• 15-08-2017   10 AM - 12:30 PM
  • Halifax, Nova Scotia - Canada

Schedule

Aug 15 10:00 AM - 12:30 PM

  • 10:00 – 10:05

    Opening Remarks by Dr. Manish Gupta

  • 10:05 – 10:35

    Invited talk by Prof. Niloy Ganguly (IIT KGP): “Opinion and Information Dynamics”

  • 10:35 – 11:05

    Invited talk by Prof. Vipin Kumar (Univ. of Minnesota): “Big Data in Climate: Opportunities and Challenges for Machine Learning”

  • 11:05 – 11:35

    Invited talk by Dr. Chid Apte (IBM): “Data Science for an Optimized Workforce”

     

  • 11:35 – 12:25

    Panel on Data Science Challenges for the Industry; Moderator – Dr. Gautam Shroff (TCS Innovation Lab). Panelists: Dr. Indrajit Bhattacharya  (TCS Innovation Lab), Dr. Karthik Sankaranarayanan (IBM Research), Dr. Navin Budhiraja (Infosys), Dr. Sugato Basu (Google), Dr. Manish Gupta (VideoKen)

  • 12:25 – 12:30

    Closing Remarks by Dr. Gautam Shroff

  • 10:05 – 10:35

    Invited talk by Prof. Niloy Ganguly (IIT KGP): “Opinion and Information Dynamics”

    Abstract: Today, social media and online social networks constantly bombard users with informations. In presence of such a heavy information overload, it becomes critical to understand which pieces of information are getting through and which are ignored. The dynamics of these users’ activities in general, is a complex evolving process and encompasses a spectrum of subprocesses. For example, in Twitter, when a user receives a tweet, she may simply re-tweet the message, or modify its content, or gain information and form opinion about a particular topic. However, the dynamics of all these processes can be naturally encapsulated by a potent mathematical devise called ‘temporal point process’, which models the rate of arrival of messages using different functional forms characterizing various phenomenons of interest. In this talk, we would discuss two key dynamical processes in Twitter, opinion dynamics of the users and hashtag propagation.

    In this first part, we aim to learn a data-driven model of opinion dynamics that is able to accurately forecast opinions of any user in future. We introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state.

    The second part of the talk is more about the complex temporal dynamics that jointly models hashtag reinforcement and hashtag competition. While the existing works have mainly focused on modeling the popularity of individual tweets rather than the popularity of the underlying hashtags, we propose Large Margin Point Process (LMPP), a novel probabilistic framework that integrates hashtag-tweet influence and hashtag-hashtag competitions, the two factors which play important roles in hashtag propagation. Furthermore, while considering the hashtag competitions, LMPP looks into the variations of popularity rankings of the competing hashtags across time.

    Biography: Niloy Ganguly is a professor of computer science, IIT Kharagpur whose research interests spans across the related fields of Online Social networks, Network Theory, Mobile Systems. Prior to his current position as a faculty at Indian Institute of Technology, Kharagpur, India, he was working as a Research Scientist in Zentrum für Hochleistungsrechnen, Technische Universität Dresden for two years. He received his Ph.D from IIEST, Shibpur and BTech from IIT Kharagpur.

    He is the founder and the head of a very active research group CNeRG (Complex Network Research Group) http://cnerg.org in IIT Kharagpur. He has been collaborating with various national and international universities and research labs including UIUC, TU Dresden, Germany, MPI PKS and MPI SWS, Germany, Microsoft Lab, Adobe Labs, HP Labs, NetApp Labs etc. He currently publishes in various top ranking international journals and conferences including ICDM, EMNLP, ACL, WWW, INFOCOM, SIGIR, SIGKDD, SIGCHI, ICWSM, CSCW, NIPS CCS, PODC, Euro Physics Letters, Physical Review E, ACM and IEEE Transactions, etc.For further information on Dr. Ganguly, please visit his website: http://www.facweb.iitkgp.ernet.in/~niloy/.

  • 10:35 – 11:05

     Invited talk by Prof. Vipin Kumar, University of Minnesota | Big Data in Climate: Opportunities and Challenges for Machine Learning

    Abstract: The climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, a massive amount of data about Earth and its environment is now continuously being generated by a large number of Earth observing satellites as well as physics-based earth system models running on large-scale computational platforms.  These massive and information-rich datasets offer huge potential for understanding how the Earth’s climate and ecosystem have been changing and how they are being impacted by humans actions.  This talk will discuss various challenges involved in analyzing these massive data sets as well as opportunities they present for both advancing machine learning as well as the science of climate change in the context of monitoring the state of the tropical forests and surface water on a global scale.
    Biography: Vipin Kumar is a Regents Professor and holds William Norris Chair in the department of Computer Science and Engineering at the University of Minnesota.  His research interests include data mining, high-performance computing, and their applications in Climate/Ecosystems and health care.  He also served as the Director of Army High Performance Computing Research Center (AHPCRC) from 1998 to 2005. His research has resulted in the development of the concept of isoefficiency metric for evaluating the scalability of parallel algorithms, as well as highly efficient parallel algorithms and software for sparse matrix factorization (PSPASES) and graph partitioning (METIS, ParMetis, hMetis).  He is currently leading an NSF Expedition project on understanding climate change using data science approaches.  He has authored over 300 research articles, and co-edited or coauthored 10 books including the widely used text book “Introduction to Parallel Computing”, and “Introduction to Data Mining”.  Kumar has served as chair/co-chair for many international conferences and workshops in the area of data mining and parallel computing, including 2015 IEEE International Conference on Big Data, IEEE International Conference on Data Mining (2002), and International Parallel and Distributed Processing Symposium (2001).  Kumar is a Fellow of the ACM, IEEE, AAAS, and SIAM.  Kumar’s foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society’s highest awards.
  • 11:05 – 11:35

    Invited talk by Dr. Chid Apte (IBM): “Data Science for an Optimized Workforce.

    Abstract: One of the core initiatives led by IBM Research – India, during it’s foundational start up years, was in the area of workforce optimization. This was a natural and timely topic for the nascent lab, given IBM’s rapid and aggressive growth of it’s workforce in India, and the deep collaborative partnership between IBM Research – India, and the Mathematical Sciences group in IBM Research – T.J. Watson Research Center, NY. Inspired by earlier work by IBM Research in Supply Chain optimization, and using an ensemble of advanced data science methodologies drawn from machine learning and optimization, a set of highly effective novel workforce optimization applications were developed and deployed, and benefits measurably quantified. This talk will discuss highlights of this decade long effort.

    Biography: Chid Apte is Director of Mathematical Sciences in the IBM Research Division, at the Thomas J. Watson Research Center in Yorktown Heights, New York, USA. He received his Ph.D. in Computer Science at Rutgers University, and B. Tech. in Electrical Engineering at the Indian Institute of Technology (Bombay). Chid has over three decades of technical experience as a research scientist and leader in the data science area. In his current role, he oversees the strategy and agenda in IBM Research for it’s Mathematical Sciences research area, which includes AI and Blockchain Solutions for Industries, Scalable Analytics, Applied Mathematics, and Operations Research.

Panel Discussion - Data Science Challenges for the Industry

The eminent panelists include Dr. Karthik Sankaranarayanan, Dr. Manish Gupta, Dr. Indrajit Bhattacharya , Dr. Navin Budhiraja and Dr. Sugato Basu. The panel discussion will be moderated by Dr. Gautam Shroff

sugato.v2
Sugato Basu

Google Research.

manish.v1
Manish Gupta

VideoKen

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Navin Budhiraja

Infosys

indra
Indrajit Bhattacharya

TCS Innovation Lab

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Gautam Shroff

TCS Innovation Lab

  • KDD 2017 Networking Session

    The KDD community in India is rapidly growing and the purpose of this event is to showcase the work that is happening both in Indian academia and industry. The event is being organized by ACM SIGKDD India chapter. The program consists of invited talks by leading researchers in the field, as well as panel discussion on data science challenges faced in India. This is the third in the series following the successful events in KDD 2015, Sydney and KDD 2016, San Francisco.

    Target Audience: Researchers interested in the state of KDD research in India could be with a view to collaborate, start up, or return to take up positions in academia or research labs.

    Organizers

    Balaraman Ravindran (IIT Madras)
    Gautam Shroff (TCS Research)
    Manish Gupta (VideoKen, India)