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Guest Talk Responsible Data Science in a Dynamic World

Prof. Wil van der Aalst, Date/Time: TUE 7 Nov at 18:30 Loca­tion: TC.1.02, Big data is chan­ging the way we do busi­ness, socia­lize, conduct rese­arch, and govern society. Data are collected on anything, at any time, and in any place. Big Data is often considered as the “new oil” and data science aims to trans­form this into new forms of “energy”....

Abstract:

Big data is chan­ging the way we do busi­ness, socia­lize, conduct rese­arch, and govern society. Data are collected on anything, at any time, and in any place. Big Data is often considered as the “new oil” and data science aims to trans­form this into new forms of “energy”: insights, diagnostics, predic­tions, and auto­mated deci­sions. Yet, the process of trans­for­ming “new oil” (data) into “new energy” (analy­tics) may nega­tively impact citi­zens, pati­ents, cust­o­mers, and employees. Syste­matic discri­mi­na­tion based on data, inva­sions of privacy, non-­trans­pa­rent life-ch­an­ging deci­sions, and inac­cu­rate conclu­sions occur ever­yw­here. Respon­sible Data Science (RDS), also referred to as Green Data Science (GDS), aims to address chal­lenges related to Fair­ness (Data science without preju­dice: How to avoid unfair conclu­sions even if they are true?), Accu­racy (Data science without guess­work: How to answer ques­tions with a guaran­teed level of accu­racy?), Confi­den­tia­lity (Data science that ensures confi­den­tia­lity: How to answer ques­tions without revea­ling secrets?), and Trans­pa­rency (Data science that provides trans­pa­rency: How to clarify answers such that they become indis­pu­table?). These FACT chal­lenges will be illus­trated using powerful process mining tech­ni­ques that are able to discover the real processes, detect devia­tions from norma­tive process models, and uncover bott­len­ecks and waste. Process mining can be used to reveal the dynamic beha­viors of workers, cust­o­mers, and other people. In our dynamic world event data is extre­mely valuable, but can also be used in an irre­s­pon­sible way. There­fore, concerns related to fair­ness, accu­racy, confi­den­tia­lity, and trans­pa­rency gene­rate new and inte­res­ting chal­lenges for the process mining disci­pline.

Short biography:

Prof.dr.ir. Wil van der Aalst is a distin­gu­ished univer­sity professor at the Tech­ni­sche Univer­siteit Eind­hoven (TU/e) where he is also the scien­tific director of the Data Science Center Eind­hoven (DSC/e). Since 2003 he holds a part-­time posi­tion at Queens­land Univer­sity of Tech­no­logy (QUT). Currently, he is also a visiting rese­ar­cher at Fonda­zione Bruno Kessler (FBK) in Trento and a member of the Board of Gover­nors of Tilburg Univer­sity. His personal rese­arch inte­rests include process mining, Petri nets, busi­ness process manage­ment, work­flow manage­ment, process mode­ling, and process analysis. Wil van der Aalst has published over 200 journal papers, 20 books (as author or editor), 450 refe­reed confe­rence/work­shop publi­ca­tions, and 65 book chap­ters. Many of his papers are highly cited (he one of the most cited computer scien­tists in the world; accor­ding to Google Scholar, he has an H-index of 135 and has been cited 80,000 times) and his ideas have influ­enced rese­ar­chers, soft­ware deve­l­o­pers, and stan­dar­di­za­tion commit­tees working on process support. Next to serving on the edito­rial boards of over 10 scien­tific jour­nals he is also playing an advi­sory role for several compa­nies, inclu­ding Fluxicon, Celonis, and Process­Gold. Van der Aalst received hono­rary degrees from the Moscow Higher School of Econo­mics (Prof. h.c.), Tsin­ghua Univer­sity, and Hasselt Univer­sity (Dr. h.c.). He is also an elected member of the Royal Nether­lands Academy of Arts and Sciences, the Royal Holland Society of Sciences and Huma­nities, and the Academy of Europe. Recently, he was awarded with a Humboldt Profes­sor­ship, Germany’s most valuable rese­arch award (five million euros), and will move to RWTH Aachen Univer­sity at the begin­ning of 2018.



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