Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.
Behtash Babadi is an Associate Professor in the Department of Electrical & Computer Engineering and a faculty member at the Institute for Systems Research and the Brain and Behavior Institute at the University of Maryland, College Park. He also holds affiliate appointments in the Program in Neuroscience & Cognitive Science and the Applied Mathematics & Statistics program. Education: Ph.D. in Engineering Sciences, Harvard University (2011) M.Sc. in Engineering Sciences, Harvard University (2008) B.Sc. in Electrical Engineering, Sharif University of Technology (2006) Research Interests: Dr. Babadi’s work focuses on statistical and adaptive signal processing frameworks for understanding neural systems. Key areas include: Neural signal processing and systems neuroscience Granger causality and functional connectivity analysis Dynamic modeling of neuronal assemblies Applications to auditory processing and cognitive recovery Scientific Contributions: His recent publications address cortical network dynamics, MEG source analysis, and robust causal inference. Notable methods include Network Localized Granger Causality (NLGC) for direct connectivity estimation and multitaper spectral analysis for neuronal spiking data. Awards: NSF CAREER Award (2016) E. Robert Kent Teaching Award (2019) GSAS Merit Fellowship (Harvard, 2010) Collaborations: Dr. Babadi collaborates with institutions like MIT, Harvard, and Massachusetts General Hospital, and participates in interdisciplinary initiatives such as the Brain and Behavior Initiative (BBI) and NIH BRAIN grants.
Rakesh Venkat is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. His research focuses on Theoretical Computer Science, including approximation algorithms, hardness of approximation, and communication complexity. Education : Ph.D., Tata Institute of Fundamental Research (TIFR), Mumbai. Research Trends : His work addresses fundamental challenges in algorithm design, such as optimizing cache misses, improving clustering algorithms, analyzing graph expansion, and exploring embedding techniques. Publications span top-tier conferences like APPROX, FSTTCS, ICALP, and ITCS, with collaborations at institutions including HUJI, TIFR, and IIT-Bombay. Teaching : Courses taught include Approximation Algorithms, Advanced Data Structures, Discrete Mathematics, and Spectral Graph Theory.
Jichun Xie is an Associate Professor in both the Department of Biostatistics & Bioinformatics and the Department of Computer Science at Duke University . He is also affiliated with the Duke Cancer Institute and the Duke Center for Statistical Genetics and Genomics . His research spans computational biology, genomics, and statistical methods for single-cell data and rare variant analysis. Education: Ph.D. in Biostatistics, University of Pennsylvania (2011) Research Focus: Dr. Xie develops computational frameworks for single-cell RNA sequencing analysis, rare variant association studies, and integrative genomics. His work bridges statistical inference with biological discovery, particularly in cancer immunology, aging, and immune disorders. Publication Trends: Recent papers emphasize single-cell data analysis (e.g., SifiNet, B-Lightning), rare variant mapping (DYNATE), and immune-genomic interactions in chronic GVHD and cancer. Methodologically, he pioneers hierarchical multiple testing and co-expression graph topologies. Grants: Duke Program of Training in Pulmonary Research (PROSPER), NIH/NHLBI (2022-2026) New computational methods for rare variant subregion detection, NIH/NHGRI (2022-2026) Training Program in Bioinformatics at the Intersection of Cancer Immunology and Microbiome, NIH/NCI (2020-2026) Labs and Teams: Dr. Xie leads the Xie Lab , which actively trains students and researchers in computational methods. The lab collaborates with immunologists and oncologists, producing tools like DART and TEAM for biomedical analysis.
Matthias Löwe is a Professor at the Institute of Stochastics within the Faculty of Mathematics and Computer Science at the University of Münster. His academic career spans several decades with continuous contributions to probability theory and statistical physics. His research focuses on deep theoretical investigations of complex stochastic systems, particularly in the context of disordered systems and random structures. Löwe's research interests center on Probability Theory , Statistical Physics , and Random Matrix Theory . His work examines phase transitions in spin systems, fluctuation phenomena in random graphs, and the mathematical foundations of neural network models. His research program consistently bridges theoretical probability with applications in statistical physics, particularly in understanding the behavior of complex interacting particle systems at critical points. His publication record shows consistent output in top probability journals including Annals of Probability , Electronic Journal of Probability , and Annales de l'Institut Henri Poincaré . His most recent work (2020-2023) focuses on propagation of chaos in mean-field models, fluctuations in Ising models on random graphs, and exact recovery problems in block spin systems. Löwe frequently collaborates with researchers including Zakhar Kabluchko, Kristina Schubert, and Jonas Jalowy. Löwe has supervised multiple doctoral students including Raphael Meiners, Mirko Ebbers, Jens Ameskamp, and Sarah Behrens. His research group has included postdoctoral researchers and doctoral candidates working on various aspects of probability theory and its applications.
Dr Ben Cardoen is a Research Fellow at the School of Mathematics, University of Birmingham, specializing in algorithm development for biomedical imaging analysis at multiple scales—from diffuse optical tomography to superresolution microscopy. His work focuses on recovering minimal causal signatures for pathologies in ageing and inflammation using sparse high-dimensional data. His educational background includes: BSc in Computer Science, University of Antwerp (2015) MSc in Computer Science, University of Antwerp (2017) PhD in Computing Science, Simon Fraser University (2024) Following postdoctoral work at the University of British Columbia (2025), he joined the University of Birmingham. Dr Cardoen designs scalable, interpretable algorithms leveraging belief theory, graph algorithms, and structural causal discovery to push beyond empirical resolution limits in imaging modalities. His research targets degenerative diseases (Alzheimer, ALS, ageing), metabolic disorders (diabetes), and infectious diseases, with emphasis on robustness to complex noise models. Key methodologies include weakly supervised learning for protein-organelle interaction analysis in volumetric point cloud data, utilizing distributed computing on SLURM-based supercomputers. No scientific awards are documented in the provided materials. Regarding academic mentorship and funding, the text indicates no formal advising roles or grant details beyond his current fellowship position. Dr Cardoen's active projects involve extending probabilistic learning algorithms for nested label recovery in multichannel superresolution microscopy, with applications spanning genomic analysis, viral infection tracking, and cellular drug response modeling.
Gitta Astrid Hildegard Kutyniok is a Professor in the Department of Physics and Technology at UiT The Arctic University of Norway. Her research spans machine learning, applied mathematics, and signal processing, with a focus on theoretical foundations and practical applications.
Dr. Vincent Nguyen serves as a Senior Lecturer in Orthoptics within the Graduate School of Health at the University of Technology Sydney. With a distinguished career spanning clinical practice, research, and academia, he brings extensive expertise in visual science and rehabilitation. His academic journey began with Honours in Orthoptics (1993) followed by a Master of Applied Science (1996), culminating in a PhD from the University of Sydney (2003) focused on binocular rivalry in visual psychophysics. Dr. Nguyen's research interests focus on low vision rehabilitation, depth perception, binocular vision, and the application of virtual reality and spatial audio technologies for assistive applications. His work bridges fundamental visual neuroscience with practical rehabilitation solutions, particularly for people with visual impairments. He has pioneered research in acoustic touch interfaces, virtual reality rehabilitation environments, and spatial audio navigation systems. His publication record demonstrates a clear trajectory from fundamental visual neuroscience to applied rehabilitation technologies. Recent work emphasizes immersive virtual reality applications for communication and physical rehabilitation, as well as innovative spatial audio systems that enhance navigation for people who are blind. His research integrates principles from neuroscience, engineering, and clinical practice to develop practical assistive solutions. Dr. Nguyen has secured significant research funding including NHMRC Ideas Grants (2023-2027) for 'Fluent Mobility for the Blind Individual Using Multimodal Auditory Sensory Augmentation' and CRC-P projects like 'ARIA - Bionic Visual-Spatial Medical Device for the Blind' (2022-2024). Current projects include 'EyeBot: AI-Powered Triage for Ophthalmology Referrals' (2025) and 'Next-Generation Extended Reality, Wearable Biosensors, and Metaverse Technologies for Medical Applications' (2024). As an educator, Dr. Nguyen teaches courses in Therapy and Rehabilitation (96037), Clinical Management of Refractive Error (96031), and Eye and Visual Systems (96027). His clinical background includes appointments as a Clinical Electrophysiologist with NSW Health (2007) and work with Vision Australia assisting people with low vision (2012). His postdoctoral work at York University's Centre for Vision Research with Professor Ian Howard focused on human depth perception, building on his foundational expertise in visual neuroscience.
John Wright is an Associate Professor at the Institute for Public Policy and Governance at the University of Technology Sydney (UTS), where he conducts research on political theory, public policy, regulation, and governance. His academic career spans multiple prestigious institutions including the London School of Economics and Political Science, London School of Hygiene and Tropical Medicine, and Australian National University. Wright's research focuses on political theory , public policy change , regulation and governance , and public health policy . His work examines critical issues such as the Australian Housing Crisis and British relations with the European Union, analyzing how policy regimes establish, consolidate, erode, and terminate meaning within policy objectives. He employs post-structuralist approaches to understand policy change as the transformation of meaning within discursive configurations. His recent publications reveal a strong focus on policy change analysis , particularly regarding European Union membership and Australian housing policy. The publications demonstrate interdisciplinary work spanning political science, health policy, regulatory studies, and economic evaluation. His research shows particular strength in analyzing how policy regimes function, how meaning is invested in policy objectives, and how these processes lead to policy continuity or change. Wright has secured research funding including the 2023 grant for Life Cycle Assessment of Hydrogen Storage Technologies for Circular and Sustainable Energy Systems . His scholarly impact is evident through numerous citations across his publications in political science, health policy, and regulatory studies. He actively engages with policy communities through external research impact activities related to the Australian Housing Crisis and British-EU relations.
Dominik Stöger is an Assistant Professor (tenure-track) in the Department of Mathematics at KU Eichstätt-Ingolstadt since 2021, affiliated with the Mathematical Institute for Data Science and Machine Learning (MIDS). His research bridges mathematical theory and data science applications. His educational background includes: B.Sc. in Mathematics, Technical University of Munich (2013) M.Sc. in Mathematics, Technical University of Munich (2015) Ph.D. in Mathematics, Technical University of Munich (2019) Stöger's research centers on mathematical foundations of data science, with emphasis on non-convex optimization in machine learning, theoretical analysis of overparameterized models, and low-rank matrix recovery. He combines optimization theory and high-dimensional probability to develop rigorous guarantees for modern algorithms, addressing critical challenges in deep learning theory. His recent publications (2020-2025) demonstrate consistent output in top venues including COLT, NeurIPS, and ICLR, with particular focus on implicit regularization phenomena and non-convex recovery guarantees. The 2025 pipeline shows active work extending theoretical boundaries in matrix sensing and neural network analysis. Stöger has received significant recognition: NeurIPS 2021 Spotlight Paper (top 3% of submissions) COLT 2025 paper presentation As a tenure-track faculty member, he maintains active collaborations across institutions (USC, TUM) and likely advises graduate students. His research program shows strong momentum with multiple concurrent projects advancing theoretical machine learning. He contributes to the research ecosystem through affiliation with MIDS, fostering interdisciplinary work in mathematical data science at KU Eichstätt-Ingolstadt.
Johanna Carolina Jokinen serves as Associate Professor at Uppsala University's Department of Human Geography, holding a PhD in Social and Economic Geography with expertise in GIS applications. Her research portfolio spans transnational migration systems, peri-urban land use transformations, and digital welfare solutions across Nordic and Latin American contexts. Her research interests focus on transnational migration dynamics , peri-urban agricultural change , and digital welfare innovations , employing mixed methods including GIS, remote sensing, and critical social analysis. Recent work examines migration patterns through social media analytics and welfare accessibility in sparsely populated regions, reflecting her commitment to methodologically innovative geographical research addressing contemporary societal challenges. Her publication trends reveal a consistent focus on migration-land use interconnections (2014-2018), evolving toward digital welfare systems and Nordic regional analysis (2020-2024). Key themes include climate-migration linkages in Bolivia, critical examinations of academic labor conditions, and innovative digital approaches to migration tracking using social media data. Co-organizer of Gender & Geography Commission sessions at AAG conferences Active participant in Nordregio research networks Treasurer for Geografiska föreningen i Uppsala since 2012 Her professional trajectory demonstrates significant engagement with international research collaborations, particularly through Nordregio reports and Nordic Council publications, while maintaining strong connections to field-based research in Bolivia. Current work emphasizes digital methodologies for understanding migration and welfare systems, positioning her at the intersection of traditional geographical analysis and contemporary data science approaches.
Professor Tadashi Wadayama serves in the Department of Computer Science within the Faculty of Engineering at Nagoya Institute of Technology. He holds a full professorship position and leads research initiatives in coding theory, signal processing, and deep learning applications for next-generation communication systems. Professor Wadayama received his B.E., M.E., and D.E. degrees from Kyoto Institute of Technology in 1991, 1993, and 1997 respectively. He began his academic career at Okayama Prefectural University in 1995 as a research associate and spent 1999-2000 as a visiting researcher at Essen University in Germany. He joined Nagoya Institute of Technology as an associate professor in 2004 and was promoted to full professor in 2010. He maintains active memberships in IEEE and the Institute of Electronics, Information and Communication Engineers (IEICE). His research spans multiple interconnected domains with primary focus on Coding Theory , Signal Processing for Wireless Communications , and Deep Learning applications . Professor Wadayama has made significant contributions to LDPC codes, MIMO signal detection, and the emerging field of deep unfolding techniques that bridge neural networks with traditional signal processing algorithms. His work increasingly incorporates physics-aware modeling of communication channels governed by partial differential equations. Recent research demonstrates strong interdisciplinary integration between information theory, machine learning, and communication engineering principles. Analysis of his recent publications reveals a clear trajectory toward developing foundational technologies for post-Shannon communication architectures. His work emphasizes ultra-large-scale coding, goal-oriented communication, digital homeostasis mechanisms, physics-embedded signal processing, and dual-process learning systems that combine fast reactive processing with deliberative meta-learning using LLM orchestrators. Fundamentals Review Best Author Award, IEICE, 2022 SRC 2010 Paper Award, Storage Research Promotion Organization, 2011 Professor Wadayama has successfully led multiple competitive research grants including JSPS Grant-in-Aid projects. He currently serves as Principal Investigator for the JST CRONOS project "Digital Cybernetics: Towards Next-Generation Communication Architecture" (2025-2031), which aims to develop foundational technologies supporting autonomous, adaptive, and robust large-scale AI systems. As IEEE Information Theory Workshop General Co-chair (2020-2021) and former chair of IEICE's Information Theory Research Committee (2020-2022), he maintains active leadership roles in the academic community. He leads the Wadayama Group at Nagoya Institute of Technology, which focuses on digital cybernetics and communication physics. The group develops physics-aware signal processing implementations, dual-process learning systems, digital homeostasis mechanisms, and system integration for next-generation communication architectures. His team collaborates with researchers from Kyoto University, Hiroshima University, Institute of Science Tokyo, and Tokyo University of Science, creating a robust research ecosystem focused on post-Shannon communication frameworks.
