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.
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.
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.
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.
Research funded by the NSF Expeditions in Computing Program and NASA
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.