Abstract: Relational database systems, the workhorse of today's information industry, have been extensively researched for over four decades, and a consensus has emerged on the implementation of most of their components. The design of the declarative query processing module, however, continues to be mired in challenging technical problems. In this talk, we will present promising new approaches to address these chronic difficulties.
Bio: Jayant Haritsa is on the faculty of the Computer Science & Automation department at the Indian Institute of Science, Bangalore, since 1993. He received a BTech degree from IIT Madras, and the MS and PhD degrees from the University of Wisconsin−Madison. He is a Fellow of ACM and IEEE.

Abstract: Deep Reinforcement Learning methods have achieved significant successes recently by marrying the representation learning power of deep networks and the control learning abilities of RL. This has resulted in some of the most significant recent breakthroughs in AI such as the Atari game player and the Alpha Go engine from Deepmind. This success has opened up new lines of research and revived old ones in the RL community. I will talk about two pieces of work that go beyond reward based RL. The first is Successor Options - on discovering hierarchical structure in problems by exploiting the properties of successor representations. The second is Risk Averse Imitation Learning (RAIL), that tries to minimize the tail risk when learning to imitate expert policies.
Bio: Prof. Ravindran is the head of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) at IIT Madras and a professor in the Department of Computer Science and Engineering. He is also the co-director of the reconfigurable and intelligent systems engineering (RISE) group at IIT Madras. He received his PhD from the University of Massachusetts, Amherst and his Master’s in research degree from Indian Institute of Science, Bangalore. He has nearly two decades of research experience in machine learning and specifically reinforcement learning. He has held visiting positions at the Indian Institute of Science, Bangalore, India and University of Technology, Sydney, Australia. He is also one of the founding executive committee members of the India chapter of ACM SIGKDD (IKDD) and is currently serving as the president of the chapter. His research interests are centred on learning from and through interactions and span the areas of complex network analysis and reinforcement learning.

Abstract:Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI, it is necessary to make these different paths meet and enable seamless human interaction. First, I will introduce for learning from rich, structured, complex and noisy data. One of the key attractive properties of the learned models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. Finally, I will discuss more recent work on "closing-the-loop" where information is solicited from humans as needed that allows for seamless interactions with the human expert. I will discuss these methods in the context of supervised learning, planning, reinforcement learning and inverse reinforcement learning.
Bio: Sriraam Natarajan is an Associate Professor at the Department of Computer Science at University of Texas Dallas. He was previously an Associate Professor and earlier an Assistant Professor at Indiana University, Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award and the IU trustees Teaching Award from Indiana University. He is an editorial board member of MLJ, JAIR and DAMI journals and is the electronics publishing editor of JAIR. He is the organizer of the key workshops in the field of Statistical Relational Learning and has co-organized the AAAI 2010, the UAI 2012, AAAI 2013, AAAI 2014, UAI 2015 workshops on Statistical Relational AI (StarAI), ICML 2012 Workshop on Statistical Relational Learning, and the ECML PKDD 2011 and 2012 workshops on Collective Learning and Inference on Structured Data (Co-LISD). He was also the co-chair of the AAAI student abstract and posters at AAAI 2014 and AAAI 2015 and the chair of the AAAI students outreach at AAAI 2016 and 2017.

Abstract: Fashion designers and fashion houses usually start conceptualizing and designing products for the new season six months to one year prior to the actual selling season–though in recent times this has been drastically reduced with the emergence of fast-fashion retailers. That’s why for most apparel retailers, and the fashion industry in general, knowing the trends customers would like to wear next season is extremely important. This talk will describe how AI based tools which can understand fashion images and articles can be used to provide a more data-driven approach for trend analysis and forecasting. I will also describe some of our recent collaborations with various fashion designers.
Bio: Vikas C. Raykar works as a researcher at IBM Research, India. An expert in machine learning he is currently focused on building machines that can understand natural language and images in par with humans. He finished his doctoral studies in the computer science department at the University of Maryland, College Park. He is also defining a roadmap for what can be done for the fashion industry, primarily leveraging deep image and text understanding together with other AI capabilities.

Abstract: Due to increase in the number of sources of data, research in cross-modal matching is becoming an increasingly important area of research. It has several applications like matching text with image, matching near infra-red images with visible images for night-time or low-light surveillance, matching sketch images with pictures for forensic applications, etc. This is an extremely challenging task due to significant differences between data from different modalities. 
In this talk, I will discuss about the different challenges of this problem and also some of the approaches we are working on for addressing this. In addition, I will also touch upon some related problems, like zero-shot learning and low-resolution face recognition.
Bio: Dr. Soma Biswas is an Assistant Professor in the Electrical Engineering department in IISc. She received her PhD degree in Electrical and Computer Engineering from University of Maryland, College Park, in 2009. Then she worked as a Research Assistant Professor at University of Notre Dame and as a Research Scientist at GE Research before joining IISc. Her research interests include image processing, computer vision, and pattern recognition.