Panel
Explainability and Reproducibility in Neuroimaging
Prof. Pamela K. Douglas
Dr. Douglas is a computational neuroscientist whose work lies at the intersection of cognitive neuroscience and artificial intelligence. Dr. Douglas completed a PhD in neuroengineering from UCLA after studying biomedical engineering at Johns Hopkins and the University of Pennsylvania. Dr. Douglas’s work spans both empirical neuroimaging data collection including: transcranial ultrasound, EEG, fMRI, and simultaneous approaches to EEG-fMRI data collection and analysis. Current research focuses on explainable deep learning, brain computational models, and computational models of white matter signaling in the brain.
Prof. Z. Jane Wang
Z. Jane Wang received the B.Sc. degree from Tsinghua University in 1996 and the M.Sc. and Ph.D. degrees from the University of Connecticut in 2000 and 2002, respectively, all in electrical engineering. She has been Research Associate at the University of Maryland, College Park from 2002 to 2004. Since Aug. 2004, she has been with the ECE dept. at the University of British Columbia (UBC), Canada, and she is currently Professor. She is Fellow of IEEE and the Canadian Academy of Engineering (FCAE). Her research interests are in the broad areas of statistical signal processing and machine learning, with current focuses on digital media and biomedical data analytics. She has been key Organizing Committee Member for numerous IEEE conferences and workshops (e.g., Co-Technical Chair for ChinaSIP2014, GlobalSIP2017 and ICIP2021). She has been Associate Editor for the IEEE TSP, SPL, TMM, TIFS, TBME, and SPM, and Area Editor of SPM, and Editor-in-Chief for IEEE SPL.
Prof. Dimitri Van De Ville
Dr. Dimitri Van De Ville received his Ph.D. degree in computer science and engineering from Ghent University, Belgium, in 2002. 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 responsible 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 Associate Professor of Bioengineering at the EPFL, jointly affiliated with the University of Geneva, Switzerland. His main research interest is in computational neuroimaging to advance cognitive and clinical neurosciences. His methods toolbox includes wavelets, sparsity, deconvolution, graph signal processing. He was a recipient of the Pfizer Research Award 2012, the NARSAD Independent Investigator Award 2014, the Leenaards Foundation Award 2016, and was elected Fellow of the IEEE in 2020, and IEEE SPS Distinguished Lecturer 2021-2022.
Dr. Van De Ville serves as an Editor for the new journal, Neuroimage: Reports since 2020, as a Senior Editor for the IEEE Transactions on Signal Processing since 2019 and as an Editor for the SIAM Journal on Imaging Science from 2018 onwards. He served as an Associate Editor for the IEEE Transactions on Image Processing from 2006 to 2009, and the IEEE Signal Processing Letters from 2004 to 2006. He was the Chair of the Bio Imaging and Signal Processing (BISP) TC of the IEEE Signal Processing Society (2012-2013) and the Founding Chair of the EURASIP Biomedical Image & Signal Analytics SAT (2016-2018). He was Co-Chair of the biennial Wavelets & Sparsity series conferences, together with Y. Lu and M. Papadakis.
Prof. Selin Aviyente
Selin Aviyente 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 and Associate Chair for Undergraduate Studies. Her research focuses on statistical and nonstationary signal processing, higher-order data representations and network science with applications to neuronal signals. She has authored more than 150 peer-reviewed journal and conference papers. She is the recipient of a 2005 Withrow Teaching Excellence Award, a 2008 NSF CAREER Award and 2021 Withrow Excellence in Diversity Award. She is currently serving as the chair of IEEE Signal Processing Society Bioimaging and Signal Processing Technical Committee, on the Steering Committees of IEEE SPS Data Science Initiative and IEEE BRAIN. She has served as an Associate Editor and Senior Area Editor for IEEE Transactions on Signal Processing, IEEE Transactions on Signal and Information Processing over Networks, IEEE Open Journal of Signal Processing and Digital Signal Processing.
Prof. Tülay Adalı
Tülay Adalı received the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, MD, the same year. She is currently a Distinguished University Professor in the Department of Computer Science and Electrical Engineering at UMBC.
Prof. Adali served as the Signal Processing Society (SPS) Vice President for Technical Directions 2019−2022, and is currently the Chair of the IEEE Brain Technical Community.
She has been active in conference organizations, and served or will serve as technical chair, 2017, special sessions chair, 2018, 2024, publicity chair, 2000, 2005, for the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), general/technical chair for the IEEE Machine Learning for Signal Processing (MLSP) and Neural Networks for Signal Processing Workshops 2001−2009, 2014, and 2023. She was the Chair of the NNSP/MLSP Technical Committee, 2003–2005 and 2011–2013, and served or is currently serving on numerous boards and technical committees of the SPS.
Prof. Adali is a Fellow of the IEEE, AIMBE, and AAIA, a Fulbright Scholar, and an IEEE SPS Distinguished Lecturer. She is the recipient of SPS Meritorious Service Award, a Humboldt Research Award, an IEEE SPS Best Paper Award, SPIE Unsupervised Learning and ICA Pioneer Award, the University System of Maryland Regents' Award for Research, and an NSF CAREER Award.
Prof. Sharon Gannot
Sharon Gannot received the BSc degree (summa cum laude) from the Technion-Israel Institute of Technology, Haifa, Israel, in 1986, and the MSc (cum laude) and PhD degrees from Tel-Aviv University, Tel Aviv, Israel, in 1995 and 2000, respectively, all in electrical engineering. In 2001, he held a Postdoctoral position with the Department of Electrical Engineering, KU Leuven, Leuven, Belgium. He is currently a Full Professor with the Faculty of Engineering, Bar-Ilan University, Israel, where he is heading the Acoustic Signal Processing Laboratory and the Data Science Program. He also serves as the Faculty Vice Dean.
Dr. Gannot took many editorial responsibilities, including Senior Area Chair for the IEEE Transactions on Audio, Speech, and Language Processing, 2013–2017 and since 2020, a member of the Senior Editorial Board of IEEE SP Magazine since 2021and a member of the SPS Education Center Editorial Board. He served as the Chair of the Audio and Acoustic Signal Processing (AASP) technical committee of the IEEE SPS, 2017-2018. Currently, he is the chair of the Data Science Initiative of IEEE SPS. He was the General Co-Chair of IWAENC 2010 and of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2013. He was selected to present tutorials and keynote addresses in many of the leading conferences in the field.
He is a co-recipient of thirteen best paper awards, including IEEE SP Letters and ICASSP, and a recipient of The 2022 EURASIP Group Technical Achievement Award. He is an IEEE Fellow for contributions to acoustical modelling and statistical learning in speech enhancement.
Prof. Borbála (Bori) Hunyadi
Borbála (Bori) Hunyadi received an MSc degree in electrical and computer engineering from the Pázmány Péter Catholic University, Budapest in 2009. In the same year she joined Stadius, Department of Electrical Engineering at KU Leuven, where she worked in close collaboration with the Laboratory for Epilepsy Research, and obtained her PhD degree in 2014. She continued working in Stadius as a postdoctoral researcher on the ERC advanced grant Biotensors for EEG-fMRI fusion, and served as the research lead on the Imec-ICON project SeizeIT developing a multimodal wearable seizure detector. In 2018 she was awarded one of the “Delft Technology Fellowships” for outstanding female academic researchers and joined the Circuits and Systems (now Signal Processing Systems) group at TU Delft as an assistant professor. She is co-director of the Delft Tensor AI Lab (DeTAIL).
Her research interests include signal processing and machine learning for biomedical pattern recognition. More specifically, she is interested in multimodal signal processing and fusion, blind source separation, tensor decompositions and wearable signal processing to better understand healthy and pathological physiology. In particular, she is leading projects on ECG, EEG and (functional) ultrasound signal processing for a variety of applications, with a special focus on neuroscience research.
She is the secretary of the IEEE EMBC Benelux chapter and vice-chair of the EURASIP technical area committee on biomedical signal and image processing.