Title: Data Science for sustainable development goals (SDGs)


Seventeen sustainable development goals (SDGs) are listed by United Nations in the year 2015 ( The scope of the panel is as follows:
"If data science researchers, select the problems related to SDGs, the societal growth can be accelerated."
The panel of eminent members share the perspectives on the above statement.



(i) Masaru Kitsuregawa (The University of Tokyo)

Bio:Masaru Kitsuregawa is the Director General of National Institute of Informatics (NII) and University Professor at the University of Tokyo. Received Ph.D. degree from the University of Tokyo in 1983. Served in various positions such as President of Information Processing Society of Japan (2013–2015) and Chairman of Committee for Informatics, Science Council of Japan(2014-2016). He has wide research interests, especially in database engineering.
For the Japanese Government, he served as a Steering Committee Chair of the Information Grand Voyage Project by the Ministry of Economy, Trade and Industry from 2007 to 2010, and as Science Advisor for the Ministry of Education, Culture, Sports, Science and Technology from 2008 to 2012. For the Science Council of Japan, he served as a Council Member from 2011 to present, and a Chair of the Informatics Committee from 2014 to 2017. In addition, he has been a Program Officer of JST CREST/PREST on Big Data from 2013 to the present, and was the President of the Information Processing Society of Japan from 2013 to 2015.
He received the ACM SIGMOD E. F. Codd Innovation Award in 2009 as the first recipient in Asia, as well as the IPSJ Contribution Award in 2011, the 21st Century Invention Award of National Commendation for Invention Japan, the IEEE Innovation in Societal Infrastructure Award in 2019, and in 2020, the Japan Academy Award. In addition, he was awarded the Medal with Purple Ribbon from the Japanese Government in 2013, and was made a Chevalier de la Légion d’Honneur by the French Government in 2016. He is an IEEE Life Fellow, an ACM Fellow, an IEICE Fellow, an IPSJ Fellow, and a China Computer Federation honorary member. Japan Academy Prize in 2020.

(ii) Jaideep Srivastava (University of Minnesota)

Bio: Jaideep Srivastava is a professor at the University of Minnesota, where he has established and led a research laboratory which conducts research in the information and knowledge aspects of computing. He has supervised 26 Ph.D. dissertations and 53 M.S. theses, and authored or co-authored over 220 papers in refereed journals and conferences. Dr. Srivastava has served on the editorial boards of various journals, including IEEE TPDS, IEEE TKDE, and the VLDB journal. He has also served as Program and Conference Chair for a number of prominent conferences, especially in the area of data mining, and is on the Steering Committee for the PAKDD series of conferences. He has delivered a number of keynote addresses, plenary talks, and invited tutorials at major conferences. Dr. Srivastava has a very active interaction with the industry, in both consulting and executive roles. Specifically, during a 2-year sabbatical during 1999-2001, he lead a corporate data mining team at ( and built a data analytics department at Yodlee ( from the ground up. More recently, he spent two years as the Chief Technology Officer for Persistent Systems (, where he built an R&D; division and oversaw the redesign of the training and technical vitalization program for 2,200+ engineers. He has provided technology and technology strategy advice to a number of large corporations including Cargill, United Technologies, IBM, Honeywell, 3M, and Eaton. He has served in an advisory capacity to a number of small companies, including Lancet Software and Infobionics. Dr. Srivastava has also played an active advisory role in the government sector. Specifically, he has served as the US federal government's expert witness in a nationally significant tax case. He is presently serving as Senior Technology Advisor to the State of Minnesota, and is on the Technology Advisory Council to the Chief Minister of Maharashtra, India. He is a Fellow of the IEEE, and has been an IEEE Distinguished Visitor.

(iii) Longbing Cao (University of Technology Sydney)

Bio:Longbing Cao is a professor and an Australian Research Council Future Fellow (Professorial level) at the University of Technology Sydney (UTS), and the founding director of UTS Advanced Analytics Institute (now Data Science Institute). He received an Australian Eureka prize, serves as the Editor-in-Chiefs of IEEE Intelligent Systems and Springer-Nature’s Journal of Data Science and Analytics, and created several data science initiatives including the IEEE International Conference on Data Science and Advanced Analytics. His broad research interest covers AI, data science, machine learning, behavior informatics, complex intelligent systems, and their enterprise applications in public and private sectors.

(iv) Santanu Chaudhury (IIT Jodhpur)

Bio:Professor Santanu Chaudhury, Professor, Department of Electrical Engineering, IIT Delhi, has assumed charge as Director, IIT Jodhpur, on 10 December 2018. Professor Chaudhury holds B.Tech. (Electronics and Electrical Communication Engineering) and Ph.D. (Computer Science & Engineering) Degrees from IIT Kharagpur.
Professor Chaudhury joined as Faculty Member in the Department of Electrical Engineering, IIT Delhi, in 1992. He was Dean, Under-Graduate Studies at IIT Delhi. He has served as Director of CSIR-CEERI, Pilani, during 2016-18. Professor Chaudhury is a recipient of the Distinguished Alumnus award from IIT Kharagpur.
Professor Chaudhury is a Fellow of Indian National Academy of Engineers (INAE) and National Academy of Sciences (NAS). He is a Fellow of International Association Pattern Recognition (IAPR). He was awarded the INSA (Indian National Science Academy) Medal for Young Scientists in 1993. He received ACCS-CDAC award for his research contributions in 2012.
A keen researcher and a thorough academic, Professor Chaudhury has about 300 publications in peer reviewed journals and conference proceedings, 15 patents and 4 authored/edited books to his credit.

(v) Yun Sing Koh (University of Auckland)

Bio:Yun Sing Koh is an Associate Professor at the School of Computer Science, The University of Auckland, New Zealand. Her main research area is Artificial Intelligence (AI) and Machine Learning (ML). Specifically focusing on several research strands: continual learning and adaptation, transfer learning anomaly detection, and data stream mining. Yun Sing is passionate about using machine learning for social good, and her research has been applied to interdisciplinary applications in environmental and health domains. Yun Sing has published 100+ peer-reviewed publications in top conferences and journals, including IJCAI, IEEE ICDE, IEEE ICDM, Machine Learning Journal and Journal of Artificial Intelligence. She won the New Zealand Royal Society Fast-Start Marsden funding (2018) and the United States Office of Naval Research Grant (2019). Yun Sing has been active in the research community, including serving as the General Co-Chair at the IEEE International Conference on Data Mining 2021 and Australasian Data Mining Conference 2022, Workshop Co-Chair at the ECML/PKDD conference 2021, Program Co-Chair of the Australasian Data Mining Conference 2018 and as the Workshop Co-Chair for the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining. She leads the Advanced Machine Learning and Data Analytics Research (MARS) Lab.