جستجوی استاد راهنمای دانشگاهی

Hatice Altugمشاهده پروفایل

استاد

• Nanophotonics
• Plasmonics
• Optical Biosensors
• +۴ مورد دیگر

Hatice Altug is a Full Professor at EPFL's Institute of Bioengineering within the School of Engineering, where she leads the Bionanophotonic Systems Laboratory. Her research integrates nanophotonics, plasmonics, and microfluidics to develop advanced biosensors for real-time molecular diagnostics. She holds dual roles in EPFL's doctoral programs and academic committees. Education: PhD in Applied Physics, Stanford University (2000-2007) B.S. in Physics, Bilkent University (1996-2000) Her research centers on creating label-free, high-sensitivity optical biosensors using nanophotonic technologies. Key innovations include dielectric metasurfaces for mid-infrared spectroscopy, AI-enhanced detection platforms, and portable nanoplasmonic imagers for point-of-care diagnostics. Her work bridges fundamental light-matter interactions with clinical applications like sepsis monitoring and cancer biomarker detection. Her publications emphasize nanophotonic biosensor design, metasurface applications, and single-cell analysis. Recent trends show increased focus on AI integration, vibrational spectroscopy, and wafer-scale manufacturing for clinical translation. Awards & Honors: Optical Society Fellow (2020) Presidential Early Career Award (PECASE, 2011) ERC Consolidator Grant (2016) IEEE Photonics Society Young Investigator Award (2011) She mentors numerous PhD students and leads interdisciplinary teams developing optofluidic platforms. Her laboratory pioneers nanoplasmonic microarrays and collaborates globally on projects like neurodegenerative disease biomarker detection. She co-directs EPFL's doctoral program in photonics and champions women in STEM through executive roles in diversity initiatives.

Carl Henrik Ekمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Bayesian Non-parametric Methods
• +۶ مورد دیگر

Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.

Pingfu Fuمشاهده پروفایل

استاد

• Survival analysis
• Clinical trials
• Cancer research
• +۴ مورد دیگر

Pingfu Fu, PhD, is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University's School of Medicine. He is also a member of the Developmental Therapeutics Program at the Case Comprehensive Cancer Center. His expertise spans biostatistics, mathematics, and computer science, with a focus on cancer research and HIV/AIDS. Dr. Fu advises researchers on study design and statistical methodology for clinical and pre-clinical studies. He teaches courses in survival data analysis and clinical trials, and was recognized as 'Professor of the Year' in 2010 by the Department of Epidemiology and Biostatistics. Education: PhD in Biostatistics (Case Western Reserve University, 2001), MS in Statistics (Case Western Reserve University, 1996), MS in Mathematics (Xiangtan University, 1988), and BS in Mathematics (Jiangxi Normal University, 1984). His research interests include survival analysis, tree-based methods, clinical trials, and statistical applications in medical research. He has co-authored numerous peer-reviewed articles, focusing on cancer disparities, radiomics, and computational pathology. Professional memberships include the American Statistical Association, American Mathematical Society, and American Cancer Society. Dr. Fu holds editorial roles at Reviews on Recent Clinical Trials , Journal of Clinical Oncology , and Journal of the National Cancer Center . His work has addressed mathematical challenges in stochastic processes and resolved statistical issues in study design and tree-based models. Notable contributions include developing risk prediction models for cancer outcomes and advancing interdisciplinary collaborations across oncology, biostatistics, and computer science. His lab focuses on integrating computational methods with clinical data to improve patient outcomes.

A.T. Charlie Johnsonمشاهده پروفایل

استاد

• Condensed Matter Physics
• Nanotechnology
• Nanomaterials
• +۴ مورد دیگر

A.T. Charlie Johnson serves as the Rebecca W. Bushnell Professor of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences, where he has been a standing faculty member since 1994. His research program focuses on nanoscale systems and has established him as a leading figure in condensed matter physics, earning recognition from major scientific societies. His educational foundation includes: Ph.D. in Physics from Harvard University (1990) B.S. in Physics from Stanford University (1984) Professor Johnson's research centers on the development and application of atomic-layer nanomaterials, particularly graphene and transition metal dichalcogenides , for fundamental studies of transport phenomena and practical biosensor applications. His group employs advanced nanofabrication techniques at Penn's Singh Center for Nanotechnology to create devices that leverage biological molecules for chemical recognition in disease diagnosis, security screening, and environmental monitoring. This work bridges condensed matter physics with biomedical engineering , yielding innovative solutions for real-world detection challenges. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) scalable graphene-based biosensor development for medical diagnostics, (2) exploration of quantum phenomena like Klein tunneling in novel nanoelectromechanical systems, and (3) interdisciplinary applications spanning oncology, planetary science, and fetal medicine. His work consistently emphasizes materials synthesis , device integration , and practical translation of nanoscale phenomena. His scientific contributions have been recognized with prestigious honors: Defense Science Study Group Fellow (2018-2019) Fellow of the American Association for the Advancement of Science (2017) Fellow of the American Physical Society (2011) Lindback Foundation Award for Distinguished Teaching (2003) David and Lucille Packard Foundation Fellowship (1994-1999) As an educator, Professor Johnson has mentored numerous graduate students and postdoctoral researchers, with notable alumni like Michael Biercuk (founder of Q-CTRL). His research has been supported through significant leadership roles including Director of the Nano/Bio Interface Center (2014-2017) and Packard Fellowship funding, enabling sustained innovation in nanotechnology. His group actively collaborates across disciplines to advance both fundamental understanding and practical applications of nanomaterials. Based at the Singh Center for Nanotechnology, Johnson leads a dynamic research team utilizing state-of-the-art facilities for nanofabrication and characterization. His laboratory maintains strong campus collaborations through secondary appointments in Electrical and Systems Engineering and Materials Science and Engineering, fostering an interdisciplinary environment for developing next-generation nanoscale devices.

Anne Magurranمشاهده پروفایل

استاد

• Biological Diversity
• Ecology
• Community Ecology
• +۵ مورد دیگر

Prof. Anne Magurran is a Professor (Re-engaged) at the School of Biology, University of St Andrews. Her research focuses on biological diversity, quantifying ecological patterns, and understanding processes influencing biodiversity change over space and time to inform conservation efforts. She holds academic qualifications including a D.Phil. from the University of Ulster and an honorary doctorate from the University of Bergen. She is a Fellow of the Royal Society of Edinburgh (FRSE) and an honorary life member of the Fisheries Society of the British Isles. Her work spans global ecosystems and involves collaborations across five continents. Key projects include the ERC-funded BioTIME initiative, analyzing biodiversity time series, and NERC-supported studies integrating taxonomic, phylogenetic, and functional diversity metrics. She has supervised PhD students such as Cher Chow, Amber Cowans, and Volodymyr Hudgins. Prof. Magurran has received prestigious awards like the Beverton Medal (2006), ASAB Medal (2021), and the CBE (2022). Her research also addresses UN SDGs related to life on land and below water. She leads interdisciplinary teams at the Centre for Biological Diversity and contributes to institutions like the Marine Alliance for Science & Technology Scotland.

Bo Zhuمشاهده پروفایل

استادیار

• Computer Graphics
• Computational Physics
• Scientific Machine Learning
• +۳ مورد دیگر

Bo Zhu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. His research focuses on computational approaches for complex physical systems, including fluid dynamics, topology optimization, and robotics control. He holds a Ph.D. from Stanford University and completed postdoctoral research at MIT CSAIL. He has been recognized with the NSF Career Award (2022) and multiple best paper awards at SIGGRAPH conferences. Education: B.E.-M.S., Software Engineering, Shanghai Jiao Tong University Ph.D., Computer Science, Stanford University Postdoc, EECS, MIT Research Interests: Develops numerical algorithms and machine learning techniques to simulate fluidic systems, soft materials, and multi-scale phenomena. His work emphasizes vorticity preservation, real-time simulation, and physics-based AI integration. Key Contributions: Pioneered Particle Flow Map (PFM) methods for fluid simulation, developed open-source libraries like SimpleX and PFM Hub, and contributed to projects like Genesis physics engine. Over 50 peer-reviewed publications in top venues (SIGGRAPH, NeurIPS, IEEE TVCG). Awards: NSF Career Award (2022) Best Paper Honorable Mention (SIGGRAPH 2025) Best Paper Award (SIGGRAPH Asia 2024) Grants & Projects: Leads NSF-funded research on Physical AI Design, collaborating with Sandia National Labs on real-time CFD solvers. Active in open-source software development for computational physics and graphics.

Jesper Rindom Jensenمشاهده پروفایل

دانشیار

• Audio Signal Processing
• Artificial Intelligence
• Speech Enhancement
• +۱۲ مورد دیگر

Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.

• Formal Verification
• Software Testing
• Programming Languages
• +۱۲ مورد دیگر

Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His work bridges formal methods, software testing, and programming languages, with a focus on enhancing the reliability of high-performance and parallel software systems. He has held key roles including Director of Research (since 2023) and previously served as Lecturer (2011–2014), Senior Lecturer (2014–2017), and Reader (2017–2020) before being promoted to Professor in 2020. His research interests include formal verification, compiler testing, GPU programming, concurrency, and fuzzing. He has made significant contributions to the verification of GPU kernels, metamorphic testing of graphics drivers, and the development of tools like GPUVerify and GraphicsFuzz. His work combines theoretical rigor with practical impact, demonstrated by the acquisition of his startup GraphicsFuzz by Google in 2018 and his subsequent roles as Senior Software Engineer and Visiting Researcher at Google. His recent publications reflect a sustained focus on compiler and system reliability, with trends in fuzzing, formal specification, and automated testing of complex systems such as WebGPU, CXL cache coherence, and large language models for code generation. His work increasingly integrates empirical validation with formal techniques to uncover subtle bugs in real-world systems. Scientific awards and recognitions include: 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Fellow of the British Computer Society Best Paper awards at EuroSys 2024, MET 2021, IISWC 2019, IWOCL 2019, and ICST 2016 Best Industry Paper at ICST 2024 ACM SIGSOFT Distinguished Paper at ISSTA 2023 ACM SIGPLAN Most Influential OOPSLA Paper Award (2012 paper), awarded in 2022 Best Student Paper at PPoPP 2014 He has advised numerous PhD students and leads a vibrant research group. He has secured significant research funding and collaborates extensively with industry and academia. His service includes leadership roles such as General Chair of PLDI 2020, PC Chair of ECOOP 2019, and Steering Committee Chair of PLDI (2022–2025). He also serves on the advisory board of PACM-PL and on program committees for top venues including POPL, OOPSLA, PLDI, ICSE, and ISSTA. He leads the FastPL research group, which focuses on the design and implementation of programming tools and techniques for reliable software. The group conducts cutting-edge research in compiler testing, formal methods, and high-performance systems, fostering collaboration across academia and industry.

• Human Geography
• Urban Studies
• Migration Studies
• +۴ مورد دیگر

Prof. Dr. Sakura Yamamura is a Junior Professor (Assistant Professor) in Digital Methods in Human Geography at RWTH Aachen University, holding a Dr. rer. nat. in Geography from the University of Hamburg (2018). Her research focuses on migration, transnationalism, superdiversity, and urban studies, with a particular emphasis on digital methods applied to spatial and social dynamics. She has held postdoctoral and visiting fellowships at institutions including the Max Planck Institute and Maastricht University. Yamamura’s work bridges geography, sociology, and economics, examining how transnational migrants shape global cities like Tokyo and the impacts of globalization on urban spaces and policies. Her recent studies address the intersectionality of entrepreneurship, pandemic effects on LGBTQ+ activism, and digital transformations in urban production. Research interests include the socio-spatial dimensions of migration, intersectional approaches to entrepreneurship, and the interplay between digital tools and human geography. Her academic contributions span peer-reviewed articles in journals like Ethnic and Racial Studies and Urban Studies , as well as edited volumes on superdiversity and global urbanization. She actively engages in interdisciplinary dialogue, addressing topics such as mediated social touch and the methodological challenges of fieldwork in transnational contexts. Yamamura’s career includes roles as a guest lecturer at Adam Mickiewicz University (Poland) and SKEMA Business School (France), reflecting her commitment to international collaboration and teaching. She has advised numerous research projects and contributes to policy discussions on migration and urban development. Her current work at RWTH Aachen emphasizes leveraging digital methods to analyze complex urban systems and transnational networks.

Katrine Eldegardمشاهده پروفایل

استاد

• Conservation Biology
• Behavioural Ecology
• Population Ecology
• +۷ مورد دیگر

Katrine Eldegard is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management (MINA), Department of Ecology and Natural Resource Management (INA). She leads BatLab Norway and contributes extensively to national and international conservation science policy. Institution: Norwegian University of Life Sciences School: Faculty of Environmental Sciences and Natural Resource Management Department: Department of Ecology and Natural Resource Management Position: Professor Her research centers on understanding how human activities and land use affect natural ecosystems and species across taxa and spatial scales. She specializes in the behavioral, population, and community-level responses of mammals, birds, and insects to anthropogenic pressures such as energy infrastructure, transport networks, and forestry. A major focus is on bat ecology and conservation, pollination dynamics, and biodiversity monitoring in boreal and agricultural landscapes. Her recent publications reveal strong trends in climate change impacts on bat morphology and distribution, pollinator-plant interactions under environmental change, and the ecological consequences of infrastructure development. These works integrate field ecology with advanced modeling and policy-relevant assessments. She has played leading roles in key scientific committees: Chair, Mammal Committee, Norwegian Red List for Species (2021) Chair, Mammal Committee, Norwegian Alien Species List (2023) Member, Norwegian Scientific Committee for Food and Environment (VKM), CITES Expert Panel Norway’s representative, UNEP/Eurobats Advisory Committee Eldegard has supervised numerous research projects and collaborated with government agencies and private partners on applied ecology. She teaches courses including NATF200 Vern og forvaltning av norsk natur and the upcoming NATF300 Conservation Science. Her work is supported by extensive fieldwork, interdisciplinary collaboration, and integration of ecological theory with practical conservation. She leads BatLab Norway, a research group dedicated to advancing knowledge on bat ecology, behavior, and conservation through innovative methods including telemetry, acoustic monitoring, and landscape analysis.

Philippe Christeمشاهده پروفایل

دانشیار

• Host-Parasite Interactions
• Evolutionary Ecology
• Behavioral Ecology
• +۷ مورد دیگر

Philippe Christe is an Associate Professor in the Department of Ecology and Evolution at the University of Lausanne (UNIL), Faculty of Biology and Medicine. His research centers on host-parasite interactions, with a focus on bats, birds, and their ectoparasites, integrating behavioral, physiological, and genetic approaches. He leads the Christe Group, which investigates evolutionary and ecological dynamics in host-parasite systems, particularly avian malaria and bat-mite relationships. He is actively involved in conservation initiatives, including the Jorat Peri-Urban Nature Park, and collaborates with KORA on human-wildlife interactions. Bachelor's degree in biology, UNIL (1989) PhD in Zoology and Animal Ecology, UNIL (1994) Philippe Christe’s research interests lie at the intersection of parasitology, evolutionary ecology, and conservation biology. He investigates how host defenses evolve in response to parasitic pressures, examining trade-offs between immunity and life history traits such as reproduction and survival. His work often integrates field studies with experimental and molecular techniques. Key model systems include Parus major (great tit) and Culex pipiens in avian malaria, and bat species with their ectoparasitic mites. He also explores broader ecological interactions, including predator-prey dynamics and wildlife conservation in human-modified landscapes. His recent publications reflect a strong emphasis on disease ecology, wildlife conservation, and molecular methods. Articles span topics such as avian malaria transmission, lynx population dynamics, snow leopard behavior, and habitat fragmentation effects on insects. These works employ advanced techniques like DNA metabarcoding, spatial modeling, and long-term monitoring, demonstrating a trend toward interdisciplinary, data-driven ecological research with direct conservation applications. Dubois Foundation Prize for an educational CD-Rom on bats (2004) Communication Award from the Faculty of Biology and Medicine of UNIL (2017) Philippe Christe has supervised numerous PhD and Master’s students, contributing significantly to training the next generation of ecologists and evolutionary biologists. His group collaborates widely with institutions such as the Museum of Zoology in Lausanne and KORA, and participates in large-scale conservation projects involving snow leopards, lynx, and bats. He has been involved in research grants related to host-parasite coevolution, wildlife monitoring, and conservation strategies. His leadership in establishing the Jorat Peri-Urban Nature Park and involvement in the UNIL Interdisciplinary Center for Mountain Research highlight his commitment to applied science and environmental stewardship. He leads the Christe Group, which conducts research on host-parasite coevolution, with a focus on bats and their ectoparasites, and birds affected by avian malaria. The group combines fieldwork, laboratory experiments, and molecular analyses, and includes post-docs, PhD students, and technical staff. Collaborations extend to researchers in Bern, Paris, and Spain, fostering an international and interdisciplinary research environment.

Akihiko Nishimuraمشاهده پروفایل

استادیار

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +۹ مورد دیگر

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

• Wireless Communications
• 6G Technology
• Signal Processing
• +۷ مورد دیگر

Erik G. Larsson is a Professor and Head of the Division for Communication Systems within the Department of Electrical Engineering (ISY) at Linköping University (LiU), Sweden. He joined LiU in September 2007 and has previously held academic and research positions at the Royal Institute of Technology (KTH), University of Florida, George Washington University, and Ericsson Research. Research Interests: Enabling technologies for 6G wireless communication Statistical inference and signal processing Network science and complex networks Decentralized and federated machine learning over networks Physical layer security and privacy Energy-efficient digital signal processing His research group, active in areas like RadioWeaves and massive MIMO, focuses on robust, efficient, and secure wireless connectivity. Recent publications highlight trends in decentralized learning, resource allocation in wireless networks, and the integration of AI into mobile networks, particularly through projects like 'Turning the Air into an AI Computer' funded by the Knut and Alice Wallenberg Foundation. Scientific Awards and Honors: IEEE Signal Processing Magazine Best Column Award (2012, 2014) IEEE ComSoc Stephen O. Rice Prize (2015) IEEE ComSoc Leonard G. Abraham Prize (2017) IEEE ComSoc Best Tutorial Paper Award (2018) IEEE ComSoc Fred W. Ellersick Prize (2019) IEEE SPS Donald G. Fink Overview Paper Award (2023) IEEE Fellow Member, Royal Swedish Academy of Sciences (KVA) Gyllene Moroten Best Teacher Award (2021) Advising and Grants: He has supervised numerous Ph.D. and Licentiate students, many of whom now hold positions at leading industry and academic institutions. His research is currently funded by major organizations including the Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research (SSF), ELLIIT, Security-Link, Swedish Research Council (VR), and EU Horizon 2020 (H2020-SNS-6GTandem). Previous sponsors include VR, KVA, NSF, ORAU, and multiple EU FP7 and H2020 projects (e.g., MAMMOET, REINDEER, 5G-Wireless). Leadership and Service: He has served as Associate Editor for IEEE Transactions on Communications and IEEE Transactions on Signal Processing, chaired technical committees and steering committees in IEEE Signal Processing Society, and held leadership roles in major conferences such as the Asilomar Conference on Signals, Systems and Computers. He was a Visiting Fellow at Princeton University in 2015.

Paul Henshallمشاهده پروفایل

مدرس ارشد

• Renewable Energy Systems
• Energy Storage Systems
• Mathematical Modelling
• +۷ مورد دیگر

Dr Paul Henshall is a Senior Lecturer in Mechanical Engineering at Oxford Brookes University, affiliated with the School of Engineering, Computing and Mathematics. His work bridges advanced thermal systems, renewable energy, and mathematical modelling, with a strong focus on sustainable technologies. His research interests include: Mathematical modelling of renewable energy systems Second-life battery energy storage Thermal management of battery packs Lithium-ion cell characterization Advanced solar thermal systems Thermochemical energy storage The analysis of his recent publications (2018–2025) reveals a consistent focus on energy storage and solar thermal technologies. His work emphasizes computational modelling, experimental validation, and sustainability assessment, particularly in second-life batteries and high-efficiency solar collectors. Key themes include battery degradation prediction, thermal stress analysis, and the integration of renewable systems in buildings and industry. Professional recognitions include: Chartered Physicist (CPhys) Member of the Institute of Physics (MInstP) Fellow of the Higher Education Academy (FHEA) Member of the Institute of Physics Energy Group Committee Paul supervises MEng and PhD students, including Dr. Farhad Salek, whose thesis focused on second-life battery systems. He teaches core modules in engineering mathematics, numerical methods, and computational modelling. He is involved with the Centre for Batteries, Electric Vehicles and Electronics and the Architectural Engineering Research Group, contributing to interdisciplinary research in sustainable energy systems.

Martin Burgerمشاهده پروفایل

استاد

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +۱۵ مورد دیگر

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.