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2017 Distinguished Lecturers


Vivek K. Goyal

Vivek K. Goyal (F) received the B.S. degree in mathematics (1993) and the B.S.E. degree in electrical engineering (1993) from the University of Iowa, where he received the John Briggs Memorial Award for the top undergraduate across all colleges. He received the M.S. degree (1995) and Ph.D. degree (1998) in electrical engineering from the University of California, Berkeley, where he received the Eliahu Jury Award for outstanding achievement in systems, communications, control, or signal processing.

Dr. Goyal was a Member of Technical Staff in the Mathematics of Communications Research Department, Bell Laboratories, Lucent Technologies, (1998–2001); and a Senior Research Engineer for Digital Fountain, Inc. (2001–2003). He was the Esther and Harold E. Edgerton Associate Professor of Electrical Engineering, Massachusetts Institute of Technology (2004–2013); Adviser, 3dim Tech, Inc. (winner of the 2013 MIT $100K Entrepreneurship Competition Launch Contest Grand Prize and 2013 MassChallenge Accelerator Gold), and was subsequently with Nest, an Alphabet company (2014–2016). He is now with the Department of Electrical and Computer Engineering of Boston University.

Dr. Goyal is an IEEE Fellow. He was awarded the IEEE Signal Processing Society Magazine Award (2002), the SPS Best Paper Award (2014), and an NSF CAREER Award. The work he supervised won student best paper awards at the IEEE Data Compression Conference in 2006 and 2011 and the IEEE Sensor Array and Multichannel Signal Processing Workshop in 2012 as well as five MIT thesis awards. He is a co-author of Foundations of Signal Processing (Cambridge University Press, 2014).

Dr. Goyal served on the IEEE Image and Multidimensional Signal Processing Technical Committee (2003–2009); IEEE Image, Video, and Multidimensional Signal Processing Technical Committee (2014); and the Steering Committee of the IEEE Transactions on Multimedia (2013). He has served as Editorial Board Member, Foundations and Trends and Signal Processing (2006-Present); Scientific Advisory Board of the Banff International Research Station for Mathematical Innovation and Discovery (2011-Present); the IEEE SPS Computational Imaging SIG (2015-Present); the IEEE Standing Committee on Industry DSP Technology (2016-Present); Technical Program Co-Chair, International Conference on Sampling Theory and Application (2015); and Conference Co-chair, SPIE Wavelets and Sparsity conference series (2006–2016).

Dr. Goyal’s research interests include computational imaging, human perception, decision making, sampling, quantization, and source coding theory.

Vivek K. Goyal
Department of Electrical and Computer Engineering
Boston University
8 St. Mary’s Street
Boston, MA 02215-2421
E: v.goyal@ieee.org

Lecture Topics

  • First-Photon Imaging and Other Extreme Optical Imaging
  • Social Learning in Decision-Making Groups
  • Teaching Signal Processing with Geometry
  • The Optimistic Bayesian: Replica Method Analysis of Compressed Sensing

Christine, Guillemot

Christine Guillemot (F) holds a PhD degree from ENST (Ecole Nationale Superieure des Telecommunications) Paris. She has been with FRANCE TELECOM, where she has been involved in various projects in the area of coding for TV, HDTV, and multimedia (November 1985 to October 1997) and she worked at Bellcore, NJ, USA, as a visiting scientist (January 1990 to mid 1991). Since November 1997, she is 'Director of Research' at INRIA, head of a research team dedicated to the design of algorithms for the image and video processing chain, with a focus on analysis, representation, compression, and editing, including for emerging modalities such as high dynamic range imaging and light fields.

Dr. Guillemot has co-authored 9 book chapters, 65 publications in peer-reviewed international journals (IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, IEEE Transactions on Information Theory, and IEEE Transactions on Circuits and Systems for Video Technology), 162 publications in international conferences (IEEE-ICASSP, IEEE-ICIP, IEEE-MMSP, Eusipco) and has co-authored 24 granted patents.

Dr. Guillemot is an IEEE Fellow. She has served as Associate Editor, IEEE Transactions on Image Processing (2000-2003, and 2014-2016); Associate Editor, IEEE Transactions on Circuits and Systems for Video Technology (2004-2006); Associate Editor, IEEE Transactions on Signal Processing (2007-2009); Associate Editor, IEEE Journal on Selected Topics in Signal Processing (2013-2015); Member, IEEE Image and Multidimensional Signal Processing Technical Committee (2001-2006); Member, IEEE Multimedia Signal Processing Technical Committee (2005-2008); Member, IEEE Image, Video, and Multidimensional Signal Processing Technical Committee (2013-Present); Senior Area Editor, IEEE Transactions on Image Processing (2016-2017); Steering Committee Member, IEEE Transactions on Multimedia (2016).

Dr. Guillemot’s research has focused over the past 20 years on numerous aspects of image and video processing: modeling, representation, compression, and communication. Her contributions concern algorithms for image and video analysis, representation, coding, communication and for inverse problems such as super-resolution, inpainting, restoration.

Christine Guillemot
Campus de Beaulieu
35042 RENNES
P: +33299847429
E: Christine.Guillemot@inria.fr

Lecture Topics

  • Sparsity and Dimensionality Reduction in Image Compression and Super-Resolution
  • Multi-View and Light Fields Processing: From Analysis, Representation, Compression to Rendering
  • From Image to Video and Multi-View Inpainting

Preferred Geographic Coverage of the Lectures

  • Europe
  • North America

Petros Maragos

Petros Maragos (F) received the M.Eng. Diploma in E.E. from the National Technical University of Athens (NTUA) in 1980 and the M.Sc. and Ph.D. degrees from Georgia Tech, Atlanta, in 1982 and 1985. In 1985, he joined the faculty of the Division of Applied Sciences at Harvard University, Boston, where he worked for eight years as professor of electrical engineering, affiliated with the Harvard Robotics Lab. In 1993, he joined the faculty of the School of ECE at Georgia Tech, affiliated with its Center for Signal and Image Processing. During periods of 1996-98 he had a joint appointment as director of research at the Institute of Language and Speech Processing in Athens. Since 1999, he has been working as a professor at the NTUA School of ECE, where he is currently the Director of the Intelligent Robotics and Automation Lab. He has held visiting scientist positions at MIT in Fall 2012 and at the University of Pennsylvania in Fall 2016.

Prof. Maragos served as Associate Editor, IEEE Transactions on Acoustics, Speech and Signal Processing (1989-1990); IEEE Transactions on Pattern Analysis and Machine Intelligence; General Chair, VCIP (1992); General Chair, ISMM (1996); General Chair, MMSP (2007); Program Chair, ECCV (2010); ECCV Workshop on Sign, Gesture and Activity (2010); Dagstuhl Symposia on Shape (2011 & 2014); IROS Workshop on Cognitive Mobility Assistance Robots (2015); General Chair, EUSIPCO (2017); Member, SPS Digital Signal Processing Technical Committee (1992-1998); IEEE SPS Image and Multidimensional Signal Processing Technical Committee (1995-1999); IEEE SPS Multimedia Signal Processing Technical Committee (2009-2012); Member, Greek National Council for Research and Technology.

Prof. Maragos is the recipient or co-recipient of several awards for his academic work, including: US NSF Presidential Young Investigator Award (1987-1992); IEEE SPS Young Author Best Paper Award (1988); IEEE SPS Best Paper Award (1994); IEEE W.R.G. Baker Prize Award for the most outstanding original paper (1995); Pattern Recognition Society's Honorable Mention Best Paper Award (1996); Best Paper Award, CVPR-2011 Workshop on Gesture Recognition.

Prof. Maragos was elected IEEE Fellow for his research contributions in 1995 and received the 2007 EURASIP Technical Achievements Award for contributions to nonlinear signal processing, systems theory, image and speech processing. In 2010, he was elected Fellow of EURASIP for his research contributions. He has been elected IEEE SPS Distinguished Lecturer for 2017-2018.

Prof. Maragos’ research and teaching interests include signal processing, systems theory, machine learning, image processing and computer vision, audio and speech/language processing, cognitive systems, and robotics. In the above areas he has published numerous papers, book chapters, and has also co-edited three Springer research books, one on multimodal processing and two on shape analysis.

Petros Maragos
National Technical University of Athens
School of E.C.E.
Athens 15773, Greece
T: +30-210-7722360
E: maragos@cs.ntua.gr

Lecture Topics

  • Multimodal Spatio-Temporal Signal Processing and Audio-Visual Perception
  • Nonlinear Signal Processing and Dynamical Systems on Lattices
  • Morphological and Variational Methods in Image Analysis and Computer Vision
  • Graph-based Methods for Clustering and Segmentation
  • Multimodal Gesture and Spoken Command Recognition in Human-Robot Interaction

Athina P. Petropulu

Athina P. Petropulu (F) received her undergraduate degree from the National Technical University of Athens, Greece, in 1986, and the M.Sc. and Ph.D. degrees from Northeastern University, Boston MA, in 1988 and 1991, respectively, all in Electrical and Computer Engineering. Since 2010, she is Professor of the Electrical and Computer Engineering (ECE) Department at Rutgers, having served as chair of the department during 2010-2016. Before that she was faculty at Drexel University.

Dr. Petropulu is an IEEE Fellow (2008) and the recipient of the 1995 Presidential Faculty Fellow Award given by NSF and the White House. She has served as Editor-in-Chief, IEEE Transactions on Signal Processing (2009-2011); IEEE Signal Processing Society Vice President-Conferences (2006-2008); Member-at-Large, IEEE Signal Processing Society Board of Governors (2004-2005); General Chair, IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2005); recipient, IEEE Signal Processing Magazine Best Paper Award (2005); recipient, IEEE Signal Processing Society Meritorious Service Award (2012); Member, IEEE SPS Fellow Reference Committee (2012-2014); and was selected as IEEE Distinguished Lecturer for the IEEE Signal Processing Society (2017-2018).

Dr. Petropulu's research interests span the area of statistical signal processing, wireless communications, signal processing in networking, physical layer security, and radar signal processing. Her research has been funded by various government industry sponsors including the National Science Foundation, the Office of Naval research, the US Army, the National Institute of Health, the Whitaker Foundation, and Lockheed Martin.

Athina Petropulu
Electrical and Computer Engineering Department
Rutgers, The State University of New Jersey
94 Brett Road, Piscataway, NJ 08854
T: +1 848-445-0414
E: athinap@rci.rutgers.edu

Lecture Topics

  • Sparse Sensing Based MIMO Radars
  • Coexistence of Radar and Communication Systems
  • Cooperative Approaches For Physical Layer Security
  • Cooperative Approaches For Improving The Performance of Wireless Networks
  • Mobile Beamforming
  • Localization of Brain Activations Based on EEG Recordings and Sparse Signal Recovery Theory

Brian M. Sadler

Brian M. Sadler (F) is the US Army Senior Research Scientist for Intelligent Systems, at the Army Research Laboratory (ARL) in Maryland. He received his undergraduate and Master’s education in electrical engineering from the University of Maryland in 1984, and the PhD degree in electrical engineering from the University of Virginia in 1993.

He was an Associate Editor for the IEEE Transactions on Signal Processing (1999-2001, 2008-2009, 2015-Present), EURASIP Signal Processing, and the IEEE Signal Processing Letters (2006-2007). He has been Guest Editor for several journals including IEEE Journal of Selected Topics for Signal Processing, IEEE Journal on Selected Areas in Communications, and the IEEE Signal Processing Magazine; Lead Guest Editor, International Journal of Robotics Research special issue on Networked Robotics; General Co-Chair, IEEE Global Conference on Signal and Information Processing (GlobalSIP 2016); Member, SPS Sensor Array and Multichannel Technical Committee (2006-2011 and 2015-Present); Member, Signal Processing for Communications Technical Committee (1999-2005); and Co-Chair, IEEE Robotics and Automation Society Technical Committee on Networked Robotics.

Dr. Sadler received the IEEE SPS Best Paper Award in 2006 and 2010, several ARL awards, three Army R&D Achievement awards, as well as the Outstanding Invention of the Year Award from the University of Maryland in 2008.

Dr. Sadler is an IEEE Fellow, an ARL Fellow, and has lectured at the Johns Hopkins University Whiting School of Engineering for 14 years.

Dr. Sadler’s research interests span intelligent systems, with an emphasis on distributed collaborative operation, including multi-agent autonomy, cognitive networking, distributed sensing and signal processing, and mixed-signal circuit architectures for low power sensing and cognition. His recent work focuses on collaborative physical agents in stressful and complex environments, “20-questions” strategies for machines to query humans, and the combination of distributed computation, control, and cognitive networking.

Brian M. Sadler
Senior Research Scientist, Intelligent Systems
Army Research Laboratory
2800 Powder Mill Road
Adelphi, MD 20783
P: +1 301-394-1239
E: brian.m.sadler6.civ@mail.mil

Lecture Topics

  • Distributed Collaborative Intelligent Systems
  • Human-Autonomy Querying and Interaction
  • Autonomous Networking


2016 Distinguished Lecturers


Jan P. Allebach

Jan P. Allebach (LF) earned his BSEE from the University of Delaware (1968-1972) and his Ph.D. from Princeton University (1972-1976). He was a faculty member at the University of Delaware from 1976 to 1983. Since 1983, he has been at Purdue University, where he is currently Hewlett-Packard Distinguished Professor of Electrical and Computer Engineering with courtesy appointments in Computer Science and Psychological Sciences.

Prof. Allebach is a Fellow of IEEE (1991), IS&T (The Society for Imaging Science and Technology) (1996), and SPIE (2007). He is recipient of the Senior Best Paper Award from the IEEE Signal Processing Society (1989) and the Bowman Award from IS&T (1998). He was named Electronic Imaging Scientist of the Year by SPIE and IS&T (2004), and received Honorary Membership from IS&T (2007), which is its highest award. He received the Daniel E. Noble Award for Emerging Technologies an IEEE Field Award (2013). He was elected to membership in the National Academy of Engineering (2014), and the National Academy of Inventors (2015). From Purdue University, he has received ten different awards for teaching, research, and mentorship.

Prof. Allebach was an Associate Editor, IEEE Transactions on Acoustics, Speech, and Signal Processing (1988-1990) and the IEEE Transactions on Image Processing (1997-1999); Member (1986-1995) and later Chair (1990-1991), Image and Multidimensional Signal Processing Technical Committee; Member, Information Forensics and Security Technical Committee (2008-2011); Secretary, SPS Board of Governors (1992-1995) and later as an elected member (1996-1998); Co-Technical Program Chair, ICASSP-93 (1993); General Co-Chair, Ninth IMDSP Workshop held in Belize City (1996). Prof. Allebach was also a past Distinguished Lecturer, IEEE Signal Processing Society (1994-1995).

Prof. Allebach has published 105 refereed journal articles and 349 conference papers. He is listed as a co-inventor on 31 issued U.S. patents and several more pending patent applications. Printing and document imaging have been central themes of his research; and he has made many seminal contributions in these areas.

Jan P. Allebach
School of Electrical and Computer Engineering
465 Northwestern Avenue
West Lafayette, IN 47907-2035
P: +1 765 494 3554 (admin Camille Hamelman)
E: allebach@purdue.edu

Lecture Topics

  • Digital Printing: the Transformation of a 2,000-year Old Technology and What It Means to You
  • Embedding Data in Printed Documents at the Printer Mechanism Level
  • The Intertwined Roles of Semantics, Aesthetics, and Quality in Color Document Imaging

Yoram Bresler

Yoram Bresler (F) received the B.Sc. (cum laude) and M.Sc. degrees from the Technion, Israel Institute of Technology, (1974 and 1981 respectively); the Ph.D degree from Stanford University, (1986), all in Electrical Engineering. Since 1987, he has been on the faculty at the University of Illinois, Urbana-Champaign, where he is currently Professor, Department of Electrical and Computer Engineering and the Department of Bioengineering, and at the Coordinated Science Laboratory. In 2003, Dr. Bresler co-founded InstaRecon, Inc., based in Champaign, Illinois, to commercialize breakthrough technology for tomographic reconstruction developed in his academic research. He currently serves as the company’s President and Chief Technology Officer.

Dr. Bresler was elected IEEE Fellow in 1999 "for contributions to computer-based imaging and sensor array processing," and in 2010, Fellow of the American Institute for Medical and Biomedical Engineering (AIMBE), "for pioneering contributions to fast tomographic reconstruction algorithms and fundamental contributions to sampling theory for fast dynamic imaging.” He holds 11 US patents and more than 20 international patents, and has received the IEEE SPS Best Paper Award (1988) and (1989). He is the recipient of a NSF Presidential Young Investigator Award (1991); the Technion (Israel Inst. of Technology) Fellowship (1995); and the Xerox Senior Award for Faculty Research (1998). He was named a University of Illinois Scholar (1999); appointed as Associate, the Center for Advanced Study of the University (2001-2002); and Faculty Fellow, the NCSA (National Center for Super Computing Applications) (2006).

Dr. Bresler has served as Associate Editor, IEEE Transactions on Signal Processing (1992-1993); on Editorial Boards, Machine Vision and Applications (1987-2006), and the SIAM Journal on Imaging Science (2007-2013); on the Senior Editorial Board, IEEE Journal on Selected Topics in Signal Processing (2006-2013); and he was Guest co-Editor, IEEE Transactions on Medical Imaging special issue on Compressed Sensing. He was a member, IEEE Image and Multidimensional Signal Processing Technical Committee (1993 – 1998) and the IEEE Bio Imaging and Signal Processing Technical Committee (2005-2009), and served on the IEEE SPS Awards Board (2003-2006).

Dr. Bresler’s interests are in multi-dimensional and statistical signal processing and their applications to inverse problems in imaging, and in particular compressed sensing, which he introduced with his students in the mid 90’s under the monikers of “spectrum-blind sampling,” and “image compression on the fly,” as well as computed tomography, magnetic resonance imaging, and learning-based signal processing.

Yoram Bresler
Professor, Dept. Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
112 CSL, 1308 West Main St. Urbana, IL 61801
P: +1-217-244-9660
E: ybresler@illinois.edu

Lecture Topics

  • The Invention of Compressive Sampling
  • Breaking the Speed Barrier in Tomography
  • Learning Sparsifying Transforms for Signal, Image, and Video Processing
  • Learning Sparse Representations for Blind Compressed Sensing in MRI and CT
  • Blind Signal Processing: Sparse Signal Reconstruction in Bilinear Inverse Problems

Peyman Milanfar

Peyman Milanfar (F) received his undergraduate education in electrical engineering and mathematics from the University of California, Berkeley in 1988, and the MS and PhD degrees in electrical engineering from the Massachusetts Institute of Technology, in 1990 and 1993 respectively. He was a Professor of EE at UC Santa Cruz (1999-2014), where he is now a visiting faculty. He was Associate Dean for Research, School of Engineering (2010-2012). He was on leave at Google-x, from 2012-2014, where he helped develop the imaging pipeline for Google Glass. He currently leads the Computational Imaging team in Google Research.

Dr. Milanfar is an IEEE Fellow “for contributions to inverse problems and super-resolution in imaging." He was Member, IEEE Signal Processing Society Awards Board (2011-2013); Editorial Board Member, SIAM Journal on Imaging Science (2010); Editor, “Super-resolution Imaging”, book published by CRC press (2009); Editorial Board Member, Image and Vision Computing (2009-2011); Member, IEEE Image, Video, Multidimensional Signal Processing (IVMSP) Technical Committee (2007-2011); Associate Editor, IEEE Transactions on Image Processing (2007-2010); Guest Editor, Journal of Applied Signal Processing, Special Issue on Super-resolution Imaging (2004); Outstanding Reviewer of the Year, IEEE Transactions on Image Processing (2006); Paper identified by Institute of Scientific Information as a leader of “emerging research front” in imaging (2005); Graduate Student Morteza Shahram Winner of the Best Student Paper at ICASSP (2005); IEEE Signal Processing Society Best Paper Award (2010).

Dr. Milanfar holds 8 US patents, several of which are commercially licensed. He has been a keynote speaker at numerous international conferences including PCS, SPIE, and ICME; and along with his students, has won several best paper awards from the IEEE Signal Processing Society.

Peyman Milanfar
Google Inc.
1600 Amphitheater Blvd.
Mountain View, CA 94043
P: +1 650 332-2311
E: peyman.milanfar@gmail.com

Lecture Topics

  • Computational Imaging: From Photons to Photos
  • A Tour of Modern Image Filtering
  • Graph Affinity-Based Image Processing

Hermann Ney

Hermann Ney (F) is a full professor of computer science at RWTH Aachen University, Aachen, Germany. He received the Diploma degree in physics from Goettingen University, Goettingen, Germany (1977), and the Dr.-Ing. degree in electrical engineering, Braunschweig University of Technology, Braunschweig, Germany (1982). In 1977, he joined Philips Research Germany, and since 1985, he headed the speech recognition group at Philips that pioneered the first prototype systems for large vocabulary continuous speech recognition and spoken dialogue systems. He was a visiting scientist, Bell Laboratories, Murray Hill, NJ (October 1988 to October 1989) and joined the computer science department of RWTH Aachen University (July 1993). Since then, he has been working on a large number of topics in automatic speech recognition and machine translation using statistical pattern recognition and machine learning.

Dr. Ney's professional activities include: Executive Board, Association of Computational Linguistics (2014-2016); Co-Chair, Interspeech 2015; Senior Area Editor, IEEE-ACM Transactions on Audio, Speech and Language Processing (2014-2016); IEEE SPS Speech and Language Processing Technical Committee (2012-2014); Co-Chair Plenary Sessions, ICASSP 2009; Co-Chair, IEEE-ACL Workshop on Spoken Language Technology (2006); Editor, ACM Transactions on Speech and Language Processing (2005-2007); Associate Editor, IEEE Transactions on Speech and Audio Processing (2001-2003); IEEE SPS Speech Processing Technical Committee (1995-2000); Editorial Board Member, Computer, Speech and Language (1993-2001); Editorial Board, Speech Communication (1993-2001); Technical Committee Member, German Association for Pattern Recognition (DAGM) (2003-2006); Executive Committee Member, German Section IEEE (1992-1998).

Dr Ney is both an IEEE Fellow and Fellow, International Speech Communication Association. He was the recipient of the Technical Achievement Award (2005); Distinguished Lecturer, International Speech Communication Association (2012-2013); awarded a senior DIGITEO chair at LIMIS/CNRS in Paris, France (2011-2013); and received the Award of Honour from the International Association of Machine Translation (2013).

Dr. Ney's main research interests are concerned with the application of machine learning methods to speech recognition and machine translation.

Hermann Ney
Lehrstuhl Informatik 6
RWTH Aachen University
Ahornstrasse 55
D-52074 Aachen, Germany
P: ++49 (241) 8021600
E: ney@cs.rwth-aachen.de

Lecture Topics

  • From Speech Recognition (ASR) to Machine Translation (MT) - What MT has learnt from ASR
  • Speech and Language Processing: Achievements and Challenges
  • The Mathematical Machinery of Speech and Language Processing
  • Discriminative Training and Log-Linear Models in ASR
  • Generative and Discriminative Modelling in Speech and Natural Language Processing
  • The Role of Bayes Decision Rule in Speech Recognition and Natural Language Processing
  • Error Bounds on Bayes Classification Error in Speech and Language Processing

Paris Smaragdis

Paris Smaragdis (F) is faculty at the Computer Science and Electrical and Computer Engineering departments at the University of Illinois at Urbana-Champaign, and a Senior Research Scientist at Adobe Research. He completed his Masters (1997), Ph.D. (2001) and postdoctoral studies (2002) at the Machine Listening Group, MIT Media Lab. He was previously a research scientist at Mitsubishi Electric Research (MERL).

Prof. Smaragdis was selected by MIT’s Technology Review as one of the year’s top young technology innovators (TR35) for his work on machine listening, in 2006. In 2015, he was elevated to IEEE Fellow “for contributions in audio source separation and audio processing”. He has been elected as a full member, Acoustical Society of America (2008), and recipient of the C.W. Gear Outstanding Junior Faculty Award (2015), an NSF CAREER grant, and multiple teaching awards at the University of Illinois.

Prof. Smaragdis was Chair, IEEE Machine Learning for Signal Processing Technical Committee (2013-2014); Chair, LVA/ICA conference steering committee (2012-2015); Member, IEEE Machine Learning for Signal Processing Technical Committee (2010-2015); Member, Audio and Acoustic Signal Processing Technical Committee (2011-present); Associate Editor, IEEE Signal Processing Letters (2012-present); and Area Editor, IEEE Transactions for Signal Processing (2015-present). Prof. Smaragdis was an organizer of the GLOBALSIP Symposium on Machine Learning for Speech Processing (2014); General Co-Chair, Machine Learning for Signal Processing Workshop (2014); Technical Chair, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (2011); and an organizer of multiple special sessions and tutorials at international conferences.

Prof. Smaragdis’ research is on applications of machine learning techniques on signal processing problems, especially as they apply to the analysis of sound mixtures. He has more than 120 publications in the areas of audio signal processing and machine learning, and holds 59 patents internationally.

Paris Smaragdis
Siebel Center for Computer Science
Office 3231, MC-258
201 N. Goodwin Ave
Urbana IL 61801, USA
P: +1 217-265-6893 (x56893)
F: +1 217-265-4035
E: paris@illinois.edu

Lecture Topics

  • Non-Negative Models for Source Separation
  • Machine Learning for Speech Enhancement
  • Machine Listening: Making Computers that Understand Sound

Josiane Zerubia

Josiane Zerubia (F) has been a permanent research scientist at INRIA since 1989 and Director of Research since July 1995. She was head of the PASTIS Remote Sensing Laboratory (INRIA Sophia-Antipolis) (mid-1995-1997) and of the Ariana Research Group (INRIA/CNRS/University of Nice) (1998-2011). Since January 2012, she has been head of Ayin Research Group (INRIA-SAM) dedicated to models of spatio-temporal structure for high resolution image processing with a focus on remote sensing and skincare imaging. She has been Professor, SUPAERO (ISAE) in Toulouse since 1999.

Prof. Zerubia was also with the Signal and Image Processing Institute of the University of Southern California as a postdoc. She also worked as a researcher, LASSY (University of Nice/CNRS) (1984-1988) and in the Research Laboratory of Hewlett Packard in France and in Palo-Alto, CA (1982-1984). She received the MSc degree from the Department of Electrical Engineering at ENSIEG, Grenoble, France in 1981, the Doctor of Engineering degree, her PhD and her `Habilitation', in 1986, 1988, and 1994 respectively, all from the University of Nice Sophia-Antipolis, France.

Prof. Zerubia is an IEEE Fellow. She was a Member, IEEE Image and Multidimensional Signal Processing Technical Committee (1997-2003); Member, IEEE Bio Imaging and Signal Processing Technical Committee (2004-2012); Member, IEEE Image, Video, and Multidimensional Signal Processing Technical Committee (2008-2013); Associate Editor, IEEE Transactions on Image Processing (1998-2002); Area Editor, IEEE Transactions on Image Processing (2003-2006), Guest Co-Editor, special issue of IEEE Transactions on PAMI (2003); Editorial Board Member, IJCV (2004-March 2013); Member-at-Large, SPS Board of Governors (2002-2004); Editorial Board Member, French Society for Photogrammetry and Remote Sensing (SFPT) (1998-Present), Foundation and Trends in Signal Processing (2007-Present), Member-at-Large, Board of Governors of the SFPT (September 2014-Present); and Associate Editor, on-line resource « Earthzine » (IEEE CEO and GEOSS).

Prof. Zerubia was Co-Chair of two workshops on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2001 and EMMCVPR 2003); Co-Chair, Workshop on Image Processing and Related Mathematical Fields (IPRM 2002); Technical Program Chair, Workshop on Photogrammetry and Remote Sensing for Urban Areas (2003); Co-Chair, special sessions at IEEE ICASSP (2006) and IEEE ISBI (2008); Publicity Chair, IEEE ICIP (2011); Tutorial Co-Chair, IEEE ICIP (2014); General Co-Chair, Workshop Earthvision at IEEE CVPR (2015); Organizing Committee Member and Plenary Talk Co-Chair, IEEE-EURASIP (EUSIPCO 2015).

Prof. Zerubia’s main research interest is in image processing using probabilistic models. She also works on parameter estimation, statistical learning and optimization techniques.

Preferred Geographic Coverage of the Lectures:
Europe till April 2016 and after October 2016, USA/Canada from May till October 2016.

Josiane Zerubia
INRIA Sophia-Antipolis Méditerranée
BP 93, 2004 Route des Lucioles
06902 Sophia-Antipolis Cedex – France
P: +33 4 92 38 78 65
F: +33 4 92 38 78 58
E: Josiane.Zerubia@inria.fr

Lecture Topics

  • Marked Point Processes For Object Detection and Tracking in High Resolution Images: Applications to Remote Sensing and Biology
  • Marked Point Processes for Object Detection in High Resolution Images: Applications to Earth Observation and Cartography
  • High Resolution Optical and SAR Satellite Image Processing for Disaster Management Using Hierarchical MRFS


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