{"id":2875,"date":"2020-08-21T09:52:54","date_gmt":"2020-08-21T13:52:54","guid":{"rendered":"https:\/\/www.bu.edu\/met\/?post_type=profile&#038;p=2875"},"modified":"2026-05-21T17:11:06","modified_gmt":"2026-05-21T21:11:06","slug":"eugene-pinsky","status":"publish","type":"profile","link":"https:\/\/www.bu.edu\/met\/profile\/eugene-pinsky\/","title":{"rendered":"Eugene Pinsky"},"content":{"rendered":"<p>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 &amp; 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&#8217;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,\u00a0and 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.<\/p>\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h2 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Research Interests<\/h2><div class=\"bu_collapsible_section\" style=\"display: none;\"><\/p>\n<ul>\n<li class=\"p1\">Performance analysis<\/li>\n<li class=\"p1\">Data Science<\/li>\n<li class=\"p1\">Machine Learning<\/li>\n<\/ul>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h2 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Courses<\/h2><div class=\"bu_collapsible_section\" style=\"display: none;\"><\/p>\n<ul><div class=\"course-feed\"><\/p>\n<li>MET CS 521 \u2013 Information Structures with Python<\/li>\n<p><\/p>\n<li>MET CS 550 \u2013 Computational Mathematics for Machine Learning<\/li>\n<p><\/p>\n<li>MET CS 577 \u2013 Data Science with Python<\/li>\n<p><\/p>\n<li>MET CS 795 \u2013 Directed Study<\/li>\n<p><\/p>\n<li>MET CS 810 \u2013 MS Thesis 1<\/li>\n<p><\/div><\/ul>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h2 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Scholarly Works<\/h2><div class=\"bu_collapsible_section\" style=\"display: none;\"><\/p>\n<p><strong>Journal Papers<\/strong><\/p>\n<p>Minfei Liang, Yuanyuan Tang, Saiteja Puppala, and Eugene Pinsky. &#8220;Evaluating Simple Strategies with Mutual Funds and ETFs to Outperform the China\u2019s Shanghai Composite Index (SCI).&#8221; <em>Journal of Risk and Financial Management<\/em> 19, no. 4 (2026): 1\u201331. <a href=\"https:\/\/doi.org\/10.3390\/jrfm19040246\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/jrfm19040246<\/a><\/p>\n<p>R. Kaur, T. Kundu, B. Sharma, K. Park, and E. Pinsky. &#8220;Operational Resilience Under Carbon Constraints: A Socio-Technical Multi-Agentic Approach to Global Supply Chains.&#8221; <em>Systems<\/em> 14, no. 4 (2026). <a href=\"https:\/\/doi.org\/10.3390\/systems14040374\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/systems14040374<\/a><\/p>\n<p>R. Kaur, T. Kundu, K. Park, and E. Pinsky. &#8220;The Carbon Cost of Intelligence: A Domain-Specific Framework for Measuring AI Energy and Emissions.&#8221; <em>Energies<\/em> 19, no. 3 (2026). <a href=\"https:\/\/doi.org\/10.3390\/en19030642\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/en19030642<\/a><\/p>\n<p>Aishwarya Malhotta, Saiteja Puppala, and Eugene Pinsky. &#8220;Duration Rotation in U.S. Treasury Fixed-Income ETFs: Evidence for a \u201dMedian\u201d Strategy.&#8221; J<em>ournal of Financial Risk and Management<\/em> 5, no. 29 (April 2026): 1\u201334. https:\/\/doi.org\/10.3390\/fintech5020029<\/p>\n<p>Claire Guo, Jiachi Zhao, and Eugene Pinsky. &#8220;Resilient Anomaly Detection in Ocean Drifters with Unsupervised Learning, Deep Learning Models, and Energy-Efficient Recovery.&#8221; <em>Oceans<\/em> 7, no. 1 (January 2026):1\u201327. <a href=\"https:\/\/doi.org\/10.3390\/oceans7010005\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/oceans7010005<\/a><\/p>\n<p>Natasya Liew, Strrya Katrakona, Sai Puppala, and Eugene Pinsky. &#8220;A Comparison of Some Brazil and Mexico ADR Rotation Strategies.&#8221; <em>Artificial Intelligence, Machine Learning and Data Science<\/em> 3, no. 4 (November 2025). <a href=\"https:\/\/doi.org\/10.51219\/JAIMLD\/eugene-pinsky\/619\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.51219\/JAIMLD\/eugene-pinsky\/619<\/a><\/p>\n<p>Sandra Hoyek, Celine Chaaya, Muhammad Abidi, Francisco A. Lamarque, Ryan S. Meshkin, Varsha Giridharan, Kavach Shah, Efren Gonzalez, Eugene Pinsky, and Nimesh Patel. &#8220;Artificial Intelligence for the Detection of Maculopathy in Pediatric Patients with Sickle Cell Disease. <em>RETINA, The Journal of Retinal and Vitreous Diseases<\/em> (September 2025). <a href=\"https:\/\/doi.org\/10.1097\/IAE.0000000000004663\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1097\/IAE.0000000000004663<\/a><\/p>\n<p>Ritvik Gupta, Tharunya Katikreddy, and Eugene Pinsky. &#8220;Analyzing Sector Performance and Investment Strategies: a Decade of FTSE Data.&#8221; <em>International Journal of Economics and Financial Modeling<\/em> 10, no. 1 (2025):75\u201385, 2025. <a href=\"https:\/\/doi.org\/10.55284\/811.v10.i1.1663\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.55284\/811.v10.i1.1663<\/a><\/p>\n<p>T. Kundu and E. Pinsky. \u201cPredicting Daily Stock Movements with Deep Learning Models.\u201d <em>Machine Learning and Applications (Elsevier)<\/em> 22, article 100744 (December 2025): 1\u201316. <a href=\"https:\/\/doi.org\/10.1016\/j.mlwa.2025.100744\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.mlwa.2025.100744<\/a><\/p>\n<p>B. Ozmen, N. Singh, K. Shah, I. Berber, F.D. Singh, E. Pinsky, N. Sinclair, I. Raymond, and G.S. Schwarz. \u201cDevelopment of a novel artificial intelligence clinical decision support system for aesthetic surgery: Aura.\u201d <em>Aesthetic Surgery Journal<\/em> 45, no. 11 (November 2025): 1206\u20131212. <a href=\"https:\/\/doi.org\/10.1093\/asj\/sjaf120\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1093\/asj\/sjaf120<\/a><\/p>\n<p>E. Pinsky and S. Shah. \u201cMomentum-based Trading Strategies in Crude Oil ETFs and Futures.\u201d <em>Technical Analysis of Stocks and Commodities<\/em> 43, no. 9 (September 2025): 20\u201323.<\/p>\n<p>S. Pattnaik and E. Pinsky. \u201cSpatio-Temporal Machine Learning for Marine Pollution Prediction: A Multi-Modal Approach for Hotspot Detection and Seasonal Pattern Analysis in Pacific Waters.\u201d <em>Toxics<\/em> 13, no. 10 (September 2025). <a href=\"https:\/\/doi.org\/10.3390\/toxics13100820\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/toxics13100820<\/a><\/p>\n<p>Ayauzhan Arystambekova and Eugene Pinsky. \u201cEnhancing Public Health Insights through AI-Driven Time-Series Analysis: Hierarchical Clustering, Hamming Distance, and Binary Tree Visualizations of Infectious Disease Trends.\u201d <em>Computer Science and Mathematics Forum<\/em> 11, no. 1 (August 2025): 1\u201321. <a href=\"https:\/\/doi.org\/10.3390\/cmsf2025011023\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/cmsf2025011023<\/a><\/p>\n<p>X. Zhang and E. Pinsky. \u201cComparing Algorithmic Trading Strategies by Analogies to Machine Learning.\u201d <em>Algorithmic Finance <\/em>(August 2025). <a href=\"https:\/\/doi.org\/10.1177\/21576203251360571\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1177\/21576203251360571<\/a><\/p>\n<p>E. Pinsky and Q. Wen. \u201cSimple Approximations and Interpretation of Pareto Index and Gini Coefficient using Mean Absolute Deviation and Quantile Functions.\u201d <em>Econometrics<\/em> 13, no. 3 (August 2025). <a href=\"https:\/\/doi.org\/10.3390\/econometrics13030030\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/econometrics13030030<\/a><\/p>\n<p>P. Talmar, A. Jain, and E. Pinsky. \u201cShould you sleep or trade bitcoin? Unveiling bitcoin\u2019s price behavior with deep learning.\u201d <em>Computer Sciences and Mathematics Forum<\/em> 11, no. 1 (August 2025): 1\u201310. <a href=\"https:\/\/doi.org\/10.3390\/cmsf2025011020\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/cmsf2025011020<\/a><\/p>\n<p>N. Liew, S. Harinatha, S. Pattnaik, K. Park, and E. Pinsky. \u201cInclusive Turnout for Equitable Policies: Using Time Series Forecasting to Combat Policy Polarization.\u201d <em>Computer Sciences and Mathematics Forum<\/em> 11, no. 1 (August 2025): 11. <a href=\"https:\/\/www.mdpi.com\/2813-0324\/11\/1\/11\" rel=\"noopener\" target=\"_blank\">https:\/\/www.mdpi.com\/2813-0324\/11\/1\/11<\/a><\/p>\n<p>Z. Wang, W. Zhang, and E. Pinsky. \u201cComparative Analysis of Temperature Prediction Models: Simple Models vs. Deep Learning Models.\u201d <em>Computer Science and Mathematics Forum<\/em> 11, no. 1 (July 2025): 1\u201316. <a href=\"https:\/\/doi.org\/10.3390\/cmsf2025011006\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/cmsf2025011006<\/a><\/p>\n<p>M. Liang, Y. Gao, and E. Pinsky. \u201cAnalyzing and Classifying Time-Series Trends in Medals.\u201d <em>Computer Sciences and Mathematics Forum<\/em> 11, no. 9 (July 2025): 1\u201312. <a href=\"https:\/\/doi.org\/10.3390\/cmsf2025011009\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/cmsf2025011009<\/a><\/p>\n<p>B. Sharma and E. Pinsky. \u201cSome Patterns of Sleep Quality and Daylight Saving Time Across Countries: A Predictive and Exploratory Analysis.\u201d <em>International Journal of Data Mining and Knowledge Processing<\/em> 15, no. 4 (July 2025): 1\u201316. <a href=\"https:\/\/doi.org\/10.5121\/ijdkp.2025.15401\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijdkp.2025.15401<\/a><\/p>\n<p>S. Pattnaik and E. Pinsky. \u201cHamming Diversification Index: A New Clustering-based metric to Understand and Visualize Time-Evolution of Patterns in Multi-Dimensional Data sets.\u201d <em>Applied Sciences<\/em> 15, no. 14 (July 2025). <a href=\"https:\/\/doi.org\/10.3390\/app15147760\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/app15147760<\/a><\/p>\n<p>K. Park, N. Liew, S. Pattnaik, A. Kures, and E. Pinsky. \u201cExploring the Transition to Low-Carbon Energy: A Comparative Analysis of Population, Economic Growth, and Energy Consumption in Oil Producing OECD and BRICS Countries.\u201d <em>Sustainability<\/em> 17, no. 13 (July 2025). <a href=\"https:\/\/doi.org\/10.3390\/su17136221\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/su17136221<\/a><\/p>\n<p>E. Pinsky and S. Shah. \u201cThe Silver Lining of Daily Bitcoin Trading.\u201d <em>Technical Analysis of Stocks and Commodities<\/em> (June 2025): 1\u20135.<\/p>\n<p>B. Ozmen, S. Nishant, K. Shah, I. Berber, E. Pinsky, A. Rampazzo, G. Schwarz, and D. Singh. \u201cDevelopment of A Novel Artificial Intelligence Clinical Decision Support Tool for Hand Surgery: HandRAG.\u201d <em>Journal of Hand and Microsurgery<\/em> (June 2025). <a href=\"https:\/\/doi.org\/10.1016\/j.jham.2025.100293\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.jham.2025.100293<\/a><\/p>\n<p>S. Pattnaik and E. Pinsky. \u201cSimilarity and Novelty Metrics: A Machine Learning Framework for Audience Extension.\u201d <em>Informatics Engineering: An International Journal <\/em>9, no. 1\/2 (June 2025): 1\u201315. <a href=\"https:\/\/doi.org\/10.5121\/ieij.2025.9201\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ieij.2025.9201<\/a><\/p>\n<p>S. Pattnaik, M. Rizinski, and E. Pinsky. \u201cRethinking Inequality: The Complex Dynamics Beyond the Kuznets Curve.\u201d <em>Data<\/em> 10, no. 88 (June 2025): 1\u201332. <a href=\"https:\/\/doi.org\/10.3390\/data10060088\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/data10060088<\/a><\/p>\n<p>V. Angampally and E. Pinsky. \u201cComparing Classifiers in the Presence of Errors in True Label Assignment in Medical Datasets.\u201d <em>Machine Learning and Applications: An International Journal (MLAIJ)<\/em> 12, no. 2 (June 2025). <a href=\"https:\/\/aircconline.com\/mlaij\/V12N2\/12225mlaij01.pdf\" rel=\"noopener\" target=\"_blank\">https:\/\/aircconline.com\/mlaij\/V12N2\/12225mlaij01.pdf<\/a><\/p>\n<p>S. Kotrakona, N. Liew, and E. Pinsky. \u201cBeyond Winners and Losers: Median Sector Rotation in the Japanese Equity Market.\u201d <em>International Journal of Data Mining and Knowledge Management<\/em> 15, no. 3 (May 2025): 1\u201315. <a href=\"https:\/\/doi.org\/10.5121\/ijdkp.2025.15301\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijdkp.2025.15301<\/a><\/p>\n<p>W. Zhang, Z. Wang, and E. Pinsky. &#8220;Mad-deviations for Hyperexponential and Hypoexponential distributions.&#8221; <em>Operations Research and Applications International Journal<\/em> 12, no. 2 (May 2025): 19\u201337. <a href=\"https:\/\/airccse.com\/oraj\/papers\/12225oraj02.pdf\" rel=\"noopener\" target=\"_blank\">https:\/\/airccse.com\/oraj\/papers\/12225oraj02.pdf<\/a><\/p>\n<p>Nahush Bhamre, Pranjal Ekhande, and Eugene Pinsky. \u201cEnhancing Naive Bayes Algorithm with Stable Distributions for Classification.\u201d <em>International Journal on Cybernetics and Informatics (IJCI)<\/em> 14, no. 2 (April 2025): 107\u2013117. 3rd International Conference on Data Mining, Big Data and Machine Learning (DBML 2025). <a href=\"https:\/\/doi.org\/10.5121\/ijci.2025.140207\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijci.2025.140207<\/a><\/p>\n<p>Jingyi Jingyi and Eugene Pinsky. \u201cSome Observations on Export\/Import Dynamics in China (2015\u20132023).\u201d <em>Social Sciences Insights Journal (SSIJ)<\/em> 3, no. 1 (April 2025). <a href=\"https:\/\/doi.org\/10.60036\/4dbeh074\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.60036\/4dbeh074<\/a><\/p>\n<p>X. Zeng and E. Pinsky. \u201cElliptical Mixture Models Improve the Accuracy of Gaussian Mixture Models with Expectation-Maximization Algorithm.\u201d <em>International Journal on Cybernetics and Informatics<\/em> 14, no. 2 (April 2025): 87\u2013106. <a href=\"https:\/\/doi.org\/10.5121\/ijci.2025.140206\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijci.2025.140206<\/a><\/p>\n<p>X. Zeng and E. Pinsky. \u201cStable Distribution Naive Bayes Achieves Higher Accuracy Than Traditional Naive Bayesian Classification.\u201d <em>International Journal on Cybernetics and Informatics<\/em> 14, no. 2 (April 2025): 71\u201386. <a href=\"https:\/\/doi.org\/10.5121\/ijci.2025.140205\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijci.2025.140205<\/a><\/p>\n<p>Deepali Chawla, Nidhi Desai, Goutham Sekharamantri, and Eugene Pinsky. \u201cMining the Authors Catalog of the Russian Academy of Sciences: Some Statistics on Leonhard Euler\u2019s Publications.\u201d <em>International Journal of Data Mining and Knowledge Management Process<\/em> 15, no. 2 (March 2025): 17\u201331. <a href=\"https:\/\/doi.org\/10.5121\/ijdkp.2025.15202\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijdkp.2025.15202<\/a><\/p>\n<p>S. Parab and E. Pinsky. \u201cAnalyzing Fashion Trends Using Hierarchical Clustering and Temporal Analysis.\u201d <em>Machine Learning and Applications International Journal (MLAIJ)<\/em> 12, no. 1 (March 2025): 107\u2013118. <a href=\"https:\/\/doi.org\/10.5121\/mlaij.2025.12107\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/mlaij.2025.12107<\/a><\/p>\n<p>S. Mandloi, A. Jalali, and E. Pinsky. \u201cAn Adaptive Hierarchical Tree-Based Clustering Approach to Outlier Detection in ETF-Focused Financial Time-Series.\u201d <em>Machine Learning and Applications International Journal (MLAIJ)<\/em> 12, no. 1 (March 2025): 131\u2013146. <a href=\"https:\/\/doi.org\/10.5121\/mlaij.2025.12109\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/mlaij.2025.12109<\/a><\/p>\n<p>E. Pinsky and S. Shah. \u201cEstimating the Accuracy of a Bagged Ensemble.\u201d <em>Machine Learning and Applications International Journal (MLAIJ)<\/em> 12, no. 1 (March 2025): 89\u2013105. <a href=\"https:\/\/doi.org\/10.5121\/mlaij.2025.12106\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/mlaij.2025.12106<\/a><\/p>\n<p>A. Singh and E. Pinsky. \u201cData Mining and Analysis of Early Modern European Enlightenment Trends in Acta Eruditorum.\u201d <em>Data Mining and Knowledge Management Journal<\/em> 15, no. 2 (March 2025): 45\u201355. <a href=\"https:\/\/doi.org\/10.5121\/ijdkp.2025.15204\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijdkp.2025.15204<\/a><\/p>\n<p>T. Sharma, A. Srivastava, and E. Pinsky. \u201cOptimizing Sector Index Rotation and Rebalancing Frequency with Data Mining: A Case Study on Indian National Stock Exchange.\u201d <em>Data Mining and Knowledge Management Journal<\/em> 15, no. 2 (March 2025): 55. <a href=\"https:\/\/doi.org\/10.5121\/ijdkp.2025.15205\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijdkp.2025.15205<\/a><\/p>\n<p>A. Warty and E. Pinsky. \u201cData Mining for Bernford Law in Ancient Roman Coins.\u201d <em>International Journal of Data Mining and Knowledge Management Process<\/em> 15, no. 2 (March 2025): 33\u201344. <a href=\"https:\/\/doi.org\/10.5121\/ijdkp.2025.15203\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijdkp.2025.15203<\/a><\/p>\n<p>T. Katikreddy, U. Damadhar, and E. Pinsky. \u201cExploring the Aspect Ratios of World Currencies: Mining the Numismatics Catalog.\u201d <em>International Journal of Data Mining and Knowledge Management Process (IJDKP)<\/em> 15, no. 2 (March 2025): 1\u201316. <a href=\"https:\/\/doi.org\/10.5121\/ijdkp.2025.15201\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/ijdkp.2025.15201<\/a><\/p>\n<p>X. Zeng and E. Pinsky. \u201cQuantile Regression with Q1\/Q3 Anchoring: A Robust Alternative for Outlier Resistant Modeling.\u201d <em>Machine Learning and Applications International Journal<\/em> 12, no. 1 (March 2025): 119\u2013130. <a href=\"https:\/\/doi.org\/10.5121\/mlaij.2025.12108\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5121\/mlaij.2025.12108<\/a><\/p>\n<p>E. Pinsky. \u201cComputation and Interpretation of Mean Absolute Deviations by Cumulative Distribution Functions.\u201d <em>Frontiers in Applied Mathematics and Statistics<\/em> vol. 11 (February 2025): 1\u201314. <a href=\"https:\/\/doi.org\/10.3389\/fams.2025.1487331\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3389\/fams.2025.1487331<\/a><\/p>\n<p>S. Kumar and E. Pinsky. \u201cSell the Losers? Keep the Winners? None of the above. Focus on the Median.\u201d <em>Algorithmic Finance<\/em> (January 2025). <a href=\"https:\/\/doi.org\/10.1177\/21576203241307779\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1177\/21576203241307779<\/a><\/p>\n<p>X. Zhang and E. Pinsky. \u201cSP-500 vs. Nasdaq-100 Price Movement Prediction with LSTM for Different Daily Periods.\u201d <em>Machine Learning with Applications (Elsevier)<\/em> vol. 19 (January 2025): 1\u201319. <a href=\"https:\/\/doi.org\/10.1016\/j.mlwa.2024.100617\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.mlwa.2024.100617<\/a><\/p>\n<p>E. Pinsky. \u201cMean Absolute Deviation (About Mean) Metric for Kurtosis.\u201d <em>Advanced Research in Sciences (ARS)<\/em> 2, no. 2 (September 2024). <a href=\"https:\/\/doi.org\/10.54026\/ARS\/1021\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.54026\/ARS\/1021<\/a><\/p>\n<p>E. Pinsky, W. Zhang, and Z. Wang. \u201cPareto Distribution of the Forbes Billionaires.\u201d <em>Computational Economics<\/em> vol. 66 (September 2024): 809\u2013834. <a href=\"https:\/\/doi.org\/10.1007\/s10614-024-10730-1\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/s10614-024-10730-1<\/a><\/p>\n<p>B. Ozmen, E. Pinsky, and G. Schwarz. \u201cFuture of Outcomes Research in Plastic Surgery: Artificial Intelligence Generated Synthetic Data and Predictive Models.\u201d <em>Journal of Plastic, Reconstructive and Aesthetic Surgery: JPRAS<\/em> vol. 94 (July 2024): 38\u201339. <a href=\"https:\/\/doi.org\/10.1016\/j.bjps.2024.05.014\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.bjps.2024.05.014<\/a><\/p>\n<p>S. Pattnaik, P. Danole, S. Mandiya, A. Foroutan, G. Mashhadiagha, Y. Khanghah, K. Isazadehfar, and E. Pinsky. \u201cAnalyzing Patterns of Injury in Occupational Hand Trauma Focusing on Press Machines: A Registry-Based Study and Machine Learning Analysis.\u201d <em>Engineering Proceedings<\/em> 68, no. 1 (July 2024): 61. <a href=\"https:\/\/doi.org\/10.3390\/engproc2024068061\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/engproc2024068061<\/a><\/p>\n<p>E. Pinsky, E. Meunier, P. Moreau, and T. Sharma. \u201cA Simple Computational Approach to Predict Long-Term Hourly Electric Consumption.\u201d <em>Engineering Proceedings<\/em> 68, no. 1 (July 2024): 59. <a href=\"https:\/\/doi.org\/10.3390\/engproc2024068059\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/engproc2024068059<\/a><\/p>\n<p>S. Pattnaik, N. Liew, A. Kures, E. Pinsky, and K. Park. \u201cCatalyzing Supply Chain Evolution: A Comprehensive Examination of Artificial Intelligence Integration in Supply Chain Management.\u201d <em>Engineering Proceedings<\/em> 68, no. 1 (July 2024): 57. <a href=\"https:\/\/doi.org\/10.3390\/engproc2024068057\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/engproc2024068057<\/a><\/p>\n<p>E. Pinsky and K. Piranavakumar. \u201cA Machine Learning-Based Approach to Analyze and Visualize Time-Series Sentencing Data.\u201d <em>Engineering Proceedings<\/em> 68, no.1 (July 2024): 50. <a href=\"https:\/\/doi.org\/10.3390\/engproc2024068050\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/engproc2024068050<\/a><\/p>\n<p>Daur Aktaukenov, Mohammed Alshaalan, Zhannar Omirbekova, and Eugene Pinsky. \u201cA Predictive Model for Oil Well Maintenance: A Case Study in Kazakhstan.\u201d <em>SOCAR: Reservoir and Petroleum Engineering<\/em> vol. 1 (April 2024): 048\u2013056. <a href=\"https:\/\/doi.org\/10.5510\/OGP20240100939\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5510\/OGP20240100939<\/a><\/p>\n<p>Saveli Goldberg, Lev Salnikov, Noor Kaiser, Tushar Srivastava, and Eugene Pinsky. \u201cCorrecting User Decisions Based on Incorrect Machine Learning Decisions.\u201d In <em>Proceedings of the Future of Information and Communications Conference (FICC)<\/em>, Berlin, Germany (April 2024).<\/p>\n<p>Maheshwaria, V. Shah, A. Sawhney, and E. Pinsky. \u201cMachine Learning-based Analysis and Effective Visualization of Mutual Funds through CUSUM and Clustering.\u201d <em>Journal of Business Data Science Research<\/em> 3, no. 1 (March 2024): 21\u201335.<\/p>\n<p>S. Goldberg, L. Salnikov, N. Kaiser, T. Srivastava, and E. Pinsky. \u201cCorrecting user decisions based on incorrect machine learning decisions.\u201d In <em>Advances in Information and Communication (FICC 2024. Lecture Notes in Networks and Systems, vol. 921)<\/em>, edited by Kohei Arai (Springer, Cham, March 2024): 12\u201320. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-54053-0_2\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-54053-0_2<\/a><\/p>\n<p>M. Ma and E. Pinsky. \u201cUsing Machine Learning to Identify Primary Features in Choosing Electric Vehicles Based on Income Levels.\u201d <em>Data Science and Management<\/em> 7, no. 1 (March 2024): 1\u20136. <a href=\"https:\/\/doi.org\/10.1016\/j.dsm.2023.10.001\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.dsm.2023.10.001<\/a><\/p>\n<p>M. Martin, M. Meunier, P. Moreau, J. Gadenne, J. Dautel, F. Catherin, E. Pinsky, and R. Rawassizadeh. \u201cADA-SHARK: A Shark Detection Framework Employing Underwater Cameras and Domain Adversarial Neural Nets.\u201d In <em>Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies<\/em> vol. 7 (January 2024): 1\u201325. <a href=\"https:\/\/doi.org\/10.1145\/3631416\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3631416<\/a><\/p>\n<p>M. Rizinski, A. Jankov, V. Sankaradas, E. Pinsky, I. Mishkovski, and D. Trajanov. \u201cComparative Analysis of NLP-Based Models for Company Classification.\u201d <em>Information<\/em> 15, no. 2 (January 2024): 77. <a href=\"https:\/\/doi.org\/10.3390\/info15020077\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3390\/info15020077<\/a><\/p>\n<p>Y. Wang and E. Pinsky. \u201cGeometry of deviation measures for triangular distributions.\u201d <em>Frontiers in Applied Mathematics and Statistics<\/em> vol. 9 (December 2023). <a href=\"https:\/\/doi.org\/10.3389\/fams.2023.1274787\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3389\/fams.2023.1274787<\/a><\/p>\n<p>E. Pinsky and S. Klawansky. \u201cMAD (about Median) vs. Quantile-based Alternatives for Classical Standard Deviation, Skew, and Kurtosis.\u201d <em>Frontiers in Applied Mathematics and Statistics<\/em> vol. 9 (June 2023). <a href=\"https:\/\/doi.org\/10.3389\/fams.2023.1206537\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3389\/fams.2023.1206537<\/a><\/p>\n<p>Rizinski, M., Jankov, A., Sankaradas, V., Pinsky, E., Miskovski, I., and Trajanov, D. \u201cCompany classification using zero-shot learning.\u201d In <em>Proceedings 20th International Conference on Informatics and Information Technologies<\/em> (May 2023): 55\u201358. ISBN 978-608-4699-16-3<\/p>\n<p>Snigdha Dubey, Gaurav Tiwari, Sneha Singh, Saveli Goldberg, and Eugene P. \u201cUsing Machine Learning for Healthcare Treatment Planning.\u201d <em>Frontiers in Artificial Intelligence<\/em> vol. 6 (April 2023). <a href=\"https:\/\/doi.org\/10.3389\/frai.2023.1124182\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3389\/frai.2023.1124182<\/a><\/p>\n<p>L. Salnikov, S. Goldberg, H. Rijhwani, Y. Shi, and E. Pinsky. \u201cThe RNA-Seq Data Analysis Shows How the Ontogenesis Defines Aging.\u201d <em>Frontiers in Aging<\/em> vol. 4 (March 2023). <a href=\"https:\/\/doi.org\/10.3389\/fragi.2023.1143334\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.3389\/fragi.2023.1143334<\/a><\/p>\n<p>A. Vasiukevich and E. Pinsky. \u201cConstructing Portfolios Using Stable Distributions: The Case of SP-500 Sectors Exchange-traded Funds.\u201d <em>Machine Learning with Applications<\/em> 10, article 100434 (December 2022). <a href=\"https:\/\/doi.org\/10.1016\/j.mlwa.2022.100434\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.mlwa.2022.100434<\/a><\/p>\n<p>Y. Yang and E. Pinsky. \u201cA Simple Rotation Strategy with Sector ETFs.\u201d <em>Technical Analysis of Stocks and Commodities<\/em> 40, no. 12 (December 2022): 36\u201340.<\/p>\n<p>Pinsky, E., Khan, R., Schena, P., and Park, K. \u201cA Clustering Approach to Analyzing NHL Goaltenders\u2019 Performance.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2022, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 450)<\/em>, edited by T. Zlateva and R. Goleva (Springer, Cham, 2022): 3\u201310. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-17292-2_1\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-17292-2_1<\/a><\/p>\n<p>J. Yust, L. Jaeseong, and E. Pinsky. \u201cA Clustering-Based Approach to Automatic Harmonic Analysis: An Exploratory Study of Harmony and Form in Mozart\u2019s Piano Sonatas.\u201d <em>Transactions of the International Society for Music Information Retrieval<\/em> 5, no. 1 (October 2022): 113\u2013128. <a href=\"https:\/\/doi.org\/10.5334\/tismir.114\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.5334\/tismir.114<\/a><\/p>\n<p>L. Salnikov, S. Goldberg, P. Sukumaran, and E. Pinsky. \u201cDNA Methylation Meta-Analysis Confirms the Division of the Genome into Two Functional Groups.\u201d <em>Journal of Cell Science and Therapy<\/em> S10 (May 2022): 352. <a href=\"https:\/\/www.longdom.org\/open-access\/dna-methylation-metaanalysis-confirms-the-division-of-the-genome-into-two-functional-groups.pdf\" rel=\"noopener\" target=\"_blank\">https:\/\/www.longdom.org\/open-access\/dna-methylation-metaanalysis-confirms-the-division-of-the-genome-into-two-functional-groups.pdf<\/a><\/p>\n<p>Saveli Goldberg and Eugene Pinsky. \u201cBuilding a meta-agent for human-machine dialogue in machine learning systems.\u201d In <em>Advances in Information and Communication (FICC 2022, Lecture Notes in Networks and Systems, vol. 439)<\/em>, edited by Kohei Arai (Springer, Cham, March 2022): 474\u2013487. <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-98015-3_33\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-030-98015-3_33<\/a><\/p>\n<p>L. Salnikov, E. Pinsky, and B. Galitsky. \u201cA Bi-directional Adversarial Explainability for Decision Support.\u201d <em>Journal of Human-Intelligent Systems Integration<\/em> 3, no. 1(February 2021): 1\u201314. <a href=\"https:\/\/doi.org\/10.1007\/s42454-021-00031-5\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/s42454-021-00031-5<\/a><\/p>\n<p>Morteza Homayounfar, Amirhossein Malekijoo, Aku Visuri, Chelsea Dobbins, Ella Peltonen, Eugene Pinsky, Kia Teymourian, and Reza Rawassizadeh. \u201cUnderstanding Smartwatch Battery Utilization in the Wild.\u201d <em>Sensors (Basel, Switzerland)<\/em> vol. 20 (2020). <a href=\"https:\/\/api.semanticscholar.org\/CorpusID:220436514\" rel=\"noopener\" target=\"_blank\">https:\/\/api.semanticscholar.org\/CorpusID:220436514<\/a><\/p>\n<p>E. Pinsky. \u201cTeaching data science by history: Kepler\u2019s laws of planetary motion and generalized linear models.\u201d In <em>Proceedings Computer Science and Education in Computer Science<\/em> 16, no. 1 (2020): 72\u201377.<\/p>\n<p>Pinsky, E. \u201cMathematical Foundations for Ensemble Machine Learning and Ensemble Portfolio Analysis\u201d (September 2018). <a href=\"https:\/\/ssrn.com\/abstract=3243974\" rel=\"noopener\" target=\"_blank\">https:\/\/ssrn.com\/abstract=3243974<\/a><\/p>\n<p>Pinsky, E., and A. Conway. \u201cMean-Value Analysis of Multifacility Blocking Models with State-Dependent Arrivals.\u201d <em>Journal of Performance Evaluation<\/em> vol. 24 (1996): 303\u2013309.<\/p>\n<p>Conway, A., E. Pinsky, and S. Tridandapani. \u201cEfficient Decomposition Methods for the Analysis of Multi-facility Blocking Models\u201d <em>Journal of the ACM<\/em> 41, no. 4 (1994): 648\u00ad\u2013675.<\/p>\n<p>Pinsky, E., A. Conway, and W. Liu. \u201cBlocking Formulae for the Engset Model.\u201d <em>IEEE\u00a0Transactions on Communications<\/em> 42, no. 6 (1994): 2213\u20132214.<\/p>\n<p>Pinsky, E., P. Stirpe. \u201cPerformance Analysis of an Asynchronous Multirate Crossbar with Bursty Traffic.\u201d <em>Computer Communication Review<\/em> 22, no. 4 (October 1992): 150\u2013160.<\/p>\n<p>\u201cA Simple Approximation for the Erlang Loss Function.\u201d <em>Journal of\u00a0Performance Evaluation<\/em> 15, no. 3 (September 1992): 155\u2013161.<\/p>\n<p>Pinsky, E., and A. Conway. \u201cComputational Algorithms for Blocking Probabilities in Circuit-Switched Networks.\u201d <em>Annals of Operations Research<\/em> 35, no. 1 (February 1992): 31\u201341.<\/p>\n<p>Pinsky, E., and A. Conway. \u201cExact Computation of Blocking Probabilities in State-Dependent Multi-Facility Blocking Models.\u201d <em>Proceedings of the IFIP WG 7.3 International Conference on Performance of Distributed Systems and Integrated Communication Networks<\/em>. North-Holland Publishing Co.,\u00a0Amsterdam, The Netherlands (1992): 383\u2013392.<\/p>\n<p>Pinsky, E., and Y. Yemini. \u201cAsymptotic Analysis of Some Packet Radio Systems.\u201d <em>IEEE\u00a0Journal on Selected Areas in Communications<\/em> 4, no. 6 (September 1986): 938\u2013946.<\/p>\n<p><strong>Book Chapters<\/strong><\/p>\n<p>Pinsky, E., Goldberg, S., Sukumaran, P., and Salnikov, L. \u201cMethylation Level Differences between the Housekeeping and the Specialized Genes Identified during Ontogenesis.\u201d In <em>Cutting Edge Research in Biology<\/em>, edited by A. Hanif (BP International, 2022). <a href=\"https:\/\/doi.org\/10.9734\/bpi\/cerb\/v2\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.9734\/bpi\/cerb\/v2 <\/a><\/p>\n<p>Pinsky, E., and A. Conway. \u201cExact Computation of Blocking Probabilities in State-Dependent Multi-Facility Blocking Models<em>.<\/em>\u201d In <em>Performance Evaluation of\u00a0<\/em><em>Distributed Systems and Integrated Communication Networks<\/em>, edited by T. Hasegawa, et al. (North-Holland Publishing Co.,\u00a0Amsterdam, The Netherlands, 1992): 383\u2013393.<\/p>\n<p>Pinsky, E., and W. Wang. \u201cApplication of the Partition Function in Revenue-Oriented Performance Analysis for Network Management.\u201d In <em>Network Management and\u00a0<\/em><em>Control<\/em>, edited by A. Kerschenbaum, et al. (Plenum Publishing, New York, 1990): 339\u2013349.<\/p>\n<p>Pinsky, E., and Y. Yemini. \u201cThe Canonical Approximation in Performance Analysis.\u201d In\u00a0<em>Computer Networking and Performance Evaluation<\/em>, edited by T. Hasegawa (North-Holland Publishing Co.,\u00a0Amsterdam, The Netherlands, 1986): 3.3.1\u20133.3.13.<\/p>\n<p>Pinsky, E., M. Sidi, and Y. Yemini. \u201cThe Canonical Approximation in the Performance Analysis of Packet Radio Networks.\u201d In <em>Current Issues in Distributed Computing\u00a0<\/em><em>and Communications<\/em>, edited by Y.Yemini (Computer Science Press, 1986): 140\u2013162.<\/p>\n<p>Pinsky, E., and Y. Yemini. \u201cA Statistical Mechanics of Some Interconnected Networks.\u201d In\u00a0<em>Proceedings of IFIP Performance <\/em><em>\u201984<\/em>, Paris, France (North-Holland Publishing Co.,\u00a0Amsterdam, The Netherlands, 1984): 147\u2013158.<\/p>\n<p><strong>Conference\/Workshop Papers<\/strong><\/p>\n<p>E. Pinsky, D. Taratynova, A. Kassenova, B. Dauletbayev, and M. Al-Shedivat. \u201cA Predictive Model of Arrival Times for Smart Shuttle Buses in Astana, Kazakhstan.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2024, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 609)<\/em>, edited by T. Zlateva and G. Tuparov (Springer, Cham, March 2025): 29\u201349. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-84312-9_2\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-84312-9_2<\/a><\/p>\n<p>E. Pinsky, S. Pattnaik, and K. Park. \u201cThe Periodic Table: Chemical Properties and Mendeleev Meets Physical Properties and Machine Learning.\u201d In Computer Science and Education in Computer Science (CSECS 2024, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 609), edited by T. Zlateva and G. Tuparov (Springer, Cham, March 2025): 97\u2013116. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-84312-9_7\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-84312-9_7<\/a><\/p>\n<p>E. Pinsky and M. Liang. \u201cAnalyzing the Geometric Ratios of Greek Vases.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2024, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 609)<\/em>, edited by T. Zlateva and G. Tuparov (Springer, Cham, March 2025): 154\u2013165. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-84312-9_11\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-84312-9_11<\/a><\/p>\n<p>E. Pinsky, G. Izbassova, and S. Minhaj. \u201cTeaching and Learning Python by Comparative Visualization.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2024, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 609)<\/em>, edited by T. Zlateva and G. Tuparov (Springer, Cham, March 2025): 310\u2013331. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-84312-9_21\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-84312-9_21<\/a><\/p>\n<p>N. Liew, S. Pattnaik, A. Kures, E. Pinsky, and K. Park. \u201cMachine Learning Innovations in Supply Chain Management: Revolutionizing Predictive Modeling for Efficiency and Growth.\u201d Boston University Symposium on Entrepreneurship &#038; Technology, Boston, Mass. (December 2024).<\/p>\n<p>A. Nittur and E. Pinsky. \u201cImpact Of Large Language Models on Entrepreneurship: Challenges for Innovation.\u201d Boston University Symposium on Entrepreneurship &#038; Technology, Boston, Mass. (December 2024).<\/p>\n<p>N. Liew, S. Pattnaik, A. Kures, K. Park, and P. Pinsky. \u201cAI Revolutionizing Supply Chain Management: Advances, Insights, and Implications for Business Backorder and Predictions.\u201d In <em>44th Annual Conference<\/em>, Strategic Management Society, Ankara, Turkey (October 2024).<\/p>\n<p>S. Pattnaik, N. Liew, A. Kures, K. Park, and P. Pinsky. \u201cMachine Learning Innovations in Supply Chain Management: Revolutionizing Predictive Modeling for Efficiency and Growth.\u201d In <em>25th Annual Conference<\/em> no. 4, Global Business and Technology Association (GBATA) (July 2024): 575\u2013586. ISBN: 1-932917-20-9.<\/p>\n<p>S. Pattnaik, K. Park, and E. Pinsky. \u201cSustainability across countries.\u201d Northeast Decision Sciences Conference, Cambridge, Mass. (April 2024).<\/p>\n<p>S. Pattnaik and E. Pinsky. \u201cSimilarity-Novelty Metric: An Alternative Algorithm for Efficient Audience Extension.\u201d Northeast Decision Sciences Conference, Cambridge, Mass. (April 2024). <\/p>\n<p>S. Klawansky, B. Balswick, M. Alsayel, I. Charvachidze, A. Manghwani, P. Almeida, D. Dalvi, J. Vora, and E. Pinsky. \u201cOn some alternative probability density metrics for analyzing empirical datasets.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2023, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 514)<\/em>, edited by T. Zlateva and G. Tuparov (Springer, Cham, March 2024): 40\u201347. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-44668-9_3\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-44668-9_3<\/a><\/p>\n<p>K. Park, E. Pinsky, N. Kaiser, A. Subramani, and Y. Ying. \u201cThe application of data analytics for understanding patterns of mergers and acquisitions and CEO characteristics in and between crisis times.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2023, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 514)<\/em>, edited by T. Zlateva and G. Tuparov (Springer, Cham, March 2024): 265\u2013280. <a href=\"https:\/\/doi.org\/10.1007\/978-3-031-44668-9_21\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-44668-9_21<\/a><\/p>\n<p>S. Pattnaik and E. Pinsky. \u201cAlpha-Based Similarity Metric in Computational Advertising: A New Approach to Audience Extension.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2023, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 514)<\/em>, edited by T. Zlateva and G. Tuparov (Springer, Cham, March 2024): 3\u201329.<a href=\"https:\/\/doi.org\/10.1007\/978-3-031-44668-9_1\" rel=\"noopener\" target=\"_blank\"> https:\/\/doi.org\/10.1007\/978-3-031-44668-9_1<\/a><\/p>\n<p>F. Elakoum, Kuma Y., Gill E., Adiraju, S, Das. S., Kiran, K., and Pinsky, E. \u201c\u2018Studying\u2019 Foreheads: ML-based Analysis of Cardan\u2019s Metoposcopy.\u201d 2nd Annual EAI Conference on Computer Science and Education in Computer Science (CSECS), Boston, Mass. (June 2023).<\/p>\n<p>R. Khan, P. Schena, K. Park, and E. Pinsky. \u201cA clustering approach to analyzing NHL goaltenders\u2019 performance.\u201d In <em>Computer Science and Education in Computer Science (CSECS 2022, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 450)<\/em>, edited by T. Zlateva and R. Goleva (Springer, Cham, March 2022): 3\u201310. <a href=\"https:\/\/doi.org\/\/10.1007\/978-3-031-17292-2_1\" rel=\"noopener\" target=\"_blank\">https:\/\/doi.org\/\/10.1007\/978-3-031-17292-2_1<\/a><\/p>\n<p>Conway, A., and E. Pinsky. \u201cStochastic Modeling and Analysis of Wavelength Division Multiplexing (WDM) Lightwave Networks.\u201d In <em>Proceedings of the IEEE Infocom \u201994<\/em>, Toronto, Canada (June 1994): 560\u2013568.<\/p>\n<p>Pinsky, E., and P. Stirpe. \u201cPerformance Analysis of High-Speed Asynchronous Circuit- Switching Communication Networks.\u201d In <em>Proceedings of the 4<sup>th<\/sup> IEEE Workshop on<\/em> <em>Computer Aided Design, Analysis and Modeling of Communication<\/em> <em>Networks and Links<\/em>, Quebec, Canada (September 1992).<\/p>\n<p>Conway, A., and E. Pinsky. \u201cA Decomposition Algorithm for the Exact Analysis of Circuit-Switched Networks.\u201d In <em>Proceedings of the IEEE Infocom \u201992<\/em>, Florence, Italy (May 1992): 996\u20131003.<\/p>\n<p>Pinsky, E., and P. Stirpe. \u201cModeling and Analysis of Hot Spots in an Asynchronous NxN Crossbar Switch.\u201d In <em>Proceedings of the 1991 Annual Conference on Parallel Processing<\/em>, Penn State University (1991): I.546\u2013I.549.<\/p>\n<p>Pinsky, E., and P. Stirpe. \u201cPerformance Analysis of an Asynchronous Multi-Rate Crossbar Network.\u201d In <em>Proceedings of the 1991 International Symposium on<\/em> <em>Communications<\/em>, Taiwan (1991).<\/p>\n<p>Pinsky, E., and P. Stirpe. \u201cThe Performance Analysis of an Asynchronous Non-Blocking Network Switch.\u201d In <em>Proceedings of the Singapore International Conference on Networks<\/em>, Singapore (September 1991): 7\u201312.<\/p>\n<p>Hsu, M., E. Pinsky, and W. Wang. \u201cModeling Hot Spots in Database Systems.\u201d Extended Abstract in <em>Proceedings of the 10<sup>th<\/sup> ACM SIGACT-SGMOD-SIGART Symposium<\/em> <em>on Principles of Database Systems<\/em>, Denver, Colorado (1991).<\/p>\n<p>Conway, A., and E. Pinsky. \u201cPerformance Analysis of Sharing Policies for Broadband Networks.\u201d In <em>Proceedings of the 7<sup>th<\/sup> International Teletraffic Congress<\/em>, Morristown, New Jersey (October 1990): 11.4.1\u201311.4.8.<\/p>\n<p>Ho, K. Y., E. Pinsky, and W. Wang. \u201cOn Improving the Efficiency of Cellular Communication Systems<em>.<\/em>\u201d Extended abstract in <em>Proceedings of the ISMM International<\/em> <em>Conference on Industrial, Vehicular and Space Applications of<\/em> <em>Microcomputers<\/em>, New York (1990).<\/p>\n<p>Pinsky, E., and W. Wang. \u201cA Revenue-Oriented Performance Analysis of Resource Sharing in Distributed Systems.\u201d Extended abstract in <em>Proceedings of the ISMM International<\/em> <em>Conference on Parallel and Distributed Computing and Systems<\/em>, New York (1990).<\/p>\n<p>Binney, C., E. Pinsky, P. Stirpe, and W. Wang. \u201cAn Overview of Ensemble: A Software Tool for revenue-Oriented Performance Analysis of Large-Scale Circuit-Switched Networks.\u201d In <em>Proceedings of the 3<sup>rd<\/sup> IEEE Internation Workshop on Computer-Aided<\/em> <em>Modeling, Analysis and Design of Communication Links and Networks<\/em>, Torino, Italy (September 1990).<\/p>\n<p>Litvak, E., and E. Pinsky. \u201cEfficient Computational Methods for Estimating Some Performance Measures in Large-Scale Communication Systems.\u201d In <em>Proceedings of the ORSA<\/em> <em>Conference on Operations Research in Telecommunications<\/em>, Boca Raton, Florida (March 1990): 8A.1\u20138A.3<\/p>\n<p>Ho, K. Y., E. Pinsky, and W. Wang. \u201cPerformance Analysis fo Some Channel Access Schemes in Cellular Communication Systems.\u201d In the <em>Proceedings of the IEEE Inforocom<\/em>, San Francisco, California (June 1990): 603\u2013610.<\/p>\n<p>Pinsky, E., and W. Wang. \u201cComputing Some Performance Bounds in Cellular Mobile Communication Systems<em>.<\/em>\u201d In <em>Proceedings of the International Conference on Computer<\/em> <em>Communications<\/em>, New Dehli, India (December 1990): PAM 3.1\u2013.3.6.<\/p>\n<p>Binney, C., E. Pinsky, and W. Wang. \u201cDesign with Ensemble: A Tool for Performance Analysis of Circuit-Switched Networks.\u201d In <em>Proceedings of the SBT\/IEEE International<\/em> <em>Telecommunication Symposium<\/em>, Rio de Janeiro, Brazil (September 1990): 14.1.1\u201314.1.15.<\/p>\n<p>E. Pinsky. \u201cApplying Statistical Physics to Performance Analysis of Large-Scale Computing Systems.\u201d Extended abstract in <em>Proceedings of the ACM Computer Science<\/em> <em>Conference<\/em>, Washington, D.C. (1990).<\/p>\n<p><\/div>\n<\/div>\n\n<div class=\"bu_collapsible_container \" aria-live=\"polite\" data-customize-animation=\"false\"><h2 class=\"bu_collapsible\" aria-expanded=\"false\"tabindex=\"0\" role=\"button\">Faculty Q&amp;A<\/h2><div class=\"bu_collapsible_section\" style=\"display: none;\"><\/p>\n<div id=\"FaculityQA\">\n<p><strong>What is your area of expertise?<\/strong><br \/>\nMy research area is in performance analysis, data science, and machine learning. Since completing my PhD in computer science at Columbia University, I have been teaching throughout my career within both academia (Boston University, MIT) and industry. For almost twenty years, I worked in data science and machine learning areas developing applications that utilize machine learning to solve real-life practical problems, primarily in computational finance and computational advertising.<\/p>\n<p>Data science and machine learning require an understanding of many interdisciplinary concepts. The practice involves mining large amounts of data and applying machine learning methods to design new algorithms to solve important practical problems. As more and more tasks become automated, machine learning has an increasing role in our life. Thus, it is important that students are taught the practical elements of data science and machine learning.<\/p>\n<hr style=\"width: 50%; text-align: center;\" \/>\n<p><strong>What courses do you teach at MET? What \u201creal-life\u201d exercises do you bring to class?<\/strong><br \/>\n<span>At the present time, I teach\u00a0<\/span><a href=\"https:\/\/www.bu.edu\/met\/courses\/graduate\/computer-science\/#course-METCS521\">Information Structures with Python (MET CS 521)<\/a><span>\u00a0and a new course,\u00a0<\/span><a href=\"https:\/\/www.bu.edu\/met\/courses\/graduate\/computer-science\/#course-METCS577\">Data Science with Python (MET CS 577)<\/a><span>. In all of my courses, I try to present many simple examples with a strong emphasis on visualization. In addition to regular homework and exams, students choose a project and present it on the last day of class.\u00a0<\/span><\/p>\n<hr style=\"width: 50%; text-align: center;\" \/>\n<p><strong>How do you see the curriculum evolving to stay current with industry trends?<\/strong><br \/>\nTraditionally, machine learning and data science courses have been often limited to the arenas of computer science and engineering. Such courses focus on computer science and assume students have a background in advanced math and science courses. At present, there is a growing need for students to learn and use data science and machine learning in real-life applications. The challenge, therefore, is to develop courses that are application driven and can be utilized by students with diverse backgrounds.<\/p>\n<p>I am very interested in helping to develop such application-driven courses that use data mining and machine learning techniques, especially in computational finance and computational advertising. As an example, consider computational advertising. Skills in this area are in increasing demand as more advertising, commerce, and delivery of services are online. In computational advertising, we try to solve the problem of finding the best advertisement for a given target. The science behind solving this problem is very complex and requires different areas of expertise such as data mining, recommendation systems, optimization, and forecasting. As data science and machine learning algorithms are increasingly employed in advertising and marketing, it becomes essential to have a basic understanding behind this science.<\/p>\n<p>I strongly believe that there is a tremendous opportunity to collaborate with my colleagues at MET and with other faculty members across the university to help develop new and extend existing courses to incorporate opportunities provided by data mining and machine learning.<\/p>\n<\/div>\n<p><\/div>\n<\/div>\n\n<div class=\"responsive-video\">\n<iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/KYTRyW7vVE0?si=cJg15859WVybyqDo&#038;control=0&#038;rel=0\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div>\n<h4 style=\"margin: 0;\">What advice do you have for new students?<\/h4>\n<blockquote><p><em>&#8220;<\/em><em>Data science and machine learning require an understanding of many interdisciplinary concepts, based on\u00a0<\/em><em>classical results from mathematics and statistics. I would encourage students to study these\u00a0fundamental sciences<\/em> <em>and look for solutions that are simple and amenable to intuitive interpretations.\u00a0As data scientists, you should have an open mind.<\/em> <em>Negative results are just as important as positive results.&#8221;<\/em><\/p><\/blockquote>\n","protected":false},"author":16254,"template":"","_links":{"self":[{"href":"https:\/\/www.bu.edu\/met\/wp-json\/wp\/v2\/profile\/2875"}],"collection":[{"href":"https:\/\/www.bu.edu\/met\/wp-json\/wp\/v2\/profile"}],"about":[{"href":"https:\/\/www.bu.edu\/met\/wp-json\/wp\/v2\/types\/profile"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/met\/wp-json\/wp\/v2\/users\/16254"}],"version-history":[{"count":22,"href":"https:\/\/www.bu.edu\/met\/wp-json\/wp\/v2\/profile\/2875\/revisions"}],"predecessor-version":[{"id":100019,"href":"https:\/\/www.bu.edu\/met\/wp-json\/wp\/v2\/profile\/2875\/revisions\/100019"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/met\/wp-json\/wp\/v2\/media?parent=2875"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}