Distinguished Lecturers

Patrice Abry

Patrice Abry (F) was born in Bourg-en-Bresse, France in 1966.  He received the degree of Professeur-Agrégé in Physical Sciences Physiques, in 1989 at Ecole Normale Supérieure de Cachan and completed a PhD in Physics and Signal Processing, at Université Claude-Bernard University in Lyon in 1994 and an “Habilitation à diriger les Recherches” (HDR) in 2001.  He is currently CNRS Senior Scientist (Directeur de recherche de Classe Exceptionnelle), at the Physics Departement  of Ecole Normale Supérieure de Lyon, Where he was in charge of the Signal, systems and Physics research team, until 2020. Since 2020, he serves as Chair of the Complex System Institute (Institut rhône-alpin des Systèmes Complexes, ixxi.fr).

Dr. Abry is an IEEE Fellow (2011) and EURASIP Fellow (2023).  He received the AFCET-MESR-CNRS prize for Best PhD in Signal Processing (1993-1994); the “Michel Monpetit – INRIA” prize, awarded by the French Academy of Sciences (2020); the “Grand Prix Institut Mines Télécom – Académie des sciences” prize, awarded by the French Academy of Sciences (2024).  He has served as Member, IEEE SPS Signal Processing Theory and Methods Technical Committee (2006-2010) and (2016-2019); Member (2019-2020) and Chair (2021-2022) of the IEEE SPS Challenge and Data Collections Committee.

Dr. Abry’s research interests include the wavelet-based statistical analysis and modeling of scale-free dynamics (self-similarity, stable processes, multifractal, 1/f processes, long-range dependence, local regularity, infinitely divisible cascades,…).  Beyond theoretical developments and contributions in multifractal analysis and stochastic process design, Dr. Abry shows a strong interest into real-world applications, such as hydrodynamic turbulence, Internet teletraffic, Heart Rate Variability, neurosciences, art investigations, and more recently pandemic monitoring.

Patrice Abry
Ecole Normale Supérieure de Lyon
France

Lecture Topics

• Robust Estimation and Uncertainty Quantification in the Assessment of the Space Time Evolution of the Covid19 Pandemic Intensity
• Internet Traffic Modeling and Analysis with Application to Cybersecurity: Automated Anomaly Detection and Classification, Low Volume Anomaly Detection, Fault IP Address Identification 
• Intrapartum Fetal Acidosis Early Detection 
• Analysis of Heart Rate Variability in Adults for Pathology Assessment 
• Infraslow Human Brain Activity Analysis 
• Selfsimilar and Anisotropic Textures Modeling, Analysis and Segmentation
• Theory and Practice of Multivariate Self Similarity Analysis in Signal and Image Processing (With Toolbox) 
• Theory and Practice of Multivariate Multifractal Analysis in Signal and Image Processing (With Toolbox) 
• Neural Network Based Multivariate Time Series or Multivariate Textures Synthesis? How Well Does it Work?

Scott T. Acton

Scott T. Acton (F) is the Lawrence R. Quarles Professor and Chair of Electrical & Computer Engineering at the University of Virginia (UVA). He is also appointed in Biomedical Engineering. Between 2019-2022, he was Program Director and acting Deputy Division Director in the Computer and Information Sciences and Engineering directorate of the U.S. National Science Foundation (NSF).  Prof. Acton received the M.S. (1990) and Ph.D. (1993) degrees at the University of Texas at Austin, and he received his B.S. degree at Virginia Tech (1988).

Prof. Acton is a Fellow of the IEEE (2013); Fellow, Artificial Intelligence Industry Alliance (2024); and Fellow, Asia-Pacific Artificial Intelligence Association (2024). At NSF, he was given the Director’s Award for Superior Accomplishment (2020). Prof. Acton was the IEEE/Eta Kappa Nu Outstanding Young Electrical Engineer (1996). At UVA, he has received the All University Teaching Award (2009) and the Educational Innovation Award (2017).

Prof. Acton’s laboratory at UVA is called VIVA - Virginia Image and Video Analysis that specializes in biological/biomedical image analysis problems. He is also co-lead of the NSF-sponsored project called Artificial Intelligence for Advancing Instruction (AIAI), which is joint work with UVA Education and Human Development.

Prof. Acton was Co-Chair, IEEE International Symposium on Biomedical Imaging (2018); General Chair (2006) and Technical Chair (2004), Asilomar Conference on Signals, Systems and Computer; served on the Board of Asilomar (2008-2023); and Editor-in-Chief, IEEE Transactions on Image Processing (2014-2018). 

Prof. Acton’s technical interests include multimodal transformers, diffusion networks, graph theory, and diffusion processes for image and video analysis.

Scott Acton
Lawrence R. Quarles Professor of Engineering and Applied Science
University of Virginia, USA

Lecture Topics

• A 40-Minute Short Course on AI
• Don’t Fear AI; Learn It!
• Using Image Analysis to Unravel Mysteries of the Brain
• Artificial Intelligence for Advancing Instruction

Selin Aviyente

Selin Aviyente (SM) received her B.S. degree with high honors in Electrical and Electronics engineering from Bogazici University, Istanbul in 1997. She received her M.S. and Ph.D. degrees, both in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor, in 1999 and 2002, respectively. She joined the Department of Electrical and Computer Engineering at Michigan State University in 2002, where she is currently a Professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering.

Prof. Aviyente is the recipient of a Withrow Teaching Excellence Award (2005); NSF CAREER Award (2008); and Withrow Excellence in Diversity Award (2021).  She has served as Chair, IEEE Signal Processing Society Bioimaging and Signal Processing Technical Committee (2022-2023); Member, Steering Committees of IEEE SPS Data Science Initiative and IEEE BRAIN; Area Editor, Special Issues for IEEE Signal Processing Magazine (2024); Associate Editor, IEEE Open Journal of Signal Processing (2020-2024) and IEEE Transactions on Information Theory.  Prof. Aviyente is serving on the organizing committees of multiple IEEE conferences including as the Technical Co-Chair of IEEE International Workshop on Machine Learning for Signal Processing (2025).

Prof. Aviyente’s research focuses on the theory and applications of time-frequency analysis, machine learning, and signal and information processing over networks. Apart from pursuing fundamental research, she has also worked on using existing tools to define and solve new problems, particularly in the area of brain connectomics.

Selin Aviyente
Michigan State University, USA

Lecture Topics

• Computer Audition
• Affective Computing
• Artificial Intelligence in Health

Mathews Jacob

Mathews Jacob (F) is a professor in the Department of Electrical and Computer Engineering and is heading the Computational Biomedical Imaging Group (CBIG). His research interests include image reconstruction, image analysis, and quantification in the context of magnetic resonance imaging.  He obtained his B.Tech in Electronics and Communication Engineering from National Institute of Technology, Calicut, Kerala, and his M.E in signal processing from the Indian Institute of Science, Bangalore. He received his Ph.D. degree from the Biomedical Imaging Group at the Swiss Federal Institute of Technology in 2003. He was a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign.

Prof. Jacob is currently the Associate Editor of the IEEE Transactions on Medical Imaging and has served as Associate Editor of the IEEE Transactions on Computational Imaging from 2016-20. He was the senior author on two Best Paper Awards (2015 & 2021) and one Best Machine Learning Paper Award (2019) from IEEE ISBI. Dr. Jacob is the recipient of the CAREER Award from the National Science Foundation in 2009, the Research Scholar Award from American Cancer Society in 2011,  the Faculty Excellence Award for Research from University of Iowa in 2021, and the Eminent Researcher Award from the Virginia Innovation Partnership Corporation. He served as the General Chair of IEEE International Symposium on Biomedical Imaging, 2020.  He was elected as a Fellow of the IEEE (2022) for contributions to computational biomedical imaging.

Mathews Jacob 
Department of Electrical and Computer Engineering
University of Virginia, USA

Lecture Topics

• Inexpensive and Faster Medical Imaging Using Learning-Based Algorithms.
• Computational MRI: Past, Present, and Future

Hanseok Ko

Hanseok Ko (SM) is Professor and Director of Intelligent Signal Processing Lab in the School of Electrical and Computer Engineering at Korea University (KU), Seoul, Korea.  He also serves as Director of the Machine Learning Big Data Institute at KU and as co-CTO of a NASDAQ-listed startup he cofounded in 2024.  He received his BS degree from Carnegie Mellon University, MS degree from Johns Hopkins University, and Ph.D. degree from the Catholic University of America, all in Electrical Engineering, in 1982, 1988, and 1993, respectively. After spending 12 years at the US Defense Department Labs in Washington DC as principal engineer and later as a faculty at UMBC, he joined the faculty of Korea University as an assistant professor in 1995. He served as Chair of the Electronics Engineering Dept (2002~2004) and Vice President of the Office of Information Technology and Services (2004~2006) at KU.  He was a visiting professor at the Johns Hopkins University (2001~2002) and University of Maryland, College Park (2009).

Prof. Ko served as General Chair, IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP 2024); General Chair, INTERSPEECH Conference (2022); General Co-Chair, IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP 2018); President-elect and President, Acoustical Society of Korea (ASK 2015-2016); Technical Program Chair, IEEE Internation Conference on Multisensor Fusion and Integration (MFI 2017); and General Chair, IEEE Advanced Video and Signal-based Surveillance Conference (AVSS 2014).

Prof. Ko is a Fellow of the International Speech Communication Association (ISCA), and Fellow of the Institute of Engineering and Technology (IET). He served as Guest Editor, Sensors Journal (2019); Associate Editor, Journal of Communication and Networks (JCN; 2007-2010); Associate Editor, E-Bridges Journal (2010~2023); and was presented with IEEE Service Merit Award (2024). He was a recipient of the Emile Bell Award by the Acoustical Society of Korea (2022); and Distinguished Service Award by KICS (Korea Information and Communication Society) (2005). He was also a recipient of the 2008 Samsung Research Excellence Award.

Prof. Ko’s research has been focusing on the challenges and solutions for realizing real-time interactive multimodal human-machine interface, connecting the physical world and human with interactive avatar through signal processing, machine learning, and generative AI.  The research interests include multimodal perception (speech, acoustics, image, emotion, gesture), NLP, and avatar-based response generation in terms of dialogue, gesture, facial expression, and text-to-speech (TTS). 

Hanseok Ko
Korea University, Korea

Lecture Topics

• Multimodal Human-Machine-Interface: Challenges and Opportunities
• Natural Styling and Few-Shot Adaptation in Text-to-Speech (TTS)
• Information Fusion to Inject Contextual Information Into Large Language Model (LLM)
• Target Speaker Extraction for Multi-Speaker Environment
• Image to Text Generation for Active LLM

Justin Dauwels

Justin Dauwels (SM) is an Associate Professor at the TU Delft (Circuits and Systems, Department of Microelectronics). He was an Associate Professor of the School of Electrical and Electronic Engineering at the Nanyang Technological University (NTU) in Singapore till the end of 2020. He was the Deputy Director of the ST Engineering – NTU corporate lab. Dr. Dauwels obtained his PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005.

Dr. Dauwels was a postdoctoral Fellow at the RIKEN Brain Science Institute (2006-2007), a research scientist at the Massachusetts Institute of Technology (2008-2010), a JSPS postdoctoral Fellow (2007), a BAEF Fellow (2008), a Henri-Benedictus Fellow of the King Baudouin Foundation (2008), and a JSPS invited Fellow (2010, 2011).

Dr. Dauwels served as Chairman, IEEE CIS Chapter in Singapore (2018 to 2020); Associate Editor, IEEE Transactions on Signal Processing (2018-2023); Associate Editor, Elsevier journal Signal Processing (since 2021); member, Editorial Advisory Board of the International Journal of Neural Systems (since 2020), and organizer, IEEE conferences and special sessions. He is also Elected Member of the IEEE Signal Processing Theory and Methods Technical Committee and IEEE Biomedical Signal Processing Technical Committee, both since 2018.

Dr. Dauwels’ research on intelligent transportation systems has been featured by the BBC, Straits Times, Lianhe Zaobao, Channel 5, and numerous technology websites. Besides his academic efforts, the team of Dr. Justin Dauwels also collaborates intensely with local start-ups, SMEs, and agencies, in addition to MNCs, in the field of data-driven transportation, logistics, and medical data analytics.

Dr. Dauwels’ research interests are in data analytics with applications to intelligent transportation systems, autonomous systems, and analysis of human behaviour and physiology.

Justin Dauwels
TU Delft, Mekelweg, Delft
E: J.H.G.Dauwels@tudelft.nl

Lecture Topics

• Deep Generative AI
• Machine Learning for Applications in Neurology
• Machine Learning for Applications in Psychiatry
• Perception Error Modelling for Autonomous Driving

Urbashi Mitra

Urbashi Mitra (F) received the B.S. and the M.S. degrees from the University of California at Berkeley in 1987 and 1989, respectively, and her Ph.D. from Princeton University in 1994. Dr. Mitra is currently the Gordon S. Marshall Professor in Engineering at the University of Southern California with appointments in Electrical & Computer Engineering and Computer Science.

Dr. Mitra was the inaugural Editor-in-Chief, IEEE Transactions on Molecular, Biological and Multi-scale Communications; member, IEEE Information Theory Society's Board of Governors (2002-2007, 2012-2017), the IEEE Signal Processing Society’s Technical Committee on Signal Processing for Communications and Networks (2012-2016), the IEEE Signal Processing Society’s Awards Board (2017-2018), and the Chair/Vice-Chair of the IEEE Communication Theory Technical Committee (2017-2020). Dr. Mitra has also served on the IEEE Signal Processing Society’s Awards Board (1/17–12/18) and Fellows Committee (1/17–12/19) and was the Society’s representative on the IEEE Transactions on Wireless Communications Steering Committee (1/14–12/16, 1/17–12/18, Chair).

Dr. Mitra has further served on the IEEE Founders Medal Committee (2020–2022), Chair (2023), the IEEE Koji Kobayashi Computers and Communications Award Committee (2019–2022). the IEEE James H. Mulligan Jr. Education Medal Committee (2013-2016, Chair 2017- 2018) and the inaugural IEEE Fourier Award for Signal Processing Committee (2013-2016).

Dr. Mitra is a Fellow of the IEEE, recipient of: the USC Viterbi School of Engineering Senior Research Award (2021), the IEEE Women in Communications Engineering Technical Achievement Award (2017), a UK Royal Academy of Engineering Distinguished Visiting Professorship (2015), a US Fulbright Scholar Award (2015), a UK Leverhulme Trust Visiting Professorship (2015-2016), IEEE Communications Society Distinguished Lecturer, Globecom Signal Processing for Communications Symposium Best Paper Award (2012), US National Academy of Engineering Lillian Gilbreth Lectureship (2012), the International Conference on Distributed Computing in Smart Systems Applications & Systems Best Paper Award (2009), Okawa Foundation Award (2001), Ohio State University’s College of Engineering Lumley Award for Research (2000), and a National Science Foundation CAREER Award (1996).

Dr. Mitra’s research interests are in wireless communications, structured statistical methods, communication and sensor networks, biological communication systems, detection and estimation and the interface of communication, sensing and control.

Urbashi Mitra
Ming Hsieh Department of Electrical & Computer Engineering
University of Southern California, USA
E: ubli@usc.edu

Lecture Topics

• Exploiting Statistical Hardness for Increased Privacy in Wireless Systems
• Digital Cousins: Ensemble Learning for Large Scale Wireless Networks
• Microbiology Inspired Molecular Communications: Transceiver Design and Scheduling
• Resource Allocation and Learning for Wireless Networks: Underwater and Radio Frequency Use Cases

Björn W. Schuller

Björn W. Schuller (F) received his diploma in 1999, his doctoral degree for his study on Automatic Speech and Emotion Recognition in 2006, and his habilitation (fakultas docendi) and was entitled Adjunct Teaching Professor (venia legendi) in the subject area of Signal Processing and Machine Intelligence for his work on Intelligent Audio Analysis in 2012 all in electrical engineering and information technology from TUM in Munich/Germany.

From 2023, he is Full Professor of Health Informatics at TUM in Munich/Germany. Since 2017, he is Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany. At the same time, he is Professor of Artificial Intelligence in the Department of Computing at Imperial College London/UK since 2018 where he heads the Group on Language Audio & Music (GLAM), previously being a Reader in Machine Learning since 2015 and Senior Lecturer since 2013. Further, he is the co-founding CEO and current CSO of audEERING GmbH – a TUM start-up on intelligent audio engineering since its launch in 2012. Dr. Schuller was also a member of the Alan Turing Institute and Royal Statistical Society Lab (Turing-RSS Lab) (2021-2022), Guest Professor, Southeast University in Nanjing, China (2021-2022), appointed as Honourary Dean of the Centre for Affective Intelligence at Tianjin Normal University, Tianjin, P.R. China (2019), Full Professor and Chair of Complex and Intelligent Systems at the University of Passau/Germany (2014-2017) where he previously headed the Chair of Sensor Systems in 2013.

Dr. Schuller is Fellow of the IEEE (2018); Fellow, International Speech Communication Association (ISCA, 2020); Fellow, British Computer Society (BCS, 2020); Fellow, Association for the Advancement of Affective Computing (AAAC, 2021); Fellow, European Laboratory for Learning and Intelligent Systems (ELLIS, 2021); Senior Member, ACM (2018). Before, he was President, Association for the Advancement of Affective Computing (AAAC, registered Charity in the UK, 2013-2015); elected member, IEEE Speech and Language Processing Technical Committee (2013-2018), and Honorary Fellow and member, TUM Institute for Advanced Study (IAS, 2013-2014).

Dr. Schuller was co-founding member and secretary of the steering committee (2009-2013) and Guest Editor, and served as Associate Editor and Editor in Chief of the IEEE Transactions on Affective Computing (2015-2018), General Chair, of AAAC/IEEE ACII 2019 and ACM ICMI 2014, and workshop and challenge organizer including the first of their kind INTERSPEECH 2009-2021 annual Computational Paralinguistics Challenges and the 2011-2019 annual Audio/Visual Emotion Challenge and Workshop and a Program Chair of INTERSPEECH 2019, ACM ICMI 2019 and 2013, IEEE SocialCom 2012, and ACII 2011 and 2015, Area Chair of the ACM Multimedia, IEEE ICASSP, IEEE ICTAI, IJCAI, EURASIP EUSIPCO.

Björn W. Schuller
University of Augsburg
Augsburg, Germanym
E: schuller@ieee.org
Website

Imperial College London
London, UK

Lecture Topics

• Computer Audition
• Affective Computing
• Artificial Intelligence in Health

Tuomas Virtanen

Tuomas Virtanen (F) is Professor at Tampere University, Finland, where he is leading the Audio Research Group. He received the M.Sc. and Doctor of Science degrees in information technology from Tampere University of Technology in 2001 and 2006, respectively. He has also been working as a research associate at Cambridge University Engineering Department, UK (2007).

Prof. Virtanen is an IEEE Fellow (2021), member, Audio and Acoustic Signal Processing Technical Committee of IEEE Signal Processing Society (2016-2022); associate editor, IEEE/ACM Transactions on Audio, Speech, and Language Processing (2016-2019); General Chair, DCASE (Detection and Classification of Acoustic Scenes and Events) workshop in 2016, 2017, and 2023; Area Chair, multiple ICASSP and WASPAA conferences; Awards Chair, WASPAA 2023; and Chair, DCASE Steering Group between (2016-2023). Prof. Virtanen has received the IEEE Signal Processing Society Best Paper Award (2012) as well as several other awards, including best paper awards of IWAENC 2018, AES 2018, IJCNN 2017, CHiME 2013, and ISMIR 2009 conferences.

Prof. Virtanen’s research interests include computational acoustic scene analysis, audio signal processing, source separation, and machine learning for audio.

Tuomas Virtanen
Tampere University
Finland
E: tuomas.virtanen@tuni.fi

Lecture Topics

• Computational Acoustic Scene Analysis: Machine Learning Tasks, Models, Data, and Applications
• Data Acquisition for Training and Evaluation of Computational Acoustic Scene Analysis Methods
• Sound Event Localization and Detection
• Detection and Classification of Acoustic Scenes and Events: An Overview of the DCASE Data Challenge
• Machine Learning for Acoustic Scene Analysis

Yimin D. Zhang

Yimin D. Zhang (F) graduated from the Northwest Telecommunications Engineering Institute (now Xidian University), Xi'an, China, in 1982, and received the Ph.D. degree in Applied Physics from the University of Tsukuba, Tsukuba, Japan, in 1988. He is an Associate Professor at the Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA. Before he joined Temple, he was a Research Professor at the Center for Advanced Communications at Villanova University, Villanova, PA.

Dr. Zhang is a Fellow of IEEE (2019); Fellow, SPIE (2020); member, Signal Processing Theory and Methods (SPTM) Technical Committee (2019-2024); member, Integrated Sensing and Communication (ISAC) Technical Working Group (since 2021); founding member, Integrated Sensing and Communication (ISAC) Emerging Technology Initiative of the IEEE Communications Society (since 2021); member, Sensor Array and Multichannel (SAM) Technical Committee (2015-2020); Senior Area Editor, IEEE Transactions on Signal Processing (since 2022); and an editor, IEEE Signal Processing journal (since 2008). He has served as Associate Editor, IEEE Transactions on Signal Processing (2010-2014, 2015-2019), IEEE Transactions on Aerospace and Electronic Systems (2020-2022), IEEE Signal Processing Letters (2006-2010), and Journal of the Franklin Institute (2007-2013). Dr. Zhang was Technical Co-Chair, IEEE Benjamin Franklin Symposium on Microwave and Antenna Sub-Systems (BenMAS) (2014); Technical Co-Chair, IEEE Sensor Array and Multichannel Signal Processing (SAM) Workshop (2018); Technical Area Chair, Asilomar Conference on Signals, Systems, and Computers (2019); and Track Co-Chair, IEEE Radar Conference (2020).

Dr. Zhang is a recipient of the 2016 IET Radar, Sonar and Navigation Premium Award, the 2017 IEEE Aerospace and Electronic Systems Society Mimno Award, the 2019 IET Communications Premium Award, and the 2021 EURASIP Best Paper Award for Signal Processing.

Dr. Zhang's research interests lie in the areas of statistical signal and array processing, information theory, compressive sensing, machine learning, computational imaging, time-frequency analysis, and optimization applied to radar, wireless communications, satellite navigation, and radio astronomy.

Yimin D. Zhang
Temple University
Philadelphia, PA, USA
E: ydzhang@temple.edu

Lecture Topics

• Sparse Array Design and Processing for Direction-of-Arrival Estimation
• Spectrum Sharing for Joint Sensing and Communications
• Information Theoretic Compressive Sensing in Massive MIMO Systems
• Time-Frequency Analyses and Array Processing

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