Eugene Pinsky

Associate Professor of the Practice, Computer Science; Coordinator, Software Development

Title Associate Professor of the Practice, Computer Science; Coordinator, Software Development
Office 1010 Commonwealth Avenue, 3rd Floor, 327
Email epinsky@bu.edu
Education PhD, Columbia University
BA, Harvard University

Dr. Eugene Pinsky first joined the faculty of Boston University in 1986, after earning his doctorate at Columbia University. He was an assistant professor of computer science in the College of Arts & Sciences until 1993, when he left BU and gained extensive industry experience designing computational methods to analyze and monitor market risk at multiple trading and investment firms, including Bright Trading, F-Squared Investments, and Harvard Management Company. He has also applied his expertise in data mining, predictive analytics, and machine learning to video advertising at Tremor Video. Dr. Pinsky’s areas of expertise include pattern recognition, clustering, regression, prediction, factor models, support vector machines, and other machine learning algorithms and methods for data analysis; multi-dimensional statistical data analysis of large time-series data; data mining and predictive analytics to uncover patterns, correlations, and trends; algorithmic trading, pricing models, financial modeling, risk, and portfolio analysis; Python, R, C/C++, VBA, Weka, MATLAB, and MySQL; Big Data technologies and visualization (Hadoop/Hive, AWS, Tableau); design and implementation of software tools for quantitative analysis; and professional curriculum and course development.

Research Interests

Courses

Scholarly Works

Recent Research Reports

Pinsky, E., (2018), Mathematical Foundations for Ensemble Machine Learning and Ensemble Portfolio Analysis, SSRN: https://ssrn.com/abstract=3243974

Pinsky, E., (2020), Teaching data science by history: Kepler’s laws of planetary motion and generalized linear models, Proceedings Computer Science and Education in Computer Science 16 (1), 72-77

Pinsky, E., and Goldberg, S. (2022). Building a Meta-agent for Human-Machine Dialogue in Machine Learning Systems. In K. Arai (Ed.), Advances in Information and Communication (pp. 474-487). Switzerland: Springer. doi:10.1007/978-3-030-98015-3_33

Pinsky, E., Goldberg, S., Sukumaran, P., and Salnikov, L. (2022). Methylation Level Differences between the Housekeeping and the Specialized Genes Identified during Ontogenesis. In A. Hanif (Ed.), Cutting Edge Research in Biology. B P International. doi:10.9734/bpi/cerb/v2

Pinsky, E., Khan, R., Schena, P., and Park, K. (2022). A Clustering Approach to Analyzing NHL Goaltenders’ Performance. In T. Zlateva, & R. Goleva (Eds.), Computer Science and Education in Computer Science (pp. 3-10). Switzerland: Springer. doi:10.1007/978-3-031-17292-2_1

Pinsky, E., and Yang, Y. H. (2022). A Simple Rotation Strategy with Sector ETFs. Technical Analysis of Stocks and Commodities, (December), 36-40.

Pinsky, E., Salnikov, L., Sukumaran, P., and Goldberg, S. (2022). DNA methylation meta-analysis confirms the division of the genome into two functional groups. Journal of Cell Science and Therapy, 13(3), 1-6. doi:10.35248/2157-7013.22.13.352

Vasiukevich, A. and Pinsky, E. (2022). Constructing portfolios using stable distributions: The case of S&P 500 sectors exchange-traded funds. Machine Learning with Applications, 10, 100434. doi:10.1016/j.mlwa.2022.100434

Yust, J., Lee, J., and Pinsky, E. (2022). A Clustering-Based Approach to Automatic Harmonic Analysis: An Exploratory Study of Harmony and Form in Mozart’s Piano Sonatas. Transactions of the International Society for Music Information Retrieval, 5(1), 113-128. doi:10.5334/tismir.114

Pinsky, E. and Klawansky, S. (2023). MAD (about Median) vs. Quantile-based Alternatives for Classical Standard Deviation, Skewness, and Kurtosis, Frontiers in Applied Mathematics and Statistics, Volume 9, doi: 10.3389/fams.2023.1206537

Dubey, S., Tiwari, G. Singh, S., Goldberg, S. and Pinsky, E. (2023), Using Machine Learning for Healthcare Treatment Planning, Frontiers in Artificial Intelligence, April, Volume 6, doi: 10.3389/frai.2023.1124182

Rizinski, M., Jankov, A., Sankaradas, V., Pinsky E, Miskovski I, Trajanov, D. (2023), Company classification using zero-shot learning, arXiv preprint arXiv:2305.01028

Salnikov, S., Goldberg, S., Rijhwani, H., Shi, Y., and Pinsky, E. (2023) The RNA-Seq Data Analysis Shows how the Ontogenesis Defines Aging, Frontiers in Aging, Insights in Molecular Mechanisms of Aging, Volume 4, 2023, doi: 10.3389/fragi.2023.1143334

Elakoum F., Kuma Y., Gill E., Adiraju, S, Das. S., Kiran, K., and Pinsky, E., (2023), “Studying” Foreheads: ML-based Analysis\\ of Cardan’s Metoposcopy,2-nd Annual EAI Conference of Computer Science and Education in Computer Science (CSECS), Boston, MA, June 2023

Pattnaik S., and Pinsky, E. (2023), Alpha-Based Similarity Metric in Computational Advertising: A New Approach to Audience Extension, 2-nd Annual EAI Conference of Computer Science and Education in Computer Science (CSECS), Boston, MA, June 2023

Klawansky S., Balswick B.,, Charvachidze I., Manghwani A., Dalvi D., Vora Y. and Pinsky E., (2023) On Some Alternative Probability Density Metrics for Analyzing Empirical Datasets, 2-nd Annual EAI Conference of Computer Science and Education in Computer Science (CSECS), Boston, MA, June 2023

Park K, Pinsky E. Kaiser N., Subramani A. and Ying Y, (2023) The Application of Data Analytics for Understanding Patterns of Mergers and Acquisitions and CEO Characteristics in and between Crisis Times, 2-nd Annual EAI Conference of Computer Science and Education in Computer Science (CSECS), Boston, MA, June 2023

Goldberg S, Salnikov L., Kaiser N., Shrivastava T. and Pinsky E. (2024), Correcting User Decisions Based on Incorrect Machine Learning Decisions, proc. Future of Information and Communications Conference (FICC), Berlin, Germany, April 2024

Ma M and Pinsky E., Using Machine Learning to Identify Primary Features in Choosing Electric Vehicles Based on Income Levels, submitted to the Journal of Data Science and Management

Wang Y. and Pinsky E. Deviation Measures for Triangular Distributions, submitted to International Journal of Data Science and Management

Kandaswamy P. and Pinsky E., A (2023) Machine Learning-based Approach to Analyze and Visualize Sentencing Data, in preparation

Journal Papers

Conway, A., E. Pinsky, and S. Tridandapani. “Efficient Decomposition Methods for the Analysis of Multi-facility Blocking Models.” Journal of the ACM 41, no. 4 (1994): 648–675.

Pinsky, E., P. Stirpe. “Performance Analysis of an Asynchronous Multirate Crossabr with Bursty Traffic.” Computer Communication Review 22, no. 4 (October 1992): 150–160.

Pinsky, E., A. Conway, and W. Liu. “Blocking Formulae for the Engset Model.” IEEE Transactions on Communications 42, no. 6 (1994): 2213–2214.

Pinsky, E., and A. Conway. “Mean-Value Analysis of Multifacility Blocking Models with State-Dependent Arrivals.” Journal of Performance Evaluation vol. 24 (1996): 303–309.

“A Simple Approximation for the Erlang Loss Function.” Journal of Performance Evaluation15, no. 3 (September 1992): 155–161.

Pinsky, E., and A. Conway. “Computational Algorithms for Blocking Probabilities in Circuit-Switched Networks.” Annals of Operations Research 35, no. 1 (February 1992): 31–41.

Pinsky, E., and A. Conway. “Exact Computation of Blocking Probabilities in State-Dependent Multi-Facility Blocking Models.” Proceedings of the IFIP WG 7.3 International Conference on Performance of Distributed Systems and Integrated Communication Networks. North-Holland Publishing Co., Amsterdam, The Netherlands (1992): 383–392.

Pinsky, E., and Y. Yemini. “Asymptotic Analysis of Some Packet Radio Systems.” IEEE Journal on Selected Areas in Communications 4, no. 6 (September 1986): 938–946.

Book Chapters

Pinsky, E. and A. Conway. “Exact Computation of Blocking Probabilities in State-Dependent Multi-Facility Blocking Models.” In Performance Evaluation of Distributed Systems and Integrated Communication Networks, edited by T. Hasegawa, et al. (North-Holland Publishing Co., Amsterdam, The Netherlands, 1992): 383–393.

Pinsky, E. and W. Wang. “Application of the Partition Function in Revenue-Oriented Performance Analysis for Network Management.” In Network Management and Control, edited by A. Kerschenbaum, et al. (Plenum Publishing, New York, 1990): 339–349.

Pinsky, E. and Y. Yemini. “The Canonical Approximation in Performance Analysis.” In Computer Networking and Performance Evaluation, edited by T. Hasegawa (North-Holland Publishing Co., Amsterdam, The Netherlands, 1986): 3.3.1–3.3.13.

Pinsky, E., M. Sidi, and Y. Yemini, “The Canonical Approximation in the Performance Analysis of Packet Radio Networks.” In Current Issues in Distributed Computing and Communications, edited by Y.Yemini (Computer Science Press, 1986): 140–162.

Pinsky, E., and Y. Yemini. “A Statistical Mechanics of Some Interconnected Networks.” In Proceedings of IFIP Performance ’84, Paris, France (North-Holland Publishing Co., Amsterdam, The Netherlands, 1984): 147–158.

Conference/Workshop Papers

Conway, A., and E. Pinsky. “Stochastic Modeling and Analysis of Wavelength Division Multiplexing (WDM) Lightwave Networks.” In Proceedings of the IEEE Infocom ’94, Toronto, Canada (June 1994): 560–568.

Pinsky, E., and P. Stirpe. “Performance Analysis of High-Speed Asynchronous Circuit- Switching Communication Networks.” In Proceedings of the 4^th IEEE Workshop onComputer Aided Design, Analysis and Modeling of Communication Networks and Links, Quebec, Canada (September 1992).

Conway, A., and E. Pinsky. “A Decomposition Algorithm for the Exact Analysis of Circuit-Switched Networks.” In Proceedings of the IEEE Infocom ’92, Florence, Italy (May 1992): 996–1003.

Pinsky, E., and P. Stirpe. “Modeling and Analysis of Hot Spots in an Asynchronous NxN Crossabr Switch.” In Proceedings of the 1991 Annual Conference on Parallel Processing, Penn State University (1991): I.546–I.549.

Pinsky, E., and P. Stirpe. “Performance Analysis of an Asynchronous Multi-Rate Crossbar Network.” In Proceedings of the 1991 International Symposium on Communications, Taiwan (1991).

Pinsky, E., and P. Stirpe. “The Performance Analysis of an Asynchronous Non-Blocking Network Switch.” In Proceedings of the Singapore International Conference on Networks, Singapore (September 1991): 7–12.

Hsu, M., E. Pinsky, and W. Wang. “Modeling Hot Spots in Database Systems.” Extended Abstract in Proceedings of the 10^th ACM SIGACT-SGMOD-SIGART Symposium on Principles of Database Systems, Denver, Colorado (1991).

Conway, A., and E. Pinsky. “Performance Analysis of Sharing Policies for Broadband Networks.” In Proceedings of the 7^th International Teletraffic Congress, Morristown, New Jersey (October 1990): 11.4.1–11.4.8.

Ho, K. Y., E. Pinsky, and W. Wang. “On Improving the Efficiency of Cellular Communication Systems.” Extended abstract in Proceedings of the ISMM International Conference on Industrial, Vehicular and Space Applications of Microcomputers, New York (1990).

Pinsky, E., and W. Wang. “A Revenue-Oriented Performance Analysis of Resource Sharing in Distributed Systems.” Extended abstract in Proceedings of the ISMM InternationalConference on Parallel and Distributed Computing and Systems, New York (1990).

Binney, C., E. Pinsky, P. Stirpe, and W. Wang. “An Overview of Ensemble: A Software Tool for revenue-Oriented Performance Analysis of Large-Scale Circuit-Switched Networks.” In Proceedings of the 3^rd IEEE Internation Workshop on Computer-Aided Modeling, Analysis and Design of Communication Links and Networks, Torino, Italy (September 1990).

Litvak, E., and E. Pinsky. “Efficient Computational Methods for Estimating Some Performance Measures in Large-Scale Communication Systems.” In Proceedings of the ORSA Conference on Operations Research in Telecommunications, Boca Raton, Florida (March 1990): 8A.1–8A.3

Ho, K. Y., E. Pinsky, and W. Wang. “Performance Analysis fo Some Channel Access Schemes in Cellular Communication Systems.” In the Proceedings of the IEEE Inforocom, San Francisco, California (June 1990): 603–610.

Pinsky, E., and W. Wang. “Computing Some Performance Bounds in Cellular Mobile Communication Systems.” In Proceedings of the International Conference on ComputerCommunications, New Dehli, India (December 1990): PAM 3.1–.3.6.

Binney, C., E. Pinsky, and W. Wang. “Design with Ensemble: A Tool for Performance Analysis of Circuit-Switched Networks.” In Proceedings of the SBT/IEEE InternationalTelecommunication Symposium, Rio de Janeiro, Brazil (September 1990): 14.1.1–14.1.15.

“Applying Statistical Physics to Performance Analysis of Large-Scale Computing Systems.” Extended abstract in Proceedings of the ACM Computer Science Conference, Washington, D.C. (1990).

What advice do you have for new students?

“Data science and machine learning require an understanding of many interdisciplinary concepts, based on classical results from mathematics and statistics. I would encourage students to study these fundamental sciences and look for solutions that are simple and amenable to intuitive interpretations. As data scientists, you should have an open mind. Negative results are just as important as positive results.”

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