Overview Dr. Jean-François DOLLINGER is a Researcher-Lecturer at CESI LINEACT (Strasbourg campus), affiliated with the Engineering and Numerical Tools research team. His academic roles include teaching Computer Science courses (undergraduate/graduate) and supervising student projects in algorithmics, programming, databases, and networks. Education: PhD in Computer Science (2011-2015), University of Strasbourg - ICube Lab MSc in Computer Science (2009-2011), University of Strasbourg (Highest Honors) BSc in Computer Science (2008-2009), University of Strasbourg (Honors) Research Interests: Focuses on edge-cloud computing, high-performance distributed systems, combinatorial optimization in IoT networks, and smart city infrastructure. Specializes in optimizing federated learning, WSN deployment strategies, and hybrid CPU/GPU execution frameworks. Advising & Collaboration: Supervises PhD/Master’s students (e.g., A. BAAHMED on federated learning, K. BOUHOUCH on OpenStack edge deployment) Collaborated with Indonesian universities (Mercu Buana) on RPL protocol extensions Research Team: Leads the Engineering and Numerical Tools group, developing frameworks for edge-cloud infrastructures and BIM-based WSN deployments in smart buildings.
Alex Wein is an Assistant Professor of Mathematics at the University of California, Davis. His research bridges theoretical computer science, statistics, and probability, with a focus on the mathematical foundations of data science. Key areas include understanding optimal algorithms for signal detection in noise, computational complexity of statistical inference (especially via the low-degree polynomial framework), tensor analysis, and applications of group actions in computational problems. Research Interests: Mathematics of data science: optimal algorithms for hidden structure detection Computational-statistical gaps via low-degree polynomials Tensors: computational challenges and applications Bayesian inference and connections to statistical physics Group actions in molecular structure determination and representation theory Recent Talks: Banff International Research Station (2024): 'Optimality of AMP Among Low-Degree Polynomials' Bernoulli-IMS Symposium (2020): 'Low-Degree Framework for Statistical Inference' Professional Service: Program committee member for COLT, STOC, FOCS Organizer of workshops on computational complexity and statistical inference
Hong Ye Tan is currently a Hedrick Assistant Adjunct Professor in Computational and Applied Mathematics at the University of California, Los Angeles (UCLA), hosted by Professor Stanley Osher. Previously, he completed his PhD at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics as a member of the Cambridge Image Analysis group and the Cantab Capital Institute for the Mathematics of Information, supervised by Professors Carola-Bibiane Schönlieb, Subhadip Mukherjee, and Junqi Tang with funding from GSK.ai. His educational trajectory is exceptional: admitted to the University of Hong Kong at age 11 in 2015 (youngest in recent history) and to Cambridge at age 13 for doctoral studies. He passed his PhD thesis with no corrections, focusing on provably convergent algorithms leveraging geometric structures in data. Tan's research centers on machine learning theory, specifically investigating why learning succeeds through interactions between problem structure, data distributions, optimizers, and network architectures. His work bridges differential geometry (manifold hypothesis, intrinsic complexity), optimization (convex learning-to-optimize, Plug-and-Play inverse problems), and sampling theory (noise-free MCMC methods). He develops theoretically grounded algorithms with practical applications in imaging and unsupervised learning, emphasizing provable convergence guarantees derived from classical mathematics. Analysis of his 13 recent publications reveals a cohesive research program connecting optimal transport theory, manifold learning, and regularization techniques. His work demonstrates how geometric insights enable efficient solutions for high-dimensional problems, particularly in image analysis where dimensionality effects transform from curse to blessing. Key themes include Wasserstein proximal methods, dataset distillation via quantization, and accelerating mirror descent through equivariance. His scientific recognition includes: Masason Foundation Fellowship GSK.ai PhD Fellowship Tan has secured research funding through the GSK.ai PhD studentship and operates within Professor Stanley Osher's group at UCLA. He maintains active collaborations from his Cambridge tenure, particularly with the Cambridge Image Analysis group. Notably, he handles 100% of coding and 98% of writing for first-author publications, actively encouraging code reuse by the community. His work continues to explore foundational questions in learning theory while developing practical tools for inverse problems and imaging science.
Hamdi Joudeh is an Associate Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He is affiliated with the Information and Communication Theory (ICT) Lab and the Signal Processing Systems (SPS) Group. His research focuses on information theory, communications, and signal processing, with applications in wireless networks and radar systems. He holds a Ph.D. in Electrical Engineering from Imperial College London and has held research positions at Technische Universität Berlin and Imperial College London. His research interests include quantum sensing, error exponents, MIMO systems, and channel coding. He leads projects such as the IT-JCAS (Information Theoretic Foundations of Joint Communication and Sensing) and ANTERRA (Beam Prediction for Fast-Moving LEO), addressing challenges in 5G/6G communication and radar technologies. He has received an ERC Starting Grant (2023) for his work on environment-scanning mobile networks. Education: Ph.D. in Electrical Engineering (Imperial College London), M.Sc. in Communications and Signal Processing (Imperial College London) Editorial Roles: Editorial board member of IEEE Transactions on Signal Processing , IEEE Communications Letters , and EURASIP Journal on Wireless Communications and Networking Labs/Teams: ICT Lab, SPS Group, and leads projects at TU/e’s Center for Wireless Technology Grants: ERC Starting Grant, TKI-HTSM/22.0547/TKI2212P11 RAIDAR, and others His recent work explores the intersection of communication and sensing, including quantum radar processing and robust beamforming techniques. He has published extensively on topics like error exponents, MIMO channel analysis, and interference management, with over 40 peer-reviewed articles.
Oliver Layton is an Associate Professor and Associate Chair of the Computer Science Department at Colby College, Maine. His research bridges computational neuroscience and machine learning, focusing on neural modeling of visual perception and self-motion estimation. He teaches courses in Mathematical Data Analysis and Visualization (CS252), Neural Networks (CS343), and Deep Learning (CS444). Postdoctoral scholar, Rensselaer Polytechnic Institute Ph.D., Cognitive and Neural Systems, Boston University B.A., Mathematics and Computational Neuroscience, Skidmore College His expertise spans computational neuroscience, neural modeling, and visual perception, with emphasis on optic flow processing and its applications. Research explores how biological systems (like primate MSTd) estimate heading and self-motion, using biologically inspired neural networks and dynamic sensory encoding models. Recent publications focus on deep learning approaches to optic flow analysis, heading perception stability, and curvilinear motion modeling. Key trends include applying convolutional neural networks to simulate MSTd tuning, investigating sparseness and ReLU activation impacts, and comparing human and AI performance in self-motion estimation tasks.
Jeffrey H. Shapiro is a Professor at the Massachusetts Institute of Technology (MIT) School of Engineering. His career began with an NSF Fellowship and a Ph.D. at MIT in 1970, followed by a brief tenure as an Assistant Professor at Case Western Reserve University before returning to MIT in 1973, where he has remained for over five decades. His research spans optical and quantum communication, sensing, and imaging, bridging physics and communication theory. Key contributions include foundational work on quantum photodetection, two-photon coherent states, and quantum illumination. His work on adaptive optics, squeezed states, and quantum key distribution has shaped modern quantum communication protocols. Current research explores zero added-loss multiplexing for entanglement distribution and hardware-efficient bosonic codes for quantum computing. Notable awards include the NSF Fellowship. Collaborations with researchers like Robert Kennedy, Horace Yuen, Prem Kumar, Franco Wong, and Isaac Chuang have driven experimental and theoretical advancements. His group at MIT’s Research Laboratory of Electronics focuses on quantum-classical boundary phenomena. Scientific Awards: NSF Fellowship
Mohammadreza MOUSAVI-KALAN is an Assistant Professor of Statistics at CREST-ENSAI. Previously, he was a postdoctoral fellow in the Department of Statistics at Columbia University. He received his Ph.D. in Electrical Engineering from the University of Southern California (USC) and his B.Sc. from Sharif University of Technology. Dr. MOUSAVI-KALAN's research focuses on theoretical foundations at the intersection of statistics and distributed computing. His primary interests include statistical machine learning, transfer learning, optimization theory, and distributed computing systems. He investigates how to design efficient algorithms that can leverage knowledge across related tasks while providing rigorous theoretical guarantees for learning procedures. His work addresses fundamental questions about sample complexity, computational efficiency, and statistical performance in modern machine learning settings. His publication record reveals a clear research trajectory from foundational work on distributed optimization (2018-2019) toward specialized topics in transfer learning and statistical hypothesis testing (2020-2025). A consistent theme across his work is establishing theoretical limits (minimax bounds, rate analyses) for practical machine learning problems. His recent publications focus on outlier detection, Neyman-Pearson classification frameworks, and transfer learning theory, demonstrating evolution toward more specialized statistical learning problems with practical applications. Dr. MOUSAVI-KALAN has established strong collaborative ties with researchers at USC, including Mahdi Soltanolkotabi, Salman Avestimehr, and Songze Li. His most influential work includes the Lagrange coded computing framework for distributed systems, which addresses critical challenges in resiliency, security, and privacy. His research bridges theoretical computer science, statistical learning theory, and practical distributed systems challenges, with implications for secure and efficient large-scale machine learning applications.
Veli Mäkinen is a Professor at the Department of Computer Science, University of Helsinki, and holds the title of Docent in the same department. He leads the Genome-scale Algorithmics research group and is affiliated with the Helsinki Institute for Information Technology (HIIT). Mäkinen serves as a supervisor for doctoral programmes in both Integrative Life Science and Computer Science at the University of Helsinki. Professor, Department of Computer Science, University of Helsinki Docent, Department of Computer Science, University of Helsinki Supervisor, Doctoral Programme in Integrative Life Science Supervisor, Doctoral Programme in Computer Science Affiliated with Helsinki Institute for Information Technology (HIIT) His research focuses on algorithmic bioinformatics, genome-scale algorithmics, and computational methods for genomic sequence analysis. Mäkinen's work encompasses data structures, string algorithms, and graph theory applied to bioinformatics challenges such as variation detection, sequence alignment, and pan-genome analysis. Recent publications highlight his advancements in graph indexing, elastic founder graphs for scalable genomic analysis, and innovative approaches to colinear chaining. These works often intersect with high-throughput sequencing and quantum computing applications in bioinformatics. Scientific awards include: Hyvä tutkija award, University of Helsinki (2007) Mäkinen has received significant funding through grants such as EU Horizon Europe's "TeamPerMed" project and multiple Finnish Academy grants for research on cancer genetics and genomic epidemiology. He is an active editor for BMC Bioinformatics and has organized key conferences like IWOCA 2016 and WABI 2017.
Tom Franken, MD, PhD, is an Assistant Professor of Neuroscience at the Department of Neuroscience, Washington University School of Medicine in St. Louis. He leads the Franken Lab which focuses on understanding how the primate brain parses complex sensory information to construct organized representations of the external world. Dr. Franken's research centers on visual perception mechanisms, particularly border ownership computation where neurons in early visual areas (V2, V4) signal which side of a border belongs to a foreground object. His work has revealed that border ownership signals are organized in columnar clusters with deep layer neurons carrying the earliest signals, supporting the hypothesis of feedback from higher brain areas. The lab employs high-channel count electrophysiology (Neuropixels) in behaving non-human primates, behavioral techniques, causal approaches, and computational methods to study these neural mechanisms. Analysis of Dr. Franken's recent publications shows a strong focus on visual scene segmentation and neural computation, with significant contributions to understanding how the brain organizes visual input into meaningful objects. His 2025 work demonstrates that brain-like border ownership signals emerge in deep recurrent artificial neural networks trained to predict natural videos, suggesting these signals are fundamental to efficient visual processing. Earlier work also extends into auditory neuroscience, particularly sound localization mechanisms. Dr. Franken's laboratory is actively recruiting researchers, indicating ongoing projects in visual and auditory neuroscience with applications to understanding conditions where perceptual organization fails, such as agnosia, schizophrenia, or autism.
Yin Sun is the Godbold Associate Professor in the Department of Electrical and Computer Engineering at Auburn University. His research focuses on timely information updates, wireless networks, and IoT, with emphasis on data freshness metrics and remote estimation systems. He earned his B.S. and Ph.D. in Electronic Engineering from Tsinghua University. Education: B.S. Electronic Engineering, Tsinghua University Ph.D. Electronic Engineering, Tsinghua University His research interests include wireless communications, cloud computing, and machine learning applications in safety-critical systems. Notable achievements include co-authoring the seminal book on Age of Information and receiving the NSF CAREER Award for his work on goal-oriented status updating. He also serves as Technical Co-Chair for the ACM MobiHoc Symposium and leads Auburn's Wireless Engineering Research and Education Center, addressing real-world communications challenges through interdisciplinary collaboration. Awards include the 2021 Best Paper Award (Journal of Communications and Networks) and contributions to food pantry inventory optimization via machine learning. His work bridges theoretical foundations with practical systems, emphasizing real-time data relevance and efficient resource allocation strategies. Advisees/Grants: Leads NSF-funded projects on semantic status updating and collaborates on applications like disease detection in agriculture. Active in advising graduate students through his lab's focus on edge computing and network optimization. Labs/Teams: Core member of Auburn's Wireless Engineering Research and Education Center, fostering multi-university collaborations in communications research.
Dr. Mark M. Wilde is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University and an Adjunct Professor in the Department of Physics and Astronomy at Louisiana State University. He holds a Ph.D. in electrical engineering from the University of Southern California. His primary research focuses on quantum information theory, quantum computing, quantum error correction, and quantum computational complexity. He has authored influential textbooks such as *Quantum Information Theory* and *Principles of Quantum Communication Theory*, widely used in graduate courses globally. Wilde’s research explores foundational aspects of quantum communication, including quantum Shannon theory, network quantum information theory, and quantum algorithms. He has contributed to advancements in quantum error correction, quantum cryptography, and thermodynamics. His work bridges theoretical insights with practical applications in quantum technologies. Notably, he has co-authored groundbreaking results on quantum channel capacities, entanglement-assisted quantum coding, and quantum hypothesis testing. Wilde has secured grants from institutions like the Simons Foundation and the National Science Foundation. His awards include the IEEE Fellow distinction and the 2018 AHP-Birkhauser Prize. He collaborates extensively, publishing over 100 peer-reviewed articles, and his research impacts fields from quantum computing to biophysics. His recent work emphasizes quantum algorithms, thermodynamic limits, and privacy-preserving quantum protocols. Education: Ph.D. in Electrical Engineering, University of Southern California (2009). Grants: Supported by NSF, Simons Foundation, and others. Labs/Teams: Leads research groups in quantum information theory and collaborates with interdisciplinary teams on quantum algorithms and applications.
Raul Castro Fernandez is a prominent researcher in data management and database systems, with a focus on data discovery, integration, and marketplaces. He has collaborated extensively with leading institutions and researchers, contributing to projects like Data Station and Nexus for secure data sharing. His work bridges theoretical innovation with practical implementations in cloud optimization, differential privacy, and LLM-driven data tools. Key Contributions : Data market frameworks, LLM applications in databases, differential privacy platforms Collaborators : Yue Gong, Samuel Madden, Michael Stonebraker, Eugene Wu, Kyle Chard Research Themes Fernandez explores automated metadata management for data catalogs, spatiotemporal data sharing with privacy guarantees, and LLM-based data discovery . His work on stateful stream processing (e.g., SABER system) and cost optimization in cloud analytics shows technical depth. Recent Trends 2023-2025 publications highlight his pivot toward LLM applications in data management, including tabular data representation and hypothesis assessment tools. He also investigates sustainability in HPC through carbon credit systems.
Tal Malkin is a Professor at Columbia University's Department of Computer Science, School of Engineering and Applied Science. Her work spans cryptography, secure computation, and data privacy, with a focus on non-malleable codes, topology-hiding communication, and privacy-preserving protocols. 2025: Peony Onion Encryption for asynchronous anonymity 2024: Structural lower bounds for pseudorandom functions 2022: XSPIR for Ring-LWE-based private retrieval Research themes include: Cryptographic Foundations: Non-malleability, garbling circuits, and zero-knowledge protocols Privacy-Preserving Systems: Differential privacy, secure multi-party computation, and database anonymity Applied Security: Biometric encryption, model watermarking, and polymer-based unclonable functions Her 15 most recent publications analyze anonymity in dynamic networks, robust watermarks for AI models, and tamper-resilient encryption. Keywords span computer science, cryptography, and machine learning. Sub-fields include secure computation, lattice-based cryptography, and function-private protocols.
Stefanie Jegelka is an Associate Professor (on leave) at MIT's Department of Electrical Engineering and Computer Science, and a Humboldt Professor at Technical University of Munich (TU Munich). She is a member of CSAIL (Computer Science and Artificial Intelligence Laboratory), IDSS (Institute for Data, Systems, and Society), and Machine Learning at MIT. Her research spans algorithmic machine learning with focus on modeling, optimization algorithms, theory, and applications. Dr. Jegelka completed her PhD at the Max Planck Institutes in Tuebingen and ETH Zurich, followed by a postdoc at UC Berkeley's AMPlab and computer vision group. Her academic journey reflects a strong foundation in both theoretical and applied aspects of machine learning. Her research focuses on exploiting mathematical structure for discrete and combinatorial machine learning problems, robustness, and scaling machine learning algorithms. Key areas include submodular optimization, graph neural networks, invariant and equivariant learning, and representation theory in machine learning. Her work bridges theoretical foundations with practical applications across various domains, with particular emphasis on how mathematical structure can enhance algorithmic performance. Dr. Jegelka's recent publications demonstrate significant contributions to understanding the theoretical properties of graph neural networks, developing methods for invariant learning, and advancing representation learning techniques. Her work consistently shows strong connections between mathematical structure and machine learning performance, with applications spanning natural language processing, computer vision, and scientific domains. Sloan Research Fellowship NSF CAREER Award DARPA Young Faculty Award Dr. Jegelka has advised numerous graduate students and postdocs, many of whom have gone on to successful careers in academia and industry. Her research has been supported by prestigious grants from NSF, DARPA, ONR, and industry partners including Google, Two Sigma, and Adobe. She serves as Program Chair for ICML 2022 and has held numerous editorial and organizational roles in the machine learning community. She leads a research group investigating fundamental questions in machine learning, particularly how mathematical structure can be leveraged to develop more efficient, robust, and scalable learning algorithms. Her group collaborates across disciplines, connecting theoretical machine learning with applications in science and engineering, and has produced influential work on submodularity, graph representation learning, and invariant learning methods.
Carlisle Rainey is an Associate Professor in the Department of Political Science at Florida State University (FSU), where he also serves as Director of the Research Intensive Bachelor’s Certificate Program. He holds a Ph.D. in Political Science from FSU (2013) and an M.S. in Mathematical Statistics (2012) and Political Science (2009). Prior to FSU, he was an Assistant Professor at Texas A&M University (2015–2018) and the University at Buffalo, SUNY (2013–2015). His research focuses on political methodology, Bayesian and computational methods, and statistical inference. Notable contributions include work on logistic regression separation, equivalence testing, and research transparency. He teaches courses in quantitative methods, political methodology, and American/Comparative Politics. Rainey’s publications appear in top journals like the American Political Science Review, American Journal of Political Science, and Political Analysis. His recent work emphasizes statistical power analysis, data availability practices, and experimental design. Active in academic service, he chairs the Political Methodology section for the 2026 APSA conference and serves on NSF review panels.
