Speakers

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Speaker Biography: Narendra Ahuja is the Donald Biggar Willet Professor in the Depart of Electrical and Computer Engineering at the University of IIIinois at Urbana-Champaign. He is a Fellow of the IEEE, American Association for Artificial Intelligence, International Association for Pattern Recognition, Association for Computing Machinery, American Assoiation for the Advancement for Computing Machinery, American Association for the Advancement of Science, and International Society for Optical Engineering. Narendra is on the editorial boards of several journals. He was the Founding Director of the International Institute of Information Technology, Hyderabad where he countinues to serve as Director International. Narendra received his Ph.D. from the University of Maryland in 1979.

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Speaker Biography: Kunal Chaudhury is currently an Assistant Professor in the Department of Electrical Engineering at the Indian Institute of Science. Prior to joining IISc, he was a research associate in the Program in Applied and Computational Mathematics at Princeton University. Kunal’s research interest is in inverse problems in image acquisition and processing, sensor network localization, protein structure calculation, and fast-scalable algorithms for image and video processing. Kunal has an M.E. in System Science and Automation from IISc, and a Ph.D. in Computers, Communication, and Information Systems from EPFL Switzerland.

Abstract: Computed Tomography (CT), a X-Ray based system has come a long way since its invention moving the 1-slice to 320 slices (wide coverage) and from single energy to dual energy. Simultaneously, the applications have seen lot of evolution with newer information being visible with the advancement of the system. The new generation of scanners with multi-energy acquisition has enabled different way of tissue analysis in CT, which was predominantly, restricted to bone, soft tissue, fat (vaguely) and air classification based on the attenuation values (or Houndsfield units). The talk will focus on walking through the advances in spectral CT, the physics behind it and possible research enabling new tissue classification through Dual Energy CT.

Speaker Biography: Bipul Das, earned his PhD from Indian Institute of Technology, Kharagpur and specialized in the area of VLSI for Ultrasound systems. In his PostDoctoral position at University of Pennsylvania, Department of Radiology, he developed his expertise in the area of image analysis for MR images. looking at atherosclerotic plaque assessment. In 2005, he joined GE Global Research in Bangalore, in the Imaging Technologies lab. He led the research in CT applications and has developed a number of algorithms in that area. Presently, he is leading the Medical Image Analysis Lab, in GE Global Research. His research interests are medical image analysis, looking into the tissue-signal interaction and tissue-based biomarker discovery. He has 30+ publications in International Journals & Conferences and 10+ filed patents.

Abstract: We are entering an era that will usher dramatic changes in most industries via exploitation of data. With the availability of abundant computing power, proliferation of data from sensors and increasing digitization of data that used to be in non-electronic form, there are opportunities to enable transformations in the way the world is run. Focusing on the healthcare industry, we present preliminary work that shows the applicability of remote sensing and data analytics to measure body vitals such as respiration and heart rate, and also to diagnose diseases such as breast cancer and atrial fibrillation (a form of cardiac arrhythmia). As more of the patients’ medical history gets captured in electronic health record systems, there is a further promise of applying real-time predictive analytics to assist doctors in practicing evidence-based medicine. We present work that significantly improves upon the state of the art for a variety of problems, including prediction of serious complications such as acute hypotensive episodes for patients in an ICU, and predicting the probability of a patient requiring ICU admission. Finally, we present some observations on the common aspects of opportunities across various industry verticals, characterizing these as technology support to enable highly personalized services at massive scale and describe some outstanding challenges.

Speaker Biography: Dr. Manish Gupta is Vice President at Xerox Corporation and Director of Xerox Research Centre India. Previously, Manish has served as Managing Director, Technology Division at Goldman Sachs India, and has held various leadership positions with IBM, including that of Director, IBM Research - India and Chief Technologist, IBM India/South Asia. From 2001 to 2006, he served as a Senior Manager at the IBM T.J. Watson Research Center in Yorktown Heights, New York, where he led the team developing system software for the Blue Gene/L supercomputer. IBM was awarded a National Medal of Technology and Innovation for Blue Gene by US President Barack Obama in 2009. Manish holds a B.Tech. degree in Computer Science from IIT Delhi and a Ph.D. from the University of Illinois at Urbana Champaign. He has co-authored over 75 papers, with more than 5,500 citations in Google Scholar (with an h-index of 41) in the areas of high-performance computing, compilers, and virtual machine optimizations, and has been granted more than 15 US patents. While at IBM, Manish received an Outstanding Innovation Award, two Outstanding Technical Achievement Awards and the Lou Gerstner Team Award for Client Excellence. Manish is an ACM Fellow.

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Speaker Biography: Vidit Jain is senior research scientist at Yahoo! Labs. He received a Ph.D. in Computer Science from the University of Massachusetts Amherst and a B.Tech. in Computer Science from the Indian Institute of Technology Kanpur. His main research interests are in large-scale, machine-learning for computer vision and text processing.

Abstract: The needs of emerging markets differ from those in developed countries. For instance, the number of doctors per capita is much smaller necessitating algorithmic workflow modifications. There exist several disease patterns which have been dealt with in developed countries which are endemic in emerging markets such as presence of tuberculosis. This often causes difficulties when the person also has cancer, specifically when treatment decisions have to be taken based on estimating the stage of the cancer. In this talk, we present some of topics undertaken by the Siemens Imaging Lab in India to address some of these problems. As an example of development of low cost solutions without compromising accuracy we present the monocular Visual Patient Movement Monitoring (MVPM) System for frameless stereotaxy. The system consists of a light weight mouth bite with dual fiducial system used for position tracking. One set of fiducials consist of easy-to-detect checkerboard whose 3D position can be tracked using a pre-calibrated off-the-shelf camera and a set of radio-opaque rods suitable for X-ray imaging. The system provides a low cost alternative to accurate position monitoring during stereotactic radiotherapy. I will also talk of other relevant research topics undertaken in the lab.

Speaker Biography: Amit Kale received the B.E. (Hons.) degree from Victoria Jubilee Technical Institute affiliated with the University of Mumbai, India, in 1996, the M.Tech. degree from the Indian Institute of Technology, Bombay, in 1998, and the Ph.D. degree from the Department of Electrical and Computer Engineering, University of Maryland, College Park, in 2003, where he worked on developing algorithms for human identification using gait. He was a Post-doctoral Researcher and then a Research Assistant Professor at the University of Kentucky Center for visualization and Virtual Environments, Department of Computer Science University of Kentucky, Lexington from 2003-2007. He joined Siemens Corporate Technology India in 2007 as a Member of Technical Staff. Presently, he heads the Imaging and Computer Vision Research Group at Siemens Corporate Technology India. He has over 40 publications in refereed international journals and conferences and he is a regular reviewer for the same. He was awarded the best paper prize for his work on background subtraction at British Machine Vision Conference in 2005 and his work on fast object detection in medical volumes in the 2nd IEEE conference on Parallel Distributed and Grid Computing. His research interests are in image and video processing, computer vision, and pattern recognition.

Abstract: With the fast proliferation of digital cameras and other image acquisition devices due to the advancement in digital photography technology, photos from the public may have good news values for making journalist reports. However, one big challenge is how to authenticate the photo contents from the public, which may come from unreliable sources. A large variety of forensics works have been proposed to address various forensic challenges based on different types of tell-tale signs. This talk introduces several techniques for: (1) Accurate detection of image demosaicing regularity as a general type of image forensics features. (2) Identification of various common image source models including digital still cameras, RAW conversion tools and the low-end mobile cameras; (3) Universal detection of a wide range of common image tampering and (4) Prevention of the image recapturing threat. These techniques help expose common image forgeries, especially those easy-to-make forgeries, which can be hardly seen directly by human eyes. The common theme behind these forensics techniques is through statistical detection of some intrinsic image regularity or tampering anomalies.

Speaker Biography: Alex C. Kot (F’06) has been with the Nanyang Technological University, Singapore, since 1991. He headed the Division of Information Engineering at the School of Electrical and Electronic Engineering for eight years and served as Associate Chair Research and Vice-Dean Research for the School of Electrical and Electronic Engineering. He is currently Professor and Associate Dean for the College of Engineering. He has published extensively in the areas of signal processing for communication, biometrics, data-hiding, image forensics and information security.

Dr. Kot served as Associate Editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING, the IEEE TRANSACTIONS ON MULTIMEDIA, the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (IEEE CSVT); and the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS PARTS I and II. He also served as Guest Editor for the Special Issues for the IEEE CSVT and the EURASIP Journal of Advanced Signal Processing (JASP). He is currently Associate Editor for the IEEE TRANSACTIONS ON INFORMATION, FORENSICS AND SECURITY, IEEE TRANSACTIONS ON IMAGE PROCESSING, and the IEEE SIGNAL PROCESSING LETTERS. He is also Editor for the EURASIP JASP, the IEEE Signal Processing Magazine, and the IEEE JOURNAL OF THE SPECIAL TOPICS IN SIGNAL PROCESSING. He serves on the IEEE CAS Visual Signal Processing and Communication, the IEEE SPS Image and Video Multi-dimensional Signal Processing and IEEE SPS Information Forensic and Security technical committees. He has served the IEEE Society in various capacities such as the General Co-Chair for the 2004 IEEE International Conference on Image Processing (ICIP) and Chair of the worldwide SPS Chapter Chairs and the Distinguished Lecturer program. He served as IEEE Fellow Evaluation Committee. He received the Best Teacher of the Year Award and is a co-author for several Best Paper Awards including ICPR, WIFS and IWDW. He was the IEEE Distinguished Lecturer in 2005 and 2006 and is a Fellow of IEEE, a Fellow of the Academy of Engineering, Singapore, and a Fellow of IES.

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Speaker Biography: Ajay Kumar (S’00–M’01–SM’07) received the Ph.D. degree from the University of Hong Kong, Hong Kong, in May 2001. He was an Assistant Professor with the Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India, from 2005 to 2007. He has been an Assistant Professor with the Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, since 2009. He is currently on the editorial board of Pattern Recognition and serves on the IEEE Biometrics Council as Vice President (Publications). He was on the editorial board of IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY from 2010 to 2013 and served on the program committees of several international conferences and workshops. He was the Program Chair of The Third International Conference on Ethics and Policy of Biometrics and International Data Sharing in 2010 and the Program Co-Chair of the International Joint Conference held in Washington, DC in 2011 and the International Conference on Biometrics in Madrid in 2013. His current research interests include biometrics with the emphasis on hand biometrics, vascular biometrics, iris and multimodal biometrics, pattern recognition with the emphasis on biometrics and defect detection using wavelets, general texture analysis, neural networks, and support vector machines. He holds three U.S. Patents and has published extensively on biometrics and computer vision-based industrial inspection.

Abstract: This talk describes social learning in the context of controlled sensing and multi-agent signal processing. Individual agents perform social learning to estimate an underlying state of nature and thereby make local decisions. How can a global decision maker use these local decisions to optimize a utility function? Two examples are considered: The first example deals with the quickest detection/estimation problem when individual agents perform social learning. The second example deals with a global decision maker that optimizes a social utility function to delay herding amongst agents. In both examples, the optimal strategy of the global decision maker is unusual: the stopping set is non convex,

Abstract: Partially Observed Markov Decision Processes (POMDPs) are widely used in machine learning and artificial intelligence as a formalism for decision making under uncertainty. This tutorial will present state-of-the-art results in POMDPs starting from an elementary level. Applications described include controlled sensing, reinforcement learning and multi-armed bandits.

Speaker Biography: Vikram Krishnamurthy was born in 1966. He received his bachelor's degree from the University of Auckland, New Zealand in 1988, and Ph.D. from the Australian National University in 1992. He currently is a professor and Canada Research Chair at the Department of Electrical Engineering, University of British Columbia, Vancouver, Canada.

Dr. Krishnamurthy's current research interests include game theory, stochastic control in sensor networks, and modeling of biological ion channels and biosensors. Dr. Krishnamurthy served as Editor in Chief of IEEE Journal Selected Topics in Signal Processing and as Distinguished lecturer for the IEEE signal processing society. He has also served as associate editor for several journals including IEEE Transactions Automatic Control and IEEE Transactions on Signal Processing. He is a Fellow of IEEE.

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Abstract: The presentation will focus on two applications of structured (or knowledge enhanced) Compressive Sensing: a) fast and accurate wideband spectrum sensing and b) radar signal processing.
The key requirement of dynamic spectrum access (DSA) in cognitive radios is fast and effective detection of channel status (to determine unused channels by primary users). The front-end architecture of a traditional receiver chain consists of analog bandpass filtering (for channelization) followed by suitable channel scanning algorithm. Based on current circuit designs that determine typical channel switching times and standard detection algorithms (such as energy detection), the mean time to complete a full scan can be quite significant, significantly exceeding desired orders for various application scenarios. We investigate the application of compressive sampling (CS) concepts to ameliorate this, requiring only sub-Nyquist sampling ADC. The talk will describe our results using two CS-inspired approaches that exploit the available a-priori side-information regarding channel occupancy (typically sparse).
The second application is Space-Time Processing with a radar transmit/receive array for accurate target detection for challenging clutter-dominated environments. Traditional approaches that do not exploit the clutter structure require significant training data, thereby reducing it’s real-time applicability. Fortunately, clutter shows sparsity in the Doppler-angle domain, which can be exploited in a suitable compressive sensing formulation. We will describe preliminary results using Dictionary Learning approach to a radar (KASSPER) dataset.

Speaker Biography: Sumit Roy (Fellow, IEEE for “contributions to cross-layer design approaches in wireless standards”) received the B. Tech. degree from the Indian Institute of Technology (Kanpur) in 1983, and the M. S. and Ph. D. degrees from the University of California (Santa Barbara), all in Electrical Engineering in 1985 and 1988 respectively, as well as an M. A. in Statistics and Applied Probability in 1988. Presently he is Integrated Systems Prof. of Electrical Engineering, Univ. of Washington and an Adjunct Professor and Consulting External Chair for ECE program. IIIT-Delhi. His research interests include analysis/design of wireless communication and sensor network systems. His recent research emphasis includes wireless LANs (802.11) and emerging 4G technologies, definition of multi-standard wireless inter-networking and cognitive radios, vehicular and underwater networks and sensor networking involving RFID technology. He spent 2001-03 on academic leave at Intel Wireless Technology Lab as a Senior Researcher engaged in systems architecture and standards development for ultra-wideband systems (Wireless PANs) and next generation high-speed wireless LANs. In 2008, he was Science Foundation of Ireland’s Walton Fellow on a sabbatical at University College, Dublin. His activities for the IEEE Communications Society (ComSoc) includes membership of several technical (Cognitive Networks, Communications Theory) and conference program committees. He has engaged in global collaborations worldwide: having served on numerous external thesis committees (Canada, Israel, Thailand, HongKong), research collaborations with academic colleagues (Singapore, Korea, New Zealand) and service on national research panels and review boards (Ireland, HongKong, Qatar). He is spending part of his 2014 sabbatical year between IIIT-Delhi, Microsoft Research Bangalore and ITRA Academy, Media Lab Asia (Dept. of Electronics and Information Technology, Govt. of India).

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Speaker Biography: Dr. Kaushik Saha joined STMicroelectronics Ltd.(previously SGS-Thomson Microelectronics Ltd) in 1996, after having obtained his B.Tech, M.Tech. and Ph.D. degrees from Indian Institute of Technology, Delhi. He joined the organization as a designer of semiconductor memories in the Memory Products Group. Subsequently, he worked for the Applications Lab of the company and was involved in the design of consumer electronics systems around the devices designed and fabricated by the company. Till 2013, he was Principal Member Technical Staff in the Advanced Systems Technologies group of STMicroelectronics, which is the group involved in research and development of future generations of devices and systems planned by the company for the markets of tomorrow.

At present he is Director, Advanced Software Team, Samsung R&D India, Delhi and heads teams engaged in embedded Linux kernel, Security and SoC R&D. His research interests are in the areas of Advanced Processor Architectures and Parallel Algorithms & Architectures for Digital Signal Processing Applications in which he holds various patents and has extensive publications. He is also associated with the Indian Institute of Technology, Delhi in the capacity of Adjunct Faculty.

Abstract: A major trend traversing both the machine learning and signal processing communities in recent years has been the broad adoption of probabilistic methods for estimation and inference, which coupled with approximation techniques have provided computationally feasible solutions to problems that were formerly considered intractable. In this talk, we highlight this trend by drawing upon two applications in the completely disparate areas of communications and bioinformatics that, however, share a very strong underlying similarity. Specifically, we consider the turbo approach to probabilistic decoding and review its application in the original communications setting it was developed and in the decoding of RNA secondary structure, a key problem in bioinformatics. The problems and solution approaches share a deep underlying similarity. In the communications setting, a single message stream is coded by two encoders separated by an interleaver. In the biological setting, Nature encodes the same secondary structure in multiple homologous ncRNA sequences occurring in different species, preserving topological structure essential for biological function. In both settings, a jointly optimal decoding of the encoded information is preferable because it offers significant improvements in accuracy compared with individual decodings. Furthermore, in both scenarios, exact jointly optimal decoding is stymied by computational complexity, which is exponential in the interleaver length for the communications setting and exponential in the number of homologs for the biological setting. An approximation “turbo decoding” approach based on iteration and feedback provides an elegant and effective solution for both problems. The talk will also highlight the differences between the problem setting and the importance of the secondary structure prediction problem for medicine and biology.

Speaker Biography: Gaurav Sharma is a professor at the Electrical and Computer Engineering Department at the University of Rochester, where, from 2008-2010, he also served as the Director for the Center for Emerging and Innovative Science (CEIS), a New York state funded center located at the University of Rochester chartered with promoting economic development through university-industry technology transfer. He received the PhD degree in Electrical and Computer engineering from North Carolina State University, Raleigh in 1996. From 1993 through 2003, he was with the Xerox Innovation group in Webster, NY, most recently in the position of Principal Scientist and Project Leader. His research interests include color science and imaging, multimedia/print security, and bioinformatics areas in which he has 49 patents and has authored over a 150 journal and conference publications. He is the Editor-in-Chief for the Journal of Electronic Imaging and the editor of the Digital Color Imaging Handbook published by CRC press in 2003. He has also served as an associate editor for the Journal of Electronic Imaging, the IEEE Transactions on Image Processing, and for the IEEE Transactions on Information Forensics and Security. Dr. Sharma is a fellow of the IEEE, a fellow of SPIE, a fellow of the Society for Imaging Science and Technology (IS&T) and has been elected to Sigma Xi, Phi Kappa Phi, and Pi Mu Epsilon. In recognition of his research contributions, he received an IEEE Region I technical innovation award in 2008.

Abstract: The confluence of multiple technology trends: Social, Big Data, Internet of Things and Mobility, is being viewed as a renewed opportunity for driving ‘Digital’ business strategies across a wide spectrum of industry verticals. A few examples from the retail and manufacturing domains will be shared. Data analytics forms the underlying enabler for all such strategic digital programs. Further, digital strategies require integrating predictive solutions into a broader prescriptive analytics framework, both in a technical as well as organizational sense. Three examples of such frameworks will be described, along with examples of specific solutions: Stream Prognostics, Prescriptive Information Fusion, and Enterprise Contextual Intelligence.

Speaker Biography: Dr. Shroff heads TCS’ Innovation Lab in Delhi that conducts applied research in data analytics, information fusion, multimedia, robotics, and virtual reality. Prior to joining TCS in 1998, Dr. Shroff had been on the faculty of the California Institute of Technology, Pasadena, USA (1990-91) and thereafter of the Department of Computer Science and Engineering at Indian Institute of Technology, Delhi, India (1991 – 1997). He has also held visiting positions at NASA Ames Research Center in Mountain View, CA, and at Argonne National Labs in Chicago. In 1994 he was conferred the ‘Young Scientist Award’ from the Department of Atomic Energy.
Dr. Shroff has published over 30 research papers in the areas of computational mathematics, parallel computation, distributed systems, software architecture and software engineering. He has written a book “Enterprise Cloud Computing” published by Cambridge University Press, UK, in October 2010. Oxford University Press published his second book, “The Intelligent Web”, in 2013. In 2012 Dr. Shroff taught a massive online open course (MOOC), via Coursera, on “Web Intelligence and Big Data”, in his capacity as an adjunct professor at IIT Delhi and IIIT Delhi. The course was attended by tens of thousands of students all over the world, especially from India and the US.
Dr. Shroff is an active member of ACM and ACM-India, and serves as the founding chair of the ACM-India SIG on Knowledge Discovery from Data (IKDD), which is also the India chapter of ACM SIGKDD.
Dr. Shroff graduated with a B.Tech in Electrical Engineering from the Indian Institute of Technology, Kanpur, India, in 1985 and received his Ph.D. in Computer Science from Rensselaer Polytechnic Institute, NY, USA, in 1990.

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