جست‌وجوی استاد و پژوهشگر

Kevin Fossezمشاهده پروفایل

استادیار

• Nuclear Theory
• Few-Body Systems
• Exotic Nuclei
• +۳ مورد دیگر

Kevin Fossez is an Assistant Professor in the Department of Physics at Florida State University, maintaining his office in Keen 206 with contact email kfossez@fsu.edu. His academic position and research output confirm active faculty status within the university's physics department. His research centers on nuclear theory with specialized focus on exotic nuclei near drip lines, few-body quantum systems, and continuum effects in nuclear structure. Employing advanced methodologies including the Gamow shell model, density matrix renormalization group (DMRG), and eigenvector continuation, he investigates proton halos, neutron configurations, and resonance states in unstable isotopes. His work bridges theoretical frameworks with experimental implications for facilities like FRIB. Analysis of his 2020-2025 publications reveals consistent emphasis on precision mass measurements of proton-dripline nuclei (notably 22Al), evolution of neutron configurations in lithium isotopes, and island of inversion phenomena in fluorine systems. His research trajectory demonstrates increasing integration of computational extrapolation techniques for continuum states, with direct relevance to rare isotope beam facilities and nuclear astrophysics.

Tatiana Podladchikovaمشاهده پروفایل

دانشیار

• Solar Physics
• Space Weather
• Coronal Mass Ejections
• +۵ مورد دیگر

Dr. Tatiana Podladchikova serves as Associate Professor, Director of the Center for Digital Engineering, and Director of the Engineering Systems Master's Program at Skolkovo Institute of Science and Technology (Skoltech). Her work bridges fundamental solar physics research with operational space weather services to protect critical infrastructure including satellites, aviation systems, and power grids from solar-induced geomagnetic disturbances. Her research centers on solar activity dynamics—particularly solar cycles, eruptive flares, coronal mass ejections (CMEs), and high-speed solar wind streams—and their terrestrial impacts. She develops predictive models for space weather phenomena using advanced data analysis techniques, with recent work integrating deep learning frameworks for solar observation translation and physics-informed neural networks for magnetic field extrapolation. Her research directly addresses infrastructure vulnerabilities during extreme space weather events through operational forecasting systems. Dr. Podladchikova's publications demonstrate a clear evolution toward AI-enhanced space weather prediction, combining multi-point solar observations (including Solar Orbiter mission data), coronal dimming analysis for early CME detection, and innovative applications like the DIRECD method for real-time propagation tracking. Her work spans fundamental solar physics, engineering applications for satellite systems, and environmental monitoring techniques. Her scientific recognition includes: International Alexander Chizhevsky Medal for Space Weather and Space Climate (2015, Belgium) Skoltech Best Professor Award (2018-2020) Outstanding Achievements in Education and Research (2022-2023) As an educator, she directs Skoltech's Engineering Systems Master's Program, leads the Space Weather Laboratory, and teaches Experimental Data Analysis and Data Analysis for Space Weather courses. Her leadership extends to public outreach initiatives including science communication events like 'Mysteries of the Sun: Space Weather of the 21st Century' lectures. She heads the Skoltech Space Weather Laboratory where her team develops operational forecasting tools, analyzes multi-spacecraft solar observations, and advances CubeSat technologies including the Skoltech-F mission. Current projects focus on real-time geomagnetic storm prediction systems and AI-driven translation of historical solar datasets to maintain observational continuity.

John Hildebrandمشاهده پروفایل

استاد

• Marine Acoustics
• Bioacoustics
• Marine Mammal Behavior
• +۴ مورد دیگر

John Hildebrand is a Distinguished Professor of Oceanography at Scripps Institution of Oceanography, University of California San Diego, and serves as an adjunct professor in the Department of Electrical and Computer Engineering. His research pioneers marine bioacoustics through innovative passive acoustic monitoring systems to study marine mammals and anthropogenic ocean noise impacts. Education Ph.D. in Applied Physics, Stanford University, 1983 B.S. in Physics and Electrical Engineering, UC San Diego, 1978 Research Focus : Hildebrand's work centers on developing acoustic technologies like the High-frequency Acoustic Recording Package (HARP) for long-term ocean monitoring. His lab deploys instruments globally to study whale communication, migration patterns, and the ecological consequences of rising ocean noise from shipping. Key discoveries include regional blue whale dialects and quantification of decades-long noise increases threatening marine mammals. His methodology combines engineering innovation with ecological fieldwork across the Arctic, Hawaii, California coast, and Gulf of Mexico. Publication Trends : Analysis of his 15 most recent publications reveals three dominant research thrusts: 1) Endangered species conservation (particularly Rice's whale monitoring in the Gulf of Mexico), 2) Climate-noise interactions in ocean soundscapes, and 3) Advanced acoustic tracking methodologies for deep-diving cetaceans. His work increasingly integrates multi-year datasets to assess anthropogenic impacts against natural variability. Awards Fellow of the Acoustical Society of America Professional Service : Hildebrand serves on the Marine Mammal Commission's board of scientific advisors and is an active member of the Society for Marine Mammalogy. His leadership in acoustic methodology development has established standardized protocols for global passive acoustic monitoring networks. Laboratory Infrastructure : He leads the Scripps Whale Acoustics Laboratory (SWAL) and collaborates with the Marine Physical Laboratory. His team maintains a global array of HARPs and develops open-source software like Where's Whaledo for acoustic localization, supporting international marine mammal research and conservation efforts.

• Computational Fluid Dynamics (CFD)
• Hypersonic Flows
• Shock Wave Interaction
• +۵ مورد دیگر

Paciorri Renato is an Associate Professor at the Department of Engineering, Sapienza University of Rome. His research focuses on computational fluid dynamics (CFD), particularly in the areas of shock wave interaction, hypersonic flows, and unstructured grid techniques. He specializes in numerical methods for simulating complex aerothermal phenomena, such as re-entry trajectories, inflatable structures, and transonic flow dynamics. His work addresses challenges in aerospace engineering, including launch vehicle design, aerocapture maneuvers, and thermal protection systems. Key research contributions include the development of shock-fitting and shock-capturing techniques for analyzing flows with strong shocks. He explores innovative methods like front-tracking for inflatable shields and unstructured re-meshing for multi-body separation problems. His studies often involve collaborations with space agencies and focus on practical applications such as spacecraft re-entry systems, launch vehicle aerodynamics, and high-speed flow simulations. Paciorri’s publications span over two decades, demonstrating expertise in turbulence modeling, plasma flows, and numerical solver validation. While no specific awards are listed, his extensive body of work contributes significantly to advancing computational methods in aerospace and mechanical engineering.

Molina Peralta, Raquelمشاهده پروفایل

پژوهشگر

• Effective Field Theories
• Hadron Physics
• Lattice QCD
• +۳ مورد دیگر

Raquel Molina Peralta is a Distinguished Researcher at the University of Valencia and a member of the Hadron and Nuclear Theory Group at IFIC. She holds a PhD in Physics (2012) and has held postdoctoral positions at George Washington University and Osaka University. Research Focus: Her work centers on Effective Field Theories (EFT) and their applications in lattice QCD Exotic hadronic states and resonances Meson-baryon interactions via chiral unitary approaches Triangle singularity mechanisms in particle decays Heavy quark mass dependencies in charmed/doubly-bottom mesons Recent Publication Trends: Analysis of Triple-charm hadrons (2025) Exotic Σ∗(1430) generation (2024) X(2866) and Zcs state detection (2022) KK̅ channel dynamics in nuclear matter (2016) Awards: CIDEGENT program funding recipient Holds the Investigador Distinguido title at University of Valencia

• Probability
• Statistics
• Extreme Events
• +۴ مورد دیگر

Anna (Ania) Panorska is a Professor at the University of Nevada, Reno within the Graduate Program of Hydrologic Sciences. She holds a Ph.D. in Mathematics (Statistics and Applied Probability program) from UC Santa Barbara (1992) and an M.S. in Statistics from UT El Paso (1988), with prior graduate coursework in Applied Mathematics at the University of Warsaw. Her career spans academia, research institutions (Desert Research Institute), industry (Blue Cross Blue Shield of Tennessee), and statistical consulting. Her educational background includes: M.S. in Statistics, University of Texas at El Paso, 1988 Ph.D. in Mathematics (Statistics and Applied Probability program), University of California, Santa Barbara, 1992 Professor Panorska's research centers on theoretical and applied probability and statistics, with specialized expertise in limit theorems and extreme event modeling within probability theory, and multivariate model development in statistics. Her work is characterized by extensive interdisciplinary collaboration across ecology, engineering, climate science, hydrology, and medicine. Key contributions include novel distributional frameworks for stochastic episodes, heavy-tailed data analysis, and extreme value applications in environmental systems. Analysis of her recent publications reveals a sustained focus on developing new probability distributions (e.g., slash, truncated Zipf, generalized Pareto variants) and their application to multivariate dependencies and extreme events. Her methodological innovations consistently bridge theoretical statistics with practical challenges in hydrology (precipitation extremes), climate science (intense weather events), and ecology (species interaction networks), demonstrating a cohesive trajectory in environmental statistics.

Jingjing Yuمشاهده پروفایل

دانشیار بالینی

• Drug Interactions
• Drug Metabolism
• Drug Transporters
• +۳ مورد دیگر

Jingjing Yu is a Clinical Associate Professor in the Department of Pharmaceutics at the University of Washington School of Pharmacy. She joined the faculty in July 2020 after serving as Associate Director of UW Drug Interaction Solutions since 2019, where she initially began as a Research Scientist and Project Manager in 2013. Her academic trajectory demonstrates steady progression from post-doctoral training at Cerep (now Eurofins) and UW Medicine to her current faculty position. Dr. Yu received her MD in Preventive Medicine and MS in Psychology from Peking University (Beijing, China), followed by MS (2005) and PhD (2007) in Biology from Rensselaer Polytechnic Institute. Her educational background spans medicine, psychology, and biology, providing a multidisciplinary foundation for her research. Her research focuses on drug-drug interaction assessment and safety evaluation, particularly in drug interaction risk prediction, clinical relevance evaluation, and management. Expertise areas include drug and medical products regulation, drug metabolism, drug transporters, in vitro to in vivo extrapolation, mechanisms of drug interactions, and translational research. She has made significant contributions to understanding intestinal drug interactions mediated by OATPs, pharmacokinetic drug-drug interactions with newly approved drugs, and inhibitors of organic anion-transporting polypeptides. Her publication record spans from 2011 to 2024, showing consistent scholarly output. Recent work demonstrates expansion into surgical faculty mentorship while maintaining her core focus on drug interactions. Her research bridges basic pharmacological mechanisms with clinical applications and regulatory considerations, making substantial contributions to drug interaction science. Dr. Yu has demonstrated leadership in managing the Drug Interaction Database content curation process and subscription services at UW Drug Interaction Solutions. Her work consistently addresses critical issues in drug development, safety evaluation, and regulatory science, with particular attention to translational applications of pharmacokinetic research.

Giorgio Marianiمشاهده پروفایل

پژوهشگر

• Generative Models
• Audio Generation
• Computer Graphics
• +۳ مورد دیگر

Giorgio Mariani is a PostDoctoral Researcher at the University of Milano-Bicocca, having completed his Ph.D. in Computer Science at Sapienza, University of Rome under the supervision of Prof. Emanuele Rodolà. His work bridges theoretical machine learning with practical applications in audio processing and computer graphics. His primary research focuses on: Generative models, particularly autoregressive and diffusion-based approaches Audio and music synthesis technologies Computer graphics applications for animation and gaming Adversarial vulnerabilities in geometric data Mariani's publication trajectory shows significant progression from geometric deep learning toward advanced audio generation techniques. His recent work on music generation using diffusion models has been accepted at ICASSP 2024-2025, with 'Latent Autoregressive Source Separation' earning an oral presentation at ICLR 2024. His foundational work on 'Generating Adversarial Surfaces via Band-Limited Perturbations' (Computer Graphics Forum, 2020) established his expertise in 3D shape analysis security. Notable achievements include: Oral presentation at ICLR 2024 (top 5% acceptance rate) Multiple paper acceptances at ICASSP 2024 and upcoming 2025 conference Research bridging audio processing, computer graphics, and security domains As a postdoctoral researcher, Mariani continues to push boundaries in generative AI for audio applications while maintaining connections to his foundational work in 3D geometric data analysis, demonstrating exceptional interdisciplinary research capabilities.

Ricardo Mantillaمشاهده پروفایل

دانشیار

• Hydrology
• Flood Forecasting
• River Networks
• +۵ مورد دیگر

Ricardo Mantilla serves as an Associate Professor in the Department of Civil Engineering at the University of Manitoba's Price Faculty of Engineering. He joined the faculty in July 2021 after previously working as an Assistant Professor at the University of Iowa in the Department of Civil and Environmental Engineering. His academic journey includes significant contributions to hydrological research and flood forecasting systems development. Ph.D. in Civil Engineering (Hydrology), University of Colorado at Boulder (2007) M.Sc. in Civil Engineering (Water Resources), Universidad Nacional de Colombia (2003) B.Sc. in Civil Engineering, Universidad Nacional de Colombia (2000) Mantilla's research focuses on Surface Hydrology, particularly the role of self-similarity in river network topology and hydraulic geometric variables in shaping flood characteristics. He is recognized for developing the Hillslope-Link Model (HLM) and contributing to the Iowa Flood Information System (IFIS). His work bridges theoretical hydrology with practical flood forecasting applications, addressing how climatic and anthropogenic changes affect flood and drought regimes globally. Mantilla's publications demonstrate a consistent trajectory from theoretical foundations to practical implementations, with recent work emphasizing physics-based modeling approaches over purely statistical methods for flood prediction under changing climate conditions. Mantilla actively seeks graduate students for his research group, emphasizing the need for strong communication skills, passion for water resources engineering, problem-solving abilities, and comfort working in diverse environments. His research has led to the development of operational flood forecasting systems that utilize High-Performance Computing resources to provide public flood information through web platforms.

• Computational Neuroscience
• Neural Networks
• Motion Processing
• +۳ مورد دیگر

Selma Souihel is a PhD Candidate at INRIA Sophia Antipolis within the Biovision team, affiliated with Université Côte d’Azur. Her research in Computational Neuroscience focuses on motion encoding, anticipation, and retino-cortical integration. She is funded by the ANR project “Trajectory” (ANR-15-CE37-0011) and collaborates with researchers from Institut de Neurosciences de la Timone, Institut de la Vision, and Institut des Neurosciences Paris-Saclay. Education: PhD in Computer Science (2019, Université Côte d’Azur); Engineering degree in bioinformatics (2016, Ecole des Mines de Saint Etienne) Her research bridges retinal and cortical modeling, emphasizing computational principles for motion extrapolation and long-range cellular interactions. Articles span mean-field approaches, motion anticipation models, and retinal dynamics, presented at international conferences like Bernstein, AREADNE, and ICMNS. She received a poster prize at the GDR Neural Net conference (2018). Teaching roles include courses on Digital Transmissions, Networks and Security, and Advanced Networking at Université Côte d’Azur (2016–2019). She also organized PhD seminars and events like MOMI2018 and Grets’17, while participating in science outreach at Inria.

• harmonic analysis
• theory of functions of a complex variable
• Banach algebra
• +۵ مورد دیگر

Saulius Norvidas is an Associate Professor and Senior Researcher at Vilnius University's Faculty of Mathematics and Informatics, working within the Interdisciplinary Statistical Research Group at the Institute of Data Science and Digital Technologies. His academic career spans over four decades, with significant contributions to mathematical analysis. His educational background includes: Graduated from the Faculty of Mechanics and Mathematics of Moscow University in 1980 Candidate of Physical and Mathematical Sciences (1986, Moscow University) Doctor of Mathematics (1993, Research Council of Lithuania) Norvidas specializes in harmonic analysis, theory of functions of a complex variable, Banach algebra, and operator theory. His research primarily focuses on characteristic functions, their properties, extensions, and applications in probability theory. He has made significant contributions to understanding the relationship between characteristic functions and their analytic properties, including uniqueness theorems, extrapolation problems, and the behavior of powers of characteristic functions. His work often bridges pure mathematics with applications in signal processing and statistical inference. An analysis of his recent publications reveals a consistent focus on characteristic functions and harmonic analysis, with particular attention to their analytic continuation properties, extension problems, and applications in reconstruction theory. His work demonstrates deep connections between complex analysis, functional analysis, and probability theory, with numerous papers exploring the intricate relationship between the analytic properties of characteristic functions and their underlying probability distributions. Norvidas is an active member of both the Lithuanian and American Mathematical Societies, reflecting his international engagement in the mathematical community. He has contributed to significant research projects, including the Lithuanian State Science and Studies Foundation project "Function and Measure Reconstruction Problems". His academic career progression shows steady advancement from assistant positions at Vilnius University (1980-1997) to Senior Research Fellow at MII (since 1993), culminating in his appointment as Associate Professor at Vilnius University (since 1997). This trajectory demonstrates his sustained contribution to mathematical research and education over several decades. Norvidas works within the Interdisciplinary Statistical Research Group, which suggests collaboration across mathematical disciplines and potential applications of his theoretical work to statistical problems and data science applications.

• Environmental Toxicology
• Exposure Assessment
• Public Health
• +۴ مورد دیگر

Marc-André VERNER is a Full Professor at the University of Montreal's School of Public Health, holding a position in the Department of Environmental Health and Occupational Health. He specializes in evaluating children's exposure to environmental contaminants and developing predictive toxicology models. His work bridges toxicology and environmental epidemiology, focusing on chemical risk assessment. Education: Bachelor's and Master's in Biology and Toxicology, Université du Québec à Montréal (2005-2007) PhD in Toxicology, Université du Québec à Montréal (2012) Postdoctoral studies at Karolinska Institutet (2012) and Harvard Medical School (2014) Research Interests: Dr. Verner's research centers on exposure assessment, environmental toxicology, and the health impacts of contaminants like PFAS, flame retardants, and pesticides. He emphasizes modeling chemical transfer across biological barriers and improving risk assessment methodologies. His work highlights the importance of minimizing early-life exposures to protect child development. Grants & Projects: Lead researcher on projects addressing PFAS exposure in breast milk and dietary sources (funded by MAPAQ, CRSNG). Investigates in vitro-to-in vivo extrapolation tools for chemical risk assessment (CRSNG). Studies hydraulic fracturing's effects on maternal and fetal exposure to environmental toxins (IRSC). Awards: 2016 Joan M. Daisey Outstanding Young Scientist Award Multiple honors from the Society of Toxicology for research excellence Teaching & Supervision: Teaches courses on risk analysis and toxicology at the graduate level. Supervises master's and doctoral students in areas such as PFAS exposure, biomonitoring, and pharmacokinetic modeling. Affiliations: Member of the CReSP (Centre de recherche en santé publique) and leads the Toxicology and Risk Analysis track in the Public Health PhD program.

• Computer Vision
• Machine Learning
• Robotics
• +۷ مورد دیگر

Yuxiong Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC). His research focuses on computer vision, machine learning, and robotics, particularly in open-world perception, meta-learning, generative modeling, and agent learning. He previously held a postdoctoral position at Carnegie Mellon University and obtained his Ph.D. from the Robotics Institute under Prof. Martial Hebert. He has received notable awards including the Best Paper Honorable Mention at ECCV 2020 and Best Paper Finalist awards at CVPR 2019 and 2022. His work bridges generative and discriminative AI, with contributions to 3D vision, human-object interaction, and vision-language models. Recent research highlights include RandAR (decoder-based visual generation), InterMimic (physics-based human-object control), and Argus (vision-centric reasoning). He teaches courses such as CS 445 (Computational Photography) and CS 598 LTL (Learning to Learn). His group actively collaborates with NVIDIA and Electronic Arts, and his work has been funded by grants from NASA and the NCSA. Current projects explore meta-learning, open-world learning, and applications in robotics, healthcare, and materials science.

Shanti Bhushanمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics (CFD)
• Turbulence Modeling
• Hypersonic Flows
• +۷ مورد دیگر

Shanti Bhushan is an Associate Professor in the Michael W. Hall School of Mechanical Engineering at Mississippi State University (MSU) and serves as Associate Director of the Center for Advanced Vehicular Systems (CAVS) and iDEELab Deputy Director . He holds a Ph.D. in Aerospace Engineering from MSU (2003), M.S. from IIT Kharagpur (2000), and B.S. from Bengal Engineering College (1998). His research focuses on advanced CFD modeling, turbulence transition mechanisms, hypersonic flows, and fluid-structure interactions with over 20 years of experience in industrial and academic collaborations. Key research areas include turbulence modeling , hypersonic trajectory optimization , and machine learning integration with CFD. He has contributed to projects at the Air Force Research Lab, Argonne National Lab, and the University of Iowa's IIHR. Awards include the 2022 Outstanding Senior Faculty Research Award and 2016 Robert T. Knapp Award (ASME). Teaching : Viscous Fluid Dynamics, Intermediate Thermodynamics Leadership : ASME CFD Technical Committee Associate Editor Interdisciplinary Work : Poultry Incubator Thermal Studies, Scramjet Engine Modeling His publications span 20+ years, emphasizing LES/RANS hybrid models , vortex dynamics , and CFD validation for naval and aerospace applications. Recent work explores machine-learned turbulence closures and hypersonic thermal management .

Ben DeVriesمشاهده پروفایل

دانشیار

• Satellite Earth observation
• Remote sensing
• Ecosystem dynamics
• +۲ مورد دیگر

Ben DeVries is an Associate Professor in the Department of Geography, Environment & Geomatics at the University of Guelph's College of Social and Applied Human Sciences. His research focuses on satellite Earth observation, remote sensing of ecosystem dynamics, and land use/land cover change. He teaches courses such as Analysis in Geography, The Earth from Space, GIS and Spatial Analysis, and Applied Geomatics. His research interests span geospatial technologies, environmental monitoring, and ecosystem dynamics, with a strong emphasis on practical applications in conservation and land management. Dr. DeVries supervises multiple graduate students working on projects ranging from wetland classification using remote sensing to Arctic lake dynamics. His recent publications reflect a focus on global forest carbon patterns, erosion modeling, and advanced satellite data applications. Research trends show integration of machine learning, large-scale geospatial analysis, and climate impact assessments across diverse ecosystems. His work employs cutting-edge methodologies including cloud computing, time-series analysis of satellite imagery, and hybrid modeling approaches to address pressing environmental challenges.