Distinguished Lecturers

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Distinguished Lecturers

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

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2021 Distinguished Lecturers

Pier Luigi Dragotti

Pier Luigi Dragotti (F) is Professor of Signal Processing in the Electrical and Electronic Engineering Department at Imperial College London. He received the Laurea Degree (summa cum laude) in Electronic Engineering from the University Federico II, Naples, Italy, (1997); the Master degree in Communications Systems from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland (1998); and PhD degree from École polytechnique fédérale de Lausanne (EPFL), Switzerland, (April 2002). Before joining Imperial College in November 2002, he was a senior researcher at EPFL working on distributed signal processing for Swiss National Competence Center in Research on Mobile Information and Communication Systems.

Prof. Dragotti has also held several visiting positions. He was a visiting student, Stanford University (1996); summer researcher, Mathematics of Communications Department at Bell Labs, Lucent Technologies, Murray Hill, NJ (2000); and visiting scientist, Massachusetts Institute of Technology (2011).

Prof. Dragotti is an IEEE Fellow (2017). He was Editor-in-Chief, IEEE Transactions on Signal Processing (2018-2020); Member, IEEE SPS Fellow Evaluation Committee (2020); Associate Editor, IEEE Transactions on Image Processing (2006-2009); Elected Member, IEEE Image, Video and Multidimensional Signal Processing Technical Committee (2008-2013) where he acted as Chair of the award sub-committee (2011-2013); Member, IEEE Signal Processing Theory and Methods Technical Committee (2013-2018); Member, Computational Imaging Technical Committee (2015-2020); and Technical Co-Chair, European Signal Processing Conference (Eusipco) (2012).

Prof. Dragotti is also the recipient of a European Research Council (ERC) Investigator Award, which is awarded to “exceptional research leaders to pursue ground-breaking, high-risk projects” (2011-2016).

Pier Luigi Dragotti
Imperial College London
London, United Kingdom
E: p.dragotti@imperial.ac.uk

Lecture Topics

  • New Sampling methods: Sparse sampling based on timing information and sampling along trajectories
  • Deep Dictionary Learning Approaches for Image Super-Resolution
  • Computational Imaging for Art investigation and for Neuroscience

Karen Livescu

Karen Livescu (SM) is an Associate Professor at Toyota Technological Institute at Chicago (TTI-Chicago). She completed her PhD in electrical engineering and computer science at Massachusetts Institute of Technology (MIT) in 2005 and her Bachelor's degree in physics at Princeton University in 1996.

Dr. Livescu is an Associate Editor, IEEE Open Journal of Signal Processing (present); Associate Editor, IEEE/ACM Transactions on Audio, Speech, and Language Processing (2014-2017), Member, IEEE Speech and Language Processing Technical Committee (2012-2017); Technical Co-Chair, IEEE Workshop on Automatic Speech Recognition and Understanding (2015, 2017, and 2019). Outside of the IEEE, she has served as Program Co-Chair, International Conference on Learning Representations (2019); Subject Editor, Speech Communication journal, and Area Chair for a number of speech processing, machine learning, and natural language processing conferences. She has won Best Paper Awards at the ACL Workshop on Representation Learning for NLP in 2016 and 2017, and a Best Student Paper Award at Interspeech (2012). Her work has been acknowledged with an Amazon AWS Machine Learning Research Award (2020) and Google Research Awards (2014, 2015), and she was awarded a Clare Boothe Luce Post-Doctoral Fellowship (2005-2007) and an NSF Graduate Research Fellowship (1997-2000).

Dr. Livescu’s main research interests are in speech and language processing and machine learning.

Karen Livescu
Toyota Technological Institute-Chicago
Chicago, IL, USA
E: klivescu@ttic.edu

Lecture Topics

  • Embeddings for Spoken Language
  • Automatic Recognition of Sign Language in Video

Venkatesh Saligrama

Venkatesh Saligrama (F) is a professor in the Departments of Electrical and Computer Engineering, Computer Science (by courtesy), and Systems Engineering at Boston University. He is a founding faculty of Computing and Data Sciences at Boston University. He holds a PhD from Massachusetts Institute of Technology (MIT).

Dr. Saligrama is an IEEE Fellow, recipient of several awards including Presidential Early Career Award, ONR Young Investigator Award, and the NSF Career Award, and he has received best paper awards at several conferences. He has served as an Associate Editor, IEEE Transactions on Signal Processing (2005-2007), IEEE Transactions on Information Theory, edited special issues for IEEE Journal of Selected Topics in Signal Processing, and the IEEE Transactions on Signal Information Processing Over Networks, has been Chair, Big Data Special Interest Group (2020), and served on Technical Program Committees of several IEEE conferences.

Dr. Saligrama’s current research interests are in Machine Learning with particular emphasis on resource-efficient learning, zero-shot and limited-shot learning, statistical testing of graphs, and more broadly on the societal impact of AI.

Venkatesh Saligrama
Boston University
Boston, MA, USA
E: srv@bu.edu

Lecture Topics

  • Machine Learning on the Edge and Resource Efficient Learning
  • Zero-Shot Learning, and Learning with Limited or no Supervision in the Target Domain
  • Testing Changes in Graphs/Networks with Applications to Social and Physical Sciences.

Dimitri Van De Ville

Dimitri Van De Ville (F) received the M.S. degree in Computer Sciences and the Ph.D. degree in Computer Science Engineering from Ghent University, Belgium, in 1998, and 2002, respectively. He was a post-doctoral fellow (2002-2005) at the lab of Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, before becoming group leader for the Signal Processing Unit at the University Hospital of Geneva, Switzerland, as part of the Centre d’Imagerie Biomédicale (CIBM). In 2009, he received a Swiss National Science Foundation professorship and since 2015 became Professor of Bioengineering at the École polytechnique fédérale de Lausanne (EPFL) (Institute of Bioengineering), jointly affiliated with the University of Geneva (Department of Radiology and Medical Informatics), Switzerland.

Dr. Van De Ville serves as Senior Editor, IEEE Transactions on Signal Processing (2019-present); Editor, SIAM Journal on Imaging Science (2018-present); Associate Editor, IEEE Transactions on Image Processing (2006 to 2009); Associate Editor, IEEE Signal Processing Letters (2004 to 2006); Chair, Bio Imaging and Signal Processing (BISP) Technical Committee (2012-2013); Founding Chair, EURASIP Biomedical Image & Signal Analytics SAT (2016-2018); Co-Chair, Biennial Wavelets & Sparsity series conferences, together with Y. Lu and M. Papadakis. He is the recipient of the Pfizer Research Award (2012); NARSAD Independent Investigator Award (2014); and the Leenaards Foundation Award (2016).

Dr. Van De Ville’s research interests include wavelets, sparsity, graph signal processing, and their applications in computational neuroimaging.

Dimitri Van De Ville
Campus Biotech/EPFL/MIPLAB
Geneva, Switzerland
E: Dimitri.VanDeVille@epfl.ch

Lecture Topics

  • Human brain imaging: dynamics of functional brain networks, whole-brain connectomics, cognitive and clinical biomarkers, computational neuroimaging, functional magnetic resonance imaging
  • Graph signal processing: spectral transforms, graph Slepians, non-parametric surrogate data generation, modularity-based graph signal processing

Dong Xu

Dong Xu (F) is Chair in Computer Engineering and ARC Future Fellow at the School of Electrical and Information Engineering, The University of Sydney, Australia. He received the B.Eng. and PhD degrees from University of Science and Technology of China, in 2001 and 2005, respectively. Before joining The University of Sydney, he worked as a postdoctoral research scientist at Columbia University (2006-2007) and a faculty member at Nanyang Technological University (2007-2015). He was selected as the Clarivate Analytics Highly Cited Researcher in the field of Engineering in 2018 and awarded the IEEE Computational Intelligence Society Outstanding Early Career Award in 2017.

He will serve/served as Program Chair, IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2021); Program Co-chair, IEEE Signal and Data Science Forum (2016); Program Co-chair, IEEE International Conference on Multimedia & Expo (ICME 2014); and Program Co-chair, Pacific-Rim Conference on Multimedia (PCM 2012). He served as a Steering Committee Member of ICME (2016-2017); Area Chair, AAAI Conference on Artificial Intelligence (AAAI 2020); International Conference on Computer Vision (ICCV 2017); ACM MM 2017, European Conference on Computer Vision (ECCV 2016); and the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012); as well as Track Chair, International Conference on Pattern Recognition (ICPR 2016). He is a member of the Image, Video, and Multidimensional Signal Processing Technical Committee (2018-2020) and Machine Learning for Signal Processing Technical Committee (2017-2020) and was a member in the Multimedia Signal Processing Technical Committee (2014-2019). He received the Best Associate Editor Award of IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT) in 2017. He is a Fellow of the IEEE and IAPR.

Prof. Xu is/was on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) and the IEEE Transactions on Neural Networks and Learning Systems (T-NNLS). He is serving/served as Guest Editor of more than ten special issues: International Journal of Computer Vision (IJCV), IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), IEEE Transactions on Cybernetics (T-CYB), IEEE Transactions on Multimedia, ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMM), Computer Vision and Image Understanding (CVIU) and other journals.

Dong Xu
The University of Sydney
Sydney, Australia
E: dong.xu@sydney.edu.au

Lecture Topics

  • Transfer learning for image and video recognition
  • Advances of machine learning in biometrics and visual applications
  • Deep learning for video compression

2020 Distinguished Lecturers

Andrea Cavallaro

Andrea Cavallaro (M) is Professor of Multimedia Signal Processing and the founding Director of the Centre for Intelligent Sensing at Queen Mary University of London, UK. He is Fellow of the International Association for Pattern Recognition (IAPR) and Turing Fellow at the Alan Turing Institute, the UK National Institute for Data Science and Artificial Intelligence. He received his Ph.D. in Electrical Engineering from the Swiss Federal Institute of Technology (EPFL), Lausanne, (2002) and was a Research Fellow with British Telecommunications (BT) (2004/2005).

Prof. Cavallaro is Chair, IEEE Signal Processing Society, Image, Video, and Multidimensional Signal Processing Technical Committee (2020-2021); Member, IEEE Circuits and Systems Society Visual Signal Processing and Communication Technical Committee (since 2015). He is Senior Area Editor, IEEE Transactions on Image Processing (2016-2021); Associate Editor, IEEE Transactions on Circuits and Systems for Video Technology (2016-2019) and IEEE Transactions on Multimedia (2016-2018); Area Editor, IEEE Signal Processing Magazine (2012-2014); and past Associate Editor, for the IEEE Transactions on Image Processing (2011-2015), IEEE Transactions on Signal Processing (2009-2011), IEEE Transactions on Multimedia (2009-2010) and IEEE Signal Processing Magazine (2008-2011). Prof. Cavallaro is a past elected member, IEEE Multimedia Signal Processing Technical Committee (2006-2009); IEEE Image, Video, and Multidimensional Signal Processing Technical Committee (2011-2016), and chair of its Awards committee (2013-2016).

Prof. Cavallaro was awarded the Royal Academy of Engineering teaching Prize in 2007; three student paper awards on target tracking and perceptually sensitive coding at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) in 2005, 2007 and 2009; and the best paper award at IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS) in 2009. Prof. Cavallaro has published over 270 journal and conference papers, one monograph on Video tracking (2011, Wiley) and three edited books: Multi-camera networks (2009, Elsevier); Analysis, retrieval and delivery of multimedia content (2012, Springer); and Intelligent multimedia surveillance (2013, Springer).

Andrea Cavallaro
Queen Mary University of London
London, UK
E: a.cavallaro@qmul.ac.uk

Lecture Topics

  • Audio-visual robotic perception
  • Body cameras and first-person vision
  • Machine learning for robust and privacy preserving analytics
  • Underwater image processing and restoration

Gaurav Sharma

Gaurav Sharma (F) is with the University of Rochester (UR), Rochester, NY, where he is a Professor in the Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology, and a Distinguished Researcher in Center of Excellence in Data Science (CoE) at the Goergen Institute for Data Science. He served as the Director for the Center for Emerging and Innovative Sciences (CEIS) (2008-2010). From 1996 through 2003, he was with Xerox Research and Technology in Webster, NY first as a member of research and technology staff and then as a Principal Scientist and Project Leader. He received the Ph.D. in Electrical and Computer Engineering in 1996 from North Carolina State University (NCSU), Raleigh, NC, and Masters degrees in Applied Mathematics from NCSU (1995) and in Electrical Communication Engineering (1992) from the Indian Institute of Science (IISc), Bangalore, India. He received his bachelor of engineering degree in Electronics and Communication Engineering in 1990 from Indian Institute of Technology, Roorkee (formerly, Univ. of Roorkee).

Dr. Sharma is a Fellow of the IEEE (2013), a Fellow of SPIE -- the international society for optics and photonics (2013), and a Fellow of the Society for Imaging Science and Technology (IS&T) (2013). He is also an elected member of Sigma Xi - the scientific research society (1997) and the Phi Kappa Phi (1994) and Pi Mu Epsilon (1995) honor societies. He received an IEEE Region I Technical Innovation Award in 2008 for “Contributions to the Theory and Practice of Color Imaging and Imaging Systems.” Dr. Sharma has been inducted into the NCSU Electrical and Computer Engineering Department Alumni Hall of Fame (2018) and awarded the M.N.S. Swamy and S.V.C. Aiya medals at IISc in 1992.

Dr. Sharma is Editor-in-Chief, IEEE Transactions on Image Processing (2018-present), Editor-in-Chief, IEEE Journal of Electronic Imaging (2011 through 2015), Associate Editor, IEEE Transactions on Information Forensics and Security (2004-2009), and for the IEEE Transactions on Image Processing (2003-2008). Dr. Sharma served as Chair, Image, Video, and Multi-dimensional Signal Processing (IVMSP) Technical Committee (2010-2011), Member, Image, Video, and Multi-dimensional Signal Processing Technical Committee (2006-2012), Information Forensics and Security Technical Committee (2010-2012), Multimedia Signal Processing Technical Committee (2015-2017), and the IEEE Signal Processing Society ‘s Industry DSP Technology Standing Committee (2005-2008). In 2015 and 2016, he served on the IEEE Signal Processing Society's Conference Board and its Executive subcommittee. Dr. Sharma was Technical Program Co-Chair for the IEEE International Conference on Image Processing (ICIP) (2012 and 2016) and Chair for the Rochester, NY SPS Chapter (2003).

Gaurav Sharma
University of Rochester
Rochester, NY, USA
E: gaurav.sharma@rochester.edu

Lecture Topics

  • Large scale visual data analytics for geospatial applications
  • Turbo-decoding and belief propagation in bioinformatics
  • Imaging arithmetic: physics u math > physics + math
  • Set theoretic feasibility and optimality frameworks for data hiding and privacy
  • High capacity data hiding for printed images
  • Color barcodes for mobile and other applications

Ami Wiesel

Ami Wiesel (SM) received the B.Sc. and M.Sc. degrees in electrical engineering from Tel-Aviv University, Tel-Aviv, Israel, in 2000 and 2002, respectively, and the Ph.D. degree in Electrical Engineering from the Technion-Israel Institute of Technology, Haifa, Israel, in 2007. He was a Postdoctoral Fellow in the Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA, during 2007–2009. He is currently an Associate Professor in the Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel. Since 2018, he is also a Visiting Researcher in Google.

Prof. Wiesel has served as Associate Editor, IEEE Transactions on Signal Processing in (2013-2017), on the Sensor Array and Multichannel Technical Committee (2013-2017), and on the Signal Processing Theory and Methods Technical Committee (2013-2015). He received the IEEE Signal Processing Society Young Author Paper Award in 2008, and co-authored award winning papers in the IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2005 and 2017).

Ami Wiesel
Hebrew University of Jerusalem
E: amiw@cs.huji.ac.il

Lecture Topics

  • Binary optimizations in signal processing
  • Multitask regressions and flood forecasting
  • Structured and robust covariance estimation

Xiao-Ping (Steven) Zhang

Xiao-Ping (Steven) Zhang (SM) received B.S. and Ph.D. degrees from Tsinghua University, in 1992 and 1996, respectively, both in Electronic Engineering. He holds an MBA in Finance, Economics and Entrepreneurship with Honors from the University of Chicago Booth School of Business, Chicago, IL. Since Fall 2000, he has been with the Department of Electrical and Computer Engineering, Ryerson University, where he is now Professor, Director of Communication and Signal Processing Applications Laboratory (CASPAL). He has served as Program Director of Graduate Studies. He is cross appointed to the Finance Department at the Ted Rogers School of Management at Ryerson University. He was Visiting Scientist at Research Laboratory of Electronics (RLE), Massachusetts Institute of Technology, in 2015 and 2017.

Dr. Zhang is a registered Professional Engineer in Ontario, Canada, and a member of Beta Gamma Sigma Honor Society. He is the General Co-Chair, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2021); General Co-Chair, IEEE Global Conference on Signal and Information Processing (GlobalSIP) Symposium on Signal, Information Processing and AI for Finance and Business (2019); and the General Co-Chair, GlobalSIP Symposium on Signal and Information Processing for Finance and Business (2017). He is an elected Member, IEEE International Conference on Multimedia and Expo (ICME) steering committee (2017-present); General Chair, IEEE International Workshop on Multimedia Signal Processing (MMSP) (2015); Publicity Chair, IEEE International Conference on Multimedia and Expo (ICME) (2006); and Program Chair, International Conference on Intelligent Computing (ICIC) (2005 and 2010). He served as Guest Editor, Multimedia Tools and Applications and International Journal of Semantic Computing. He is a tutorial speaker in ACM Multimedia (ACMM) (2011); IEEE International Symposium on Circuits and Systems (ISCAS) (2013); IEEE International Conference on Image Processing (ICIP) (2013); IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014); International Joint Conference on Neural Networks (IJCNN) (2017); and IEEE International Symposium on Circuits and Systems (ISCAS) (2019).

Dr. Zhang is Senior Area Editor, IEEE Transactions on Signal Processing (2017-present) and IEEE Transactions on Image Processing (2019-present). He was Associate Editor, IEEE Transactions on Signal Processing (2012-2016), IEEE Transactions on Image Processing (2015-2019), IEEE Transactions on Multimedia (2012-2016), IEEE Transactions on Circuits and Systems for Video Technology (2015-2019), and IEEE Signal Processing Letters (2011-2015). He is an elected Member of Image, Video, and Multidimensional Signal Processing Technical Committee (2014-present), Multimedia Signal Processing Technical Committee (2014-2019), Signal Processing Theory and Methods Technical Committee (2018-present), Sensor Array and Multichannel Technical Committee (2019-present), and Industry DSP Technology Standing Committee (2011-2016) of IEEE Signal Processing Society.

Dr. Zhang’s research interests include image and multimedia content analysis, statistical signal processing, machine learning, and applications in big data, finance, and marketing.

Xiao-Ping Zhang
Ryerson University
Toronto, Canada
E: xzhang@ee.ryerson.ca

Lecture Topics

  • signal processing, ai and big data for finance, economics, and business
  • passive localization and tracking for internet of things
  • graphical probabilistic modeling, machine learning and signal processing on graph with applications in multimedia

Qing Zhao

Qing Zhao (F) is a Joseph C. Ford Professor of Engineering at Cornell University. Prior to that, she was a Professor in the Electrical and Computer Engineering Department at University of California, Davis (2004-2015). She received a Ph.D. degree in Electrical and Computer Engineering from Cornell in 2001.

Dr. Zhao is a Fellow of IEEE (2013), a Marie Skłodowska-Curie Fellow of the European Union Research and Innovation Program (2018), and a Jubilee Chair Professor of Chalmers University during her 2018-2019 sabbatical leave. She received the IEEE Signal Processing Magazine Best Paper Award (2010) and the IEEE Signal Processing Society Young Author Best Paper Award (2000). She is a coauthor of Student Paper Award at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2006) and the first place winner of Student Paper Contest at IEEE Asilomar Conference on Signals, Systems, and Computers (2006).

Dr. Zhao is a member of the IEEE Signal Processing Magazine Senior Editorial Board since 2018. She was an elected member of the Signal Processing Theory and Method Technical Committee (2012-2015) and the Signal Processing for Communications and Networking Technical Committee (2006-2011); Organizing Committee Member, IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (2018, 2005); Symposium Co-Chair, IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2013); Co-Chair, IEEE Global Communications Conference (GLOBECOM) (2011). She served as Associate Editor, IEEE Transactions on Signal Processing (2006-2009) and edited several special issues for IEEE Journal of Selected Topics in Signal Processing (2013, 2008), IEEE Journal on Selected Areas in Communications (2012), and IEEE Signal Processing Magazine (2007).

Dr Zhao’s research interests include statistical inference, sequential decision theory, stochastic optimization, machine learning, and algorithmic theory with applications in infrastructure, communications, and social-economic networks.

Qing Zhao
Cornell University
Ithica, NY, USA
E: qz16@cornell.edu

Lecture Topics

  • Active hypothesis testing for anomaly detection in large-scale complex networks
  • Multi-armed bandits for online learning
  • Adversarial machine learning: label efficiency, prediction accuracy, and robustness
  • Stochastic convex optimization and approximation: classical results and recent advances
  • Distributed no-regret learning in multi-agent systems

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