, computationally efficient, and statistically principled approaches to acquiring actionable knowledge from data. Most problems I’ve worked on have had spatial-temporal flavor and address pertinent real-world societal and engineering challenges. Throughout my research work, I have developed and applied tools from kernel learning, information theory, representation learning, causal inference, econometrics, and mathematical programming (convex and integer).

Interests

Technical: Machine learning, causal inference, learning in spatial-temporal and sequential settings, statistics, optimization, scientific and technical computing

Applications: Mangagment Science and Service Operations, Business Analytics, Data Science for Policy, Biostatistics and Bioinformatics, Computational Chemistry

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Writings

[1]    

Publications

[1]     D. J. Penney, H. H. Kristoffersen, J. Y. Zhou, J. C. Robins, S. K. Buratto, and H. Metiu, “Methanol disproportionation to formaldehyde on VO+ modified reduced rutile TiO2”, Santa Barbara, CA, Dec. 2020.

[2]     X. Shao, H. Cheng, J. Zhou, J. Zhang, Y. Zhu, C. Yang, A. D. Narzo, J. Yu, Y. Shen, Y. Li, S. Xu, Z. Zhang, J. Chen, J. Cheng, and K. Hao, “Prenatal exposure to ambient air multi-pollutants significantly impairs intrauterine fetal development trajectory,” Ecotoxicology and Environmental Safety, vol. 201, p. 110 726, 2020, issn: 0147-6513. doi: https://doi.org/10.1016/j.ecoenv.2020.110726.

[3]     J. Zhou, B. Chen, and N. Zhou, “Cross-cancer genome analysis on cancer classification using both unsupervised and supervised approaches,” in Big Data – BigData 2020, ser. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12402, Series Title: Lecture Notes in Computer Science, Springer International Publishing, 2020, pp. 206–219, isbn: 978-3-030-59611-8. doi: 10.1007/978-3-030-59612-5_15.

[4]     M. Casey, B. Chen, J. Zhou, and N. Zhou, “A machine learning approach to prostate cancer risk classifcation through use of RNA sequencing data,” in Big Data – BigData 2019, ser. Lecture Notes in Computer Science (including sub- series Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11514, ISSN: 16113349, Springer International Publishing, 2019, pp. 18–32, isbn: 978-3-030-23550-5. doi: 10.1007/978-3-030-23551-2.

[5]     J. Zhou and P. Z. Maymin, “Examining stock pricing anomalies,” Computational Essay, Waltham, Massachusetts, Jul. 2019.

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