Organizers

Contact

Milan Vukićević
Contact: milan.vukicevic@fon.bg.ac.rs

Vlasta Sikimić
Contact: vlasta.sikimic@uni-tuebingen.de

Miloš Jovanović
Contact: milos.jovanovic@fon.bg.ac.rs

Boris Delibašić
Contact: boris.delibasic@fon.bg.ac.rs

Sandro Radovanović
Contact: sandro.radovanovic@fon.bg.ac.rs

Hadis Anahideh Contact: hadis@uic.edu

Abolfazl Asudeh Contact: asudeh@uic.edu

Biographies

Milan Vukićević is an associate professor in Center for Machine Learning and Decision Making, Faculty of Organizational Sciences, University of Belgrade. He has experience of development and application of ML, AI and DM algorithms in different application areas with special focus on fairness and interpretability. He was a researcher on may international projects including “Modeling decision-making in complex socio-technical environments” (project financed by Office for Naval Research (ONR). Grant No. N00014-18-S-B001, 2019-2022) problems of fairness of decision making and machine learning algorithms are addressed.

• Regular PC member of IEEE International Conference on Big Data BigData conference (2022, 2020, 2019). Organizing team at the International Conference on Decision Support System Technology (ICDSST 2019).
• Organizational committee at NSF US-Serbia & West Balkan Data Science Workshop 2018
• Organizational committee at South-East European Forum on Data Science 2016,
• Organizational committee at Computational Decision Making and Data Science workshop 2017 and 2018.

Vlasta Sikimić is a Research Fellow in Philosophy at the Cluster of Excellence – Machine Learning for Science and the Hector Research Institute of Education Sciences and Psychology of the University of Tübingen. Her research focus is on Philosophy of Science, Philosophy of AI, Empirical Philosophy, Logic, Science Policy, and Animal Ethics. More specifically, she works on data-driven approaches to optimizations of scientific reasoning.

• Co-organizer of the Making Responsible Decisions in and about Science event with Nancy Cartwright and Helen Longino (2021), virtually hosted by the Weizsäcker Center of the University of Tübingen
• Co-organizer of the Workshop Series Ethics of Covid-19 Science (2021), virtually hosted by the PhilSci India Group, the Weizsäcker Center of the University of Tübingen, the Centre for Philosophy of Science at the University of Geneva, and Forum ADVISE
• Co-organizer of the lecture series on Moral and Legal Rights of Nonhuman Animals (2017/2018), University of Belgrade, Serbia
• Member of the Organizing Committee of the Formal Models of Scientific Inquiry Conference (2017), Bochum, Germany
• Member of the Organizing Committee of Algebra and Coalgebra meet Proof Theory Workshop (ALCOP 2016), Vienna, Austria
• Co-organizer of the Second Belgrade Graduate Conference in Analytic Philosophy and Logic (2015), University of Belgrade, Serbia

Miloš Jovanović is an associate professor in Center for Machine Learning and Decision Making, Faculty of Organizational Sciences, University of Belgrade. He has a strong background in the development and application of ML algorithms. Also, he has experience with interpretable models, white box algorithm design, and optimization. He has experience in scientific collaboration taking part in multiple scientific projects including (Whibo, 2009, SCOPES, 2016, and ONR, 2018). Those projects included fairness, fgorithm design, model interpretability, and scalability of the machine learning models. Additionally, he was visiting scholar at Temple University (Philadelphia, PA, USA) working on Graphs (DARPA funded) project. He has extensive experience in organization of workshops and conferences including:

• Regular PC member of IEEE International Conference on Big Data BigData conference (2022, 2020, 2019). Organizing team at the International Conference on Decision Support System Technology (ICDSST 2019).
• Organizational committee at NSF US-Serbia & West Balkan Data Science Workshop 2018
• Organizational committee at South-East European Forum on Data Science 2016,
• Organizational committee at Computational Decision Making and Data Science workshop 2017 and 2018.

Sandro Radovanović is an assistant professor in Center for Machine Learning and Decision Making, Faculty of Organizational Sciences, University of Belgrade. He has the experience of designing and developing ML and AI algorithms, as well as in social aspects of ML applications. His research interest includes designing machine learning algorithms and decision support systems with a focus on fairness and (in)equality. He has experience in scientific collaboration taking part in multiple scientific projects including (SCOPES 2016, ONR 2018, and Teach4Edu 2020). He has extensive experience in organization of workshops and conferences including:

• Organizing Committee member (Editorial Board) at XVIII International Symposium of Organizational Sciences - SymOrg 2022.
• Mini-track session co-chair on Fairness in Algorithmic Decision Making at 55th Hawaii International Conference on System Sciences.
• Registration and Grants co-chair, Virtual co-chair, and Social media co-chair at ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’22).
• Diversity and Inclusion co-chair and Registration and Grants co-chair at ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21).
• Organizing team at the International Conference on Decision Support System Technology (ICDSST 2019).
• Technical, Web Support and Virtual Conference Coordinator at NSF US-Serbia & West Balkan Data Science Workshop 2018. Local organizer at South-East European Forum on Data Science 2016, Computational Decision Making and Data Science workshop 2017 and 2018.

Boris Delibašić is full professor in Center for Machine Learning and Decision Making, Faculty of Organizational Sciences, University of Belgrade. He is an experienced researcher with a strong research record in the areas of Machine Learning and Decision making. He has extensive experience in project management being PI in a number of successful international and national research projects including (ONR 2018, SCOPES, 2016, Whibo, 2009). He has extensive experience in organization of workshops and conferences including:

• EWG-DSS Area Chair for data-driven decision-making at ICDSST conferences since 2015.
• Mini-track session co-chair on Fairness in Algorithmic Decision Making at 55th Hawaii International Conference on System Sciences.
• Organizational committee at NSF US-Serbia & West Balkan Data Science Workshop 2018
• Organizational committee at South-East European Forum on Data Science 2016,
• Organizational committee at Computational Decision Making and Data Science workshop 2017 and 2018.

Hadis Anahideh research objectives center around Black-box Optimization, Active Learning, Machine Learning, and Algorithmic Fairness. She primarily seeks to develop innovative learning and optimization methodologies, which have potential utility for multiple fields within the engineering operations and design, and social systems. Anahideh recently joined UIC and teaches Data Science I and II. Prior to joining UIC, she worked in the Operations Research and Advanced Analytics Department at American Airlines as an OR scientist. Viewing research as teamwork, Dr. Anahideh firmly believes the power of developing meaningful relationships with whom she works is critical to successful learning and research.

Abolfazl Asudeh is an assistant professor of Computer Science at the University of Illinois Chicago and the director of Innovative Data Exploration Laboratory (InDeX Lab). He is a VLDB Ambassador, and a regular PC member of Database flagship venues. His research spans to different aspects of Big Data and Data Science, including Data Management, Information Retrieval, and Data Mining, for which he designs efficient, accurate, and scalable algorithmic solutions. Data Equity Systems, Algorithmic Fairness, and Data-centric AI are his major focus in research. His research interest also includes: Ranking, Social Networks Analysis, Large-Scale Computation on Limited Resources, Computational Fact Checking, Data management for Machine Learning, Web Databases, and applied Computational Geometry.