جست‌وجوی استاد و پژوهشگر

Aram Harrowمشاهده پروفایل

استاد

• Quantum Information Science
• Quantum Algorithms
• Quantum Computing
• +۳ مورد دیگر

Aram Harrow is a Professor of Physics at the Massachusetts Institute of Technology (MIT) , affiliated with the MIT Center for Theoretical Physics and MIT Center for Quantum Engineering . He focuses on quantum information science and quantum algorithms , with additional interests in representation theory and optimization . His recent work explores quantum computing applications in chemical physics and statistical mechanics . Undergraduate and graduate degrees in Physics at MIT Faculty positions: MIT (2013-present), University of Washington (2010-12), University of Bristol (2005-10) Research Interests: His work bridges quantum information theory and many-body physics , including: Quantum algorithm design for chemistry and optimization Quantum circuit complexity and t-designs Entanglement dynamics in quantum systems Quantum-classical hybrid computing models Key Publications: Recent articles demonstrate quantum speedups for biomolecular free energy calculations , Hamiltonian simulation , and jet clustering algorithms . His research combines quantum complexity theory with practical implementations on near-term quantum devices. Scientific Awards: 2023 Simons Investigator 2018 APS Bennett Award 2017 IEEE Best Paper Award 2016 Kavli Frontiers Fellow Mentorship: He advises current PhD students Shankar Balasubramanian , Angus Lowe , and Norah Tan , with 12 former advisees including Anand Natarajan and Saeed Mehraban . His 2026 recruitment seeks one new graduate student.

Freddy Bouchetمشاهده پروفایل

پژوهشگر

• Statistical Physics
• Climate Dynamics
• Turbulence
• +۵ مورد دیگر

Freddy Bouchet is a Directeur de Recherche at CNRS and a Professeur attaché at École Normale Supérieure de Paris (ENS-PSL). His work bridges mathematical physics, climate science, data science, and statistical mechanics , focusing on turbulent flows, climate extremes, and large deviation theory . He will lead the Laboratoire de Météorologie Dynamique (LMD) starting 2025. Research Themes : Statistical mechanics of geophysical flows (Jupiter's jets, ocean currents). Large deviation theory for rare events in turbulence and climate. Non-equilibrium phase transitions in atmospheric/oceanic systems. Ensemble inequivalence in systems with long-range interactions. Scientific Awards : Three Physicists Prize Collaborations : Tapio Schneider, Antoine Venaille, J. Laurie, O. Zaboronski, B. Dubrulle, A. Venaille. Labs & Teams : Climate and Statistical Mechanics group at ENS de Lyon Future director of Laboratoire de Météorologie Dynamique (LMD/IPSL) Publications span climate dynamics, turbulence, statistical mechanics, and large deviation theory , with applications to Jupiter's atmosphere, ocean vortices, and non-equilibrium systems . His work often challenges paradigms like Tsallis non-extensive statistics.

• X-ray Optics
• Nanoimaging
• Synchrotron Radiation
• +۳ مورد دیگر

Ulrich Vogt is a Professor in Applied Physics at Kungliga Tekniska Högskolan (KTH) and leads the X-ray Optics and Nanoimaging group within the Bio-Opto-Nano unit. He serves as Vice-head of the Applied Physics department for undergraduate education. His research focuses on developing advanced X-ray microscopy techniques, particularly at synchrotron facilities like MAX IV’s NanoMAX beamline. He specializes in X-ray optics, nanoimaging, and diffractive optical elements for applications in materials science, biology, and medicine. Key contributions include the design of the NanoMAX beamline, optimization of X-ray zone plates via metal-assisted chemical etching, and advancements in multi-beam ptychography. Vogt has pioneered compact X-ray microscopy systems using laser-plasma sources and liquid-jet targets. His work integrates nanofabrication, computational imaging, and synchrotron instrumentation to achieve sub-100 nm resolution in hard and soft X-ray regimes. Teaching responsibilities include courses on experimental physics, photonics, and X-ray applications. His lab collaborates internationally on projects like the European XFEL, emphasizing high-brightness sources and radiation-resistant optics. Recent innovations include adaptive multi-beam ptychography and stereo X-ray imaging for 3D nanoscale visualization. Research highlights span over 100 peer-reviewed articles, with a focus on coherence characterization, beamline instrumentation, and nanostructured materials. Vogt’s grants include a Röntgen-Ångström Cluster award supporting multi-beam ptychography and cryo-microscopy advancements.

• Particle and high energy physics
• Astronomical sciences
• Nuclear and plasma physics
• +۳ مورد دیگر

Wayne Springer is a Professor in the Department of Physics & Astronomy at the University of Utah, with a career spanning over 25 years. He has been actively involved in experimental particle astrophysics, ultra-high-energy cosmic ray (UHECR) physics, and gamma-ray astronomy. Ph.D. in Physics from University of Maryland (1991) B.S. in Physics from University of Maryland (1985) Postdoctoral training at University of Maryland and University of Alberta His research focuses on particle astrophysics, cosmic ray detection, and gamma-ray astronomy. He has made significant contributions to the development of the HiRes and Telescope Array cosmic ray observatories, as well as the HAWC and SWGO gamma-ray observatories. His recent work includes deployment of the Trinity neutrino detector prototype and serving as SWGO project manager for Chile site infrastructure. Article trends show strong emphasis on TeV gamma-ray observations (HAWC, SWGO), cosmic ray diffusion mechanisms, dark matter searches, and high-energy astrophysical source characterization (pulsars, microquasars, supernova remnants). He has secured multiple NSF grants for particle astrophysics research and leads detector working groups in international collaborations. Professor Springer actively participates in astronomy outreach, co-developing observatories and implementing computational physics teaching tools with Gradescope auto-graders for enhanced pedagogy. His work bridges experimental high-energy physics, detector development, and multiwavelength astrophysical studies.

Anders Sejr Hansenمشاهده پروفایل

استادیار

• 3D Genome Structure
• Gene Expression Regulation
• Chromatin Dynamics
• +۵ مورد دیگر

Anders Sejr Hansen is an Assistant Professor of Biological Engineering at MIT, leading the Hansen Lab focused on understanding 3D genome structure and its functional implications. He holds a PhD from Harvard University and completed postdoctoral training at UC Berkeley. His research integrates advanced imaging, genomics, and computational methods to study chromatin dynamics, enhancer-promoter interactions, and their roles in gene regulation across health and disease. Education: Bachelor's/Master's in Chemistry, University of Oxford (2010) PhD in Chemistry and Chemical Biology, Harvard University (2015) Postdoctoral Research, UC Berkeley (2015–2020) Research Interests: His work spans molecular mechanisms of genome organization, development of novel microscopy techniques (e.g., MINFLUX, expansion microscopy), and computational models for 3D genomics. Key areas include chromatin dynamics, loop extrusion by cohesin/condensin, and the impact of 3D structure on gene expression in cancer and aging. Awards: NIH K99 Pathway to Independence Award (2019) NIH Director’s New Innovator Award (2020) Pew-Stewart Scholar for Cancer Research (2021) NSF CAREER Award (2024) NIH Director’s Transformative Research Award (2024) Advising & Grants: Hansen mentors PhD students and postdocs, including notable advisees Viraat Goel and Domenic Narducci. His lab has secured major grants from NIH, NSF, and private foundations, supporting interdisciplinary projects in imaging, genomics, and synthetic biology. Labs/Teams: The Hansen Lab at MIT collaborates with institutions globally, advancing technologies like Region Capture Micro-C (RCMC) and deep learning models (e.g., Cleopatra) for high-resolution genome mapping. The lab also explores synthetic biology approaches to engineer genome structures.

Matthias Ihmeمشاهده پروفایل

استاد

• Combustion Science
• Fluid Dynamics
• Computational Fluid Dynamics
• +۵ مورد دیگر

Matthias Ihme is a Professor in the Department of Mechanical Engineering and Photon Science Directorate at Stanford University. His research focuses on large-eddy simulation (LES) of turbulent reacting flows, aeroacoustics, combustion-generated noise, numerical methods, and high-order schemes. He holds a Ph.D. from Stanford University (2008), an M.Sc. in Computational Engineering from the University of Erlangen (Germany, 2002), and a Dipl.-Ing. in Mechanical Engineering from Munich University of Applied Sciences (Germany, 2000). His work bridges computational fluid dynamics, combustion science, and photon science, with notable contributions to supercritical fluid dynamics, machine learning integration in fluid simulations, and high-fidelity atmospheric transport modeling. Recent research emphasizes ultrafast cluster dynamics, shock-induced interface behavior, and stochastic ignition mechanisms in advanced fuel systems. Publications highlight interdisciplinary advancements, including physics-informed ML frameworks for reacting flows and experimental studies using X-ray photon correlation spectroscopy. His projects often involve high-performance computing and collaboration with national labs like SLAC.

Yulia Gelمشاهده پروفایل

استاد

• Uncertainty Quantification
• Artificial Intelligence
• Topological Data Analysis
• +۶ مورد دیگر

Yulia Gel is a Professor in the Department of Statistics at Virginia Tech and serves as a Part-Time Program Director-Expert at the National Science Foundation (NSF). She holds a MSc (summa cum laude) and PhD in Mathematics from Saint Petersburg State University (Russia) and completed a postdoc in Statistics at the University of Washington. Her research focuses on uncertainty quantification in AI, statistical foundations of data science, spatio-temporal processes, and applications in climate science, healthcare, and blockchain analytics. She has received prestigious awards including the NSF Director’s Award (2023), ASA Distinguished Achievement Medal (2018), and TIES Abdel El-Shaarawi Award (2014). Gel has led grants on wildfire prediction, climate informatics, and blockchain data science. She serves on editorial boards of Statistica Sinica, Electronic Journal of Statistics, and Technometrics, and organizes workshops on AI for climate sustainability and fragile Earth systems. Her research group develops topological and geometric methods for graph neural networks, with applications to digital twins, environmental justice, and public health. Education: MSc (1997), PhD (2000) in Mathematics from Saint Petersburg State University; Postdoc in Statistics at University of Washington (2001–2003). Past roles include Professor at University of Texas at Dallas (2015–2024) and Associate Professor at University of Waterloo (2004–2014). Selected visiting positions include NASA Jet Propulsion Lab (2016–2017) and Isaac Newton Institute (2016–2017). She has pioneered statistical software packages like snowboot and funtimes for network inference and time-series analysis. Awards highlight her contributions to environmetrics and statistical methodologies. Current projects include NSF-funded research on AI-driven wildfire prediction and blockchain analytics for climate resilience. Her lab’s recent work emphasizes topological methods (e.g., zigzag persistence) for graph-based forecasting and adversarial robustness.

Jacqueline McClearyمشاهده پروفایل

استادیار

• Astrophysics
• Cosmology
• Weak Gravitational Lensing
• +۴ مورد دیگر

Dr. Jacqueline McCleary is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in observational cosmology with a focus on galaxy clusters and dark matter. She leads research using weak gravitational lensing to study cosmic structures, collaborating on projects like the COSMOS-Web (JWST), SuperBIT (balloon telescope), and LoVoCCS surveys. Her work leverages multi-wavelength data from space, stratospheric, and ground-based observatories. Education: M.S. in Astronomy (New Mexico State University), M.S. and Ph.D. in Physics (Brown University), Postdoctoral Fellow at NASA's Jet Propulsion Laboratory. She transitioned to Northeastern as an ADVANCE Future Faculty Fellow before becoming a tenure-track faculty member in 2022. Research Interests: Dark matter interactions, galaxy cluster dynamics, gravitational lensing techniques, and next-generation observational platforms. Her team develops advanced algorithms and instrumentation for high-resolution imaging. Recent Contributions: COSMOS-Web has enabled unprecedented observations of distant galaxies using JWST, while SuperBIT's stratospheric flights provide diffraction-limited data. Key publications focus on lensing surveys, data reduction techniques, and dark matter-halo relationships. Awards: Recognized as a Northeastern ADVANCE Future Faculty Fellow. Media Engagement: Regularly comments on space exploration, asteroid risks, and cosmic phenomena for public outlets.

Fraser Kingمشاهده پروفایل

استادیار

• Climate and Climate Change
• Radiation and Remote Sensing
• Synoptic Meteorology
• +۱۳ مورد دیگر

Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.

Andreas Jungمشاهده پروفایل

دانشیار

• High Energy Physics
• Particle Physics
• Detector Development
• +۳ مورد دیگر

Andreas Jung is an Associate Professor of Physics and Astronomy at Purdue University, affiliated with the CMS experiment at CERN. His research focuses on understanding the electroweak scale stabilization via precision measurements of top quark interactions, Higgs boson studies, and detector R&D. He also explores quantum algorithms for high-energy physics and supply chain optimization. Jung earned his Ph.D. from the University of Heidelberg (2009) and a diploma from the University of Dortmund (2004). Education: Ph.D. in Physics, University of Heidelberg, 2009 (Dissertation: D* Meson Cross Section Measurement) Diploma in Physics, University of Dortmund, 2004 (Commissioning of H1 Fast Track Trigger) Research Interests: High Energy Physics, Particle Physics, Detector Development, Quantum Computing Applications, Material Science for Detectors, and Collider Experiments. His work includes analyzing top quark spin correlations, quantum annealing for vertex reconstruction, and carbon fiber composites for CMS upgrades. Awards: Senior Distinguished Researcher fellowship at Fermilab LHC Physics Center (2019) 3-year PhD scholarship from German Research Society (2004–2007) Teaching & Leadership: Teaches courses on particle physics and data science. Serves as Convener of CMS TOP Physics Analysis Group and leads detector mechanics R&D. Engages in quantum computing collaborations with DoD and industry partners. Labs/Teams: Jung Research Group at Purdue, CMS Collaboration, and Purdue Quantum Science & Engineering Institute (PQSEI). Active in detector development for the High-Luminosity LHC upgrade, including carbon fiber support structures and silicon pixel detectors.

Matt LeBlancمشاهده پروفایل

استادیار

• Particle Physics
• High Energy Physics
• Jet Physics
• +۷ مورد دیگر

Matt LeBlanc is an Assistant Professor of Physics (Research) at Brown University, affiliated with the CMS Collaboration since 2024 and previously a core member of the ATLAS Collaboration from 2010–2023. His research focuses on experimental particle physics, particularly the analysis of hadronic objects and final states at the Large Hadron Collider (LHC). He employs advanced data science techniques, including machine learning and optimal transport algorithms, to study jet physics and search for new particles beyond the Standard Model. LeBlanc has contributed to jet reconstruction, calibration, and novel analysis methods in LHC data. Education: Ph.D. in experimental particle physics from the University of Victoria (Canada). Postdoctoral appointments at the University of Arizona, CERN, and the University of Manchester (UK). Research Interests: Jet substructure, hadronic object reconstruction, dark matter searches, QCD studies, radiation-hard detector development (e.g., MALTA sensors), and applications of AI/ML in physics. His work bridges experimental particle physics with computational science, addressing challenges in data processing for the High-Luminosity LHC era. Key Contributions: Leader of physics analyses in the ATLAS Collaboration, coordinator for hadronic object reconstruction/calibration, developer of jet energy scale algorithms, and pioneer in applying optimal transport and topic modeling to particle physics data. His recent work emphasizes efficient data pipelines and simulations for future collider experiments.

Nathaniel Alan Brunsellمشاهده پروفایل

استاد

• Biometeorology
• Remote Sensing
• Climate Change Impacts
• +۶ مورد دیگر

Nathaniel Alan Brunsell is a Professor and Director of the Environmental Studies Program at the University of Kansas. His research focuses on biometeorology, remote sensing of surface energy balance, and climate change impacts. He leads the Environmental Studies Program, overseeing interdisciplinary environmental research and education. Key research areas include land-atmosphere interactions, turbulence measurements using scintillometry and eddy covariance, and the ecological consequences of regional climate change. He collaborates on NASA missions like ECOSTRESS to advance evapotranspiration measurement capabilities. His grants include leadership in the Konza Cluster for the AmeriFlux Network and contributions to studies on urban heat islands and carbon cycle science. Brunsell teaches courses such as Land-Atmosphere Interactions, Atmospheric Turbulence, and Microclimatology, integrating theoretical and applied aspects of environmental science. His work bridges field observations, remote sensing, and numerical modeling to address challenges in climate science, sustainable agriculture, and environmental policy.

Jonathan E. Martinمشاهده پروفایل

استاد

• Synoptic Meteorology
• Atmospheric Dynamics
• Jet Stream Variability
• +۳ مورد دیگر

Jonathan E. Martin is a Professor in the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin–Madison since 1994. He received his Ph.D. in Atmospheric Sciences from the University of Washington in 1992. Research Focus: Mid-latitude cyclones, jet stream dynamics, and weather-climate interactions Key Methods: Multi-scale observational analysis, high-resolution mesoscale modeling, potential vorticity diagnostics Recent Projects: Jet stream waviness trends, subtropical-midlatitude cyclone transformation, snow cover-cyclone feedback His 2025-2024 publications examine extratropical cyclone precipitation distribution, Pacific basin rapid cyclogenesis, and Earth system model validation for cyclone occlusion. Earlier work (2023-2019) addresses: Jet superposition climatology Self-organizing map analysis of jet variability Cyclone-induced Southern Hemisphere waviness Nonlinear jet retraction mechanisms Tornado outbreak jet influences Historical cold pool contraction trends Awarded the 2021 Atmospheric Sciences Librarians International Choice Runner Up for his historical meteorology work, he also authored the textbook Mid-Latitude Atmospheric Dynamics: A First Course (2013). His research spans cyclogenesis, frontogenesis, and climate change impacts on jet streams, with applications in extreme weather forecasting and climate modeling validation.

• Graph Analytics
• Parallel Algorithms
• Multicore Computing
• +۶ مورد دیگر

Kamesh Madduri is an Associate Professor in the Department of Computer Science and Engineering at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His research focuses on graph analytics, parallel algorithms, and high-performance computing for large-scale data analysis. NSF CAREER Award (2013) His work contributes to the development of scalable graph partitioning algorithms, extreme-scale sparse data analytics, and heterogeneous computing frameworks. Recent projects include multilayer network analysis (NetSplicer) and GPU-accelerated graph processing (Jet). Key research areas include network science, computational biology, and distributed-memory graph algorithms. His publications highlight applications in genomic workflows, advertising keyphrase recommendation (Graphite/BroadGen), and large-scale hydrology data management. Collaborative Research: CCRI (2021-2023) SHF: Medium: NetSplicer (2020-2024) PPoSS: Extreme-scale Sparse Data Analytics (2018-2022) XPS: Genomic Workflows Acceleration (2014-2020) EAGER: SME Manufacturing Integration (2024-2026)

Harish Nagaraj Dixitمشاهده پروفایل

دانشیار

• Interfacial flows
• Moving contact lines
• Drop and bubble dynamics
• +۵ مورد دیگر

Dr. Harish Nagaraj Dixit is an Associate Professor in the Department of Mechanical & Aerospace Engineering at the Indian Institute of Technology Hyderabad , where he leads a multidisciplinary research group focusing on Interfacial flows , Hydrodynamic stability theory , Vortex dynamics , and Electrohydrodynamics . His academic journey began with a B.Tech from Osmania University (2003), followed by an M.S. at IIT Madras (2005), and culminated in a Ph.D. from JNCASR, Bengaluru (2010), where his dissertation explored Vortices with density stratification . The group employs theoretical, computational, and experimental approaches to investigate phenomena such as drop impact mechanics , contact line dynamics , and vortex pattern formation . They operate a state-of-the-art laboratory equipped with high-speed cameras and lasers for advanced fluid experimentation. Recent publications span topics including electrospinning , tear-film stability , and vortex shedding in oscillating flows . Dr. Dixit teaches advanced courses like ME 5310: Incompressible Fluid Flow and ME 7100: Advanced Mathematical Tools , emphasizing conceptual understanding and rigorous analytical methods. His research advisory includes multiple Ph.D. and M.Tech students working on problems from drop impact to electrohydrodynamic jetting . Collaborations extend to institutions such as the University of British Columbia and IIT Madras .