Dragutin Petkovic

Dragutin Petkovic

Coordinator for Graduate Certificates in AI Ethics & SW Engineering
Phone: (415) 338-2261
Email: petkovic@sfsu.edu


IEEE LIFE Fellow, Director, Computing for Life Sciences (CCLS)


Dragutin Petkovic (IEEE Life Fellow since 2018, IEEE Fellow since 1998) received Ph.D. degree in electrical engineering from UC Irvine in 1983 in the area of biomedical image analysis, and B.S. and M.S. degrees in electrical engineering and multidisciplinary studies respectively from University of Belgrade, Serbia with emphasis on image and signal analysis. He is currently a professor of computer science at San Francisco State University (SFSU) since 2003 where he was computer science department chair from 2003 to 2015. In 2019, he founded and co-leads the multidisciplinary SFSU Graduate Certificate in Ethical Artificial Intelligence together with SFSU Department of Philosophy and School of Business. He was a founder and Director of SFSU Center for Computing for Life Sciences from 2005 till 2018 and collaborated on multiple NIH grants with Stanford University in the areas of Artificial Intelligence (AI) for bioinformatics. He held positions at VMware as a Senior Director, Applications, and as senior manager and researcher at IBM Almaden Research center, San Jose (1983-2000). His research focus included content based retrieval (he was founder of trend-setting IBM Query by Image Content QBIC project). For last 10 years his work combines AI and ease of use and has a goal to bring technology closer to people and users. Due to his concerns about the state of the ethics and trustworthiness of AI systems and their possibly negative implications to society, his recent focus is on explainable and trustworthy AI to which he contributes by papers, books, workshops, talks and importantly by education of broader community via SFSU Certificate of Ethical AI.

  • AI Ethics and Explainability
  • Software Engineering Machine learning
  • Computing for Life Sciences
  • Usability
  • Machine Learning
  • Coordinator, Graduate Certificate in AI Ethics
  • Coordinator, Graduate Certificate in SW Engineering
  • Best session speaker at AAAI Symposium in the session “Socially Responsible AI for Well-being”, March 2023, San Francisco.
  • IEEE LIFE Fellow since 2018
  • IEEE Fellow since 1998 (for leadership in content based retrieval area and IBM’s QBIC project)
  • Blue Chip Award from Lou Gerstner, IBM CEO
  • IBM Research Awards for technical work
  • 14 US patents, over 70 peer reviewed publications

A. Personal Statement

My scientific and technical focus is in four areas:

  • Ethics and Trustworthiness of AI – best practices and verification
  • Improving and promoting explainability of Machine Learning (ML);
  • Applications and explainability of ML using Random Forest algorithms
  • Usability engineering for general SW systems and for AI/ML applications

My contributions are based on combination of my academic experience (teaching, publications, conferences and workshops) as well as industry experience (in corporate R&D and startups). I am motivated by strong desire to make ML/AI technologies ethical, trustworthy and of benefits to the well-being of society. My goals also include education of students of all backgrounds in ML/AI, modern SW engineering and design and evaluation of easy to use of SW systems.

B. Positions

  • Consulting in Machine Learning, trustworthy AI (ongoing)
  • Professor, CS Department, San Francisco State University, 2020 – present
  • One of the three faculty in charge of SFSU Graduate Certificate in AI Ethics, 2019 to present
  • Professor, Associate Chair, CS Department, San Francisco State University, 2015 - 2020
  • Chair, CS Department, San Francisco State University, 2003 – 2015
  • Founder and Director, SFSU Center for Computing for Life Sciences, 2005 – 2018
  • VMware, Senior Director, Applications, 2001 – 2002
  • Dotcast, Palo Alto, Sr. Director/VP of SW, 2000-2001
  • IBM Almaden Research center, San Jose, Senior Manager.  1987-2000
  • IBM Almaden Research center, San Jose, Researcher,  1983-1987

C. Contributions to Science (selected works)

Ethical and Trustworthy AI; Improving explainability of machine learning

SFSU Graduate certificate in Ethical AI

One of the founders and current coordinator and teacher in SFSU Graduate Certificate in Ethical AI, which involves SFSU Computer Science, Business and Philosophy departments

Press on our Graduate Certificate in Ethical AI

Research in improving explainability of AI and applications of Random Forest ML technology

Selected papers

  • D. Petkovic, "It is Not “Accuracy vs. Explainability”—We Need Both for Trustworthy AI Systems," in IEEE Transactions on Technology and Society, vol. 4, no. 1, pp. 46-53, March 2023, doi: 10.1109/TTS.2023.3239921.
  • C. Montemayor, D. Kleinrichert, D. Petkovic: “San Francisco State University Graduate Certificate in Ethical AI – overview and early experiences”, AAAI Symposium, Session on ““Socially Responsible AI for Well-being”, March 2023, San Francisco (best session speaker award for D. Petkovic)
  • Petkovic D., Alavi A., Cai D., Wong M. (2021) Random Forest Model and Sample Explainer for Non-experts in Machine Learning – Two Case Studies. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_5
  • D. Petkovic, R. Altman, M. Wong, A. Vigil: “Improving the explainability of Random Forest classifier – user centered approach”, Pacific Symposium on Biocomputing PSB 2018, Hawaii

Workshops and books devoted to AI Ethics and Explainability (leadership role)

  •  “Explainable Deep Learning AI: Methods and Challenges”, Elsevier, 1st Edition, 2023 (D. Petkovic one of four editors), https://www.elsevier.com/books/explainable-deep-learning-ai/benois-pineau/978-0-323-96098-4
  • 2-nd Workshop on Explainable and Ethical AI, ICPR 2022, Montreal, August 2022,  https://xaie-icpr.labri.fr/
  • Workshop on Explainable DL/AI, at ICPR 2020, held on line January 2021; https://edl-ai-icpr.labri.fr/
  • Petkovic D, Kobzik L, Ganaghan R, Workshop on “AI Ethics and Values in Biomedicine – Technical Challenges and Solutions”, Pacific Symposium on Biocomputing, PSB 2020, Hawaii January 3-7, 2020
  • D. Petkovic (Chair), L. Kobzik, C. Re: Workshop on “Machine learning and deep analytics for biocomputing: call for better explainability”, Pacific Symposium on Biocomputing PSB 2018, Hawaii

Applications of  ML to various biomedical problems

In our collaboration with Stanford we have applied machine learning to problems of functional site detection on 3D molecular models, where we applied and evaluated variety of ML technologies (Naïve Bayesian, Random Forest, SVM). We showed that ML performs well and can predict functional sites with acceptable false  alarm rate. We have also built a WWW site allowing users to visualize results. Work was supported by NIH grant.

  • L. Buturovic, M. Wong, G. Tang, R. Altman, D. Petkovic: “High precision prediction of functional sites in protein structures”, PLoS ONE 9(3): e91240. doi:10.1371/journal.pone.0091240
  • Okada K, Flores L, Wong M, Petkovic D, “Microenvironment-Based Protein Function Analysis by Random Forest”, Proc. ICPR - International Conference on Pattern Recognition, Stockholm, 2014

Bioinformatics projects with Stanford University

My role was in managing the projects and engaging in usability and SW engineering aspects of it

  • M. Wong et all: “Search and Visualization of gene-drug-disease interaction for pharmacogenocis and precision medicine research using Gene-Dive”, J. of Biomedical Informatics, May 2021
  • Wilson, Jennifer & Wong, Mike & Stepanov, Nicholas & Petkovic, Dragutin & Altman, Russ. (2021). “PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus”. JAMIA Open. 4. 10.1093/jamiaopen/ooab079.
  • Wilson, Jennifer & Wong, Mike & Chalke, Ajinkya & Stepanov, Nicholas & Petkovic, Dragutin & Altman, Russ. (2019). “PathFXweb: a web application for identifying drug safety and efficacy phenotypes” Bioinformatics (Oxford, England). 35. 10.1093/bioinformatics/btz419.

Investigation and application of novel teaching methods for SW engineering education

In my academic career, as a teacher and researcher, I combined by industry expertise in SW engineering with my research in ML to investigate how we can better assess and predict student learning of teamwork by using ML. Our focus was on using objective and quantitative measures of student team behavior to predict student teams bound to fail. This work has been applied in ongoing teaching of SE classes at SFSU jointly with Fulda University in Germany and before with FAU in Florida and received NSF TUES funding.

  • D. Petkovic, S. Barlaskar, J. Yang, R. Todtenhoefer: “From Explaining How Random Forest Classifier Predicts Learning of Software Engineering Teamwork to Guidance for Educators” Frontiers of Education FIE 2018, October 2018, San Jose CA
  • D. Petkovic, M. Sosnick-Pérez, K. Okada, R. Todtenhoefer, S. Huang, N. Miglani, A. Vigil: “Using the Random Forest Classifier to Assess and Predict Student Learning of Software Engineering Teamwork” Frontoiers in Education FIE 2016, Erie, PA, 2016
  • D. Petkovic: “Using Learning Analytics to Assess Capstone Project Teams”, IEEE Computer, Issue No.01 - Jan. (2016 vol.49). (invited)
  • Dragutin Petkovic, Marc Sosnick-Pérez, Shihong Huang, Rainer Todtenhoefer, Kazunori Okada, Swati Arora, Ramasubramanian Sreenivasen, Lorenzo Flores, Sonal Dubey: “SETAP: Software Engineering Teamwork Assessment and Prediction Using Machine Learning”, Proc. FIE2014, Madrid, Spain 2014

Querying Images by Content

After my Ph. D studies, while at IBM I applied image processing and machine learning to the problem of querying by image content. Our QBIC project (query by image content) work is considered one of the pioneering works in this area and produced first results showing this problem is feasible to solve. I was manager of the team as well as technical contributor and initiator of the project. For this work I was awarded IEEE Fellowship in 1998

  • M. Flickner et al.: "Query by Image and Video Content: The QBIC System", IEEE Computer, Special Issue on Content Based Retrieval, September 1995, pp. 23-32. (over 5500 citations as of 2018)
  • C. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, G. TaubinQBIC project: querying images by content, using color, texture, and shape”, SPIE, 1993, San Jose (over 2800 citations as of 2018)

D.  Additional Information: Research Support and/or Scholastic Performance

SFSU PI of NIH sub-grant 2R01LM005652-19A1 (collaboration with PI Prof. R. Altman, Stanford University) ”Text Mining for High-fidelity Curation and Discovery of Gene-drug-phenotype Relationships “. This grant includes several sub-projects. Role includes project management, SW Engineering, usability and UI design and evaluation, mentoring SFSU graduate students involved in the project.

SFSU PI of NIH sub-grant U54EB020405  “Mobility Data Integration to Insight”, (collaboration with Stanford PI Prof. S. Delp), Role includes project management, SW engineering, usability and UI design and evaluation, mentoring SFSU graduate students involved in the project. Completed 2018

SFSU PI of NIH sub-grant (collaboration with PI Prof. Russ Altman, Stanford University NIH U54 GM072970) on Physics Based Simulation of Biological Structures Simbios. Role included project management, mentoring SFSU graduate students involved in the project, completed 2013 .

PI of collaborative NSF TUES grant 1140172 “ Transforming the Understanding, Assessment and Prediction of Teamwork Effectiveness in Software Engineering Education using Machine Learning”. Role included: main PI, data collection in joint SW Engineering class with Fulda Germany and FAU, Florida, application of machine learning for assessment of student success, mentoring of graduate students. Completed 2016

SFSU PI of NIH sub-grant (collaboration with PI Prof. Russ Altman, Stanford University NIH R01 LM005652) on “Annotating Functional Sites in 3-D Biological Structures”. Role includes project management, SW Engineering, applications of machine learning, usability and UI design and evaluation, mentoring SFSU graduate students involved in the project. Completed 2016.