Slobodan Vucetic


Director, Center for Hybrid Intelligence (CHI) (former Center for Cognitive Computing)

Professor, Department of Computer and Information Sciences (CIS)

Temple University

Address: 1925 N. 12th St., 314 SERC
Philadelphia, PA 19122-6094

Telephone: (215) 204-5535
Fax: (215) 204-5082
Office: 314 SERC



  • (December 2020) Co-organizing a NAACL Workshop on Data Science with Human in the Loop: Language Advances
  • (October 2020) Our paper about job title normalization accepted at COLING 2020, the first author is Phong Ha, an undergraduate student
  • (September 2020) New NSF Future of Work project aimed at helping young adults with neurodevelopmental disabilities have careers in IT
  • (August 2020) Co-organizing a KDD Workshop on Data Science with Human in the Loop
  • (Spring/Summer 2020) Two papers resulting from our NSF project about cancer epidemiology published in Epidemiology and in Cancer Epidemiology and Prevention Biomarkers
  • (May 2020) Our paper about scalable SVMs got accepted at ICML 2020
  • (May 2020) Our paper on the state of the art protein secondary structure prediction published in PLOS One
  • (January 2020) New ONR research project aimed at advancing additive technology
  • (December 2019) Congratulations to Maxim for defending his Ph.D. thesis
  • (September 2019) New NSF Growing Convergence Research project aimed at better understanding epistasis in biology

Research Interest

  • Human Factors in Artificial Intelligence
  • Artificial Intelligence
  • Data Science
  • Applied Machine Learning
  • Biomedical Informatics
  • Bioinformatics

I am interested in solving real-life data science problems through development of novel machine learning algorithms. I am also interested in building software that has intelligent behavior and can enhance human capabilities. My research is driven by open data science problems in a wide array of disciplines such as Public Health, Biomedicine, Physical Sciences, Education, Marketing, Social Sciences, Traffic Engineering, and Industrial Engineering.

  • Personalized Virtual Job Assistant for Neurodiverse Individuals  (funded by the National Science Foundation)
  • Understanding Epistasis: the Key for Genotype to Phenotype Mapping (funded by the National Science Foundation)
  • Deep Learning for Representation of Medical Claims (funded by the National Institutes of Health
  • Space-Time Models for Health Geographic Analysis (funded by the National Science Foundation)
  • Customizing Therapy for Individuals with Autism (funded by the National Science Foundation)
  • Discriminative Modeling of Spatial-Temporal Data in Remote Sensing (funded by the National Science Foundation)
  • Dynamic Evolution of Smart-Phone Based Emergency Communications Network (funded by the National Science Foundation)
  • Computational Advertising (supported by Yahoo! Faculty Research and Engagement Program)
  • Memory-Constrained Predictive Data Mining (funded by the National Science Foundation)
  • Machine Learning for Distributed Fault Diagnosis (supported by ExxonMobil)
  • Bioinformatics – Genomics, Analysis of Protein Disorder, Proteomics, Biomedical Text Mining (funded by Pennsylvania Department of HealthNIH

Recent Representative Publications

(for the full list, check my Google Scholar page and a list with many preprints)

  • Bai, T., Egleston, B.L., Bleicher, R., Vucetic, S.Medical Concept Representation Learning from Multi-Source Data, International Joint Conference on Artificial Intelligence (IJCAI), Macao, 2019.
  • Zhang, S., He, L., Dragut, E., Vucetic, S.How to Invest my Time: Lessons from Human-in-the-Loop Entity Extraction, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Anchorage, 2019.
  • Bai T., Vucetic, S.Improving Medical Code Prediction from Clinical Text via Incorporating Online Knowledge Sources, The World Wide Web Conference (WWW), San Francisco, 2019.
  • Maiti, A., Vucetic, S., Spatial Aggregation Facilitates Discovery of Spatial Topics, Annual Meeting of the Association for Computational Linguistics (ACL), Florence, 2019.
  • Shapovalov, M., Vucetic, S., Dunbrack, Jr, R.L., A New Clustering and Nomenclature for Beta Turns Derived from High-Resolution Protein StructuresPLoS Computational Biology, 15 (3), 2019.
  • Vucetic, S., Chanda, A.K., Zhang, S., Bai, T., Maiti, A., Peer Assessment of CS Doctoral Programs Shows Strong Correlation with Faculty CitationsCommunications of the ACM, 61:09, 70-77, 2018.
  • Bai, T., Chanda, A.K., Egleston, B.L., Vucetic, S.EHR Phenotyping via Jointly Embedding Medical Concepts and Words into a Unified Vector SpaceBMC Medical Informatics and Decision Making, 2018.
  • Bai, T., Zhang, S., Egleston, B.L., Vucetic, S.Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), London, 2018.
  •  Zhang, S., He, L., Vucetic, S., Dragut, E., Regular Expression Guided Entity Mention Mining from Noisy Web Data, Empirical Methods in Natural Language Processing Conference (EMNLP), Brussels, 2018.


  • Aniruddha Maiti (Ph.D., joined 2015, M.S. from Indian Institute of Technology-Kharagpur)
  • Ashis Chanda (Ph.D., joined 2016, M.S. from University of Dhaka)
  • Ziyu Yang (Ph.D., joined 2017, B.S. from University of Science and Technology of China)
  • Saman Enayati (Ph.D., joined 2017, B.S. from Amirkabir University of Technology)
  • Sandro Hauri (Ph.D., joined 2018, M.S. from Swiss Federal Institute of Technology in Zurich)
  • Tamara Katic (Ph.D. joined 2019, M.S. from University of Novi Sad)
  • Sai Shi (Ph.D., joined 2019, M.S. from University of California-Irvine)
  • Piyush Borole (P.D., joined 2019, M.S. from University of Pennsylvania)
  • Beth Garrison (Ph.D., joined 2019, B.S. from Temple University)
  • Hanzi Xu (Ph.D., joined 2020, M.S. from Georgetown University)
  • Dalvir Singh (4+1 student)
  • Shakirah Cooper (undergraduate)
  • Abigail Liu (undergraduate)
  • Tuan Lam (undergraduate)
  • Sameera Rachakonda (undergraduate)
  • Annica Chiu (undergraduate)
  • Rachel Lazzaro (undergraduate)
  • Carlos Gonzalez (undergraduate)
  • Maxim Shapovalov (Ph.D., 2020, first position: Postdoc, Fox Chase Cancer Center)
  • Tian Bai (Ph.D., 2019, first position: Software Engineer, Google)
  • Shanshan Zhang (Ph.D., 2019, first position: Research Scientist, Facebook)
  • Vladimir Coric (Ph.D., 2014, first position: Lead Data Scientist, SEI Investments)
  • Nemanja Djuric (Ph.D., 2013, first position: Research Scientist, Yahoo! Labs, current: Uber ATC)
  • Liang Lan (Ph.D., 2012, first position: Research Scientist, Huawei Noah Ark Lab, current: Assistant Prof., Hong Kong Baptist University)
  • Mihajlo Grbovic (Ph.D., 2012, first position: Research Scientist, Yahoo! Labs, current: Airbnb)
  • Vuk Malbasa (Ph.D., 2011, first position: Postdoc, Texas A&M University, current: Assistant Prof., University of Novi Sad)
  • Zhuang Wang (Ph.D., 2010, first position: Research Scientist, Siemens Corporate Research, current: Facebook)

I am interested in advising self-motivated graduate students interested in machine learning, cognitive computing, and data science. I am looking both at the traditional computer science students and at the non-CS students with a strong background in mathematics (with degrees such as electrical engineering, physics, operations research, applied math, statistics). Interested students are encouraged to contact me by e-mail. Please, send me your CV and write a short description about your academic background and research interests.

Recent Teaching

  • Fall 2020: Machine Learning (CIS 5526)
  • Spring 2020: Principles of Data Science (CIS 3715)
  • Fall 2019: Machine Learning (CIS 5526)
  • Spring 2019: Principles of Data Science (CIS 3715)
  • Fall 2018: Machine Learning (CIS 5526)
  • Spring 2018: Principles of Data Science (CIS 3715)
  • Spring 2017: Principles of Data Science (CIS 3715)
  • Fall 2015: Machine Learning (CIS 5526)
  • Fall 2015: Math Concepts in Computing I (CIS 1166)
  • Spring 2015: Principles of Data Science (CIS 3715)
  • Spring 2015: Math Concepts in Computing II (CIS 2166)
  • Fall 2014: Machine Learning (CIS 5526)
  • Fall 2013: Introduction to Computer Science (CIS1001)
  • Fall 2013: Machine Learning (CIS 5526)