Stefano Fortunati is an Assistant Professor at Telecom SudParis within the ISTeC research centre. His work focuses on advanced signal processing, radar systems, and machine learning applications in communication and sensing. He has contributed to robust statistical methods, MIMO radar technologies, and environmental monitoring using underwater gliders. His research interests include semiparametric estimation, reinforcement learning for cognitive systems, and performance bounds in parametric models. Notable contributions address elliptical distributions, target detection in dynamic environments, and distributed sensor networks. Recent work explores integrated sensing and communication (ISAC) systems, massive MIMO radar architectures, and anomaly detection algorithms. He has published extensively on topics such as compressed sensing applications and sensor calibration in airborne systems.
Prof. Marc CASTELLA is a Lecturer at Telecom SudParis, part of the SOP (Signal and Optimization Processing) unit. His research focuses on signal processing, particularly in blind source separation, nonlinear reconstruction, and optimization techniques. He has extensively contributed to areas such as sparse signal recovery, neural networks, and tensor decomposition. His work often addresses challenges in noisy environments and nonlinear systems. Recent publications include advancements in clipped signal recovery, motor torque estimation, and global optimization methods. Though no specific awards are listed, his prolific publication record reflects his impactful contributions to signal processing and applied mathematics. He collaborates with researchers like Jean-Christophe Pesquet and Arthur Marmin on projects involving polynomial systems and neural network models. His work is applied in diverse fields from industrial electronics to data analysis.
José L. Abellán is a Ramón y Cajal Fellow (Tenure-Track Associate Professor) and European R3 researcher at the University of Murcia's Department of Computer Engineering and Technology. He leads the EcoArTech research group and holds a Ph.D. in Computer Science from the University of Murcia (2012). His career includes postdoctoral positions at Boston University and previous faculty roles at Universidad Católica de Murcia. Dr. Abellán's research focuses on architectural enhancements for GPU systems and customized accelerators targeting machine learning and fully homomorphic encryption applications. His work spans hardware/software co-design, microarchitectural extensions for privacy-preserving computation, and efficient parallel processing. His extensive publication record demonstrates consistent contributions to GPU architecture, processing-in-memory, hardware acceleration for cryptography, and graph neural networks. Recent work has appeared in top computer architecture venues including MICRO, ASPLOS, and HPCA. Awards and Honors HiPEAC Paper Awards (2019, 2020, 2023, 2024) Best Paper Award at IPDPS 2011 Top Picks in Hardware and Embedded Security 2024 European R3 Certificate (2024) Dr. Abellán leads the EcoArTech research team and serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and Frontiers in Electronics. He is a Senior Member of IEEE and active in the HiPEAC European network.
Dirk Slock is a Professor at EURECOM's Communication Systems department. His research focuses on advanced signal processing for wireless communications, including transmitter/receiver design for 4G/5G systems, Massive MIMO, stochastic geometry, and audio signal processing. He has contributed to areas like interference management, compressive sensing, and Bayesian methods. Slock teaches courses on statistical signal processing and wireless communication techniques. His notable awards include IEEE Fellow (2006) and EURASIP Fellow (2015). Collaborations with students like Christo Kurisummoottil Thomas have yielded Best Student Paper Awards at SPAWC 2018. His work addresses challenges in cell-free MIMO, semi-blind channel estimation, and secure communication systems. Recent research trends explore ultra-massive MIMO signal detection, dynamic channel prediction with tensor methods, and cell-free network optimization. His publications span 639 entries, emphasizing practical implementations of theoretical signal processing advancements.
Maben Rabi is a Professor at the Department of Information Technology and Communication, University of Southeast Norway. His work focuses on cyber-physical systems, networked control, and hybrid systems with applications in intelligent transportation and robotics. Current projects: CriSp (Research Council) and SafeSmart (KK Foundation). Research keywords: Cyber-Physical Systems, Networked Control, Hybrid Systems, Digital Society, DigiTech. Research Trends : His recent publications emphasize road friction estimation for autonomous vehicles, nonlinear control in relay feedback systems, probabilistic sampling for networked estimation, and packet loss modeling in vehicular networks. These works bridge control theory, cyber-physical systems, and transportation safety. Teaching : Courses include Technology Project (ITD25018) and Digital Control and Cyber-Physical Systems (ITD30019) .
VASDEKIS VASILEIOS is a Professor of Statistics at the Department of Statistics, School of Information Sciences and Technology, Athens University of Economics and Business (AUEB), where he has been a faculty member since 1999. He previously served as Assistant Professor (2003–2009), Associate Professor (2009–2016), and Lecturer (1999–2003). He held administrative roles including Head of Department (2016–2020) and Vice-Chancellor for Academic Affairs and Personnel (2020–2024). Education: D.Phil. in Statistics, University of Oxford, UK (1989–1993) M.Sc. in Applied Statistics, University of Oxford, UK (1988–1989) B.A. in Mathematics, University of Athens, Greece (1983–1988) His research focuses on longitudinal data analysis , latent variable models , and composite likelihood methods , with applications in clinical trials, psychology, and public health. He has developed statistical methodologies for correlated binary data, multivariate ordinal responses, and random effects models. His work bridges theoretical statistics with practical applications in medicine and social sciences. The recent publications show a strong trend in psychometrics , model diagnostics , and longitudinal modeling , particularly using composite likelihood and goodness-of-fit techniques. His interdisciplinary collaborations extend to developmental psychology and nutrition studies. Scientific Awards: Fellow of the Royal Statistical Society (UK) IKY Scholarship for Doctoral Research (1989–1993) He has supervised multiple doctoral students, including Evgenia Tzompanaki, Antonia Korre, and Ioanna Athanassopoulou, and postgraduate researcher Kostas Florios. He has secured research funding through projects such as PYTHAGORAS, ARISTEIA II, and internal AUEB grants. His professional service includes teaching workshops on SPSS and statistical methods for health professionals, often in collaboration with pharmaceutical companies like JANSSEN-CILAG. He is an active member of professional societies, including the Royal Statistical Society, the American Mathematical Society, and the Bernoulli Society. He has contributed to European research networks such as DAFNE, focusing on food consumption data analysis.
Ken M. L. Yiu is a Professor in the Department of Computing at Hong Kong Polytechnic University , Faculty of Engineering. He received his PhD and Bachelor's degree from the University of Hong Kong in 2006 and 2002, respectively, and was previously affiliated with Aalborg University (2006–2009). He is a leading researcher in databases, with a focus on spatiotemporal data, query processing, and multidimensional data management. PhD, University of Hong Kong (2006) Bachelor of Computer Engineering, University of Hong Kong (2002) His research interests lie at the intersection of database systems and spatial analytics. He investigates efficient indexing, query optimization, and privacy-preserving techniques for large-scale spatial and temporal datasets. His recent work explores learned index structures, GPU-accelerated query processing, and high-dimensional data retrieval. He has made significant contributions to spatial query processing, trajectory analytics, and location-based services. The trends in his recent publications (2021–2025) reflect a strong focus on high-performance database systems, including GPU acceleration (GHive), perfect hashing on GPUs (GPH), and learned cardinality estimation. His work increasingly integrates machine learning with traditional database techniques, as seen in AlayaDB for LLM inference and learning-based query optimization. He also continues to advance core database problems such as spatial indexing, trajectory analysis, and similarity search. SSTD 2025 10-Year Impact Award Ken Yiu has successfully led multiple competitive research projects funded by the Hong Kong GRF, including grants on learned index structures (2024–2026), smart memory for vector data mining (2021–2023), and efficient spatial data management (2017–2019). He has supervised numerous PhD and MPhil students, many of whom now hold academic positions (e.g., Bo Tang at SUSTech, Yu Li at HDU) or work in top tech companies (e.g., Huawei, Alibaba). His professional service is extensive, including roles as PI for major grants, area chair (ICDE 2024), and program committee member for top conferences like SIGMOD, VLDB, and ICDE. He is actively involved in research groups and projects related to database systems, particularly in spatiotemporal data management and efficient query processing. His lab collaborates closely with students and co-supervisors like Bo Tang on topics such as trajectory mining, spatial indexing, and learned databases. The research group maintains strong ties with international institutions and contributes to major open problems in database performance and scalability.
Samuel Adeyemo is a Postdoctoral Teaching Fellow in the Engineering Department at Calvin University. His career spans teaching high school chemistry, physics, and mathematics in Nigeria, a graduate research assistantship at West Virginia University developing machine learning algorithms for industrial systems, and a role as a process engineer at Dangote Cement PLC. He specializes in data-driven modeling and machine learning applications in chemical processes. Education: B.Sc (Ile-Ife), M.Sc (unspecified institution) His research focuses on sparse model selection, Bayesian parameter estimation, and robust machine learning for industrial systems. Recent publications emphasize hybrid AI/first-principles models and surrogate modeling under constraints. He has contributed to both computational chemistry and control theory domains. Key trends in his publications include: Integration of machine learning with industrial process modeling Development of sparse and constrained models Application of Bayesian methods to chemical engineering systems Comparative studies of neural networks and traditional ML approaches His professional experience combines industrial practice (cement manufacturing) and academic research, with a recurring emphasis on technical education and mentorship.
Antoine LEDENT is a tenure-track Assistant Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), holding a PhD in Mathematics from the University of Luxembourg (2017) and undergraduate/graduate degrees from Clare College, University of Cambridge. His academic trajectory includes a postdoctoral position at TU Kaiserslautern under Prof. Marius Kloft, establishing his foundation in theoretical computer science. His educational background spans rigorous mathematical training at Cambridge followed by doctoral research on stochastic differential equations at Luxembourg, directly informing his current theoretical work. This path transitioned into computer science through postdoctoral research in algorithmic learning theory. Professor LEDENT specializes in Statistical Learning Theory and Recommender Systems, with groundbreaking contributions to generalization bounds for deep neural networks, contrastive learning frameworks, and matrix completion theory. His research uniquely bridges abstract mathematical principles with practical AI applications, particularly in high-dimensional data analysis and robust recommendation engines, emphasizing provable guarantees over empirical results. Analysis of his 15 most recent publications reveals a dominant focus on theoretical machine learning, with 70% concerning generalization analysis in deep learning and matrix completion. His work consistently targets top-tier venues (ICML, NeurIPS, AAAI), demonstrating expertise in transforming mathematical insights into scalable AI solutions, especially for non-IID data and uncertainty-aware systems. Key recognitions include: Outstanding Meta Reviewer (ICML 2025, top 2%) Area Chair appointments for NeurIPS/ICML 2025 Action Editor for TMLR (2024) Certificate of Excellence in Reviewing (KDD 2023) Top Reviewer distinctions at NeurIPS/ICML/ICLR (2022-2024) He actively mentors PhD candidates including AISG Scholarship recipient NONG Minh Hieu and SENARATH ARACHCHIGE Dilan Dinushka, offering fully funded 4-year positions with pathways to Presidential Scholarships. His recruitment targets mathematically strong candidates for projects in Statistical Learning Theory, Matrix Completion, or Recommender Systems, with stipends exceeding 6000 SGD/month for exceptional performers. Based in Singapore's AI hub at SMU—ranked #41 globally in AI (CSRankings)—he operates within SCIS's dynamic ecosystem that connects academia with regional AI startups and industry partners, leveraging Singapore's strategic position at the Asia-Pacific research nexus.
Pia Addabbo is a Lecturer at the Department of Engineering , University of Sannio , with expertise in remote sensing and satellite data analysis. Her work focuses on GNSS reflectometry, wind speed estimation, and hyperspectral imaging applications. Department: Engineering Academic Rank: Lecturer Research Interests: Addabbo's research spans Remote Sensing , Geophysics , Environmental Monitoring , and Signal Processing . She has contributed to: GNSS reflectometry for ocean wind speed retrieval UAV systems in photovoltaic plant maintenance Phase altimetry techniques for surface height measurement Sparse learning algorithms in radar profiling Publication Trends (2015–2024): 15 articles highlight her focus on satellite-based environmental monitoring (CYGNSS, Sentinel-1), microwave reflectometry , and machine learning applications in remote sensing. Key collaborations include Silvia Ullo, Maurizio Di Bisceglie, and Carmela Galdi.
Grant Morgan is a Professor in the Department of Educational Psychology at Baylor University's School of Education, where he also serves as Associate Dean for Research and Outreach and Program Director for the Quantitative Methods graduate program. He holds a Ph.D. in Educational Research & Measurement from the University of South Carolina and has been a faculty member at Baylor since 2012. Educational Background: Ph.D. in Educational Research & Measurement, 2012, University of South Carolina, Columbia M.S. in Human Resources Management (Organization Performance track), 2005, Western Carolina University, Cullowhee B.S. in Psychology, 2003, Clemson University Dr. Morgan's research focuses on latent variable models , psychometrics , classification , and nonparametric statistics . He conducts methodological investigations using Monte Carlo simulations and applies advanced quantitative models in interdisciplinary contexts. His work emphasizes validity in psychological measurement and accurate estimation in latent variable frameworks. His recent publications reflect a strong trend in Bayesian factor analysis, latent class modeling, robust estimation for ordinal data, and the generation of nonnormal distributions using mixture models. These works span top-tier journals such as Psychological Methods , Structural Equation Modeling , and Language Assessment Quarterly , highlighting his contributions to both theoretical and applied psychometrics. Scientific Awards and Recognition: Three-time recipient of Distinguished Paper Awards from AERA-affiliated organizations Nominated for Cornelia Marschall Smith Professor of the Year Nominated for Division D Early Career Award Dr. Morgan is actively involved in academic leadership and service. He has served as Chair of the Structural Equation Modeling SIG at AERA, is a board member of a regional AERA-affiliated organization, and regularly serves as a panelist for the National Science Foundation and U.S. Department of Education. He advises on externally funded research projects totaling over $20 million and mentors graduate students in quantitative methods. He serves on the editorial board of the Journal of Psychoeducational Assessment and reviews for leading methodological journals including Structural Equation Modeling , Psychometrika , and Multivariate Behavioral Research . He leads the Quantitative Methods specialization, teaching courses such as Psychometric Theory, Item Response Theory, Latent Variable Models, and Nonparametric Statistics. His lab and research team focus on advancing methodological rigor in educational and psychological measurement through simulation studies and real-world applications.
Saikat Chatterjee is a Professor in the Department of Information Science and Engineering at the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH). He is also a Fellow of Digital Futures and maintains visiting researcher positions at Karolinska Institute, Karolinska Hospital (specializing in 'AI for Health Care'), and Oslo University Hospital in Norway. His primary research interests span Signal Processing and Machine Learning, with specific focus on signal modeling (sparsity, compressive sensing, dynamical systems), statistical signal processing, statistical machine learning, deep learning, speech/audio/image processing, medical data analytics, life science data analysis, perception for autonomous systems, distributed machine learning, and explainable AI (XAI). He particularly emphasizes explainable machine learning, having a strong background in signal processing and statistical machine learning, with growing passion for medical data analysis due to its societal importance. SSF - Swedish Foundation for Strategic Research Region Stockholm European Union Digital Futures Vinnova WASP Companies: Ericsson, Scania, Saab Professor Chatterjee is actively involved in teaching, serving as examiner and course responsible for various degree projects and courses including Machine Learning and Data Science, Pattern Recognition and Machine Learning, and Speech and Audio Processing. His research group has produced significant work across multiple domains, with notable publications in Bioinformatics and smart city applications, demonstrating the breadth of his research impact from healthcare to urban systems.
Dr. Nigel Metcalfe is an Assistant Professor at Durham University, affiliated with the Department of Physics and the Institute for Computational Cosmology. His academic responsibilities include serving on the Laboratories Committee, managing teaching databases, coordinating computer classrooms, and supervising Level 4 projects. He is also an academic adviser and tutor for the Level 3 Problem Solving Computing Course. His research centers on galaxy evolution, cosmology, and large-scale cosmic structures. Key interests include: Deep galaxy counts and clustering analysis Interpreting data through theories of galaxy formation Leading roles in international surveys: Chair of the Pan-STARRS Data Reduction Group, member of VST Atlas and DESI Imaging Validation teams He leverages computational tools to study galaxy distribution and cosmological parameters. Metcalfe's recent publications (2017–2024) focus on: Quasar clustering and dark matter halo mapping Galaxy underdensities (e.g., the 'Local Hole') Pan-STARRS data processing and calibration Machine learning applications in astronomy His work emphasizes survey-based cosmology, instrumentation, and statistical analysis of cosmic structures. He mentors students in computational cosmology and oversees research projects. While no awards are listed, his leadership in major collaborations (Pan-STARRS, VST Atlas) highlights his field impact. Metcalfe co-leads the Durham team within these consortia, focusing on data validation and cosmological inference.
Manil Dev Gomony is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology, and a researcher affiliated with Nokia Bell Labs. His expertise spans low power digital hardware design, neural network optimization for wireless systems, and hardware/software co-design for edge computing. Education : Master’s (2010) and PhD (2015) in Electrical Engineering from Linköping University and Eindhoven University of Technology, respectively. His research focuses on dynamic neural network architectures, wireless receiver design, and memory-efficient edge learning systems. Recent work includes energy-saving frameworks for neural receivers and scalable hardware implementations for training recurrent neural networks on edge devices. Key contributions include the LLRSymNet architecture for LLR estimation, MEAN (Mixture-of-Experts Based Neural Receiver), and optimized frameworks for Forward Propagation Through Time (FPTT) training methods. His 2024 publications demonstrate up to 59.2% power savings and 8.2-fold memory reduction in edge applications. He leads educational activities in Digital Integrated Circuits and Systems on Silicon , and his work is funded by the Dutch Organization for Scientific Research (NWO) under the Self-Healing Neuromorphic Systems project. Research Groups : Electronic Systems, Application Specific Integrated Circuit Lab, Center for Wireless Technology Eindhoven.
Götz Pfander is a Professor of Mathematics at the Catholic University of Eichstätt-Ingolstadt , holding the Chair of Mathematics - Scientific Computing . He has held previous academic roles at Philipps-Universität Marburg (W2 Numerical Analysis), Jacobs University Bremen (Associate/Assistant Professor), and visiting positions at institutions including MIT, NYU Courant, and TU München. His leadership roles include Dean and Vice Dean of the Faculty of Mathematics and Geography (2019-2023) and Speaker of the Mathematical Institute of Machine Learning and Data Science (MIDS) since 2022. PhD in Mathematics (University of Maryland, 1999), advised by John J. Benedetto Master of Arts in Mathematics (University of Maryland, 1998) Studies in Mathematics and Psychology (Johannes Gutenberg University Mainz, Freie Universität Berlin) Pfander's research focuses on numerical harmonic analysis, operator sampling theory, and time-frequency analysis, with applications in digital communications and signal processing. His work bridges Gabor frames, wavelet transforms, and uncertainty principles to solve problems in OFDM channel modeling, sparse signal recovery, and quantum information theory. Notable contributions include: Sampling theory for pseudodifferential operators with bandlimited Kohn-Nirenberg symbols Uncertainty principles for joint time-frequency representations on finite Abelian groups Design of robust Gabor systems for wireless communication channels Wavelet-based periodicity detection for biomedical signals His recent publications (2022-2024) emphasize exponential bases for interval partitions, cube tiling constraints, and complex-valued neural network approximation. Scientific awards include the Max Kade Fellowship and John von Neumann Visiting Professor title. He serves as Editor in Chief of Sampling Theory, Signal Processing, and Data Analysis and chairs the International Conference on Sampling Theory and Applications .
