December 16th, 2019

IEEE Signal Processing Society, Bangalore Chapter


Department of Electrical Engineering

Indian Institute of Science (IISc), Bangalore

invite you to the following talk

Title: From compressed sensing to deep learning: tasks, structures, and models.

Date and time: December 18, 2019; 11.30 AM.

Coffee will be served during the talk.

Venue: Multimedia Classroom, Department of Electrical Engineering, IISc.

Speaker: Prof. Yonina Eldar, Weizmann Institute of Science, Israel.

Host faculty: Dr. Chandra Sekhar Seelamantula, EE, IISc.


The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal and image processing. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Consequently, conversion to digital has become a serious bottleneck.  Furthermore, the resulting digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power.  In the context of medical imaging sampling at high rates often translates to high radiation dosages, increased scanning times, bulky medical devices, and limited resolution.

In this talk, we present a framework for sampling and processing a large class of wideband analog signals at rates far below Nyquist in space, time and frequency, which allows to dramatically reduce the number of antennas, sampling rates and band occupancy.


Our framework relies on exploiting signal structure and the processing task.  We consider applications of these concepts to a variety of problems in communications, radar and ultrasound imaging and show several demos of real-time sub-Nyquist prototypes including a wireless ultrasound probe, sub-Nyquist MIMO radar, super-resolution in microscopy and ultrasound, cognitive radio, and joint radar and communication systems. We then discuss how the ideas of exploiting the task, structure and model can be used to develop interpretable model-based deep learning methods that can adapt to existing structure and are trained from small amounts of data. These networks achieve a more favorable trade-off between increase in parameters and data and improvement in performance, while remaining interpretable.

Biography of the speaker:

Yonina C. Eldar received the B.Sc. degree in Physics in 1995 and the B.Sc. degree in Electrical Engineering in 1996 both from Tel-Aviv University (TAU), Tel-Aviv, Israel, and the Ph.D. degree in Electrical Engineering and Computer Science in 2002 from the Massachusetts Institute of Technology (MIT), Cambridge. From January 2002 to July 2002 she was a Postdoctoral Fellow at the Digital Signal Processing Group at MIT.

She is currently a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel. She was previously a Professor in the Department of Electrical Engineering at the Technion, where she held the Edwards Chair in Engineering. She is also a Visiting Professor at MIT, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford. She is a member of the Israel Academy of Sciences and Humanities (elected 2017), an IEEE Fellow and a EURASIP Fellow.

Dr. Eldar has received numerous awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014), and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), the Award for Women with Distinguished Contributions, the Andre and Bella Meyer Lectureship, the Career Development Chair at the Technion, the Muriel & David Jacknow Award for Excellence in Teaching, and the Technion’s Award for Excellence in Teaching (twice). She received several best paper awards and best demo awards together with her research students and colleagues including the SIAM outstanding Paper Prize and the IET Circuits, Devices and Systems Premium Award, and was selected as one of the 50 most influential women in Israel.

She was a member of the Young Israel Academy of Science and Humanities and the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing, a member of the IEEE Sensor Array and Multichannel Technical Committee and serves on several other IEEE committees. In the past, she was a Signal Processing Society Distinguished Lecturer, member of the IEEE Signal Processing Theory and Methods and Bio Imaging Signal Processing technical committees, and served as an associate editor for the IEEE Transactions On Signal Processing, the EURASIP Journal of Signal Processing, the SIAM Journal on Matrix Analysis and Applications, and the SIAM Journal on Imaging Sciences. She was Co-Chair and Technical Co-Chair of several international conferences and workshops.

She is author of the book “Sampling Theory: Beyond Bandlimited Systems” and co-author of the books “Compressed Sensing” and “Convex Optimization Methods in Signal Processing and Communications,” all published by Cambridge University Press.

December 3rd, 2019

Indian Institute of Science
Centre for BioSystems Science and Engineering

BSSE Seminar

(Organized by IEEE Signal Processing Society Bangalore Chapter)

9th December 2019 (Monday), 11:00 AM, MRDG Seminar Hall, 1st floor, Biological Sciences Building

Title: A Small Rearguard Action in the Age of Big Data and Machine Learning: Mechanistic Models in Computational Physiology

Speaker: Dr. George Verghese, MIT, Cambridge, Massachusetts

Abstract: The talk will draw some contrasts between phenomenological or empirical models (e.g., regression, neural networks) and mechanistic models (e.g., circuit analogs). Mechanistic models focus on meaningful component parts/subprocesses of the phenomenon of interest, and on their interconnections/interactions, which then generate the range of possible system behaviors. Examples will be given of mechanistic models for aspects of cardiovascular, cerebrovascular and respiratory physiology, and application of these models to extracting interpretable information from relevant data obtained in clinical or ambulatory settings.

Bio: Dr. George Verghese received his BTech from the Indian Institute of Technology, Madras in 1974, his MS from the State University of New York, Stony Brook in 1975, and his PhD from Stanford University in 1979, all in Electrical Engineering. Since 1979, he has been with MIT, where he is the Henry Ellis Warren (1894) Professor, and Professor of Electrical and Biomedical Engineering, in the Department of Electrical Engineering and Computer Science. He was named a MacVicar Faculty Fellow at MIT for the period 2011-2012, for outstanding contributions to undergraduate education.Verghese is also a principal investigator with MIT’s Research Laboratory of Electronics (RLE). His research interests and publications are in the areas of dynamic systems, modeling, estimation, signal processing, and control. Over the past decade, his research focus has shifted from applications in power systems and power electronics entirely to applications in biomedicine. He directs the Computational Physiology and Clinical Inference Group in RLE. He is an IEEE Fellow, and has co-authored two texts: Principles of Power Electronics (with J.G. Kassakian and M.F. Schlecht, 1991), and Signals, Systems and Inference (with A.V. Oppenheim, 2015).


November 21st, 2019
The IEEE Signal Processing Society, Bangalore Chapter and Department of Electrical Engineering, Indian Institute of Science, welcomes you to following talk.
Location and Date : MMCR, EE, Thursday Nov. 21, 4 pm (Coffee at 345pm).
Speakers : Dr. Sivaram Garimella and Kishore Nandury


Title: Semi-supervised Learning for Amazon Alexa.
State-of-the-art Acoustic Models (AM) are large, complex deep neural networks that typically comprise millions of model parameters. Deep neural networks can express highly complex input-output relationships and transformations, but the key to getting the best performance out of them is the availability of large amounts of matched acoustic data – matched to the desired dialect, language, environmental/channel condition, microphone characteristic, speaking style, and so on. Since it is both time consuming and expensive to transcribe large amounts of matched acoustic data for every desired condition, we leverage Teacher/Student based Semi-Supervised Learning technology for improving the AM. Our training leverages vast amount of un-transcribed data in addition to multi-dialect transcribed data yielding up to 7% relative word error rate reduction over the baseline model, which has not seen any unlabelled data.
Sri Garimella is a Senior Manager heading the Alexa Machine Learning/Speech Recognition group in Amazon, India. He has been associated with Amazon for more than 7 years. He obtained PhD from the Department of Electrical and Computer Engineering, Center for Language and Speech Processing at the Johns Hopkins University, Baltimore, USA in 2012. And Master of Engineering in Signal Processing from the Indian Institute of Science, Bangalore, India in 2006.
Kishore Nandury is an Applied scientist in Alexa ASR team in Amazon Bangalore. Prior to Amazon, he has worked in Intel, Sling media & NVIDIA graphics. He has obtained Masters degree in Signal Processing from Indian Institute of Science in 2005.
Host Faculty:  Sriram Ganapathy
November 8th, 2019

CNRS, France, Indian Institute of Science, Bangalore, and IEEE Signal Processing Society Bangalore Chapter invite you to the following talk

Title: Some contributions to Bayesian approaches for target tracking in radar applications

Speaker: Prof. Eric Grivel, IMS Lab, Bordeaux, France

Venue: Golden Jubilee Seminar Hall, ECE Department, Indian Institute of Science Bangalore

Date and time: November 13, 2019; 4 PM – 5 PM.

Abstract: Detecting and tracking maritime or ground targets is one of the application fields for surveillance by airborne radar systems. More particularly, the purpose is to estimate the trajectories of one or more moving objects over time based on noisy radar measurements. When dealing with one target, one approach consists in using a Kalman filter by making an assumption on the type of the target motion and the parameters of the motion model. However, these assumptions are not necessarily well-suited to the situation. In addition, when dealing with maneuvering targets, the motion model may often change over time. Finally, false detections may appear and have a bad influence on the estimation of the target position. To address these issues, multiple-model algorithms, joint tracking and classification approaches and Bernoulli filtering can be considered. The purpose of this talk is to present some variants based on these concepts.

It should be noted that the methods that will be presented were developed with PhD students and a French colleague.

Biography of the speaker: Eric Grivel received his PhD in signal processing in 2000 in Bordeaux (France). He joined Bordeaux Institute of Technology (Bordeaux INP), in 2001 as an assistant professor  and then as a professor in 2011.  For more than 20 years, he has been with the Signal & Image research group at IMS lab (which is a joint research unit for the French National Center for Scientific Research CNRS, University of Bordeaux and Bordeaux INP). His research activities deal with statistical signal processing with applications in speech and audio processing, mobile communication systems, radar processing, GPS navigation and biomedical.

October 29th, 2019

The IEEE Signal Processing Society, Bangalore Chapter and Department of Electrical Engineering, Indian Institute of Science, welcomes you to following talk.

Location and Date : MMCR, EE, Thursday Oct. 31, 4-5 pm

Speaker : Dr. Hemant Misra (Vice President, Head of Applied Research, Swiggy)  

Abstract : Topic models such as Latent Dirichlet Allocation (LDA: have been used extensively in the last decade for tasks such as information retrieval, topic discovery, dimensionality reduction etc. In the current presentation, the application of LDA for the task of text-segmentation ( has been explained. Results on multiple datasets are shown to demonstrate the performance of the proposed LDA based system vis-a-vis other standard methods. The talk also uncovers challenge faced by the dynamic programming (DP) algorithm used in proposed LDA based segmentation and how it was overcome.

Talk will also cover some of the exciting things we are doing at Swiggy in the Applied Research team in the areas of speech, computer vision (CV) and natural language processing (NLP).

Speaker Bio : Dr. Hemant Misra is an active researcher in the areas of text and signal processing, speech/speaker recognition, machine learning, healthcare applications and education. He did his MS (1999) from IIT, Madras, and PhD from EPFL (2006). Then he held post-doc positions at Telecom ParisTech, University of Glasgow, and Xerox Research Centre Europe.  After having successful stints at Philips (Healthcare) Research, IBM’s India Research Lab and Citicorp Services India, currently Hemant is ‘VP – Head of Applied Research’ at Swiggy.

Host Faculty : Sriram Ganapathy (EE)

October 9th, 2019

We are happy to announce the formation of the Ramaiah Institute of Technology Signal Processing Society Student Branch Chapter from October 2, 2019. 

September 8th, 2019

Dept. of Electrical Communication Engineering and


IEEE Signal Processing Society Bangalore Chapter


invite you to the following seminar:


Title: Large Scale Data Analytics for Airborne Imagery

Speaker: Prof. Gaurav Sharma, Univ. of Rochester

Time and Date: 11 AM, September 10, 2019

Venue: Golden Jubilee Hall, ECE




The widespread availability of high resolution aerial imagery covering wide geographical areas is spurring a revolution in large scale visual data analytics. Specifically, modern aerial wide area motion imagery (WAMI) platforms capture large high resolution at rates of 1-3 frames per second. The sequences of images, which individually span several square miles of ground area, represent rich spatio-termporal datasets that are key enablers for new applications. The effectiveness of such analytics can be enhanced by combining WAMI with alternative sources of rich geo-spatial information such as road maps or prior georegistered images. We present results from our recent research in this area covering three topics. First, we describe a novel method for pixel accurate, real-time registration of vector roadmaps to WAMI imagery based on moving vehicles in the scene. Next, we present a framework for tracking WAMI vehicles across multiple frames by using the registered roadmap and a new probabilistic framework that allows us to better estimate associations across multiple frames in a computationally tractable algorithm. Finally, in the third part, we highlight, how we can combine structure from motion and our proposed registration approach to obtain 3D georegistration for use in application such as change detection. We present results on multiple WAMI datasets, including nighttime infrared WAMI imagery, highlighting the effectiveness of the proposed methods through both visual and numerical comparisons.


Speaker Biography


Gaurav Sharma is a professor in the Electrical and Computer Engineering Department and a Distinguished Researcher in Center of Excellence in Data Science (CoE) at the Goergen Institute for Data Science at the University of Rochester. 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 data analytics, cyber physical systems, signal and image processing, computer vision, and media security; areas in which he has 52 patents and has authored over 200 journal and conference publications. He currently serves as the Editor-in-Chief for the IEEE Transactions on Image Processing. From 2011 through 2015, he served as the Editor-in-Chief for the Journal of Electronic Imaging and, in the past, has 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. He is a member of the IEEE Publications, Products, and Services Board (PSPB) and chaired the IEEE Conference Publications Committee in 2017-18. He is the editor of the Digital Color Imaging Handbook published by CRC press in 2003. 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.

September 3rd, 2019


Department of ECE, Indian Institute of Science

IEEE Bangalore Section

IEEE Signal Processing Society Bangalore Chapter

Welcome you to a 

Special Lecture


Title:  Brain Inspired Automated Concept and Object Learning: Vision, Text, and Beyond

Speakers: Vwani Roychowdhury (UCLA) and 

Thomas Kailath (Stanford) 

Venue: ECE Golden Jubilee Seminar Hall

             Department of  ECE, IISc

Day/ Date: Friday, 6 September 2019

Time: 3-5 pm

High Tea at 5pm


Brains are endowed with innate models that can learn effective informational and reasoning prototypes of the various objects and concepts in the real world around us. A distinctive hallmark of the brain, for example, is its ability to automatically discover and model objects, at multi-scale resolutions, from repeated exposures to unlabeled contextual data and then to be able to robustly detect the learned objects under various non-ideal circumstances, such as partial occlusion and different view angles. Replication of such capabilities in a machine would require three key ingredients: (i) access to large-scale perceptual data of the kind that humans experience, (ii) flexible representations of objects, and (iii) an efficient unsupervised learning algorithm. The Internet fortunately provides unprecedented access to vast amounts of visual data. The first part of this work will focus on our recent work that leverages the availability of such data to develop a scalable framework for unsupervised learning of object prototypes—brain-inspired flexible, scale, and shift invariant representations of deformable objects (e.g., humans, motorcycles, cars, airplanes) composed of parts, their different configurations and views, and their spatial relationships. We apply our framework to various datasets and show that our approach is computationally scalable and can construct accurate and operational part-aware object models much more efficiently than in much of the recent computer vision literature. We also present efficient algorithms for detection and localization in new scenes of objects and their partial views. The second part of this work will focus on processing large scale textual data, wherein our algorithms can create semantic concept-level maps from unstructured data sets. Finally we will conclude with the outlines of a general framework of contextual unsupervised learning that can remove many of the scalability and robustness limitations of existing supervised frameworks that require large amounts of labeled training sets and mostly act as impressive memorization engines. 

Short Bio:

Vwani Roychowdhury is a Professor of Electrical and Computer Engineering at UCLA and received his BTech and PhD degrees in Electrical Engineering from IIT Kanpur and Stanford University, respectively. Prof. Roychowdhury’s expertise lies in combining tools from a number of disciplines, including computer science, engineering, information theory, mathematics, and physics, and solving fundamental problems in multiple disciplines. His research interests have spanned a diverse set of topics related to combinatorics and theoretical computer science, Artificial Neural Networks, nanoelectronics and device modeling, quantum computing, quantum information and cryptography, the physics of information processing and computation, Bioinformatics, and more recently, brain inspired machine learning and brain modeling. He has published more than 250 journal and conference papers, and coauthored several books. He has mentored more than 25 Ph.D. students and 20 post-doctoral fellows and is always seeking collaborations with problem solvers and seekers. He also cofounded four silicon valley startups; one of these,, was founded in Jan. 2017, pioneered the unsupervised distillation of Concept Graphs from billions of documents, raised upwards of 45M US dollars in investment and was acquired in February 2017. 

Short Bio of Prof. Kailath:

Thomas Kailath received a B.E. (Telecom) degree in 1956 from the College of Engineering, Pune, India, and S.M. (1959) and Sc.D. (1961) degrees in electrical engineering from the Massachusetts Institute of Technology. He then worked at the Jet Propulsion Labs in Pasadena, CA, before being appointed to Stanford University as Associate Professor of Electrical Engineering in 1963. He was promoted to Professor in 1968, and appointed as the first holder of the Hitachi America Professorship in Engineering in1988. He assumed emeritus status in 2001, but remains active with his research and writing activities. He also held shorter-term appointments at several institutions around the world. 

His research and teaching have ranged over several fields of engineering and mathematics: information theory, communications, linear systems, estimation and control, signal processing, semiconductor manufacturing, probability and statistics, and matrix and operator theory. He has also co-founded and served as a director of several high-technology companies. He has mentored an outstanding array of over a hundred doctoral and postdoctoral scholars. Their joint efforts have led to over 300 journal papers, a dozen patents and several books and monographs, including the major textbooks: Linear Systems (1980) and Linear Estimation (2000). 

He received the IEEE Medal of Honor in 2007 for “exceptional contributions to the development of powerful algorithms for communications, control, computing and signal processing.” Among other major honors are the Shannon Award of the IEEE Information Theory Society; the IEEE Education Medal and the IEEE Signal Processing Medal; the 2009 BBVA Foundation Prize for Information and Communication Technologies; the Padma Bhushan, India’s third highest civilian award; election to the U.S. National Academy of Engineering, the U.S. National Academy of Sciences, and the American Academy of Arts and Sciences; foreign membership of the Royal Society of London, the Royal Spanish Academy of Engineering, the Indian National Academy of Engineering, the Indian National Science Academy, the National Academy of Sciences,India, the Indian Academy of Sciences, and TWAS (The World Academy of Sciences). 

In November 2014, he received the 2012 US National Medal of Science from President Obama “for transformative contributions to the fields of information and system science, for distinctive and sustained mentoring of young scholars, and for translation of scientific ideas into entrepreneurial ventures that have had a significant impact on industry.”

July 31st, 2019

IEEE Signal Processing Society Bangalore Chapter invites you to the following event:




Presented by     Harsha Kikkeri

                             CEO, Holosuit Pte Ltd, Mysore

Venue:               ECE Dept, Golden Jubilee Hall, Indian Institute of Science

Date/Time:        Friday, 2nd August 2019, 2-6 pm


Program :

1:30-2pm –         On the spot Registration (if available)

2-3 pm –             Talk:   How to build a Humanoid Robot and Control it

3-3:15 pm –       Q & A

3:15 – 3:45pm   XPrize Humanoid $10 million challenge Presentation

3:45-4:15pm –  Q & A

4:15-4:30          Tea break

4:30-6pm          Workshop on Robots and Virtual Reality and discussion.

Please register here


Abstract: How to build a Humanoid Robot and Control it?

Building Humanoid Robots is the holy grail of robotics. From Honda’s Asimov, to Boston Dynamics Atlas there have been various attempts to build general purpose humanoid robots. This talk will explore various challenges involved in building an end-to-end humanoid robot and will showcase some of the solutions to some of these challenges. Some of the challenges this talk will focus on will be building the hardware in the form factor with the accuracy required without making it cost prohibitive, while still able to control it. The kind of simulation and learning involved in mapping and navigating unpredictive terrain, grasping and manipulating objects using hands, bipedal motion and control, environmental understanding, task planning, learn by demonstration capabilities, the underlying open sources tools and hardware sensors/actuators which are required for a Humanoid Robot. There will be a workshop later which will showcase some robots.


Harsha Kikkeri

Harsha Kikkeri is the CEO of Holosuit pte Ltd where he is building HoloSuit – An AI enabled full body analytics platform which acts as a virtual trainer for your body. He has over 18 years experience working on IoT, augmented/virtual reality, aerial and ground robots with expertise in drones, sensor fusion and machine learning. He did pioneering research at Microsoft Robotics in USA building robots which could learn by demonstration. He has won numerous leadership awards including Gold Star from Microsoft, Excellence Award from Infosys, Bharat Petrleoum Scholarship and has won numerous chess tournaments. He has Masters in Electrical Engineering from Syracuse University, NY and BE Electronics from SJCE, Mysore, India. He holds 44 international patents from US, Europe, China, Japan and other countries. He is a TedX speaker. He had the honor of working under Dr TVSreenivas at IISc which led him into the field of digital signal processing and eventually into Robotics.

June 13th, 2019


Department of Electrical Engineering (EE), IISc and IEEE SPS Bangalore Chapter invite you to the following seminar:

Title: “Physics-Based Vision and Learning ”
(Joint Work with Yunhao Ba and Guangyuan Zhao)

Speaker: Dr. Achuta Kadambi

Achuta Kadambi

Host faculty : Dr. Chandra Sekhar Seelamantula

Time & Venue : 14th June, 2019, Friday on 4:00 PM at MMCR first floor, Electrical Engineering Department, IISc.

Abstract: Today, deep learning is the de facto approach to solving many computer vision problems. However, in adopting deep learning, one may overlook a subtlety: the physics of how light interacts with matter. By exploiting these previously overlooked subtleties, we will describe how we can rethink the longstanding problem of 3D reconstruction. Using the lessons learned from this prior work, we will then discuss the future symbiosis between physics and machine learning, and how this fusion can transform many application areas in imaging.

Biography: Achuta Kadambi is an Assistant Professor of Electrical and Computer Engineering at UCLA, where he directs the Visual Machines Group. The group blends the physics of light with artificial intelligence to give the gift of sight to robots. Achuta received his BS from UC Berkeley and his PhD from MIT, completing an interdepartmental doctorate between the MIT Media Lab and MIT EECS. Please see his group web page for research specifics: