SCS Recruiting Seminar: Abolfazl Asudeh

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TITLE: Towards More Responsible Data-driven Decisions


Human decision makers often receive assistance from data-driven algorithmic systems for evaluating objects, including individuals. When there are multiple criteria to be considered, two common ways of supporting the decision making are (i) to assign a score to each object and rank them accordingly, or (ii) to offer a small set of maxima representatives that include the “best” for different users.

The evaluations and decisions made based on data can have significant consequences for the individuals evaluated, as well as society. For example, a company may promote high-ranked employees and fire low-ranked ones. University rankings is an example of social impact where it is well-documented that the ranking formula has a significant effect on policies adopted by universities. Another important example is highlighted by ProPublica: judges in the US consider the scores assigned to the individuals based on their criminal record and their background as guidance when sentencing criminals.

My research’s goal is to ensure that decisions based on data are made responsibly, that is, properties such as fairness, stability, diversity, and transparency are satisfied.


Abolfazl (Abol) Asudeh is a research fellow at the computer science and engineering department of the University of Michigan, Ann Arbor, working with Professor H. V. Jagadish. At Michigan, Asudeh leads Mithra, a project on responsible data management and data ethics. Asudeh completed his Ph.D. in computer science at the University of Texas at Arlington in 2017, where he worked in DBXLAB with Professor Gautam Das. He finished his academic education with the Outstanding Ph.D. Student Award. Asudeh has also done two internships with Microsoft and Microsoft Research in 2014 and 2016. His research interests include responsible data science and data ethics, big data management and analysis, ranking and top-k query processing, compact maxima representatives, data mining, machine learning, algorithm design, computational geometry, and web data retrieval. Asudeh has a strong publication record in SIGMOD and VLDB in the past few years, including recognitions such as an invitation to the “Best of VLDB.”



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