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.

Office Hours: 12:00-2:00PM, on Fridays

Research Interests

Courses

Scholarly Works

Recent Journal Publications

V. Angampally and E. Pinsky. Comparing Classifiers in the Presence of Errors in True Label Assignment in Medical Datasets. Machine Learning and Applications: An International Journal (MLAIJ), 12(2), June 2025. URL: https://aircconline.com/ mlaij/V12N2/12225mlaij01.pdf .

A. Arystambekova and E. Pinsky. Enhancing Public Health Insights through AI-Driven Time-Series Analysis: Hierarchical Clustering, Hamming Distance, and Binary Tree Visualizations of Infectious Disease Trends. Computer Science and Mathematics Forum, 11(1):1–21, August 2025. URL: https://doi.org/10.3390/cmsf2025011023 .

N. Bhamre, P. Ekhande, and E. Pinsky. Enhancing Naive Bayes Algorithm with Stable Distributions for Classification. International Journal on Cybernetics and Informatics (IJCI), 14(2):107–117, April 2025. 3rd International Conference on Data Mining, Big Data and Machine Learning (DBML 2025). URL: https://doi.org/10.5121/ijci.2025.140207 .

D. Chawla, N. Desai, G. Sekharamantri, and E. Pinsky. Mining the Authors Catalog of the Russian Academy of Sciences: Some Statistics on Leonhard Euler’s Publications. International Journal of Data Mining and Knowledge Management Process, 15(2):17–31, March 2025. URL: http://doi.org/10.5121/ijdkp.2025.15202 .

J. Jingyi and E. Pinsky. Some Observations on Export/Import Dynamics in China (2015-2023). Social Sciences Insights Journal (SSIJ), 3(1), April 2025. doi:10.60036/ 4dbeh074 .

T. Katikreddy, U. Damadhar, and E. Pinsky. Exploring the Aspect Ratios of World Currencies: Mining the Numismatics Catalog. International Journal of Data Mining and Knowledge Management Process (IJDKP), 15(2):1–16, March 2025. URL: http: //doi.org/10.5121/ijdkp.2025.15201 .

S. Kotrakona, N. Liew, and E. Pinsky. Beyond Winners and Losers: Median Sector Rotation in the Japanese Equity Market. International Journal of Data Mining and Knowledge Management Journal, 15(3):1–15, May 2025. URL: https://doi.org/10. 5121/ijdkp.2025.15301 .

S. Kumar and E. Pinsky. Sell the Losers? Keep the Winners? None of the above Focus on the Median. Algorithmic Finance, January 2025. URL: http://doi.org/10. 1177/21576203241307779 .

T. Kundu and E. Pinsky. Predicting Daily Stock Movements with Deep Learning Models. Machine Learning and Applications (Elsevier), 22(100744):1–16, December 2025. URL: https://doi.org/10.1016/j.mlwa.2025.100744 .

M. Liang, Y. Gao, and E. Pinsky. Analyzing and Classifying Time-Series Trends in Medals. Computer Sciences and Mathematics Forum, 11(9):1–12, July 2025. URL: https://doi.org/10.3390/cmsf2025011009 .

N. Liew, S. Harinatha, S. Pattnaik, K. Park, and E. Pinsky. Inclusive Turnout for Equitable Policies: Using Time Series Forecasting to Combat Policy Polarization. Computer Sciences and Mathematics Forum, 11(1):11–0, August 2025. URL: https: //www.mdpi.com/2813-0324/11/1/11 .

S. Mandloi, A. Jalali, and E. Pinsky. An Adaptive Hierarchical Tree-Based Clustering Approach to Outlier Detection in ETF-Focused Financial Time-Series. Machine Learning and Applications International Journal (MLAIJ), 12(1):131–146, March 2025. URL: http://doi.org/10.5121/mlaij.2025.12109 .

B. Ozmen, S. Nishant, K. Shah, I. Berber, E. Pinsky, A. Rampazzo, G. Schwarz, and D. Singh. Development of A Novel Artificial Intelligence Clinical Decision Support Tool for Hand Surgery: HandRAG. Journal of Hand and Microsurgery, June 2025. URL: https://doi.org/10.1016/j.jham.2025.100293 .

B. Ozmen, N. Singh, S. Kavach, I. Berber, F.D. Singh, E. Pinsky, N. Sinclair, I. Raymond, and G.S. Schwarz. Development of a novel artificial intelligence clinical decision support system for aesthetic surgery: Aura. Aesthetic Surgery Journal, page sjaf120, 06 2025. arXiv:https://academic.oup.com/asj/advance-article-pdf/ doi/10.1093/asj/sjaf120/63537569/sjaf120.pdf , doi:10.1093/asj/sjaf120 .

S. Parab and E. Pinsky. Analyzing Fashion Trends Using Hierarchical Clustering and Temporal Analysis. Machine Learning and Applications International Journal (MLAIJ), 12(1):107–118, March 2025. URL: http://doi.org/10.5121/mlaij.2025.12107 .

K. Park, N. Liew, S. Pattnaik, A. Kures, and E. Pinsky. Exploring the Transition to Low-Carbon Energy: A Comparative Analysis of Population, Economic Growth, and Energy Consumption in Oil Producing OECD and BRICS Countries. Sustainability, 17(13), July 2025. URL: https://doi.org/10.3390/su17136221 .

S. Pattnaik, M. Rizinski, and E. Pinsky. Rethinking Inequality: The Complex Dynamics Beyond the Kuznets Curve. Data, 10(88):1–32, June 2025. URL: https://doi.org/ 10.3390/data10060088 .

S. Pattnaik and E. Pinsky. Spatio-Temporal Machine Learning for Marine Pollution Prediction: A Multi-Modal Approach for Hotspot Detection and Seasonal Pattern Analysis in Pacific Waters. Toxics, 13(10), Septemver 2025. URL: https://doi.org/ 10.3390/toxics13100820 .

S. Pattnaik and E. Pinsky. Hamming Diversification Index: A New Clustering-based metric to Understand and Visualize Time-Evolution of Patterns in Multi-Dimensional Data sets. Applied Sciences, 15(14), July 2025. URL: https://doi.org/10.3390/ app15147760 .

S. Pattnaik and E. Pinsky. Similarity and Novelty Metrics: A Machine Learning Framework for Audience Extension. Informatics Engineering: An International Journal, 9:1–15, June 2025. URL: https://doi.org/10.5121/ieij.2025.9201 .

E. Pinsky. Computation and Interpretation of Mean Absolute Deviations by Cumulative Distribution Functions. Frontiers in Applied Mathematics and Statistics, 11:1–14, February 2025. URL: https://doi.org/10.3389/fams.2025.1487331 .

P. Talmar, A. Jain, and E. Pinsky. Should you sleep or trade bitcoin? unveiling bitcoin’s price behavior with deep learning. Computer Sciences and Mathematics Forum, 11(1):1–10, August 2025. URL: https://doi.org/10.3390/cmsf2025011020 .

E. Pinsky and S. Shah. Estimating the Accuracy of a Bagged Ensemble. Machine Learning and Applications International Journal (MLAIJ), 12(1):89–105, march 2025. URL: http://doi.org/10.5121/mlaij.2025.12106 .

E. Pinsky and S. Shah. The Silver Lining of Daily Bitcoin Trading. Technical Analysis of Stocks and Commodities, pages 1–5, June 2025.

E. Pinsky and S. Shah. Momentum-based Trading Strategies in Crude Oil ETFs and Futures. Technical Analysis of Stocks and Commodities, 43(9):20–23, September 2025.

E. Pinsky and Q. Wen. Simple Approximations and Interpretation of Pareto Index and Gini Coefficient using Mean Absolute Deviation and Quantile Functions. Econometrics, 13(3), August 2025. URL: https://doi.org/10.3390/econometrics13030030 .

T. Sharma, A. Srivastava, and E. Pinsky. Optimizing Sector Index Rotation and Rebalancing Frequency With Data Mining: A Case Study on Indian National Stock Exchange. Data Mining and Knowledge Management Journal, 15(2):55, March 2025. URL: http://doi.org/10.5121/ijdkp.2025.15205 .

B. Sharma and E. Pinsky. Some Patterns of Sleep Quality and Daylight Saving Time Across Countries: A Predictive and Exploratory Analysis. International Journal of Data Mining and Knowledge Processing, 15(4):1–16, July 2025. doi:10.5121/ijdkp. 2025.15401 .

A. Singh and E. Pinsky. Data Mining and Analysis of Early Modern European Enlightenment Trends in Acta Eruditorum. Data Mining and Knowledge Management Journal, 15(2):45–55, March 2025. URL: http://doi.org/10.5121/ijdkp.2025.15204 .

Z. Wang, W. Zhang, and E. Pinsky. Comparative Analysis of Temperature Prediction Models: Simple Models vs. Deep Learning Models. Computer Science and Mathematics Forum, 11(1):1–16, July 2025. URL: https://doi.org/10.3390/cmsf2025011006 .

A. Warty and E. Pinsky. Data Mining for Bernford Law in Ancient Roman Coins. International Journal of Data Mining and Knowledge Management Process, 15(2):33–44, March 2025. URL: http://doi.org/10.5121/ijdkp.2025.15203 .

X. Zeng and E. Pinsky. Qantile Regression with Q₁/Q₃Anchoring: a Robust Alternative for Outlier Resistant Modeling. Machine Learning and Applications International Journal, 12(1):119–130, March 2025. URL: http://doi.org/10.5121/mlaij.2025.12108 .

X. Zeng and E. Pinsky. Elliptical Mixture Models Improve the Accuracy of Gaussian Mixture Models with Expectation-Maximization Algorithm. International Journal on Cybernetics and Informatics, 14(2):87–106, April 2025. URL: https://doi.org/10. 5121/ijci.2025.140206 .

X. Zeng and E. Pinsky. Stable Distribution Naive Bayes Achieves Higher Accuracy Than Traditional Naive Bayesian Classification. International Journal on Cybernetics and Informatics, 14(2):71–86, April 2025. URL: https://doi.org/10.5121/ijci.2025.140205 .

W. Zhang, Z. Wang, and E. Pinsky. Mad-deviations for Hyperexponential and Hypoexponential distributions. Operations Research and Applications International Journal, 12(2):19–37, May 2025. URL: https://airccse.com/oraj/papers/ 12225oraj02.pdf .

X. Zhang and E. Pinsky. Comparing Algorithmic Trading Strategies by Analogies to Machine Learning. Algorithmic Finance, page 21576203251360571, August 2025. URL: https://doi.org/10.1177/21576203251360571 .

X. Zhang and E. Pinsky. SP-500 vs. Nasdaq-100 Price Movement Prediction with LSTM for Different Daily Periods. Machine Learning with Applications (Elsevier), 19:1–19, January 2025. URL: http://doi.org/10.1016/j.mlwa.2024.100617 .

D. Aktaukenov, M. Alshaalan, A. Omirbekova1, and E. Pinsky. A Predictive Model for Oil Well Maintenance: A Case Study in Kazakhstan. SOCAR: Reservoir and Petroleum Engineering, 1:048–056, 2024. URL: https://doi.org/10.5510/OGP20240100939 .

S. Goldberg, L. Salnikov, N. Kaiser, T. Srivastava, and E. Pinsky. Correcting user decisions based on incorrect machine learning decisions. In Kohei Arai, editor, Advances in Information and Communication, pages 12–20, Cham, 2024. Springer Nature Switzerland. URL: http://doi.org/10.1007/978-3-031-54053-0_2 .

A. Maheshwaria, V. Shah, A. Sawhney, and E. Pinsky. Machine Learning-based Analysis and Effective Visualization of Mutual Funds through CUSUM and Clustering. Journal of Business Data Science Research, 3(1):21–35, 2024.

M. Martin, M. Meunier, I. Rennes, P. Moreau, J. Gadenne, J. Dautel, F. Catherin, E. Pinsky, and R. Rawassizadeh. ADA-SHARK: A Shark Detection Framework Employing Underwater Cameras and Domain Adversarial Neural Nets. volume 7, pages 1–25. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, January 2024. URL: http://doi.org/10.1145/3631416 .

B. Ozmen, E. Pinsky, and G. Schwarz. Future of Outcomes Research in Plastic Surgery: Artificial Intelligence Generated Synthetic Data and Predictive Models. Journal of Plastic, Reconstructive and Aesthetic Surgery: JPRAS, 94:38–39, 2024. URL: http: //doi.org/10.1016/j.bjps.2024.05.014 .

S. Pattnaik, P. Danole, S. Mandiya, A. Foroutan, G. Mashhadiagha, Y. Khanghah, K. Isazadehfar, and E. Pinsky. Analyzing Patterns of Injury in Occupational Hand Trauma Focusing on Press Machines: A Registry-Based Study and Machine Learning Analysis. Engineering Proceedings, 68(61), 2024. URL: http://doi.org/10.3390/ engproc2024068061 .

S. Pattnaik, N. Liew, A. Kures, E. Pinsky, and K. Park. Catalyzing Supply Chain Evolution: A Comprehensive Examination of Artificial Intelligence Integration in Supply Chain Management. Engineering Proceedings, 68(57), 2024. URL: https://doi.org/10.3390/engproc2024068057 .

E. Pinsky. Mean Absolute Deviation (About Mean) Metric for Kurtosis. Advanced Research in Sciences (ARS), 2(2), 2024. URL: http://doi.org/10.54026/ARS/1021 .

E. Pinsky, E. Meunier, P. Moreau, and T. Sharma. A Simple Computational Approach to Predict Long-Term Hourly Electric Consumption. Engineering Proceedings, 68(69), 2024.

E. Pinsky and K. Piranavakumar. A Machine Learning-Based Approach to Analyze and Visualize Time-Series Sentencing Data. Engineering Proceedings, 68(50), 2024. URL: http://doi.org/10.3390/engproc2024068050 .

E. Pinsky, W. Zhang, and Z. Wang. Pareto Distribution of the Forbes Billionaires. Computational Economics, 9:1–28, September 2024. URL: http://doi.org/10.1007/ s10614-024-10730-1 .

M. Rizinski, A. Jankov, V. Sankaradas, E. Pinsky, I. Mishkovski, and D. Trajanov. Comparative Analysis of NLP-Based Models for Company Classification. Information, 55, 2024. URL: https://doi.org/10.3390/info15020077 .

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

F. Elakoum, 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

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

L. Salnikov, S. Goldberg, H. Rijhwani, Y. Shi, and E. Pinsky. The RNA-Seq Data Analysis shows How the Ontogenesis Defines Aging. Frontiers in Aging, 4, March 2023. URL: http://doi.org/10.3389/fragi.2023.1143334 .

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

M. Ma and E. Pinsky. Using Machine Learning to Identify Primary Features in Choosing Electric Vehicles Based on Income Levels. Data Science and Management, 2023. URL: http://doi.org/10.1016/j.dsm.2023.10.001 .

Y. Wang and E. Pinsky. Geometry of deviation measures for triangular distributions. Frontiers in Applied Mathematics and Statistics, 9, December 2023. URL: http://doi. org/10.3389/fams.2023.1274787 .

S. Goldberg and E. Pinsky. Building a meta-agent for human-machine dialogue in machine learning systems. In Kohei Arai, editor, Advances in Information and Communication, pages 474–487, Cham, March 2022. Springer International Publishing. URL: http://doi.org/10.1007/978-3-030-98015-3_33 .

E. Pinsky, 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

L. Salnikov, S. Goldberg, P. Sukumaran, and E. Pinsky. DNA Methylation Meta-Analysis Confirms the Division of the Genome into Two Functional Groups. Journal of Cell Science and Therapy, 13(353), 2022. URL: http://doi.org/10.1101/ 2022.01.10.475724 .

A. Vasiukevich and E. Pinsky. Constructing Portfolios Using Stable Distributions: The Case of SP-500 Sectors Exchange-traded Funds. Machine Learning with Applications, 10:100434, 2022. URL: http://doi.org/10.1016/j.mlwa.2022.100434 .

Y. Yang and E. Pinsky. A Simple Rotation Strategy with Sector ETFs. Technical Analysis of Stocks and Commodities, 40(12):36–40, December 2022.

J. Yust, L. Jaeseong, and E. Pinsky. A Clustering-Based Approach to Automatic Harmonic Analysis: An Exploratory Study of Harmony and Form in Mozart’s Piano Sonatas. Trans. Int. Soc. Music. Inf. Retr., 5(1):113–128, 2022. URL: http://doi. org/10.5334/tismir.114 .

L. Salnikov, E. Pinsky, and B. Galitsky. A Bi-directional Adversarial Explainability for Decision Support. Journal of Human-Intelligent Systems Integration, 3(1), 2021. URL: http://doi.org/10.1007/s42454-021-00031-5 .

M. Homayounfar, A. Malekijoo, A. Visuri, C. Dobbins, E. Peltonen, E. Pinsky, K. Teymourian, and R. Rawassizadeh. Understanding Smartwatch Battery Utilization in the Wild. Sensors (Basel, Switzerland), 20, 2020. URL: https://api. semanticscholar.org/CorpusID:220436514 .

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

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.

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

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 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.

Conference and Workshop Publications

E. Pinsky, D. Taratynova, A. Kassenova, B. Dauletbayev, and M. Al-Shedivat. A Predictive Model of Arrival Times for Smart Shuttle Buses in Astana, Kazakhstan. In T. Zlateva and G. Tuparov, editors, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, volume 609, pages 29–49, Sofia, Bulgaria, March 2025. European Alliance for Innovation (EIA), Springer. 20-th EAI Computer Science and Education Conference, CSECS 2024. URL: http://doi.org/10.1007/978-3-031-84312-9_2 .

E. Pinsky, S. Pattnaik, and K. Park. The Periodic Table: Chemical Properties and Mendeleev meets Physical Properties and Machine Learning. In T. Zlateva and G. Tuparov, editors, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, volume 609, pages 97–116, Sofia, Bulgaria, March 2025. European Alliance for Innovation (EIA), Springer. 20-th EAI Computer Science and Education Conference, CSECS 2024. URL: http://doi.org/ 10.1007/978-3-031-84312-9_7 .

E. Pinsky and M. Liang. Analyzing the Geometric Ratios of Greek Vases. In T. Zlateva and G. Tuparov, editors, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, volume 609, pages 154–165, Sofia, Bulgaria, March 2025. European Alliance for Innovation (EIA), Springer. 20-th EAI Computer Science and Education Conference, CSECS 2024. URL: http://doi.org/ 10.1007/978-3-031-84312-9_11 .

E. Pinsky, G. Izbassova, and S. Minhaj. Teaching and Learning Python by Comparative Visualization. In T. Zlateva and G. Tuparov, editors, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, volume 609, pages 310–331, Sofia, Bulgaria, March 2025. European Alliance for Innovation (EIA), Springer. 20-th EAI Computer Science and Education Conference, CSECS 2024. URL: http://doi.org/10.1007/978-3-031-84312-9_21 .

S. Klawansky, B. Balswick, M. Alsayel, I. Charvachidze, A. Manghwani, P. Almeida, D. Dalvi, J. Vora, and E. Pinsky. On some alternative probability density metrics for analyzing empirical datasets. In T. Zlateva and G. Tuparov, editors, Computer Science and Education in Computer Science, volume 514, pages 40–47. Springer, March 2024. 19-th EAI Computer Science and Education Conference, CSECS 2024. URL: http://doi.org/10.1007/978-3-031-44668-9_3 .

N. Liew, S. Pattnaik, A. Kures, E. Pinsky, and K. Park. Machine Learning Innovations in Supply Chain Management: Revolutionizing Predictive Modeling for Efficiency and Growth. Boston, MA, December 2024. Second International Symposium on The Paradigm Shift: Entrepreneurship in Next Generation.

N. Liew, S. Pattnaik, A. Kures, K. Park, and P. Pinsky. AI Revolutionizing Supply Chain Management: Advances, Insights, and Implications for Business Backorder and Predictions. In 44-th Annual Conference, Ankara, Turkey, October 2024. SMS.

A. Nittur and E. Pinsky. Impact Of Large Language Models on Entrepreneurship: Challenges for Innovation. Boston, MA, December 2024. Second International Symposium on The Paradigm Shift: Entrepreneurship in Next Generation.

K. Park, E. Pinsky, N. Kaiser, A. Subramani, and Y. Ying. The application of data analytics for understanding patterns of mergers and acquisitions and ceo characteristics in and between crisis times. In T. Zlateva and G. Tuparov, editors, Computer Science and Education in Computer Science, volume 514, pages 265–280, Boston, USA, March 2024. European Alliance for Innovation (EIA), Springer. 19-th EAI Computer Science and Education Conference, CSECS 2024. URL: http://doi.org/10.1007/ 978-3-031-44668-9_21 .

S. Pattnaik and E. Pinsky. Alpha-Based Similarity Metric in Computational Advertising: A New Approach to Audience Extension. In T. Zlateva and G. Tuparov, editors, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, volume 514, pages 3–29, Boston, USA, March 2024. European Alliance for Innovation (EIA), Springer. 19-th EAI Computer Science and Education Conference, CSECS 2024. URL: http://doi.org/10.1007/ 978-3-031-44668-9_1 .

S. Pattnaik, N. Liew, A. Kures, K. Park, and P. Pinsky. Machine Learning Innovations in Supply Chain Management: Revolutionizing Predictive Modeling for Efficiency and Growth. In 25-th Annual Conference, number 4, pages 575–586. Global Business and Technology Association (GBATA), July 2024. ISBN: 1-932917-20-9.

S. Pattnaik, K. Park, and E. Pinsky. Sustainability across countries. Cambridge, MA, April 2024. Northeast Decision Sciences Conference.

S. Pattnaik and E. Pinsky. Similarity-Novelty Metric: An Alternative Algorithm for Efficient Audience Extension. Cambridge, MA, April 2024. Northeast Decision Sciences Conference.

R. Khan, P. Schena, K. Park, and E. Pinsky. A clustering approach to analyzing NHL goaltenders’ performance. In Tanya Zlateva and Rossitza Goleva, editors, Computer Science and Education in Computer Science, pages 3–10, Cham, 2022. Springer Nature Switzerland. URL: http://10.1007/978-3-031-17292-2_1 .

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).

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.

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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