Distinguished Lecturers

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Distinguished Lecturers

The following is a list of Signal Processing Society's distinguished lecturers.

Distinguished Lecturers Page Image

2018 Distinguished Lecturers

 

Rick Blum

Rick S. Blum (F) received a B.S. in Electrical Engineering from the Pennsylvania State University (1984) and his M.S. and Ph.D in Electrical Engineering from the University of Pennsylvania (1987 and 1991).

Dr. Blum was a member of technical staff at General Electric Aerospace in Valley Forge, Pennsylvania (1984 to 1991) and he graduated from GE`s Advanced Course in Engineering. Since 1991, he has been with the Electrical and Computer Engineering Department at Lehigh University in Bethlehem, Pennsylvania where he is currently a Professor and holds the Robert W. Wieseman Endowed Research Professorship in Electrical Engineering.

Dr. Blum was on the editorial board for the Journal of Advances in Information Fusion of the International Society of Information Fusion. He was Associate Editor, IEEE Transactions on Signal Processing (2000-2002) and IEEE Communications Letters. He has edited special issues for IEEE Transactions on Signal Processing, IEEE Journal of Selected Topics in Signal Processing and IEEE Journal on Selected Areas in Communications. He was Member, Sensor Array and Multichannel Signal Processing Technical Committee (2009-2014); Member, Signal Processing for Communications and Networking Technical Committee (1999-2001); Member, Communications Theory Technical Committee of the IEEE Communications Society; and Member, Awards Committee of the IEEE Communications Society.

Dr. Blum is a Fellow of the IEEE “for scientific contributions to detection, data fusion and signal processing with multiple sensors”, which acknowledges contributions to the field of sensor networking. He is a former IEEE Signal Processing Society Distinguished Lecturer, an IEEE Third Millennium Medal winner, a Member of Eta Kappa Nu and Sigma Xi, and holds several patents. Dr. Blum was awarded an ONR Young Investigator Award and an NSF Research Initiation Award.

Dr. Blum’s research interests include signal processing for smart grid, communications, sensor networking, radar and sensor processing.

Rick S. Blum
Robert W. Wieseman Professorship in Electrical Engineering
Professor of Electrical and Computer Engineering
Electrical and Computer Engineering Dept.
Lehigh University
19 Memorial Drive West
Bethlehem, PA 18015-3084
P: +1 610 758 3459
F: +1 610 758 6279
E: rblum@eecs.lehigh.edu

Lecture Topics

  • Cyber Attacks on Internet of Things Systems
  • Cyber Attacks on Sensor Systems for Estimation and Detection
  • Cyber Attacks on Smart Grid Systems
  • Improved IEEE 1588 Synchronization using Estimation Theory and Low Complexity
  • Realistic Performance Bounds on Passive Radar Systems
  • Ordering for Distributed Estimation and Detection

Kevin Bowyer

Kevin W. Bowyer (F) is the Schubmehl-Prein Professor of Computer Science and Engineering at the University of Notre Dame. Professor Bowyer earned his undergraduate degree at George Mason University, and his PhD in Computer Science at Duke University. He previously served on the faculty at the University of South Florida, the Swiss Federal Technical Institute and Duke University.

Professor Bowyer received an IEEE Technical Achievement Award from the IEEE Computer Society, “for pioneering contributions to the science and engineering of biometrics” (2014). Professor Bowyer is a Fellow of the IEEE, “for contributions to algorithms for recognizing objects in images”, and a Fellow of the IAPR, “for contributions to computer vision, pattern recognition and biometrics”. He has served as Editor-in-Chief, IEEE Transactions on Pattern Analysis and Machine Intelligence; Editor-In-Chief, IEEE Biometrics Compendium; and is currently serving on the editorial board of IEEE Access (for which was “Reviewer of the Month” in March 2016). He is serving as General Chair of the 2019 IEEE Winter Conference on Applications of Computer Vision.

Professor Bowyer’s most recent book is the Handbook of Iris Recognition, edited with Dr. Mark Burge.

Kevin Bowyer
Department of Computer Science & Engineering
384 Fitzpatrick Hall
University of Notre Dame
Notre Dame, IN 46556
P: +1 574 631 9978
E: kwb@nd.edu
http://www.cse.nd.edu/~kwb

Lecture Topics

  • Iris Matching and De-Duplication of Voter Registration Lists
  • Detection of Contact-Lens-Based “Spoofing” In Iris Recognition
  • Lessons from Analyzing a Large, Operational Iris-Recognition Dataset
  • Biometric Recognition of Identical Twins
  • Synthesis of Realistic-Looking Face Images

Geert Leus

Geert Leus (F) received the M.Sc. and Ph.D. degree in Electrical Engineering from the KU Leuven, Belgium, in June 1996 and May 2000, respectively. He is now an "Antoni van Leeuwenhoek" Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands.

Dr. Leus is a Fellow of the IEEE and a Fellow of EURASIP. He received the IEEE Signal Processing Society Young Author Best Paper Award (2002); the IEEE Signal Processing Society Best Paper Award (2005); was Member-at-Large, Board of Governors of the IEEE Signal Processing Society (2015-2016); Chair, IEEE Signal Processing for Communications and Networking Technical Committee (2009-2010); Editor in Chief, EURASIP Journal on Advances in Signal Processing; Editorial Board Member, IEEE Transactions on Signal Processing (2007-2010); Editorial Board Member, IEEE Transactions on Wireless Communications; Editorial Board Member, IEEE Signal Processing Letters (2002-2005); and Editorial Board Member, the EURASIP Journal on Advances in Signal Processing. Currently, Dr. Leus is a Member of the IEEE Sensor Array and Multichannel Technical Committee (2012-Present); Associate Editor of Foundations and Trends in Signal Processing; and Editor-in-Chief, EURASIP Signal Processing.

Dr. Leus’ research interests are in the broad area of signal processing, with a specific focus on wireless communications, array processing, sensor networks, and graph signal processing.

Geert Leus
Mekelweg 4, 2628 CD
Delft, The Netherlands
P: +31 15 27 84327
Secretary: +31 15 27 81372
F: +31 15 27 86190
E: g.j.t.leus@tudelft.nl
http://cas.et.tudelft.nl/~leus

Lecture Topics

  • Compressive Sensing for Power Spectral Estimation
  • Sparse Sensing for Statistical Inference
  • Graph Signal Processing: Filtering and Sampling
  • Stationary Graph Processes and Spectral Estimation
  • Subsampling for Graph Power Spectral Estimation
  • Prediction-Correction Methods for Time-Varying Optimization
  • Compressive Ultrasound Imaging

Marco Lops

Marco Lops (SM) was born and educated in Naples (Italy), where he received his Laurea and Ph. D. degrees from “Federico II” University. He was Assistant Professor and Associate Professor with the Department of Electronic Engineering at Federico II. Since 2000, he has been with the Department of Electrical and Information Engineering at University of Cassino and Southern Latium as a Professor. He was a visiting Research Fellow with the University of Connecticut and Rice University, and visiting Professor with University of Minnesota (2008) and Columbia University (2009). In 2009-2012, he was with Enseeiht, University of Toulouse (France) as a Full Professor (on leave of absence from University of Cassino and Southern Latium) and as a visiting Professor.

Dr. Lops served two terms as Member, Sensor Array and Multichannel Signal Processing Technical Committee (2009-2015); Associate Editor, Journal of Communications and Networks, IEEE Transactions on Information Theory, IEEE Signal Processing Letters, and he has been serving his second term as Associate Editor, IEEE Transactions on Signal Processing (2015-Present). In 2015, he was co-recipient of the 2014 Best Paper Award from the Journal of Communication and Networks. During 2018-2020, he will be serving as Distinguished Lecturer for the IEEE Signal Processing Society.

Dr. Lops’ research interests are in the field of detection and estimation, with emphasis on radar signal processing and signal processing for communications.

Marco Lops
Department of Electrical and Information Engineering
University of Cassino and Southern Latium
Via Di Biasio, 43
Cassino – Italy
P: +3907762993742 (office)
P: +393396028672 (mobile)
E: lops@unicas.it

Lecture Topics

  • Track-Before-Detect for Early Detection and Track Initiation
  • Trade-Offs in MIMO Radar
  • Co-Existence Between Radar and Communication Systems
  • Wireless Communications in a Dynamic Scenario: A Random-Set-Theoretic Approach

Wing-Kin (Ken) Ma

Wing-Kin Ma (F) received the B.Eng. degree in electrical and electronic engineering from the University of Portsmouth, Portsmouth, U.K., in 1995, and the M.Phil. and Ph.D. degrees, both in electronic engineering, from The Chinese University of Hong Kong (CUHK), Hong Kong, in 1997 and 2001, respectively. He is currently an Associate Professor with the Department of Electronic Engineering, CUHK. From 2005 to 2007, he was also an Assistant Professor with the Institute of Communications Engineering, National Tsing Hua University, Taiwan, R.O.C. Prior to becoming a faculty member, he held various research positions with McMaster University, Canada; CUHK; and the University of Melbourne, Australia.

Dr. Ma is an IEEE Fellow. He is currently Senior Area Editor, IEEE Transactions on Signal Processing; Associate Editor, IEEE Transactions on Signal Processing (2008-2011); Associate Editor and Guest Editor of several journals, which include IEEE Signal Processing Letters (2012), IEEE Journal of Selected Areas in Communications, and IEEE Signal Processing Magazine. He was a tutorial speaker in EUSIPCO (2011) and ICASSP (2014). He is a Member, Signal Processing Theory and Methods Technical Committee (2012-Present); and Member, Signal Processing for Communications and Networking Technical Committee (2015-Present). Dr. Ma received the Research Excellence Award by CUHK (2013–2014), the IEEE Signal Processing Magazine Best Paper Award (2015), and the IEEE Signal Processing Letters Best Paper Award (2016).

Dr. Ma’s research interests are in signal processing, optimization and communications, with recent activities focused on MIMO transceiver designs and interference management, and structured matrix factorization with application to blind signal separation and hyperspectral remote sensing.

Wing-Kin Ma
Department of Electronic Engineering
The Chinese University of Hong Kong
Shatin, N.T., Hong Kong
T: +852 3943 4350
F: +852 2603 5558
E: wkma@ee.cuhk.edu.hk

Lecture Topics

  • Semidefinite Relaxation: From Classical Concepts to Recent Advances
  • Hyperspectral Unmixing in Remote Sensing: Learn the Wisdom There and Go Beyond (Machine Learning Included)
  • MIMO Transceiver Designs and Optimization: Beyond Beamforming and Perfect Channel Information

Patrick Wolfe (Data Science Lecturer)

Patrick J. Wolfe (SM) received B.S.E.E. and B.Mus. degrees from the University of Illinois at Urbana-Champaign (1998) and his Ph.D. from the University of Cambridge (2003) as U.S. National Science Foundation Graduate Research Fellow. After teaching at Cambridge from 2001–2003, he joined the faculty of Harvard University (2004) and received the Presidential Early Career Award for Scientists and Engineers from the White House (2008). In 2012, he returned to the UK to take up an Established Career Fellowship in the Mathematical Sciences at University College London (UCL), where he also served as a Royal Society Research Fellow and as founding Executive Director of UCL’s Big Data Institute. In 2017, he was appointed the Frederick L. Hovde Dean of Science at Purdue University.

Dr. Wolfe is also a trustee and non-executive director of the Alan Turing Institute, the U.K.’s National Institute for Data Science, and serves on the board of its commercial subsidiary. Previously the Institute’s Deputy Director and recently named its first honorary fellow, he played a leading role in establishing the institute and shaping its priorities through an extensive program of engagement with a diverse range of experts and stakeholders. He has provided expert advice on applications of data science to policy, societal, and commercial challenges, including to the U.S. and U.K. governments and to a range of public and private bodies—including most recently the U.K. Food Standards Agency as an inaugural member of its Science Council. Dr. Wolfe is currently Chair, IEEE SPS Big Data Special Interest Group and serves on the steering committee of the IEEE SPS Data Science Initiative, as well as Co-Chair, Data Science Section of the Institute for Mathematical Statistics.

Dr. Wolfe has received awards for his research from a number of international bodies, including the Royal Society, the Acoustical Society of America, and the IEEE. He is active in the global mathematics, statistics, and physical sciences communities, and most recently was an organizer and Simons Foundation Fellow at the Isaac Newton Institute for Mathematical Sciences 2016 semester research program on Theoretical Foundations for Statistical Network Analysis.

Patrick J. Wolfe, Frederick L. Hovde Dean
College of Science, Purdue University
150 N. University Street
West Lafayette, IN 47907
P: +1 765 494 1730
E: patrick@purdue.edu
Assistant: Angie Teel
E: teel@purdue.edu

Lecture Topics

  • Core Technical Challenges in Data Science
  • Data Science for Policy and Public Good
  • Data Science Education and Academic Programs
  • Data Governance, Data Sharing, Legal Frameworks, and Ethics
  • Algorithms, Artificial Intelligence, and The Law
  • Setting Up Successful Data Science Initiatives: Lessons Learned
  • Big Network Data: Challenges and Opportunities
  • Understanding the Behavior of Large Networks
  • Nonparametric Statistics for Network Analysis and Comparison

 

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
http://bu.edu/ece/people/vivek-goyal/

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
INRIA
Campus de Beaulieu
35042 RENNES
FRANCE
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
http://cvsp.cs.ntua.gr/maragos

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
Professor
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
www.ece.rutgers.edu
www.ece.rutgers.edu/~cspl

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

 

 

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