Distinguished Lecturers

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Distinguished Lecturers

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

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2023 Distinguished Lecturers

Nancy F. Chen

Nancy F. Chen (SM) is a senior scientist, principal investigator, and group leader at I2R (Institute for Infocomm Research), A*STAR (Agency for Science, Technology, and Research), Singapore. Dr. Chen’s research focuses on conversational artificial intelligence (AI) and natural language generation with applications in education, healthcare, journalism, and defense. Speech evaluation technology developed by her team is deployed at the Ministry of Education in Singapore to support home-based learning to tackle challenges that arose during the COVID-19 pandemic. Dr. Chen also led a cross-continent team for low-resource spoken language processing, which was one of the top performers in the NIST (National Institute of Standards and Technology) Open Keyword Search Evaluations (2013-2016), funded by the IARPA (Intelligence Advanced Research Projects Activity) Babel program. Prior to I2R, A*STAR, Dr. Chen worked at MIT Lincoln Laboratory on multilingual speech processing and received her Ph.D. from MIT and Harvard in 2011.

Dr. Chen has received numerous awards, including Singapore 100 Women in Tech (2021), Young Scientist Best Paper Award at MICCAI (Medical Image Computing and Computer Assisted Interventions) (2021), Best Paper Award at SIGDIAL (Special Interest Group on Discourse and Dialogue) (2021), the Procter & Gamble (P&G) Connect + Develop Open Innovation Award (2020), the 11th L’Oréal UNESCO (United Nations Educational, Scientific and Cultural Organization) For Women in Science National Fellowship (2019), Best Paper Award at APSIPA (Asia-Pacific Signal and Information Processing Association) (2016), Outstanding Mentor Award from the Ministry of Education in Singapore (2012), the Microsoft-sponsored IEEE Spoken Language Processing Grant (2011), and the NIH (National Institute of Health) Ruth L. Kirschstein National Research Award (2004-2008).

Dr. Chen has been active in the international research community. She is Program Chair, ICLR (International Conference on Learning Representations) (2023); Board Member, ISCA (International Speech Communication Association) (2021-2025); elected Member, IEEE Speech and Language Processing Technical Committee (2016-2018, 2019-2021); Senior Area Editor, IEEE Signal Processing Letters (2021-2022); Associate Editors of IEEE/ACM Transactions on Audio, Speech, and Language Processing (2020-2023), Neurocomputing (2020-2021), Computer Speech and Language (2021- present); IEEE Signal Processing Letters (2019-2021); and Guest Editor, special issue of “End-to-End Speech and Language Processing” in the IEEE Journal of Selected Topics in Signal Processing (2017).

Dr. Chen’s research interests include conversational language intelligence, spoken language processing, natural language processing, which are connected to deep learning, multimodal processing, and machine learning.

Nancy F. Chen
E: nancychen@alum.mit.edu

Lecture Topics

  • Summarizing Conversations: From Meetings to Social Media Chats
  • Controllable Neural Language Generation
  • Conversational Technology Embodying Minimalism, Multilingualism, and Multimodality
  • Conversational Intelligence and Language Technology for Virtual Nurses
  • Speech and Dialogue Technology for Virtual Language Tutors

Woon-Seng Gan

Woon-Seng Gan (SM) is Professor of Audio Processing and Director of the Smart Nation Lab in the School of Electrical and Electronic Engineering at Nanyang Technological University (NTU), Singapore. He received his BEng (1st Class Hons) and Ph.D. degrees, both in Electrical and Electronic Engineering from the University of Strathclyde, UK in 1989 and 1993, respectively, and joined the faculty of Nanyang Technological University as a lecturer in 1993. He also served as the Head of the Information Engineering Division (2011-2014), and Director of the Centre for Infocomm Technology (2016-2019) at NTU.

Prof. Gan served as Technical Program Chair, IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP 2022); Member, IEEE Signal Processing Society Technical Directions Board (2021-2023); Member, Applied Signal Processing Systems Technical Committee (2021-2022); President-elect, Asia Pacific Signal and Information Processing Association (APSIPA) (2023-2024); and General Chair, Asia Pacific of Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (2017).

Prof. Gan is a Fellow of the Audio Engineering Society (AES), and Fellow of the Institute of Engineering and Technology (IET). He is currently serving as Senior Area Editor, IEEE Signal Processing Letters (2019-); Associate Technical Editor, Journal of Audio Engineering Society (JAES; 2013-); Senior Editorial Member, APSIPA Transactions on Signal and Information Processing (ATSIP; 2011-); and Associate Editor, EURASIP Journal on Audio, Speech, and Music Processing (EJASMP; 2007-). He served as Associate Editor, IEEE Signal Processing Letters (SPL; 2015-19); Associate Editor, IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP; 2012-15); and was presented with an Outstanding TASLP Editorial Board Service Award in 2016. He was also a recipient of the 2017 APSIPA Sadoaki Furui Prize Paper Award.

Prof. Gan’s research has been concerned with the connections between the physical world, signal processing and machine learning, and sound control, which resulted in the practical demonstration and licensing of spatial audio algorithms, psychoacoustic signal processing applied to the soundscape evaluation, audio intelligence monitoring at the edge, and active noise control for headphones and open apertures.

Woon-Seng Gan
Nanyang Technological University
Singapore
E: ewsgan@ntu.edu.sg

Lecture Topics

  • Signal Processing and Deep Learning for Practical Active Noise Control
  • Augmented/Mixed Reality Audio for Hearables: Sensing, Control and Rendering
  • Audio Intelligence and Urban Sound Monitoring at the Edge
  • Perceptual Evaluation on Augmenting Urban Soundscape

Danilo P. Mandic

Danilo P. Mandic (F) is a professor in signal processing with Imperial College London, UK, and has been working in the areas of machine intelligence, statistical signal processing, big data, learning on graphs, and bioengineering.

He is a Fellow of the IEEE and a current President of the International Neural Networks Society (INNS). Dr. Mandic is a Director of the Financial Machine Intelligence Lab at Imperial College. He has published two research monographs on neural networks, entitled “Recurrent Neural Networks for Prediction”, Wiley 2001, and “Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural models”, Wiley 2009 (both first books in their respective areas), and has co-edited books on Data Fusion (Springer 2008) and Neuro- and Bio-Informatics (Springer 2012). He has also co-authored a two-volume research monograph on tensor networks for Big Data, entitled “Tensor Networks for Dimensionality Reduction and Large Scale Optimization” (Now Publishers, 2016 and 2017), and more recently a research monograph on Data Analytics on Graphs (Now Publishers, 2021).

Dr. Mandic is a recipient of several awards: Dennis Gabor Award (2019); IEEE Signal Processing Society Best Paper Award (2018); Outstanding Paper Award in the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021); and the President Award for Excellence in Postgraduate Supervision at Imperial College (2014) and holds six patents.

Dr. Mandic was Technical Chair, ICASSP (2019); Senior or Associate Editor for IEEE Signal Processing Magazine (2022-present, and 2011-2017), IEEE Transactions on Neural Networks and Learning Systems (2008-2013), IEEE Transactions on Signal Processing (2007-2010), and IEEE Transactions on Signal and Information Processing over Networks (2014-2018). He was appointed by the World University Service (WUS) as a Visiting Lecturer within the Brain Gain Program (BGP), in 2015.

Danilo P. Mandic
Imperial College London
London, United Kingdom
E: d.mandic@imperial.ac.uk

Lecture Topics

  • Data Analytics on Graphs: A New Paradigm in Machine Intelligence
  • Hearables: A New Arena for Artificial Intelligence for Personalised Health
  • Tensor Decompositions for Big Data Applications
  • Convolutional Neural Networks Demystified: A Matched Filtering Approach to Fully Interpretable CNNS
  • Innovation Starts with Education: Bringing Research into the Curriculum
  • Smart Machine Intelligence for Smarter Power Grid

Gesualdo Scutari

Gesualdo Scutari (F) is the Thomas and Jane Schmidt Rising Star Professor in the School of Industrial Engineering at Purdue University (with a courtesy appointment in the School of Electrical and Computer Engineering). He is the Thrust Leader on Optimization at the Purdue Center for Resilient Infrastructures, Systems, and Processes (CRISP), and has been the Scientific Director for the area of Big-Data Analytics at the Cyber Center (Discovery Park) at Purdue University (2015-2016). He received the Laurea Degree (summa cum laude) in Electrical Engineering from the University of Rome "La Sapienza," Rome, Italy (2002), and PhD degree from the University of Rome "La Sapienza," Rome, Italy (2006). He has also held several research and visiting posiitons, at the University of California at Berkeley, Berkeley, CA; the Hong Kong University of Science and Technology, Hong Kong; the University of Rome, "La Sapienza," Rome, Italy; and the University of Illinois at Urbana-Champaign, Urbana, IL.

Prof. Scutari is an IEEE Fellow (2020). He was elected Member, IEEE Signal Processing Society Signal Processing for Communications and Networking Technical Committee (2012-2014); Associate Editor, IEEE Signal Processing Letters (2012-2013); Associate Editor, IEEE Transactions on Signal Processing (2013-2017); Associate Editor, IEEE Transactions on Signal and Information over Networks (2017-2020); Senior Area Editor, IEEE Transactions on Signal Processing (2017-2020); Associate Editor, SIAM Journal on Optimization (2018 – present); and Guest Editor of the IEEE Signal Processing Magazine, Special Issue on “Non-Convex Optimization for Signal Processing and Machine Learning”, (2020).

Prof. Scutari is the recipient of several awards, including the NSF Faculty Early Career Development (CAREER) Award (2013), the UB Young Investigator Award (2013), the Anna Maria Molteni Award for Mathematics and Physics from the Italian Scientists and Scholars in North America Foundation (ISSNAF) (2015), the IEEE Signal Processing Society Young Author Best Paper Award (2015), and the IEEE Signal Processing Society Best Paper Award (2020).

Prof. Scutari’s primary research interests include computatonal optimization, statistical inference, multiagent networks, and game theory.

Gesualdo Scutari
Purdue University
West Lafayette, In. USA
E: gscutari@purdue.edu

Lecture Topics

  • Some Reflections on Distributed Optimization for Machine Learning: Beyond the Common Wisdom
  • Communication Efficient Distributed Machine Learning
  • Bringing Statistical Thinking in Distributed Optimization: Vignettes from High-Dimensional Statistical Inference
  • Unlocking Acceleration Via Data Similarity

Gordon Wetzstein

Gordon Wetzstein (SM) is an Associate Professor of Electrical Engineering and, by courtesy, of Computer Science at Stanford University. He is the leader of the Stanford Computational Imaging Lab and a faculty co-director of the Stanford Center for Image Systems Engineering. At the intersection of computer graphics and vision, artificial intelligence, computational optics, and applied vision science, Prof. Wetzstein's research has a wide range of applications in next-generation imaging, wearable computing, and neural rendering systems.

Prof. Wetzstein received a Diplom (with honors) in Media System Sciences from the Bauhaus University in Germany (2000-2006), he completed his Ph.D. in Computer Science at the University of British Columbia in Canada (2006-2011), and he was a Postdoctoral Associate at the MIT Media Lab (2011-2014). Since 2014, he has been a faculty member at Stanford University.

Prof. Wetzstein is the recipient of several awards and fellowships, including an SPIE Early Career Achievement Award (2020), a Presidential Early Career Award for Scientists and Engineers (PECASE, 2019), an ACM SIGGRAPH Significant New Researcher Award (2018), a Sloan Fellowship (2018), a Scientist of the Year Award, IS&T Electronic Imaging (2017), an NSF CAREER Award (2016), an Okawa Research Grant (2016), a Google Faculty Research Award (2015), a Terman Faculty Fellowship (2014), a National Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship (2012-2014), and an Alain Fournier Ph.D. Dissertation Annual Award for the Best Canadian Computer Graphics PhD Thesis (2011). Prof. Wetzstein and his team have won several Best Paper and Best Demo Awards, for example at the IEEE Virtual Reality Conference (2022), the IEEE International Conference on Computational Photography (ICCP; 2011, 2014, 2016, 2022), the OSA Frontiers in Optics Conference (2018), the ACM SIGGRAPH Emerging Technologies program (2018), and they also received Best Paper Honorable Mentions at NeurIPS (2019) and Eurographics (2016).

Prof. Wetztstein has been an active member of the IEEE community, having served as an Area Chair at the IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR; 2018, 2023) and the IEEE/CVF International Conference on Computer Vision (ICCV; 2021); Conference Chair, IEEE International Conference on Computational Photography (ICCP; 2017) and the IEEE CVPR Workshop on Computational Cameras and Displays (CCD; 2012, 2013); Associate Editor, IEEE Transactions on Computational Imaging (2016-2020), and as part of the International Program Committee, IEEE International Conference on Computational Photography (ICCP; 2013-2023) and the IEEE CVPR Workshop on Computational Cameras and Displays (CCD; 2012-2023).

Gordon Wetzstein
Stanford University
Stanford, CA, USA
E: gordon.wetzstein@stanford.edu

Lecture Topics

  • Next-generation Computational Imaging and Display Systems Engineering based on End-to-end Optimization of Physical Structure and Signal Processing
  • Efficient Neural Scene Representation, Rendering, and Generation
  • Beyond the Metaverse - Towards Human-centric XR

2022 Distinguished Lecturers

Yuejie Chi

Yuejie Chi (SM) received Ph.D. and M.A. in Electrical Engineering from Princeton University in 2012 and 2009, and B.E. (Hon.) in Electrical Engineering from Tsinghua University, Beijing, China, in 2007. After a stint at The Ohio State University from 2012 to 2017, she is with the department of Electrical and Computer Engineering at Carnegie Mellon University since 2018, where she is now a Professor with courtesy appointments in the Machine Learning Department and CyLab. At Carnegie Mellon, she held the inaugural Robert E. Doherty Early Career Development Professorship from 2018 to 2020.

Prof. Chi is the recipient of the IEEE SPS Best Student Paper Award (2012), IEEE SPS Young Author Best Paper Award (2013), Google Faculty Research Award (2013), ORAU Ralph E. Powe Junior Faculty Enhancement Award (2014), AFOSR Young Investigator Program Award (2015), ONR Young Investigator Program Award (2015), NSF CAREER Award (2017), ONR Director of Research Early Career Grant (2019), Presidential Early Career Award for Scientists and Engineers (PECASE) (2019), the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award (2019) for contributions to high-dimensional structured signal processing, and named a Goldsmith Lecturer by IEEE Information Theory Society (2021).

Prof. Chi was an invited plenary speaker at the Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS) (2019) and the SIAM Conference on Imaging Science (2020). Within the IEEE Signal Processing Society, she was Member, Data Science Initiative (2020-2021); Elected Member, Sensor Array and Multichannel (SAM) Technical Committee (2019-2021); Elected Member, Signal Processing Theory and Methods (SPTM) Technical Committee (2016-2018); Elected Member, Machine Learning for Signal Processing (MLSP) Technical Committee (2016-2018); Associate Editor, IEEE Transactions on Signal Processing (since 2018), IEEE Transactions on Information Theory (since 2021), IEEE Transactions on Pattern Recognition and Machine Intelligence (since 2020), and guest edited a special issue on “Rethinking PCA for Modern Data Sets: Theory, Algorithms, and Applications”, which appeared in the August 2018 issue of Proceedings of the IEEE.

Prof. Chi’s research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning and inverse problems, with applications in sensing and societal systems, broadly defined.

Yuejie Chi
5000 Forbes Avenue
Pittsburgh, PA 15213
E: yuejiechi@cmu.edu

Lecture Topics

  • Nonconvex Optimization Meets Low-Rank Matrix Estimation
  • Spectral Methods for Data Science
  • Harnessing Sparsity over the Continuum for Superresolution
  • Reinforcement Learning: Fundamentals, Algorithms, and Theory
  • Communication-Efficient Distributed Machine Learning

Yue Lu

Yue M. Lu (SM) is Gordon McKay Professor of Electrical Engineering and of Applied Mathematics at Harvard University. He received the M.Sc. degree in mathematics and the Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign, both in 2007. He was a postdoctoral researcher (2007-2010) at the Audiovisual Communications Laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, before he joined the Harvard faculty. He is also fortunate to have held visiting appointments at Duke University in 2016 and at the École Normale Supérieure (ENS) in 2019.

Dr. Lu serves as Associate Editor, IEEE Transactions on Signal Processing (2018 - present); Associate Editor, IEEE Transactions on Image Processing (2014 - 2018); Member, Signal Processing Theory and Methods (SPTM) Technical Committee (2016 - 2021); Member, Sensor Array and Multichannel (SAM) Technical Committee (2022 - present); Member, Machine Learning for Signal Processing (MLSP) Technical Committee (2019 - 2021); Member, Image, Video, and Multidimensional Signal Processing (IVMSP) Technical Committee (2015 - 2017); and Member, aq5Management Committee of the IEEE Transactions on Artificial Intelligence (2020 - 2021). He is a recipient of the ECE Illinois Young Alumni Achievement Award (2015), and he has received best paper awards at several conferences (IEEE International Conference on Image Processing (ICIP) (2006), ICIP (2007), IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2011), IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2014), and IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2017).

Dr. Lu’s research interests include theoretical and algorithmic aspects of high-dimensional statistical signal processing.

Yue Lu
Harvard University
Boston, MA, USA
E: yuelu@seas.harvard.edu

Lecture Topics

  • Exploring and Exploiting High-Dimensional Phenomena in Statistical Estimation and Learning
  • A Friendly Tour of Sharp Asymptotic Methods for Signal Processing Researchers

Erik Meijering

Erik Meijering (F) is a Professor of Biomedical Image Computing in the School of Computer Science and Engineering (CSE), University of New South Wales (UNSW), Sydney, Australia. He received his MSc degree in Electrical Engineering from Delft University of Technology (1996) and his PhD degree in Medical Image Analysis from Utrecht University (2000), both in the Netherlands. Before moving to UNSW (in 2019), he was a Postdoctoral Fellow (2000-2002) at the Swiss Federal Institute of Technology in Lausanne, Switzerland, and an Assistant Professor (2002-2008) and later Associate Professor (2008-2019) at Erasmus University Medical Center in the Netherlands.

Prof. Meijering is an IEEE Fellow (2019) and has served as a Technical Committee Member, Bio Imaging and Signal Processing (BISP) (2005-2010); Chair, BISP TC (2018-2019), the IEEE EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP, since 2007), the cross-Society IEEE Life Sciences Technical Community (LSTC, 2018-2020), and the IEEE SPS Technical Directions Board (2018-2019). He is an Associate Editor for the IEEE Transactions on Medical Imaging (since 2004), Biological Imaging (since 2020), International Journal on Biomedical Imaging (2006-2009), and the IEEE Transactions on Image Processing (2008-2011). He was Guest Editor for special issues of the IEEE Signal Processing Magazine (on Quantitative Bioimaging: Signal Processing in Light Microscopy (January 2015), and on Deep Learning in Biological Image and Signal Processing (March 2022)) and the IEEE Transactions on Image Processing (on Molecular and Cellular Bioimaging (September 2005)).

Prof. Meijering was Technical Program Chair, IEEE International Symposium on Biomedical Imaging (ISBI) (2006, 2010, 2018); Steering Committee Chair, ISBI (2005-2008, 2018-2020); Workshop Chair, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2020), and co-organized several conferences: the BioImage Informatics Conference (BII) (2014, 2017) and the Workshop on BioImage Computing (BIC) (since 2016). He also served/serves on a great variety of other international conference, advisory, and review boards.

Prof. Meijering’s multidisciplinary research interests are in computer vision, artificial intelligence, machine and deep learning, for quantitative biomedical image analysis.

Erik Meijering
University of New South Wales
Sydney, Australia
E: erik.meijering@unsw.edu.au

Lecture Topics

  • Deep Learning in Bioimage Analysis
  • Object Tracking in Biological Imaging
  • Artificial Intelligence in Medical Imaging

Hiroshi Sawada

Hiroshi Sawada (F) received the B.E., M.E. and Ph.D. degrees in information science from Kyoto University, Kyoto, Japan, in 1991, 1993 and 2001, respectively. He joined NTT Corporation in 1993. He is now a Senior Distinguished Researcher and an Executive Manager at the NTT Communication Science Laboratories, Kyoto, Japan.

Dr. Sawada is an IEEE Fellow for contributions to blind source separation of speech and audio signals; Fellow, Institute of Electronics, Information and Communication Engineers (IEICE); Member, International ICA Steering Committee; and Member, Acoustical Society of Japan (ASJ). He received the IEEE Signal Processing Society Best Paper Award (2014), the SPIE ICA Unsupervised Learning Pioneer Award (2013), the MLSP Data Analysis Competition Award (2007), the ICMI Outstanding Paper Award (2007), the IEEE Circuit and System Society Best Paper Award (2001), the IEICE Technical Achievement Award (2016), the IEICE Best Paper Award (2017), the IEICE Best Paper Award (2004), and the Kenjiro Takayanagi Achievement Award (2020). He is a co-author of the paper that won the IEEE SPS Young Author Best Paper Award (2019).

Dr. Sawada has served as an Associate Editor, IEEE Open Journal of Signal Processing; Associate Editor, IEEE Transactions Audio, Speech and Language Processing; Member or Associate Member, Audio and Acoustic Signal Processing (AASP) Technical Committee; Plenary Chair, IWAENC 2018; Publications Chair, WASPAA 2007; and Communications Chair, IWAENC 2003. Regarding IEEE activities in Japan, he served as Vice-Chair, IEEE SPS Kansai Chapter. During that period, he organized and directed the student Local Arrangement team for ICASSP 2012 held in Kyoto, Japan. He also served as the Chair of the IEICE Technical Committee on Signal Processing, leading the Japanese signal processing research community.

Dr. Sawada’s research interests include statistical signal processing, audio source separation, array signal processing, machine learning, latent variable model, graph-based data structure, and computer architecture.

Hiroshi Sawada
NTT Communication Science Laboratories
Kyoto, Japan
E: sawada.hiroshi@ieee.org

Lecture Topics

(Actual contents of each talk are negotiable with the inviting chapter)

  • Basic Concepts of Blind Source Separation (BSS) for Speech and Music: Entropy, Non-Gaussianity, Non-Stationarity, Low-Rankness
  • Primary Methods for BSS: Independent Component Analysis (ICA) and Nonnegative Matrix Factorization (NMF)
  • Advanced BSS Methods by Integrating or Extending ICA and NMF: Independent Low-Rank Matrix Analysis (ILRMA), Full-Rank Spatial Covariance Analysis (FCA and Fastfca), and Multi-Channel NMF
  • Nonnegative Matrix and Tensor Factorization for Data Analysis

Tanja Schultz

Tanja Schultz (F) is a computer scientist specializing in speech processing. She is the former president of the International Speech Communication Association. She was a faculty member at Carnegie Mellon University from 2000 to 2007 and at the Karlsruhe Institute of Technology from 2007 to 2015 before moving to the University of Bremen in 2015. Prof. Schultz received her doctoral and diploma degree in Informatics from University of Karlsruhe, Germany in 2000 and 1995. Since 2007, Prof. Schultz directs the Cognitive Systems Lab and since 2015, is full Professor (W3), Chair for Cognitive Systems, Faculty of Mathematic/Informatics at University Bremen, Germany.

Prof. Schultz received the FZI Award for an outstanding Ph.D. thesis (2001), the ISCA Best Journal Award for her publication on language independent acoustic modeling (2002) and on Silent Speech Interfaces (2015), ISCA Best Demo Award (2006), the Plux Wireless Award (2011) for the development of Airwriting, the Alcatel-Lucent Research Award for Technical Communication (2012), the Google Research Award and the Otto-Haxel Award (2013), as well as several best paper awards. In 2002, she was part of a group of eight researchers who won the Allen Newell Medal for Research Excellence for their work on automatic speech translation.

Prof. Schultz is an IEEE Fellow (2020); a Fellow of the International Speech Communication Association (2016), and a Member of the European Academy of Sciences and Arts. She served as a member for numerous conference committees, as Associate Editor, IEEE Transactions on Speech and Audio Processing (2002-2004); Editorial Board Member, Speech Communication, Elsevier (since 2004); Associate Editor, ACM Transactions on Asian Language Information Processing (TALIP) (since 2010); Lead Editor, Special Issue on Biosignal-based Spoken Communication, IEEE Transaction on Audio, Speech, and Language Processing (2016); Lead Editor, Special Issue on Speech & Dementia, CSL (2019); and served as board member and elected President of the International Speech Communication Association ISCA for ten years.

Prof. Schultz’s research activities focus on human-machine communication with a particular emphasis on multilingual speech processing and human-machine interfaces. Together with her team, she investigates the processing, recognition and interpretation of biosignals, i.e. human signals resulting from physical and mental activities, to enable human interaction with machines in a natural way.

Tanja Schultz
University of Bremen
Bremen, Germany
E: tanja.schultz@uni-bremen.de

Lecture Topics

  • Speech Communication Systems Beyond Acoustics
  • From Human to Robot Everyday Activity

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

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