جست‌وجوی استاد و پژوهشگر

• Photonics
• Optoelectronics
• Nanotechnology
• +۴ مورد دیگر

Dr. Weilu Gao is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Utah. He holds a B.S. from Shanghai Jiao Tong University (2011) and a Ph.D. from Rice University (2016), followed by postdoctoral research there until 2019. Before joining Utah, he worked as a Photonics Designer at Lightmatter Inc. (2019–2020). His research focuses on photonics/optoelectronics of nanomaterials, including carbon nanotubes and 2D materials, with applications in computing, sensing, and energy. He has over 90 publications and 5,800+ citations. Research interests include reconfigurable photonics for machine learning, chiral photonic materials, and scientific computing using optical neural networks. Key projects involve developing diffractive optical neural networks (DONNs) for PDE-solving and energy-efficient computing, programmable chiral heterostructures, and wafer-scale aligned carbon nanotube architectures. His work bridges nanomaterial science with optical engineering, emphasizing scalable fabrication and cross-disciplinary applications. Notable achievements include publishing in Nature Communications , Advanced Photonics Research , and ACS Photonics . He leads the Weilu Gao Lab, which actively collaborates on NSF-funded projects (e.g., 2022 NSF award for carbon nanotube-based semiconductors). Professional activities include organizing workshops on chiral photonics and presenting at conferences like ECS Meetings. Grants include NSF funding for semiconductor research and collaborations with institutions like the University at Buffalo and Tokyo Metropolitan University. His lab recruits students and postdocs in scientific computing, photonics, and nanomaterials.

Daniel Romeroمشاهده پروفایل

استاد

• UAV Communications
• Wireless Communications
• Time-Series Analysis
• +۵ مورد دیگر

**Daniel Romero** is a **Professor** in the **Department of Information and Communication Technology** at the **University of Agder**, Norway. His research focuses on UAV communications, time-series analysis using machine learning and network science, and decentralized processing for sensor networks. He holds a Ph.D. in Signal Theory and Communications from the University of Vigo (2015), an M.Sc. in Signal Theory (2011), and a Telecommunication Engineering degree (2009). **Education**: Ph.D. in Signal Theory and Communications, University of Vigo (2015) M.Sc. in Signal Theory and Communications, University of Vigo (2011) Telecommunication Engineering, University of Vigo (2009) **Research Interests**: His work spans UAV communication systems (focusing on low-latency, high-reliability networks), time-series analysis for complex systems (using ML and network science), and decentralized computation in sensor networks to improve robustness and hardware efficiency. Recent projects include radio map estimation for mmWave beam alignment, spoofing detection via graph neural networks, and aerial base station placement optimization. **Publications**: Over 30+ peer-reviewed articles in top venues like IEEE Transactions on Wireless Communications and ICC. Recent trends emphasize radio map estimation (2023–2024), UAV-enabled spectrum surveying (2022), and robust D2D communications (2022). **Advising & Grants**: Teaches PhD courses (Statistical Signal Processing, Advanced Optimization) and leads the **Advanced Signal Processing Lab (ASL)**. Collaborates with the **CIEM (Center for Integrated Emergency Management)** on crisis-related communication systems. **Labs/Teams**: Directs the Advanced Signal Processing Lab (ASL.uia.no) and contributes to CIEM, applying ML and signal processing to emergency management challenges.

Jayadev S. Athreyaمشاهده پروفایل

دانشیار

• Dynamical Systems
• Geometric Topology
• Number Theory
• +۴ مورد دیگر

Jayadev S. Athreya is an Associate Professor in the Department of Mathematics at the University of Washington, where he also serves as Director of the Washington Experimental Mathematics Lab. His primary affiliation is with the College of Liberal Arts & Sciences. He holds dual roles as a Professor of Mathematics and Professor of the Comparative History of Ideas. Athreya co-directs the Pacific Institute for the Mathematical Sciences and is a founder of the Washington Experimental Mathematics Lab, emphasizing interdisciplinary experimental mathematics. His research interests span dynamical systems, geometric topology, number theory, and algebraic geometry. He focuses on translation surfaces, billiards dynamics, geometric flows, and probabilistic methods in geometry. His work often intersects with problems in ergodic theory, Teichmüller theory, and moduli spaces. Athreya has an extensive publication record, with recent work exploring billiard complexity in regular polygons, linear flows on translation prisms, and spectral properties of marked tori. His papers frequently address counting problems, asymptotic distribution of geometric objects, and connections between number theory and dynamical systems. He has taught advanced courses such as Quasiconformal Maps and Teichmüller Theory, Complex Analysis, and Elementary Number Theory. His pedagogical approach emphasizes hands-on exploration through the Washington Experimental Mathematics Lab, fostering collaborative research projects with undergraduates. Athreya is committed to accessible mathematics education, reflecting his alignment with Federico Ardila-Mantilla's axioms on equity and inclusivity in mathematical experiences. His work bridges theoretical research with educational outreach, advocating for mathematics as a universal, adaptable tool.

Kirsty Hassallمشاهده پروفایل

استادیار

• Applied Statistics
• Bayesian Modeling
• Agricultural Statistics
• +۵ مورد دیگر

Kirsty Hassall is an Assistant Professor in Applied Statistics at the Department of Statistics, University of Warwick, having joined the department in 2024. She has previously worked for nearly 10 years as an applied statistician in the agri-environment sector, bringing extensive practical experience to her academic role. Dr. Hassall holds a PhD in "The temporal and spatial analysis of single cell gene expression" from the University of Warwick (2015). She is a Chartered Statistician with the Royal Statistical Society (2019) and has served as a committee member (2017-2021) and currently as Secretary (2021-present) of the British and Irish Region of the International Biometric Society (IBS-BIR). Her research focuses on developing robust hybrid models that combine empirical, mechanistic, and stochastic elements. With expertise spanning Bayesian networks, spatially coherent clustering techniques, and farm typology development, Dr. Hassall applies statistical methods to solve complex problems in agricultural and environmental science. Her work bridges traditional statistical consultancy (mixed models, multivariate statistics, experimental design) with methodological developments for real-world applications. Analysis of Dr. Hassall's recent publications reveals a strong interdisciplinary focus at the intersection of statistics, agriculture, and environmental science. Her work demonstrates consistent application of advanced statistical techniques to address pressing challenges in food security, sustainable agriculture, and environmental management. Key trends include the development of Bayesian Belief Networks for soil quality assessment, spatial modeling for crop production estimation, and statistical approaches to address gender dimensions in agricultural research. Chartered Statistician (Royal Statistical Society, 2019) Dr. Hassall is actively involved in research supervision and teaching, currently offering the ST344: Professional Data Science course. She has secured funding for PhD research, including a fully funded EPSRC scholarship project titled "Unravelling the sound underground: Detecting and estimating earthworm abundance using low-frequency vibrations." She also organizes the "Statistical Challenges in the Agri-Environmental Sector" workshop at the University of Warwick. Through her extensive publication record and active participation in professional societies, Dr. Hassall has established herself as a researcher who effectively bridges statistical methodology with practical applications in the agri-environmental sector. Her collaborative approach is evident in her numerous multi-institutional research projects across the UK, Africa, and Europe.

Suchuan Dongمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• High-Order Numerical Methods
• Multiphase Flows
• +۷ مورد دیگر

Suchuan Dong is a Professor in the Department of Mathematics at Purdue University , affiliated with the Center for Computational and Applied Mathematics . His work bridges Computational Mathematics and Machine Learning , focusing on High-Order Numerical Methods and Multiphase Flows . Academic Background Post-Doc in Applied Mathematics, Brown University (2004) Ph.D. in Mechanical Engineering, SUNY Buffalo (2001) M.S. in Physics, Zhejiang University (1995) B.S. in Aerospace Engineering, National University of Defense Technology (1992) His research centers on Neural Network-Based Numerical Methods and Data-Driven Scientific Computing , with applications to Computational Fluid Dynamics , Contact Line Dynamics , and High-Performance Computing . Publications highlight Physics-Informed Neural Networks , Energy-Stable Schemes , and Extreme Learning Machines for PDEs. The articles reflect trends in Neural Network Applications to Dynamic PDEs , Phase Field Modeling , and High-Dimensional Computing , often combining High-Order Numerical Methods with Interfacial Phenomena . His recent work focuses on Exact Time Integration Algorithms and Hidden-Layer Concatenation for stability and efficiency. He leads research in the Center for Computational and Applied Mathematics , emphasizing Thermodynamically Consistent Modeling and Flow-Structure Interactions . His teaching includes MA-36600: Ordinary Differential Equations (Spring 2025).

Alex Townsendمشاهده پروفایل

دانشیار

• Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۶ مورد دیگر

Alex Townsend is an Associate Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds the Stephen H. Weiss Junior Fellowship and has been recognized for both research and teaching excellence. His research focuses on numerical analysis, scientific computing, and theoretical aspects of deep learning, with contributions to spectral methods, low-rank techniques, and computational algebraic geometry. Education: Townsend earned a DPhil (PhD) in Mathematics from the University of Oxford in 2014. Research Interests: Townsend's work spans several areas: novel spectral methods for differential equations, low-rank matrix and tensor techniques, theoretical foundations of deep learning, and computational algebraic geometry. His research emphasizes developing fast, accurate, and robust numerical algorithms with applications in science and engineering. Teaching & Mentoring: Townsend is a dedicated educator, having taught courses at MIT and Cornell on topics ranging from linear algebra and numerical analysis to advanced graduate-level subjects like kernel-based learning and top-ten algorithms of the 20th century. He has mentored numerous PhD students and postdocs, many of whom now hold academic and industry positions. Awards & Honors: 2022 Stephen H. Weiss Teaching Award 2022 Simons Fellowship in Mathematics 2018 SIAG/LA Early Career Prize 2015 Leslie Fox Prize in Numerical Analysis Grants & Funding: Townsend has secured significant funding, including an NSF CAREER grant (2021), to support his work on operator learning and spectral methods. Labs & Collaborations: While not tied to a specific lab, his research frequently intersects with computational mathematics and machine learning communities. He collaborates widely, contributing to open-source tools like Chebfun and Diskfun.

• Algorithm Design
• Discrete Optimization
• Approximation Algorithms
• +۲ مورد دیگر

Nikhil Bansal is Patrick C. Fischer Professor of Theoretical Computer Science at the University of Michigan. His research focuses on algorithms for discrete optimization problems, discrepancy theory, and scheduling. Research Contributions: Developed foundational techniques in randomized rounding and algorithmic discrepancy minimization. Current investigations include online algorithms, approximation methods for scheduling, and quantum-classical complexity separations. Teaching: Offers courses on randomized algorithms, linear algebra, and approximation algorithms. Created instructional materials on discrepancy theory and semidefinite programming. Professional Service: Serves on editorial boards for Journal of the ACM and Theory of Computing. Organized international workshops on discrepancy theory and optimization.

Maria Laura CARRANZAمشاهده پروفایل

استاد

• Ecology
• Remote Sensing
• Plant Invasion Dynamics
• +۵ مورد دیگر

Prof. Maria Laura CARRANZA is a Full Professor at the University of Molise, Department of Biosciences and Territory. Her work focuses on ecology, remote sensing, and environmental conservation, particularly in Mediterranean coastal ecosystems. She leads the Envix Lab, emphasizing vegetation mapping, invasive species dynamics, and climate change impacts. Her research integrates multi-source remote sensing (e.g., Sentinel, UAVs) with field data to assess biodiversity, dune ecosystems, and human-environment interactions. Key projects include monitoring plant invasions (e.g., Acacia saligna), coastal resilience, and citizen science contributions to ecological data collection. Education background and formal qualifications are not explicitly detailed in the provided text. Her research spans over 15 years, with recent emphasis on Mediterranean dunes, urban biodiversity, and global change effects on alpine grasslands. She collaborates internationally, notably in Argentina (Gran Chaco forests) and Pakistan (yak herding dynamics). Research interests include functional traits of plant communities, spectral diversity metrics, and conservation strategies for threatened ecosystems. Her work highlights the role of protected areas in mitigating litter accumulation and preserving cultural services. She has contributed to policy frameworks like Natura 2000 and the EU’s Invasive Species Directive through applied research. Publications emphasize the synergy between remote sensing and field ecology, with over 30 peer-reviewed articles since 2016. Her work on Acacia saligna invasion models and dune eco-geomorphology via UAVs has advanced management practices. She also explores educational innovations, such as online course efficacy for field subjects like bird recognition.

Chris Warnerمشاهده پروفایل

پژوهشگر

• Atmospheric Science
• Climate Modeling
• Geophysical Fluid Dynamics
• +۳ مورد دیگر

Chris Warner is a Researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP) within the Faculty of Mathematics at the University of Cambridge. His research focuses on atmosphere-ocean dynamics, particularly gravity wave parameterization, stratospheric dynamics, and climate modeling. He has contributed to advancements in general circulation models (GCMs) and the Met Office Unified Model, emphasizing realistic simulations of atmospheric processes such as the Quasi-Biennial Oscillation (QBO). His work integrates satellite data (e.g., CRISTA) with theoretical models to understand gravity wave momentum flux and spectral characteristics. Key areas include filtering mechanisms for nonorographic gravity waves and the development of ultra-simple parameterization schemes to improve climate model accuracy. Collaborations involve institutions like NASA and the Met Office, addressing challenges in mesopause physics and stratospheric-tropopause coupling. Publications span over two decades, demonstrating sustained contributions to both atmospheric science and instrumentation (e.g., microelectronic probe techniques). No scientific awards are explicitly mentioned in the provided materials. His research group is part of DAMTP’s Atmosphere-Ocean Dynamics section, contributing to theoretical and applied studies in geophysical fluid dynamics.

Peter Behrensdorff Poulsenمشاهده پروفایل

پژوهشگر

• Solar Cell Engineering
• Photovoltaics
• Drone Engineering
• +۵ مورد دیگر

Peter Behrensdorff Poulsen serves as Solar Photovoltaic Systems Group Leader at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU). His work spans photovoltaic research, solar-powered lighting systems, and drone-based inspection technologies within DTU's College of Engineering. His research focuses on Solar Cell Engineering , Photovoltaic Modules , and Drone Applications for renewable energy systems. Key areas include bifacial photovoltaics, electroluminescence imaging diagnostics, building-integrated photovoltaics, and ultra-efficient solar-powered lighting solutions. His work significantly contributes to UN Sustainable Development Goals related to affordable clean energy and sustainable cities. Recent publications reveal strong trends in AI-driven PV diagnostics , bifacial module performance in northern climates, and dark-sky compatible lighting systems . His research bridges fundamental photovoltaic science with practical applications in urban infrastructure and environmental sustainability. Best Poster Award at 44th IEEE Photovoltaic Specialists Conference Best Poster Award at 48th IEEE Photovoltaic Specialists Conference Characterizing the Performance of Daylight Filters for Electroluminescence Imaging Poster Award at 35th European Photovoltaic Solar Energy Conference Poster Prize at Sustain 2017 Poulsen leads multiple research grants including the Ultra-efficient Dark Sky-compatible Solar-powered Outdoor Lighting project (2024-2026) and previously managed the DronEL project (2017-2019) for drone-based PV inspection. His work connects photovoltaic engineering with practical lighting applications through the Lighting Color and Radiation Laboratory. He actively collaborates with industry partners on building-integrated photovoltaics and solar-powered infrastructure solutions, with notable projects including the Plateau Sun Hub public charging stations and Black Si BIPV panel development.

Nishant Agarwalمشاهده پروفایل

دانشیار

• Theoretical Cosmology
• Quantum Field Theory in Curved Spacetime
• Inflationary Cosmology
• +۴ مورد دیگر

Nishant Agarwal is an Associate Professor in the Department of Physics & Applied Physics at the University of Massachusetts Lowell (UML). He is affiliated with the Kennedy College of Sciences, contributing to theoretical cosmology and quantum field theory research. His academic background includes a Ph.D. in Astronomy and Space Sciences from Cornell University (2011), and prior degrees from institutions like the Indian Institute of Technology Kanpur and St. Stephen's College, Delhi. Agarwal's research focuses on theoretical cosmology, particularly inflationary models, dark energy, quantum field theory in curved spacetime, and large-scale structure formation. His work explores the interplay between quantum mechanics, gravity, and cosmological observations, with contributions to understanding cosmic microwave background anisotropies and non-Gaussian features in the early universe. His publications span topics ranging from open quantum cosmological systems to constraints on massive gravity theories, reflecting his expertise in both theoretical frameworks and observational implications. Notable contributions include studies on cosmic expansion dynamics and the development of novel methods for analyzing large-scale structure surveys. Education: Ph.D., Astronomy and Space Sciences, Cornell University, 2011 M.S., Astronomy and Space Sciences, Cornell University, 2009 M.S., Physics, Indian Institute of Technology Kanpur, 2006 B.S., Physics, St. Stephen's College, Delhi University, 2004 Agarwal has received several honors, including the Institute for Gravitation and the Cosmos Postdoctoral Fellowship (2014) and a Gravity Research Foundation essay award (2013). His research is supported by grants such as the Templeton Foundation-funded 'CosmoArchaeology: Digging for the Initial State' (2012), emphasizing interdisciplinary exploration of cosmic origins. His work bridges fundamental physics and cosmological observations, with ongoing projects addressing quantum gravitational effects, early universe dynamics, and observational tests of gravity theories. Agarwal collaborates widely, contributing to initiatives like the Sloan Digital Sky Survey III and advancing theoretical methods in cosmology.

Alex Kontorovichمشاهده پروفایل

استاد

• Automorphic Forms and Representations
• Homogeneous Dynamics
• Harmonic Analysis
• +۵ مورد دیگر

Alex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, where he holds the academic rank of Professor. His primary affiliation is with the Department of Mathematics, School of Arts and Sciences. He currently serves as Managing Editor of the Journal of the Association for Mathematical Research and is Executive Director of Rutgers MathCorps . During the 2024-2025 academic year, he is on leave, visiting Princeton University and the Institute for Advanced Study (IAS). Kontorovich's research focuses on automorphic forms, homogeneous dynamics, harmonic analysis, and number theory, with interdisciplinary connections to data science and machine learning. His work bridges pure mathematics with computational aspects, including sphere packing geometry and arithmetic dynamics. He has held distinguished visiting roles, such as the 2020-21 Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath), where he contributed to academic content and exhibits. His academic career includes teaching advanced courses like Graduate Complex Analysis, Automorphic Representations, and History of Mathematics. Notable awards include the Simons Foundation Fellowship, von Neumann Fellowship at IAS, and Alfred P. Sloan Research Fellowship. Grants include multiple NSF awards (regular, CAREER, FRG) and a Binational Science Foundation grant. His research explores topics like spectral gaps, thin groups, and applications of modular forms to equidistribution problems. Kontorovich’s scholarly contributions extend beyond academia: he serves on the Scientific Board of Quanta Magazine , advises the Lean Focused Research Organization , and participates in editorial and strategic roles across institutions. His work emphasizes the interplay between theoretical mathematics and computational methods, shaping modern research in number theory and dynamics.

• Solar Physics
• Stellar Astronomy
• Heliophysics
• +۵ مورد دیگر

Dr. Hannah Schunker is a Senior Lecturer in the School of Information and Physical Sciences at the University of Newcastle. She holds an ARC Future Fellowship (2022–2026) and is a Research Advantage Women in Research Fellow. Her expertise lies in solar and stellar physics, focusing on helioseismology and asteroseismology to study the solar dynamo and magnetic field dynamics. She previously worked at the Max Planck Institute for Solar System Research in Germany. Education: Doctor of Philosophy, Monash University Bachelor of Science, University of Adelaide Research Interests: Dr. Schunker investigates the Sun's magnetic field, solar dynamo mechanisms, and space weather impacts. Her work includes analyzing solar oscillations, magnetic flux emergence, and asteroseismology of Sun-like stars. She discovered the 'Schunker Effect,' where helioseismic waves are perturbed by sunspot magnetic fields. Key Contributions: Developed methods to predict space weather by analyzing active region emergence. Advanced understanding of subsurface flows and magnetic field interactions. Contributed to the PLATO mission for exoplanet and stellar asteroseismology studies. Awards & Grants: ARC Future Fellowship (2022) ABC Science collaborations (e.g., 'Solar Storms,' 'Fusion') Claire Corani Prize (1999) Teaching & Supervision: Lectures include Advanced Physics II , Introduction to Astronomy , and Photonics . She coordinates multidisciplinary laboratory research projects. Labs/Teams: Active in solar physics research groups within the University of Newcastle and collaborates internationally on helioseismology and asteroseismology projects.

Maxime Ferreira Da Costaمشاهده پروفایل

دانشیار

• Signal Processing
• Data Science
• Inverse Problems
• +۶ مورد دیگر

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

• Mathematical Physics
• Partial Differential Equations
• Probability
• +۳ مورد دیگر

Felipe Hernández is an Assistant Professor in the Department of Mathematics at Pennsylvania State University. He is affiliated with the Department of Mathematics, located in McAllister Building, and can be contacted at felipeh@psu.edu. His research interests span Mathematical Physics, Partial Differential Equations, and Probability, with a focus on quantum systems, stochastic processes, and theoretical analysis of complex systems. His work explores topics such as quantum diffusion in disordered systems, decoherence dynamics, and the interplay between classical and quantum mechanics. He also investigates computational methods like neural networks to enhance accuracy in quantum chemistry calculations and explores foundational questions in quantum computing, such as BosonSampling complexity. Recent contributions include studies on Hodge systems, divergence-free vector fields, and endpoint estimates in functional analysis. While no specific awards or grants are listed, his research demonstrates a commitment to advancing mathematical physics and computational methods through rigorous theoretical analysis and interdisciplinary approaches.