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📼 About Me

My name is Konstantinos Pelechrinis and I joined the University of Pittsburgh in Fall 2010, where I am an Associate Professor at the School of Computing and Information and I lead the Network Data Science Lab. Prior I received my Phd degree from the Computer Science department at the University of California at Riverside, under the supervision of Prof. Srikanth V. Krishnamurthy. Before joining UCR, I obtained my Diploma degree from the Electrical and Computer Engineering department of the National Techincal University of Athens, where I worked with Prof. Vasileios Maglaris at the Network Management and Optimal Design Laboratory.

🧮 Education

• PhD, University of California, Riverside, Computer Science, 2008 - 2010 (Advisor: Srikanth V. Krishnamurthy - GPA: 4.0/4.0)
  PhD Thesis: Security and Performance Considerations in Wireless Networks
• MSc, University of California, Riverside, Computer Science, 2006-2008 (Advisor: Srikanth V. Krishnamurthy - GPA: 4.0/4.0)
  Master Thesis: Design of a measurement driven anti-jamming system.
• Diploma, National Technical University of Athens, Electrical and Computer Engineering, 2001-2006 (Advisor: Vasileios Maglaris - GPA: 9.42/10.0)
  Dimploma Thesis: Analysis of Intrusion Detection Systems - theoretical aspects and statistical modeling of data fusion algorithms.

📁 RESEARCH

My research interest include:

• Network science
• Urban informatics
• Sports analytics

In more detail, my research interestes includes network science and the development of metrics and models for complex systems. I am involved in: (a) developing models and algorithms for intelligent urban services, (b) understanding the social, spatial, temporal and network dynamics of the behavior of people as captured through LBSNs, and (c) studying the effect of LBSNs on bussinesses.

I am also interested in the development of evaluation metrics for sports teams and players, as well as in the theoretical modeling of sports that will allow us understand deeper the game.

🪙🪙 Funding

• "DeepHoops: AI and Machine Learning applications for gaining an edge in basketball", Mark Cuban Foundation.
• "RAPID: Understanding and Enhancing Internet Connectivity of Underserved Communities During the COVID-19 Crisis", National Science Foundation - Division of Computer and Network Systems.
• "CPS: TTP Option: Medium: Building a Smart City Economy and Information Ecosystem to Motivate Pro-Social Transportation Behavior", National Science Foundation - Division of Computer and Network Systems.
• "Models and Metrics for Composite Socio-Spatial Networks", Army Research Office - Young Investigator award.

🎓 TEACHING

I have taught the following classes (material is available only through the University's blackboard):

• INFSCI 0610, Networks and Information - Undergraduate
• INFSCI 0530, Decision Making in Sports (Fall 2021) - Undergraduate
• INFSCI 1530 (previously INFSCI 1040), Data Mining - Undergraduate
• INFSCI 1091, Moneyball 2.0: Winning in Sports with Data (Spring 2018) - Undergraduate
• INFSCI 1071, Applications of Networks - Undergraduate
• INFSCI 2595, Machine Learning - Graduate
• CMPINF 2221, Applied Bayesian Analysis - Graduate
• TELCOM 2125/INFSCI 2125, Network Science and Analysis (Spring 2013, Spring 2014, Spring 2015, Spring 2016) - Graduate, PhD
• TELCOM 2121, Network Management - Graduate, MST
• TELCOM 2310, Computer Networks - Graduate, MST/PhD
• INFSCI 1071/TELCOM 2310, Applications of Networks/Computer Networks - Cross listed course (BSIS, MSIS and MST)
• INFSCI 3350, Doctoral Seminar on Location Based Social Networks - Graduate, PhD

🪙🪙Funding

• "CyberTraining: CDL: Security-assured Data Science Workforce Development in Pennsylvania", National Science Foundation - Office of Advanced Cyberinfrastructure
• "A Curriculum for Security Assured Health Informatics", National Science Foundation - Division of Graduate Education.

📚 SELECTED PUBLICATIONS

Selected Publications

Following is a selected list of publications. A full publication list can be found here, while datasets from these publications can be found here and on my GitHub page with sample scripts as well.

Journals:

• X. Liu and K. Pelechrinis, "Uncovering and estimating complementarity in urban lives", in EPJ Data Science 14, 9 (2025). https://doi.org/10.1140/epjds/s13688-025-00527-z.
• A. Damoulaki, I. Ntzoufras, and K. Pelechrinis, "Lasso multinomial performance indicators for in-play basketball data", in Computational Statistics (2025). https://doi.org/10.1007/s00180-025-01604-7.
• K. Pelechrinis, X. Liu, P. Krishnamurthy and A. Babay, "Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot", in PLoS ONE 18(6): e0287262. https://doi.org/10.1371/journal.pone.0287262.
• K. Pelechrinis, "Quantifying Implicit Biases in Refereeing using NBA Referees as a Testbed", in Nature Scientific Reports, 13, 4664. https://doi.org/10.1038/s41598-023-31799-y. March 2023.
• K. Pelechrinis, W. Winston, "The Hot Hand in the Wild", in PLoS ONE 17(1): e0261890. https://doi.org/10.1371/journal.pone.0261890.
• X. Liu, K. Pelechrinis, "Excess demand prediction for bike sharing systems", in PLoS ONE 16(6): e0252894. https://doi.org/10.1371/journal.pone.0252894.
• K. Pelechrinis, W. Winston, "A Skellam regression model for quantifying positional value in soccer", in Journal of Quantitative Analysis in Sports, vol. 17, no. 3, 2021.
• S. Daraei, K. Pelechrinis, D. Quercia, "A data-driven approach for assessing biking safety in cities", in EPJ Data Sci. 10, 11 (2021). https://doi.org/10.1140/epjds/s13688-021-00265-y
• K. Pelechrinis, W. Winston, "A Skellam regression model for quantifying positional value in soccer", to appear in the Journal of Quantitative Analysis in Sports.
• X. Ge, P. Chrysanthis, K. Pelechrinis and D. Zeinalipour, "Serendipity-based Points-of-Interest Navigation", to appear in ACM Transactions on Internet Technology.
• S. Mallepalle, R. Yurko, K. Pelechrinis and S. Ventura, "next-gen-scraPy: Extracting NFL Tracking Data from Images to Evaluate Quarterbacks and Pass Defenses", in Journal of Quantitative Analysis in Sports,vol. 6, no. 2, 2020.
• R. Yurko, F. Matano, L. F. Richardson, N. Granered, T. Pospisil, K. Pelechrinis and S. Ventura, "Going Deep: Models for Continuous-Time Within-Play Valuation of Game Outcomes in American Football with Tracking Data", in Journal of Quantitative Analysis in Sports,vol. 6, no. 2, 2020.
• S. Hajiseyedjavadi, Y. Lin and K. Pelechrinis, "Learning Embeddings for Multiplex Networks using Triplet Loss", in Springer Applied Network Science, vol. 4, no. 1, 2019.
• X. Wen, Y. Lin and K. Pelechrinis, "Event Analytics via Discriminant Tensor Factorization"," in Transactions on Knowledge Discovery from Data (TKDD), vol.12, no. 6, 2018.
• J.B. Colditz, K. Chu, G.E. Switzer, K. Pelechrinis, B.A. Primack, "Online data to contextualize waterpipe tobacco smoking establishments surrounding large US universities". to appear in Health Informatics Journal.
• A. Fairchild, K. Pelechrinis and M. Kokkodis, "Spatial Analysis of Shots in MLS: A Model for Expected Goals and Fractal Dimensionality", to appear in Journal of Sports Analytics.
• K. Zhang, K. Pelechrinis, and T. Lappas, "Effects of Promotions on Location-based Social Media: Evidence from Foursquare", International Journal of Electronic Commerce, 22:1, 36-65, DOI: 10.1080/10864415.2018.1396118, 2018.
• K. Pelechrinis, C. Zacharias, M. Kokkodis and T. Lappas, "Economic Impact and Policy Implications from Urban Shared Transportation: The Case of Pittsburgh’s Shared Bike System", in PLoS ONE 12(8): e0184092. https://doi.org/10.1371/journal.pone.0184092.
• S. Kotiloglou, P. Repoussis, T. Lappas and K. Pelechrinis, "Personalized Multi-Period Touristic Tour Recommendations", in Tourism Management 62: 76-88, 2017.
• K. Pelechrinis and E. Papalexakis, "The Anatomy of American Football: Evidence from 7 Years of NFL Game Data", in PLoS ONE 11(12): e0168716. doi:10.1371/journal.pone.0168716.
• E. Papalexakis, B. Hooi, K. Pelechrinis and C. Faloutsos, "Power-Hop: A Pervasive Observation for Real Complex Networks", in PLoS ONE 11(3): e0151027. doi:10.1371/journal.pone.0151027.
• K. Pelechrinis and D. Wei, "VA-index: Quantifying Assortativity Patterns in Networks with Multidimensional Nodal Attributes", in PLoS ONE 11(1): e0146188. doi:10.1371/journal.pone.0146188.
• E. Galbrun, K. Pelechrinis and E. Terzi, "Urban Navigation Beyond Shortest Route: The Case of Safe Paths", in Elsevier Information Systems, Volume 57, pp. 160–171, April 2016.
• K. Pelechrinis and P. Krishnamurthy, "Socio-Spatial Affiliation Networks", in Elsevier Computer Communications, Volume 73, Part B, pp. 251–262, January 2016.

Conferences:

• I. Nikolaou, K. Pelechrinis, and E. Terzi, "Understanding team collapse via probabilistic graphical models", in ACM SIGKDD '25, Toronto, Canada, August, 2025.
  
  
• X. Wen, Y. Lin, Y. Ahn, and K. Pelechrinis, "FacIt: Factorizing Tensors into Interpretable and Scrutinizable Patterns", in IEEE VIS '19, Vancouver, BC, Canada, October,2019.
• A. Sicilia, K. Pelechrinis and K. Goldsberry. "DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data", in ACM SIGKDD '19, Anchorage, AK, August, 2019.
  
  


• E. Papalexakis and K. Pelechrinis, "tHoops: A Multi-Aspect Analytical Framework for Spatio-Temporal Basketball Data", in ACM CIKM ’18, Torino, Italy, October, 2018.
• K. Pelechrinis, "LinNet: Probabilistic Basketball Lineup Evaluation Through Network Embedding", in ECML/PKDD ’18, Dublin, Ireland, September, 2018.
• K. Zhang, J. Xu, M. R. Min, G. Jiang, K. Pelechrinis and H. Zhang, "Automated IT System Failure Prediction: A Deep Learning Approach”" in IEEE Big Data ’16, Washington, D.C., December, 2016.
• X. Wen, Y. Lin and K. Pelechrinis, "PairFac: Event Analytics through Discriminant Tensor Factorization", in ACM CIKM '16, Indianapolis, IN, October, 2016.
• K. Zhang, J. Xu, M. R. Min, G. Jiang, K. Pelechrinis and H. Zhang, "Automated IT System Failure Prediction: A Deep Learning Approach", in IEEE Big Data 2016, Washington, D.C., December, 2016
• K. Zhang and K. Pelechrinis,"Do Street Fairs Boost Local Businesses? A Quasi-Experimental Analysis Using Social Network Data", in ECML/PKDD 2016, Riva Del Garda, Italy, September, 2016

🗞️ Tutorials, Talks and Keynotes

• "PittSmartLiving: Rethinking Public Transportation through Incentives", invited talk at the 2nd Workshop on Data-driven Intelligence Transportation at ACM SIGKDD 2019, Anchorage, AL, August, 2019.
• "Winning in Basketball with Data and Machine Learning", Keynote at Math Sports International, Athens, Greece, July, 2019.
• "Athlytics: How Teams are Turning to Data to Gain an Edge", Keynote at Sports Mediathon, Buenos Aires, Argentina, September, 2018.
• "Soccer Analytics: Past, Present and Future", tutorial at Sports Mediathon, Buenos Aires, Argentina, September, 2018.
• "Winning in Sports with Data and Machine Learning", tutorial at Sports Mediathon, Buenos Aires, Argentina, September, 2018.
• "PittSmartLiving: Rethinking Smart Cities". TEDxUniversityOfPittsburgh, Pittsburgh, PA, 2018.
• "Winning in Sports with Data and Machine Learning". MIT Sloan Sports Analytics Conference 2018, Boston, MA.
• "Athlytics: Winning in Sports with Data", with Evangelos Papalexakis. ACM WSDM 2018, Marina Del Rey, CA.
• "Athlytics: Data Mining and Machine Learning for Sports Analytics", with Ben Alamar and Evangelos Papalexakis. ACM SIGKDD 2017, Halifax, Nova Scotia, Canada.
• "An Introduction to Computational Urban Science", with Daniele Quercia. International Conference on Social Computing, Behavioral-Cultural Modeling, Prediction and Behavior Representation in Modeling and Simulation 2016. Washington, DC.
• "The Web of Cities and Mobility",with Danile Quecia, Anastasios Noulas and Bruno Goncalves. AAAI ICWSM 2016, Cologne, Germany
• "Integrating and analyzing heterogeneous information: The Case of Urban Informatics". IEEE IRI 2015, San Francisco, CA.
• "Urban Informatics and the Web", with Daniele Quercia. ACM WWW 2015, Florence, Italy.

📄 CV

You can find my full CV here.