Common Keynote Speakers

• Prof. Mani Chandy, Caltech, USA

Big Data Applications as Commodities

Abstract:This talk discusses four rapidly growing trends on their impact on people. The trends are: (1) Big Data, AI, signal processing, and other areas have applications that are increasingly commoditized. Open source, free libraries have excellent software in many domains. Companies are offering powerful Big Data and AI applications in the cloud. The key problem of combining these applications is being partially solved within each vendor's ecosystem. (2) At the same time, hardware in the form of sensors, onboard computers such as the Raspberry Pi, and computers leased in the cloud are getting ever cheaper. (3) Streams of data on the web from Twitter, RSS feeds, video cams are increasingly available. (4) The web enables people to find experts, anywhere in the world, who do short-term IT projects. These trends enable non-experts to build ever more powerful applications that monitor multiple massive streams of data at ever-lower cost to identify and detect critical patterns. Hobbyists, school children, startups, and established companies will build more of such applications, and this has a profound impact on society. This talk attempts at predicting where these trends will lead in ten years and their impact on society.

Bio: Dr. K. Mani Chandy is the Simon Ramo Professor, Emeritus at the California Institute of Technology. He got his Bachelors in Electrical Engineering at the Indian Institute of Technology, Madras, in 1965; MS in Electrical Engineering at the Polytechnic Institute of New York in 1966; and a PhD in Operations Research at the Massachusetts Institute of Technology in 1969. He taught at the University of Texas at Austin, from 1969 to 1987, and at the California Institute of Technology from 1987 to 2014. He served as chairman of the Computer Science department at U.T. and as Executive Officer at Caltech. He has written books on performance modeling, concurrent programming, and event processing. He has written several papers on queuing networks, computer and communications performance modeling, distributed simulation, the development and verification of concurrent programs, compositional programming notations for parallel programs, and the detection of critical events from streams of data.

Dr. Chandy received the A. A. Michelson Award from the Computer Measurement Group in 1985 for his work on computer performance modeling. He became a Fellow of the IEEE in 1990. He was inducted into the United States National Academy of Engineering in 1995 for “contributions to computer performance modeling, parallel discrete-event simulation, and systematic development of concurrent programs”. He received the IEEE Koji Kobayashi Award in 1996 for “fundamental contributions to the theory and practice of computer and communications performance modeling”. His paper on distributed global snapshots, with Leslie Lamport, was placed in the ACM Operating Systems “Hall of Fame” in 2013 and was awarded the ACM Edsger W. Dijkstra prize in 2014. He received the IEEE Harry H. Goode Award in 2017 with Jayadev Misra for their work on concurrent systems. He got a distinguished alumnus award from IIT-Madras.

• Prof. Sriram Pemmaraju, University of Iowa, USA

Distributed and Parallel Algorithms in All-to-All Communication Model

Abstract: Massive datasets from various domains are becoming commonplace and these are increasingly being processed by clusters of off-the-shelf computers. Typically, computers in these clusters can all communicate with each other, but bandwidth for communication is severely limited and usually the cost of communication is significantly higher than cost of computation. We consider different clean and simple models that capture these constraints and describe techniques for designing “super-fast” algorithms for fundamental problems in these models. We consider classical distributed symmetry breaking problems (e.g., Maximal Independent Set) and distributed clustering problems (e.g., Metric Facility Location). We also discuss attempts to find lower bounds for problems that seem resistant to fast algorithms. A combination of information-theoretic ideas and communication complexity reductions have been used for the few lower bounds that exist in these models.

Bio: Dr. Sriram Pemmaraju is a professor in computer science at the University of Iowa, and the director of graduate studies there. His research is on theoretical aspects of distributed algorithms, especially the role of randomization in the design of these algorithms and the use of communication complexity and information-theoretic techniques to prove distributed computing lower bounds. His research is supported by the National Science Foundation and National Institutes of Health and it has received several best paper awards. He has mentored 10 PhD students so far and has received several outstanding graduate mentor nominations.

Invited Speaker

• Dr. Saptarshi Ghosh, Indian Institute of Technology, Kharagpur

Leveraging Online Social Media for Post-Emergency Relief Operations

Abstract: Online Social Media (OSM) such as Twitter, Facebook and WhatsApp are increasingly being used to post and gather real-time information during emergency events, such as earthquakes, floods and terror attacks. During such events, OSM are used by the affected population for reporting situational information, as well as by the agencies responding to the disaster. Basically, human users act as 'social sensors' in providing valuable information from the site of the disaster, and this information is critical for coordinating the relief operations. There are several challenges in utilizing OSM during disasters, which include retrieving critical situational information from lot of conversational content, classifying and summarizing the information, guarding against misinformation, and so on. These tasks become all the more more challenging due to the informal nature of the user-generated content posted on OSM. This talk will discuss some of the aforementioned challenges, along with some recent research studies that have attempted to address these challenges.

Bio: Dr. Saptarshi Ghosh (http://cse.iitkgp.ac.in/~saptarshi/) is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India. His research interests include Information Retrieval, Data Mining, Natural Language Processing, and Social network analysis. He completed his Ph.D. in Computer Science from the same institute, and has been a Humboldt Post-doctoral Fellow at Max Planck Institute for Software Systems, Germany. Prior to joining IIT Kharagpur, he was an Assistant Professor at Indian Institute of Engineering Science and Technology Shibpur, India. He has published more than 50 papers in peer-reviewed conferences and journals. He has been awarded several awards including the Institution of Engineers (India) Young Engineer Award in Computer Engineering, the IIT Kharagpur Faculty Excellence Award, and the Max Planck-India Mobility Grant.