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.
César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.
Amy Bastine is a Researcher at the School of Engineering within the ANU College of Systems & Society at the Australian National University. Her work focuses on advanced audio signal processing, acoustics, and machine learning applications in spatial audio environments. She is affiliated with the Information & Signal Processing cluster, emphasizing interdisciplinary research. Her research interests include room acoustic modeling, spatial audio capture using spherical microphone arrays, and developing neural network-based solutions for sound field analysis. She explores topics like active noise control, source localization in reverberant environments, and immersive audio technologies. Recent projects involve creating datasets for acoustic analysis and optimizing algorithms for real-world applications. Her publications from 2022-2025 highlight trends in physics-informed neural networks for sound field estimation, sparse representation techniques, and multi-channel ANC systems. These contributions aim to bridge theoretical acoustics with practical implementations in wearable devices and recording studios. No scientific awards or grants are explicitly listed in the provided texts. Her research has been applied to datasets like room impulse responses and spherical microphone array measurements. She collaborates on projects involving acoustic imaging, HRTF interpolation, and environmental noise mitigation strategies.
Dr. Joe Guinness serves as an Associate Professor and Director of Undergraduate Studies in the Department of Statistics and Data Science within Cornell University's College of Agriculture and Life Sciences (CALS). His research focuses on developing computationally efficient methods for analyzing large spatial-temporal datasets, with applications spanning earth sciences, environmental monitoring, epidemiology, and precision agriculture. His work bridges theoretical statistics with practical implementation through the development of the GpGp R package for Gaussian process computation. Dr. Guinness specializes in spatial statistics and Gaussian process modeling, particularly advancing Vecchia approximations for scalable computation. His research addresses critical challenges in interpolating satellite data, modeling environmental processes, and developing statistical frameworks for large-scale datasets. Current projects include applications in climate change modeling, soil chemistry analysis, medical imaging, and wildlife disease surveillance, with emphasis on computational efficiency and accurate uncertainty quantification. His publication record demonstrates significant contributions to scalable spatial statistics, with recent work focusing on Vecchia approximations, Gaussian process learning, and applications to earth science problems. His research shows consistent progression toward more efficient computational methods while expanding into new application domains including epidemiology (chronic wasting disease modeling) and sports science (Vaporfly shoe impact analysis). Cornell Atkinson Academic Venture Fund (AVF) seed grant (2021) supporting vital interdisciplinary collaborations Dr. Guinness actively mentors doctoral students, currently advising Megan Gelsinger at Cornell while having graduated five PhD students from North Carolina State University. His research is supported by collaborative grants across multiple disciplines, including environmental science, agriculture, and public health initiatives. The GpGp R package he developed has become a standard tool for efficient Gaussian process computation in spatial statistics. His research group develops computational frameworks for analyzing massive spatial datasets, with particular emphasis on earth science applications requiring innovative approaches to handle satellite observations, climate model output, and environmental monitoring data. Current projects integrate statistical methodology development with practical implementation for real-world environmental challenges.
Zachary Bradshaw is a Professor and Graduate Coordinator in the Department of Mathematical Sciences at the University of Arkansas, part of the Fulbright College of Arts & Sciences. His research focuses on fluid dynamics and mathematical analysis of the Navier-Stokes equations, exploring topics like flow separation, uniqueness criteria, and eddy formation. He holds a PhD from the University of Virginia (with Zoran Grujic) and was a postdoc at the University of British Columbia (with Tai-Peng Tsai). He has advised one PhD student (Patrick Phelps) and is currently mentoring undergraduate researcher Zachary Akridge. Education: PhD in Mathematics from the University of Virginia; Postdoctoral research at the University of British Columbia. Research Interests: Fluid dynamics, mathematical analysis of PDEs, Navier-Stokes equations, SQG flows, and nonlinear flows. His work emphasizes rigorous theoretical analysis and has been supported by the Simons Foundation. Advising & Grants: Supervised 1 PhD graduate and 1 undergraduate researcher. Acknowledges Simons Foundation support. Serves as Graduate Coordinator, overseeing graduate programs in the department.
Saurabh Sihag is an Assistant Professor in the Department of Electrical & Computer Engineering at the University at Albany's College of Nanotechnology, Science, and Engineering. He holds a PhD from Rensselaer Polytechnic Institute and completed postdoctoral research at the University of Pennsylvania. His research develops theoretical foundations for graph-structured data processing, with applications in network neuroscience. Specific interests include covariance neural networks, graph signal processing, and explainable AI for neuroimaging analysis. Current projects focus on brain age prediction and neural network interpretability. Publications center on machine learning theory with neuroinformatics applications. Recent work demonstrates innovations in graph neural network architectures, structural equation modeling for brain networks, and transfer learning methodologies. Awards: J. Baliga Fellowship Charles M. Close '62 Doctoral Prize Laboratory Focus: Network Inference and Statistical Learning Lab Computational Neuroscience Group
Daniel Irving Bernstein is Assistant Professor in Tulane University's Department of Mathematics. His research spans combinatorics, discrete geometry, and algebraic statistics, with particular focus on matroid theory, rigidity theory, and geometric combinatorics. He teaches undergraduate and graduate courses in linear algebra, topology, and geometric combinatorics. His research explores: Combinatorial structures in algebraic statistics Geometric rigidity and reconstructibility problems Matroid representations and lifts Algebraic methods in machine learning Recent work develops connections between combinatorial geometry and statistical learning theory. His publications demonstrate: Applications of matroid theory to statistical thresholds Geometric approaches to matrix completion Combinatorial foundations of deep learning Tropical geometric methods in phylogenetics He maintains active collaboration with computational biology and machine learning researchers.
James Brusey is a Professor of Computer Science at Coventry University , leading AI for Cyberphysical Systems within the Centre for Data Science . With a PhD from RMIT University (2003) and over 15 years of industry experience, he specializes in Machine Learning , Reinforcement Learning , and wireless networked sensing for real-world applications. Research Interests span Reinforcement Learning for Cyberphysical Systems Thermal Comfort Optimization in Vehicles and Buildings Wireless Sensing for Safety-Critical Applications Sim2Real Challenges in Autonomous Systems Research Trends in his recent work include Advancing RL for Human-Centric Systems Multi-Objective Optimization in Energy Applications Wireless Sensor Networks for Refugee Camps Machine Learning in Anaerobic Digestion and HVAC Systems Expertise includes EU H2020 DOMUS project (2018-2022) for electric vehicle thermal comfort £35 million in grants across 25 projects Mentorship for 20+ PhD students Collaborations extend to Jaguar Land Rover TUV-NEL Rolls-Royce British Council International Projects
Panagiotis Tsakalides is Professor of Computer Science at the University of Crete and Head of the Signal Processing Laboratory at FORTH-ICS. His research develops statistical signal processing and machine learning methods with applications in computational imaging, sensor networks, and remote sensing. He has secured over €15M in research funding through 22 projects, including: Horizon Europe TITAN Project (€2.5M): Frugal AI for astrophysics applications H2020 PHYSIS Project (€1M): Sparse processing for hyperspectral systems FP7 CS-ORION Project (€1.3M): Compressed sensing for aerial surveillance Research specialties include non-Gaussian signal modeling, compressed sensing architectures, and distributed processing algorithms. Applications span medical imaging, environmental monitoring, and space systems.
Hongbin Li holds the Charles and Rosanna Batchelor Memorial Chair Professorship in the Department of Electrical and Computer Engineering at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science. Education includes PhD in Electrical Engineering from University of Florida (1999). Research focuses on: Advanced signal processing algorithms Machine learning for RF systems Compressive sensing methodologies Wireless communications architectures Radar and sensor network technologies Recent publications concentrate on automotive radar systems, physical-layer authentication, sub-Nyquist sampling techniques, and intelligent reflecting surface applications. Honors include IEEE Fellow, Provost's Award for Research Excellence, IEEE Jack Neubauer Memorial Award, and fellowships in AAIA and AIIA. His research is funded by DARPA, NSF, ONR, AFOSR, ARO, and other agencies.
