Patrick Rebeschini is a Lecturer in the Department of Statistics at the University of Oxford. Before joining Oxford, he held positions at Yale University, including Lecturer in Computer Science and Postdoctoral Associate at the Yale Institute for Network Science. He earned his Ph.D. in Operations Research and Financial Engineering from Princeton University, supervised by Ramon van Handel. His research spans high-dimensional probability, statistics, and optimization, focusing on designing computationally efficient and statistically optimal algorithms for machine learning and artificial intelligence. His recent activities include organizing workshops on Online Learning and Game-Theoretic Statistics (2025), serving as Senior Area Chair for ICML 2025, and chairing sessions at the IMS Annual Meeting (2022). He co-organizes the joint Maths-Stats colloquium series at Oxford and contributes to doctoral training as a Co-Investigator for the Imperial-Oxford StatML Centre for Doctoral Training (CDT) and the Fundamentals of AI Erlangen Hub. Scientific Awards: 2019 Oxford MPLS Teaching Award Excellence in Teaching Award from Princeton Engineering Council (2013) Grants & Collaborations: Co-Investigator for StatML CDT and Fundamentals of AI Erlangen Hub Member of Bernoulli Society, Institute of Mathematical Statistics, and ELLIS Teaching: Organized reading groups on learning theory (2017–2025) Module Leader for doctoral courses on Online Learning and Reinforcement Learning (2023) Supervisor for UNIQ+ DeepMind summer interns (2022–present)
Marco Tomamichel is a Senior Lecturer and Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) fellow at the Centre for Quantum Software and Information, which is part of the Faculty of Engineering and Information Technology at the University of Technology Sydney. He received his Master of Science in Electrical Engineering and Information Technology from ETH Zurich, where he was awarded the prestigious ETH Medal, followed by a doctorate in Theoretical Physics from the same institution. Prior to joining the University of Technology Sydney, Dr. Tomamichel held research positions at the Centre for Quantum Technologies in Singapore and the University of Sydney. Dr. Tomamichel's research lies at the intersection of information theory, computer science, and quantum physics, with a particular focus on the mathematical foundations of quantum information theory. His work explores entropy and other information measures, and addresses theoretical questions in quantum communication and cryptography when resources are limited. His research examines quantum information processing with noisy and limited resources, cryptography in a quantum world, mathematical foundations of quantum information, and other applications of quantum information theory in physics, engineering, and computer science. His recent publications demonstrate a strong focus on quantum information theory, with particular emphasis on entropies, divergences, and their applications. His work spans fundamental theoretical developments in quantum information measures, quantum cryptography protocols, quantum communication theory, and connections to quantum thermodynamics and machine learning. The recurring theme across his publications is the rigorous mathematical treatment of quantum information processing tasks, often focusing on finite-resource scenarios that are relevant for near-term quantum technologies. Dr. Tomamichel has received significant recognition for his research, including: Australian Research Council Discovery Early Career Researcher Award (DECRA) ETH Medal for Master's degree Dr. Tomamichel is available for Masters Research and PhD student supervision. His funded research projects include "Securing the quantum internet with high-dimensional quantum systems" (ARC Discovery Projects), "Topological algebra, first-order logic, and computability" (Royal Society of New Zealand), and "Enhancing Communication using Small Quantum Devices" (ARC DECRA Scheme). Dr. Tomamichel is actively involved in the quantum information research community, serving as an editor for "Quantum - the open journal for quantum science" and participating in numerous conference organizing committees including TQC, QIP, and Quantum Cryptography conferences.
Patrik Höstmad is an Associate Professor in Applied Acoustics at Chalmers University of Technology, Sweden. His work is centered on the broad domain of sound and vibration, with applications spanning vehicle acoustics, environmental noise, urban planning, and ground-borne vibrations. He is actively involved in both research and teaching, with a strong publication record and leadership roles in multiple national and international projects. Education: While specific degrees are not listed in the provided text, his extensive publication record from 2000 onwards, including a doctoral dissertation in 2005 titled "Modelling Interfacial Details in Tyre/Road Contact — Adhesion Forces and Non-Linear Contact Stiffness," indicates advanced academic training in acoustics and mechanical engineering. Research Interests: His research encompasses: Vehicle acoustics and ride comfort Environmental noise and urban sound planning Ground-borne noise from railways and infrastructure Tyre/road interaction and noise generation Aeroacoustics and high-speed train noise Human perception of sound and vibration Acoustic simulation and auralisation tools Publication Trends: His recent publications (2020–2025) focus heavily on the integration of sound and vibration in urban and automotive contexts. Topics include the development of frameworks for predicting railway noise, ride comfort in electric vs. combustion vehicles, and the use of simulation tools for urban noise visualization. Earlier work delved into detailed modeling of tyre/road contact mechanics and high-frequency sound fields. Projects & Funding: He has led or co-led 11 major research projects since 2011, funded by agencies such as the Swedish Transport Administration (Trafikverket), VINNOVA, the Swedish Research Council (VR), and the European Space Agency. These projects address pressing issues like urban noise pollution, sustainable transport, and infrastructure vibration control. Collaborations: He collaborates extensively with researchers across disciplines, including human factors engineering, fluid dynamics, architecture, and urban planning. Notable collaborators include Jens Forssén, Xiaojuan Wang, Anna-Lisa Osvalder, and Niklas Andersson.
Tetsuya Kawanishi is a Professor at the Department of Electrical and Fundamental Systems , Waseda University , with a career spanning institutions like UC San Diego and Kyoto University. His work bridges optical communications , millimeter-wave systems , and terahertz technology . Education: PhD in Engineering, Kyoto University Professional Memberships: IEEE, IEICE, JSAP Research Interests focus on microwave photonics , radio-over-fiber (RoF) systems , and high-speed optoelectronic devices . He pioneered linear-cell radar systems for transportation and optically reconfigurable phase shifters for 5G/6G. His field trials include 240 km/h train communication systems and THz backhaul networks . Scientific Awards include the Young Scientist Award (URSI-GA99) and multiple Waseda Research Awards (2022–2024). His recent papers highlight trends in terahertz communications , metasurface antennas , and high-speed photodetectors , reflecting his leadership in next-generation wireless infrastructure .
Tsuyoshi Hasegawa is a Professor at Waseda University's School of Advanced Science and Engineering, holding a Ph.D. in Physics from Tokyo Institute of Technology. With over two decades of research experience including positions at National Institute for Materials Science (NIMS) and Yokohama City University, he leads cutting-edge work in nanoionics and neuromorphic computing. His research focuses on atomic switches , reservoir computing , and memristive systems , with particular expertise in Ag 2 S-based physical reservoirs that process information through ionic diffusion and filament formation. Hasegawa's work bridges materials science, electronics, and artificial intelligence, developing hardware solutions for edge computing that operate with minimal power consumption. Analysis of his 15 most recent publications reveals a strong trend toward practical implementations of physical reservoir computing, with applications in optical signal processing, tactile sensation, game theory systems, and deep learning hardware. His team consistently achieves high accuracy rates (81-98%) while addressing critical challenges like fabrication yield, noise sensitivity, and operational stability. SSDM Award 2020 for Quantum Point Contact Switch Tsukuba Prize 2017 Japan Society of Applied Physics Outstanding Paper Award 2012 NIMS Chairman's Research Achievement Award 2010 Science and Technology Prize of MEXT 2007 Hasegawa maintains active collaborations with institutions including NIMS and RIKEN, securing significant research funding for nanoarchitectonics projects. His laboratory specializes in in-materio computing using atomic switch networks, with recent work demonstrating million-cycle endurance and direct optical signal processing capabilities. Current research emphasizes practical deployment of reservoir computing systems for robotics and edge AI applications.
Catherine von Reyn is an Associate Professor at Drexel University's School of Biomedical Engineering, Science & Health Systems , where she leads the Neural Circuit Engineering (NCE) Laboratory . Her research focuses on how neural circuits process sensory information to guide behavioral decisions, with applications in neurodegenerative disorders. PhD, Bioengineering, University of Pennsylvania, 2010 BS, Mechanical Engineering, Georgia Institute of Technology, 2003 Her work combines cell type-specific genetic engineering , whole-cell patch clamp in behaving animals , computational modeling , and detailed behavioral analyses to unravel sensorimotor circuit mechanisms. Recent studies explore neural modulation technologies , visuomotor transformations , and neurodegenerative pathology . Key awards include the Pennsylvania CURE Grant (2020-2021) . She has contributed to foundational research on Drosophila escape pathways , calpain-mediated proteolysis , and neurotechnology education tools . The NCE Laboratory investigates circuit development, aiming to engineer repair strategies for neurological disorders. Collaborative projects span traumatic brain injury , neuroinflammation , and neurodevelopmental models .
Christof Teuscher is a Professor at Portland State University's Maseeh College of Engineering & Computer Science. His research focuses on developing disruptive computing paradigms through interdisciplinary approaches that merge computer science, physics, biology, and cognitive science. Education: PhD in Computer Science, Swiss Federal Institute of Technology Lausanne (EPFL), 2004 M.Sc. in Computer Science, EPFL, 2000 Research Interests: Non-classical computation Neuromorphic and thermodynamic computing Machine learning optimization Complex adaptive systems Reservoir computing in biological/chemical systems Recent Academic Contributions: 2025 course: Hardware for AI/ML (ECE 410/510) Labs developing reversible computing architectures NSF REU and altREU programs for undergraduates Artist-in-the-Lab (AiL) interdisciplinary program Advising & Lab Activities: Supervising PhD student Byron Gregg Published 300-page research book with students Presentations at IEEE ICRC and Green Computing conferences
Ram Kats is a Research Fellow in the Department of Industrial Engineering at the University of Trento, Italy, specializing in theoretical and applied control systems with applications spanning engineering, biology, and epidemiology. His research expertise encompasses: Stability analysis of nonlinear/time-delay systems using averaging methods Data-driven control and system identification techniques Mathematical biology applications including epidemic modeling and systems biology Advanced control of partial differential equations and stochastic systems Numerical methods for exponential analysis and signal processing Analysis of his 2023-2025 publications reveals a cohesive research trajectory centered on developing mathematically rigorous control frameworks for complex dynamical systems. His work consistently bridges theoretical advances in stability analysis with practical applications in biological systems, particularly through innovative approaches to time-delay estimation, epidemic model control, and stochastic PDE boundary control. The publications demonstrate strong interdisciplinary collaboration, frequently integrating techniques from applied mathematics, signal processing, and systems biology. Ram Kats actively collaborates with researchers including Giulia Giordano, Daniele Proverbio, and Francesca Calà Campana, with publications appearing in top-tier journals such as IEEE Transactions on Automatic Control, Automatica, and SIAM Journal on Control and Optimization.
Piotr Drozdowski is a Professor at the Krakow University of Technology in the Faculty of Electrical and Computer Engineering and Department of Electrical Engineering . His research focuses on induction motors, control systems, and power electronics. Academic Rank: Professor Contact: Email: piotr.drozdowski@pk.edu.pl Professor Drozdowski's research interests span: Control systems for multiphase induction motors Mathematical modeling of electrical machines Passive filtering in traction substation power supplies Fault diagnosis via zero-sequence current analysis Energy recovery in DC traction systems His publications highlight trends in: Multiphase induction generator control under variable speeds and loads Field-oriented control for nine-phase systems Harmonics mitigation using LC filters Simulation tools like Simulink and Spice Applications in railway traction and renewable energy No scientific awards were documented. There is no information about advisees or grants in the provided materials. Activities include: Modeling and control of Diesel Rotary UPS Passive filtering for arc furnace networks Energy recovery systems in railway infrastructure Multi-phase motor drive stability analysis
Paul-Albert Anselm Schneide is a Research Fellow in the Department of Food Science within the Faculty of Science at the University of Copenhagen, actively contributing to the Design and Consumer Behavior section. His research integrates advanced chemometric methodologies with analytical chemistry to address complex data challenges in food science applications. His primary research interests center on developing innovative algorithms for multilinear data analysis, particularly shift-invariant models that resolve peak misalignments and shape variations in chromatography-mass spectrometry datasets. This work spans theoretical chemometrics framework development, practical signal processing workflows for suspect screening, and applications in sensory-driven food quality prediction. Key focus areas include non-negative tensor decomposition, gas/liquid chromatography data optimization, and crowd-sourced sensory data integration for predictive modeling in food systems. Analysis of his recent publications reveals a cohesive research trajectory advancing chemometric techniques for analytical chemistry data, with increasing emphasis on real-world food science applications. His 2023-2025 work demonstrates progression from foundational shift-invariant tri-linearity models to sophisticated frameworks handling "in-between" data structures, culminating in practical implementations for wine quality prediction and complex sample analysis. This trend highlights his dual expertise in mathematical methodology development and domain-specific food science problem-solving.
Marco Mugnaini is an Associate Professor at the Department of Information Engineering and Mathematical Sciences (DIISM) at the University of Siena, where he has been serving since 2019. He is Director of the LEEME (Lab of Electronics, Electrotecnics and Electronic Measurements) and manages the electronics training laboratory. His academic journey began with a Laurea Degree in Electronics Engineering from the University of Florence (1999), followed by a Ph.D. in Reliability Availability and Logistics (2003). His educational background includes: Ph.D. in Reliability Availability and Logistics, University of Florence, 2003 Laurea Degree (110/110 cum Laude) in Electronics Engineering with major in Non-Linear Automatic Controls, University of Florence, 1999 High School diploma from Liceo Scientifico Antonio Gramsci Firenze, 1993 Mugnaini's research spans multiple interdisciplinary domains at the intersection of reliability engineering, electronic measurements, and biomedical applications. His work focuses on sensor technology development, particularly for challenging environments like gas turbines and biomedical applications. He combines traditional engineering approaches with modern machine learning techniques to solve complex measurement and monitoring problems. His research has practical applications in industrial safety, robotics, healthcare diagnostics, and infrastructure monitoring. His recent publications reveal a strong trend toward integrating sensor technology with advanced data analysis methods. The research spans from soft robotics and wearable sensors to turbomachinery monitoring and neurodegenerative disease detection. A notable pattern is the development of reliable measurement systems for extreme or specialized environments, often incorporating machine learning for enhanced performance and reliability. His scientific recognition includes: Best National Ph.D. thesis in the maintenance context from CNIM (2003) IEEE Senior Member status IEEE Instrumentation and Measurement Distinguished Lecturer Labview Associate Developer certification Green Belt and CAP certifications Mugnaini actively mentors students through thesis projects and has secured significant research funding including STECH (400k€), ATENE (380k€), and Drone Box VIDA (200k€) projects. He serves as Editor in Chief of the International Journal of Instrumentation Technology and Associate Editor for the International Journal of Power Electronics and Drives. His leadership extends to being a member of the Editorial Board for MDPI's 'Safety' Journal since 2015 and serving on multiple conference organizing committees. His laboratory, LEEME, focuses on developing innovative measurement solutions for industrial and biomedical applications. The lab maintains active collaborations with industry partners including the National Railway Company RFI and has produced several spin-off companies including SENSIA SrL (2008) and DESMOWEB SrL (2009).
Dr. Andreas Artemiou serves as Professor, Vice Rector for Academic Affairs and Quality Assurance, and Dean of the Technology and Innovation School at the University of Larnaca's Department of Information Technologies. His leadership spans academic administration and cutting-edge statistical research with global collaborations. His academic foundation includes: BSc in Mathematics and Statistics from University of Cyprus (2005) MSc and PhD in Statistics from Pennsylvania State University (2008, 2010) Artemiou's research pioneers statistical methods for high-dimensional datasets, specializing in dimension reduction, kernel techniques, and machine learning applications. His work bridges theoretical innovation with practical implementations across engineering, computer science, and medical sciences, particularly evident in pandemic-related mortality analysis and cytometry data processing. Recent publications (2021-2024) reveal accelerating focus on SVM-based dimension reduction, sparse modeling, and medical applications. The trajectory shows increasing interdisciplinary impact, with 2024 works emphasizing matrix data analysis and time-series dimension reduction for real-world health crises. His professional recognition includes: New Researcher Fellow at Statistics and Applied Mathematical Sciences Institute Artemiou actively contributes to major collaborative initiatives without explicit grant details. His editorial role at Computational Statistics and Data Analytics journal and board membership in the European Statistical Computing Association highlight academic leadership. Current projects drive innovation in cytometry analysis and pandemic mortality modeling. He directs CytoPy - an autonomous cytometry analysis framework - and leads pandemic mortality research through the international CMOR consortium, demonstrating commitment to translating statistical theory into public health solutions.
Afzal Chamroo serves as an Associate Professor in the Department of Automatic Control and Systems at the School of Engineering (ENSIP), University of Poitiers, France. He is actively affiliated with the LIAS laboratory (Laboratoire d'Informatique et d'Automatique pour les Systèmes), maintaining research sites at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil. His academic foundation includes a 2006 PhD in Automatic Control and Robotics from Université des Sciences et Technologie de Lille - Lille I, with doctoral research focused on Piecewise Continuous Systems for online identification and real-time control applications. Chamroo's research program centers on advanced control methodologies, particularly piecewise continuous control systems, vision-based robotics, and industrial process applications. His work demonstrates consistent innovation in nonlinear system stabilization, trajectory tracking for underactuated mechanisms, and real-time monitoring solutions for critical infrastructure. Key application domains include power grid stability, induction motor fault detection, hydrogeological modeling, and thermal system control. Publication trends reveal an evolution from foundational work on piecewise control in bioprocesses (2008) and robotic systems (2009-2010) toward contemporary challenges in industrial load monitoring (2024), power grid frequency analysis (2023), and physics-informed machine learning for fault detection (2022). His research maintains strong industry relevance through collaborations with power systems and manufacturing sectors. Within LIAS, Chamroo contributes to the Automatic Control team's mission of advancing real-time control theory and implementation. The laboratory's collaborative structure enables cross-disciplinary work spanning data engineering, real-time systems development, and industrial project deployment, providing robust infrastructure for his ongoing research in control systems engineering.
Erik Etien is an Associate Professor with HDR (Habilitation à Diriger des Recherches) in Automatic Control and Systems at the University of Poitiers, France. He is affiliated with the Laboratory of Sensorial Applications Engineering (LIAS) which maintains facilities at both ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) and ISAE-ENSMA. His research focuses on automatic control systems with particular expertise in: Induction motor control and fault detection Permanent magnet synchronous motor/generator diagnostics Sensorless control techniques Signal processing for condition monitoring Digital twin technology for predictive maintenance Software sensors and estimation algorithms Dr. Etien's recent publications demonstrate a strong focus on developing advanced signal processing techniques for fault detection in electrical machines, particularly using tacholess (speed sensor-free) approaches. His work often combines model-based methods with adaptive filtering techniques like Kalman filters, with increasing incorporation of machine learning approaches in recent years. His research spans applications across multiple industries including wind energy, industrial automation, and power systems, with demonstrated practical implementations in transformers, vacuum processes, and electromechanical systems. He has co-authored two books on data acquisition systems and sensorless vector control of asynchronous machines, establishing his expertise in the field. His laboratory work at LIAS involves close collaboration with researchers including Sebastien Cauet, Laurent Rambault, and Anas Sakout on various control systems projects.
Yifan Chen is an Assistant Professor in Computer Science and Mathematics at Hong Kong Baptist University (HKBU), specializing in efficient machine learning with focus on non-parametric models and neural networks involving intensive matrix operations such as Transformers and Graph Neural Networks (GNNs). Affiliation: Department of Computer Science and Mathematics, Hong Kong Baptist University Location: Hong Kong SAR, China Dr. Chen received his Ph.D. in Statistics from the University of Illinois Urbana-Champaign in 2023 under the supervision of Prof. Yun Yang, with additional collaborations with Prof. Ruoqing Zhu, Prof. Heng Ji, and Prof. Jingrui He. Prior to his doctoral studies, he earned his B.S. in Statistics from Fudan University in 2018, advised by Prof. Juan Shen and Prof. Chenghong Zhang. Dr. Chen's research broadly focuses on understanding the statistical structures of modern machine learning algorithms and applying these insights to real-world computational challenges. His work particularly emphasizes non-parametric models and neural networks with intensive matrix operations, including Transformers (language models) and graph neural networks. His research bridges theoretical statistical learning with practical computational efficiency, exploring how to make machine learning algorithms both theoretically sound and computationally feasible for large-scale applications. Analysis of Dr. Chen's publication record reveals a strong focus on computational efficiency in machine learning, with recurring themes in graph-based learning, kernel methods, and optimization techniques. His work demonstrates expertise in both theoretical foundations and practical implementations, with publications spanning top-tier conferences including NeurIPS, ICML, KDD, and AAAI. Many of his papers address fundamental challenges in scaling machine learning algorithms while maintaining statistical guarantees, reflecting his commitment to both theoretical rigor and practical applicability. Scientific awards and recognition include: Dissertation Completion Fellowship from University of Illinois Graduate College (2023) ICML 2023 Grant Award (2023) Shanghai Outstanding Graduate from Shanghai Municipal Education Commission (2018) NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme grant (2024) Dr. Chen actively mentors PhD students and research assistants, currently supervising Yifan Xu and Yujia Yin as PhD students, and Yifan Wu as a visiting research assistant. His group has produced notable alumni including Mingchen Jiang (now PhD student at Institute of Science Tokyo), Jiahao Ma (now PhD student at HKU), Xichen Ye (now incoming PhD student at Fudan), and Guoming Li (now incoming PhD student at NUS). He teaches advanced topics in artificial intelligence and machine learning, as well as applied linear algebra for computing, emphasizing the connections between theoretical foundations and practical applications. Dr. Chen maintains active collaborations with researchers at institutions including University of Illinois Urbana-Champaign, Fudan University, Shanghai University, and industry partners like Amazon Alexa AI and IBM Research.
