ICIIT 2018
Panel

December 11 - 14, 2018
College of Engineering Guindy (CEG)
Anna University, Chennai, INDIA

ICIIT 2018 : Panel

Friday December 14, 03:45 - 05:15   |   Ada Lovelace Auditorium, IST Dept.


Data platform: to democratize proprietary access from industry to academia for world-class science


Data platforms (e.g., Kaggle) allow companies to share data sets, challenges and kernels in particular for students to apply machine learning to real problems. Global, research conferences (KDD, CIKM) announce competitions/cups to focus on research areas of relevance (information retrieval, data mining). Still, established/emerging enterprises may not have figured out yet a way to open up their precious, assets (data, infrastructure and audience) in a seamless fashion to academic researchers. This panel with experts having complementary/contradicting perspectives could explore issues still outstanding (e.g., middleware (privacy, scale), industry (IP, monetization), academia (replicability of results, open sourcing solutions).


Dr. Muthusamy Chelliah

Moderator : Muthusamy Chelliah

Flipkart, India



Muthusamy Chelliah heads external research collaboration for Flipkart – who is a pioneer in the nascent online shopping market in a vibrant, emerging economy (India). He holds a PhD degree in Computer Science from Georgia Tech., Atlanta with a focus in distributed systems. He then spent 15 years with HP as a scientist and architect in US and India working on various areas like middleware, OS security, fault-tolerant systems and cloud computing. He then moved to Yahoo where he held a similar role as now engaging academia on solving problems relevant to industry leveraging research in ML, IR, NLP and data mining. He’s passionate about catalyzing industry-relevant data science in global universities. He has published articles in conferences like IEEE SRDS as well as journals like TKDE and delivered tutorials in ICWS.




Dr. Gaurav Aggarwal

Gaurav Aggarwal

Ola, India



A Computer Science BTech from IIT Madras and PhD from University of Maryland, Gaurav has over fifteen years of experience in building computer vision and machine learning solutions both in academic and industrial settings. Gaurav has 35+ research papers in international conferences and journals, he has written as part of his research career. Gaurav currently works in the capacity of head of Data Science at Ola Cabs where he leads several efforts to bridge the gap between profitability for the business and customer delight. Before joining Ola, he founded a technology start-up called Fashiate that was acquired by Snapdeal where he worked as the head of Multimedia Research Group for over two years. Gaurav has also worked as Senior Research Scientist at Yahoo Labs and as assistant professor at University of Notre Dame.




Mr. Thiruvalluvan M.G

Thiruvalluvan M.G.

Aqfer, India



Thiruvalluvan M G is VP (engg.) at Aqfer systems, specializing in enterprise marketing data lake. He was a key contributor with Altiscale - a big data startup, which got acquired by SAP. Before that, he was Senior Principal Architect with the Cloud Platform Group in Yahoo, Bangalore for many years. He works in cloud infrastructure products and deployment solutions. He has worked in different areas of Internet scale systems in search and advertising before cloud technologies. His interests include large scale distributed computing, deployment automation, software engineering and agile methods for software project execution. He holds an undergraduate degree in ECE from College of engineering, Guindy, Chennai.




Dr. Balaraman Ravindran

Balaraman Ravindran

Indian Institute of Technology Madras, India



I am a professor at the Department of Computer Science and Engineering, and I head the Robert Bosch Centre for Data Science and AI at the Indian Institute of Technology Madras. I completed my Ph.D. at the Department of Computer Science, University of Massachusetts, Amherst. I worked with Prof. Andrew G. Barto on an algebraic framework for abstraction in Reinforcement Learning.
My current research interests span the broader area of machine learning, ranging from Spatio-temporal Abstractions in Reinforcement Learning to social network analysis and Data/Text Mining. Much of the work in my group is directed toward understanding interactions and learning from them.



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