جست‌وجوی استاد و پژوهشگر

Anne-Laure Dalibardمشاهده پروفایل

استاد

• Geophysical fluids
• Boundary layers in fluid mechanics
• Congestion models
• +۵ مورد دیگر

Anne-Laure Dalibard is a Professor at Sorbonne University's Faculty of Science and Engineering, affiliated with the Jacques-Louis Lions Laboratory (UMR CNRS 7598). She also serves as a Junior member of the Institut Universitaire de France (2020-2025) and was previously a part-time professor at the École Normale Supérieure in Paris (2021-2024). Her research focuses on mathematical analysis of fluid mechanics with applications to geophysical and oceanographic phenomena. Education: Student at ENS Ulm (2001-2005) PhD at CEREMADE, Paris-Dauphine University (defended October 8, 2007) Dalibard's research centers on geophysical fluids, boundary layers in fluid mechanics, congestion models, roughness models, scalar conservation laws, and homogenization theory. She specializes in asymptotic analysis of fluid equations relevant to oceanographic models, particularly those involving rotating fluids and boundary layer phenomena. Her work bridges rigorous mathematical analysis with practical applications in environmental fluid dynamics. Her recent publications demonstrate a consistent focus on boundary layer phenomena in fluid mechanics, with particular emphasis on geophysical applications. She has made significant contributions to understanding boundary layers in rotating fluids, congestion models in Navier-Stokes systems, and wave phenomena in stratified fluids. Her mathematical approach typically involves rigorous analysis of partial differential equations with singular perturbations, often using asymptotic methods, homogenization theory, and kinetic formulations. Scientific Awards: Junior member of the Institut Universitaire de France (2020-2025) Principal Investigator for ERC Starting grant BLOC (2015-2020) Leader of ANR BOURGEONS project (2023-2027) Dalibard leads substantial research initiatives including the ANR BOURGEONS project (2023-2027), which involves over 30 researchers, PhD students, and post-docs working on fluid dynamics aspects relevant to geophysical flows. She has supervised several PhD students including Jean Rax and Gabriela Lopez-Ruiz, and mentored post-doctoral researchers such as Frédéric Marbach, Marc Briant, and Matthew Paddick. Her research has been supported by prestigious grants from the European Research Council and the French National Research Agency. She is actively involved with the Jacques-Louis Lions Laboratory at Sorbonne University and collaborates extensively with researchers across France and internationally. Her work often intersects with oceanographic applications, connecting mathematical theory with environmental fluid dynamics problems.

• Numerical analysis
• Scientific computing
• Partial differential equations
• +۳ مورد دیگر

Kirill Serkh is an Assistant Professor in the Department of Mathematics at the University of Toronto, with a cross-appointment to the Department of Computer Science. His research focuses on advanced numerical methods for solving complex mathematical problems. Key Research Areas: Numerical analysis, Scientific computing, Partial differential equations, Numerical linear algebra, Quadrature and approximation theory, Special functions His recent work explores high-order numerical schemes for PDEs on non-smooth domains, adaptive methods for oscillatory integrals, and efficient evaluation of Newtonian potentials. He has contributed to the development of hybrid boundary integral methods and spectral techniques for challenging computational problems. While no specific scientific awards are mentioned in the provided text, his publications demonstrate expertise in computational mathematics and interdisciplinary applications in fluid dynamics, wave propagation, and machine learning. His methodological innovations span both theoretical and applied domains.

John Mackayمشاهده پروفایل

دانشیار

• Analysis on metric spaces
• Geometric group theory
• Hyperbolic groups
• +۴ مورد دیگر

John Mackay is an Associate Professor (Reader) in the School of Mathematics at the University of Bristol. His research focuses on geometric analysis, particularly analysis on metric spaces, geometric group theory, hyperbolic groups, and conformal dimension. He is a member of both the Probability, Analysis and Dynamics and Pure Mathematics institutes at Bristol, where he maintains an active research program and teaches undergraduate mathematics courses. His educational background includes undergraduate studies at the University of Edinburgh, PhD work at the University of Michigan under Bruce Kleiner (2003-2008), followed by a J.L. Doob research assistant professorship at the University of Illinois (2008-2010) and a postdoc at the University of Oxford (2011-2013) working with Cornelia Drutu before joining Bristol. Mackay's research explores the intersection of geometry, analysis, and group theory, with particular emphasis on conformal dimension, quasiconformal geometry, and the structure of hyperbolic and relatively hyperbolic groups. His work often involves studying metric spaces through their geometric and analytic properties, with connections to harmonic analysis and topology. His publication record shows consistent output since 2008, with recent work focusing on Poincaré profiles, asymptotic dimension, and the conformal dimension of hyperbolic groups. His most recent publications extend to 2025, indicating active ongoing research. The publications demonstrate a progression from foundational work in conformal dimension toward more complex applications involving random groups, Lie groups, and relatively hyperbolic structures. Among his significant contributions is the co-authored book "Conformal Dimension: theory and application" published by the AMS in 2010. Mackay actively contributes to the mathematical community through conference organization, including the "Interactions between Geometry, Dynamics and Group Theory" workshop at Bristol in January 2020, and upcoming events including the "Actions on graphs and metric spaces (OGG Workshop)" at the Isaac Newton Institute for September 2025 and the "Probability, Analysis and Dynamics conference" at Bristol for April 2025.

Sami Äyrämöمشاهده پروفایل

دانشیار

• Computer Science
• Machine Learning
• Health Science
• +۱۱ مورد دیگر

Sami Äyrämö is an Associate Professor at the Faculty of Information Technology , University of Jyväskylä. His research bridges machine learning and health science , focusing on innovative applications in biomechanics , medical imaging , and exercise physiology . Specializes in automated scoring systems for medical diagnostics Pioneer in domain-specific transfer learning for healthcare data Develops synthetic data for wellbeing sector innovation His work spans colorectal cancer tissue analysis , ACL injury risk modeling , and dementia detection from speech , with recent studies applying cluster analysis and deep learning to sports biomechanics challenges. Current projects include the WellbeingDataLab initiative for synthetic exercise data, and collaborations with the Computational Data Science Research Group on spectral imaging and health analytics.

• Coastal hydrodynamics
• Sediment transport
• Morphological changes
• +۴ مورد دیگر

Suzana Ilic is a Senior Lecturer at the Lancaster Environment Centre, Lancaster University, specializing in coastal processes and management. Her research focuses on understanding coastal hydrodynamics, sediment transport, and morphological changes along coastlines. She develops practical tools for coastal managers to predict future changes, addressing complex challenges of coastal structures' influence on natural systems including shoreline evolution, water quality, and habitat preservation. Dr. Ilic's research interests center on coastal hydrodynamics and sediment transport processes, with emphasis on developing predictive tools for coastal management. Her work aligns with major environmental initiatives including the Foresight programme on coastal flooding and erosion, Water Framework directives, and offshore sustainable energy development. She employs integrated methodologies combining field monitoring, numerical modeling, and community engagement to address contemporary coastal challenges. Her recent publications demonstrate a strong focus on coastal morphodynamics, with increasing attention to interdisciplinary approaches connecting physical processes with social dimensions of coastal management. The research spans multiple geographical contexts including the UK's Holderness coast and Morecambe Bay, as well as international sites like Croatia, reflecting her global perspective on coastal challenges. Dr. Ilic actively supervises PhD students including Juan Montalvan Lume and leads significant research projects such as NERCOUTE Inclusive Natural Environment Futures (2025-2026) and Adaptation Pathways (2025-2026). Her professional engagement extends to the North West Regional Flood and Coastal Committee and various workshops, demonstrating her commitment to translating research into practical coastal management solutions.

Jane Luuمشاهده پروفایل

استاد

• Astronomy
• Astrophysics
• Planetary Science
• +۳ مورد دیگر

Jane Luu is a Professor II at the Centre for Planetary Habitability (UiO), specializing in comets, asteroids, and interstellar objects . Her research focuses on dynamic processes in small Solar System bodies, including fragmentation, disintegration, and dust behavior. She collaborates with international teams using facilities like the Nordic Optical Telescope (NOT) and WIYN Telescopes. Email: jane.luu@geo.uio.no Address: Sem Sælands vei 13, Svein Rosseland's house, 0371 Oslo Her work includes studies on comet outbursts (e.g., 12P/Pons-Brooks), disintegration mechanisms (C/2019 J2), and interstellar interlopers like 2I/Borisov and ’Oumuamua, where she proposed fractal dust aggregate models. Recent publications examine episodic activity in asteroids (6478 Gault) and discus-shaped comets (C/2014 B1).

• Distributed Signal Processing
• Wireless Communications
• Machine Learning
• +۳ مورد دیگر

Dr. Erma Perenda serves as Professor and Chair of Distributed Signal Processing at RWTH Aachen University, Germany, leading research within the Department of Distributed Signal Processing. Her contact details include email perenda@dsp.rwth-aachen.de and phone +49 241 80-27879, with office location at Kopernikusstraße 16, 52074 Aachen in the ICT Cubes facility. Her research spans: Distributed Signal Processing Wireless Communications Machine Learning (Deep Reinforcement Learning, Federated Learning) Modulation Classification AI-driven Network Optimization She focuses on solving real-world challenges in wireless systems including hardware impairments, channel variations, and energy efficiency through advanced AI techniques. Analysis of her 2018-2024 publications reveals consistent innovation in applying multi-agent deep reinforcement learning to wireless power allocation, developing robust modulation classification methods resilient to channel impairments, and implementing federated learning for industrial edge computing. Her work bridges theoretical machine learning with practical wireless communication constraints. Scientific Awards: No awards documented in available sources Advising and Grants: No student advisees or grant information provided Labs and Teams: Leads Distributed Signal Processing research group at RWTH Aachen University Based in ICT Cubes building focusing on wireless AI systems

Ryo Ikeshiroمشاهده پروفایل

استادیار

• Sound Art
• Sonification
• Computational Creativity
• +۳ مورد دیگر

Ryo Ikeshiro is an Assistant Professor at the School of Creative Media, City University of Hong Kong, and co-director of the spatial audio art/research unit SoundLab. His work bridges sound art, computational creativity, and cultural studies through immersive installations, algorithmic audio-visual systems, and sonification techniques. PhD in Creative Practice (Goldsmiths, University of London) MPhil in Music (University of Cambridge) BMus (King's College London) Ikeshiro's research interrogates the materiality of sound through: Multichannel Ambisonics and directional audio Neural network-driven temporal dislocation Sonification of climate data and historical soundscapes Machine learning for artistic interpretation Interplay of identity and technology East Asian ideophonic traditions His 2010-2024 publications and installations reveal cross-disciplinary engagement with: Fractal mathematics in audiovisual art Algorithmic composition systems Interactive installation technologies Sonic cartography Historical memory in sound Collaborative research frameworks SoundLab, which he co-directs, develops spatial audio research at the intersection of: Technical innovation Cultural representation Experimental pedagogy Public engagement International artistic exchange Practice-based research

Harsha Gangammanavarمشاهده پروفایل

دانشیار

• Optimization under Uncertainty
• Stochastic Programming
• Large-Scale Computational Optimization
• +۳ مورد دیگر

Harsha Gangammanavar is an Associate Professor in the Department of Operations Research and Engineering Management (OREM) at Southern Methodist University (SMU), affiliated with the Data Science Institute. He holds a Ph.D. in Operations Research and M.S. in Electrical Engineering from The Ohio State University, and a B.E. in Electronics and Communication Engineering from Visvesvaraya Technological University, India. Education: Ph.D. in Operations Research, The Ohio State University M.S. in Electrical Engineering, The Ohio State University B.E. in Electronics and Communication Engineering, Visvesvaraya Technological University Research Interests: His work focuses on stochastic programming, large-scale computational optimization, and their applications in infrastructure systems, healthcare, and wireless communication. Key areas include optimization under uncertainty, stochastic decomposition methods, and scalable algorithms for power systems and renewable energy integration. Recent Activities & Awards: Recipient of NSF XTRIPODS grant for collaborative research in data science (2024). DOE Office of Science grant for multiscale stochastic optimization (2022). ONR grant for decomposition-based stochastic models in discrete-event systems (2022). Awarded INFORMS Undergraduate Student Paper Award (2021) and INFORMS Minority Affairs Poster Competition (2016). Advising & Grants: Current advisees include Ph.D. students Ishara A.A.D.H., Jackson Forner, Chhavi Sharma, and Zhiyuan Zhang. Former students Niloofar Fadavi, Sakitha Ariyarathne, and others have secured roles in industry and academia. Active in securing grants from AFOSR, ONR, DOE, and NSF. Labs & Teams: Leads research on stochastic optimization algorithms, power grid resilience, and healthcare applications through interdisciplinary collaborations. Open to motivated students for Ph.D. research in optimization and data science.

Yannis Stylianouمشاهده پروفایل

استاد

• Speech Signal Processing
• Audio Signal Processing
• Biomedical Signal Processing
• +۳ مورد دیگر

Yannis Stylianou is Professor of Speech Processing at University of Crete and Senior Research Scientist at Apple. Former positions include AT&T Labs Research, Bell-Labs, and Toshiba Cambridge Research Lab. IEEE Fellow with PhD from ENST-Paris and over 200 publications. Research spans: Adaptive speech/audio modeling Neural speech synthesis/enhancement Biomedical signal processing Awards include: IEEE Fellowship French Ministry Research Fellowship ENST Graduate Scholarship Recent work focuses on neural TTS architectures, intelligibility enhancement, and multimodal synthesis. Organizes annual International Summer School on Speech Processing.

Spencer N. Axaniمشاهده پروفایل

استادیار

• Neutrino Physics
• Particle Astrophysics
• Cosmic Rays
• +۳ مورد دیگر

Spencer N. Axani is an Assistant Professor in the Department of Physics and Astronomy at the University of Delaware (UD), associated with the College of Arts & Sciences. He also serves as Associate Director of the Delaware NASA Space Grant. His research focuses on experimental neutrino physics, multi-messenger astrophysics, cosmic rays, and nuclear physics, with particular emphasis on developing particle detector technologies. He earned a Diploma in Power Engineering from Southern Alberta Institute of Technology, followed by a B.Sc. (Honors Physics) from the University of Alberta, and M.Sc. and Ph.D. in Physics from MIT. Prior to UD, he was a Postdoctoral Associate at MIT. Key research areas include neutrino oscillations, cosmic ray anisotropy, and detector development such as the IceCube Neutrino Observatory and the CosmicWatch muon detector project. The CosmicWatch initiative provides low-cost educational muon detectors for students. Axani's work involves collaborations with institutions like the Bartol Research Institute and the American Physical Society (APS). His contributions span theoretical and experimental advancements in particle physics, with publications addressing neutrino emission from astrophysical sources, atmospheric neutrino studies, and neutrino detector technologies. His lab spaces include 303 Sharp Lab (R&D) and 012 Sharp Lab (Scintillator Lab). He actively participates in projects like IceCube, KamLAND-Zen, and NuDot. His research highlights include analyses of cosmic ray anisotropy, constraints on heavy neutral leptons, and multi-messenger observations of blazars.

David Castañónمشاهده پروفایل

استاد

• Stochastic Control
• Estimation Theory
• Game Theory
• +۶ مورد دیگر

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

Vasily Krylovمشاهده پروفایل

پژوهشگر ارشد

• Representation Theory
• Mathematical Physics
• Algebraic Geometry
• +۵ مورد دیگر

Vasily Krylov is a Benjamin Peirce Postdoctoral Fellow at Harvard University's CMSA, mentored by Prof. Dan Freed and supported by the Simons Foundation via the Simons Collaboration on Global Categorical Symmetries. He completed his PhD in Mathematics at MIT under Prof. Roman Bezrukavnikov. His research focuses on the intersection of representation theory, mathematical physics, and algebraic geometry, with a special emphasis on symplectic resolutions, integrable systems, 3D-mirror symmetry, and Coulomb branches. Affiliations: Harvard CMSA (2020–present), MIT PhD (2015–2020) Education: PhD in Mathematics (MIT, 2020), Prior studies include seminars on geometric representation theory and quantum groups. His work spans geometric realizations of algebraic structures, such as asymptotic affine Hecke algebras and Bethe subalgebras in Yangians, with applications to character formulas and symplectic duality conjectures. He co-organizes the Geometry and Quantum Theory (GQT) seminar at Harvard and has presented at institutions like the Perimeter Institute and Skoltech. Key research directions include: Proving Hikita-Nakajima conjecture for ADHM spaces and Slodowy slices Geometric Satake equivalence and Coulomb branch constructions Applications of Kazhdan-Lusztig theory to affine Lie algebras His recent articles address symplectic resolutions, Frobenius pushforwards on compactifications, and integrable crystal structures in representation theory.

Gurpreet Singh Gillمشاهده پروفایل

پژوهشگر ارشد

• MEMS/NEMS Technology
• Thin-Film Materials
• Optical MEMS
• +۳ مورد دیگر

Gurpreet Singh Gill is a Research Fellow at The University of Western Australia (UWA), School of Engineering, Department of Electrical, Electronic and Computer Engineering. He holds a Ph.D. from UWA (2022) and prior degrees from Punjab Technical University and Sri Guru Granth Sahib World University, Punjab, India. His research focuses on MEMS/NEMS, thin-film materials, optical MEMS, and infrared sensing/imaging technologies. He previously worked at the Central Electronics Engineering Research Institute, India, and currently leads projects in micro/nano electromechanical systems and their applications in optoelectronics and infrared sensing. **Education**: B.Tech in Electronics & Communication Engineering (2013, Punjab Technical University) M.Tech in Electronics & Communication Engineering (2015, Sri Guru Granth Sahib World University) Ph.D. in Microelectronics (2022, The University of Western Australia) **Research Interests**: MEMS/NEMS device design and applications Thin-film materials for optical and infrared systems Optical MEMS and optoelectronic devices Infrared sensing and imaging technologies **Awards**: Nanoscale Advances Poster Prize (2019) ADHOC Postgraduate Scholarship (2017) Scholarship for International Research Fees (SIRF) (2017) **Grants & Advising**: Gill has received scholarships for international research fees and postgraduate support. He collaborates with interdisciplinary teams and leads research projects funded by UWA and external grants. His work contributes to UN SDGs related to affordable and clean energy, industry innovation, and infrastructure. **Labs & Teams**: Active in UWA’s Microelectronics Research Group and collaborates with global institutions on MEMS-based sensing systems.

Michael Choiمشاهده پروفایل

استادیار

• Markov Chains
• Stochastic Algorithms
• Statistical Physics
• +۶ مورد دیگر

Michael Choi is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA). Previously, he held a joint appointment with Yale-NUS College (2022–2025) and worked at the School of Data Science (SDS) at the Chinese University of Hong Kong, Shenzhen. He earned his PhD from Cornell University’s School of Operations Research and Information Engineering (ORIE), advised by Prof. Pierre Patie, and holds an undergraduate degree in Actuarial Science from the University of Hong Kong. His research focuses on Markov chains and processes, stochastic algorithms (e.g., MCMC, simulated annealing, Langevin dynamics), and their applications in statistical physics, optimization, Bayesian statistics, information theory, game theory, and theoretical computer science. He explores interdisciplinary connections with quantum computation, control theory, and computational chemistry. Choi actively contributes to the academic community, serving as an Associate Editor of Statistics and Computing since August 2023. He has presented at numerous international conferences and workshops, including the IMS APRM 2026 (Hong Kong), INFORMS International Meeting 2025 (Singapore), and BayesComp 2025 (Singapore). His work bridges theoretical foundations with practical computational methods in stochastic systems.