جستجوی استاد راهنمای دانشگاهی

Todd Adamsمشاهده پروفایل

استاد

• Particle Physics
• High Energy Physics
• Neutrino Physics
• +۳ مورد دیگر

Todd Adams is a Professor in the Department of Physics at Florida State University . He leads research in particle physics (high energy experiment) with the CMS Experiment at CERN and previously the D0 Experiment at Fermilab , focusing on searches for new physics in underexplored datasets through long-lived particles , machine learning techniques , and charged particle detection . Education : PhD in Experimental Particle Physics from University of Notre Dame (1997); Postdoctoral researcher at Kansas State University (1997-2001) His research includes electromagnetic calorimeter studies for CMS, calorimeter upgrade investigations , and Monte Carlo simulation leadership for D0. He pioneered searches for neutral long-lived particles and top quark decay anomalies , co-authored key publications in Physical Review Letters and Journal of High Energy Physics , and served as Faculty Senate President and Board of Trustees member at FSU. Notable affiliations include: Collaborations : CMS, D0, NuTeV, NuSOnG Laboratories : CERN (Geneva), Fermilab (Chicago), Florida State High Energy Physics Group Key contributions: Co-convenor of D0 Monte Carlo Simulations and New Physics Signatures groups Expert in heavy quark production , dimuon analysis , and neutral current studies Publications on Higgs boson discovery implications, supersymmetry , and anomalous gauge couplings Scientific Awards : Fellow, American Association for the Advancement of Science Multiple Fermilab Result of the Week highlights (2006, 2008, 2013) Contributor to CMS Thesis Award Committee He advises graduate students in experimental particle physics and contributes to detector technology development, particularly in timing studies , calibration , and trigger systems . His research program will continue through the LHC's 2035 operations with ongoing CMS data analysis.

Teruki Kamonمشاهده پروفایل

استاد

• Dark Matter
• Supersymmetry
• Particle Physics
• +۲ مورد دیگر

Teruki Kamon is a Professor at Texas A&M University affiliated with the Mitchell Institute for Fundamental Physics & Astronomy. As an active member of the CDF and CMS collaborations, he conducts experimental particle physics research at hadron colliders with focus on dark matter phenomenology. His research centers on testing particle physics models for cold dark matter, with Supersymmetry (SUSY) as a leading candidate. He pioneered SUSY searches in final states involving electrons, muons, and third-generation particles like tau leptons, specializing in: Compressed mass spectrum signatures Vector boson fusion topologies Soft tau lepton analyses Missing transverse momentum techniques Recent publications (2017-2020) demonstrate consistent leadership in LHC-based searches for supersymmetry and new physics, including unique contributions to CMS detector upgrades. His group dominates research on compressed-mass SUSY in vector boson fusion processes. No information regarding awards, student advising, or grant funding was provided in available sources.

Fay Couceiroمشاهده پروفایل

استاد

• Environmental Pollution
• Microplastics Research
• Contaminant Fate and Transport
• +۴ مورد دیگر

Professor Fay Couceiro is a Professor of Environmental Pollution in the School of Civil Engineering and Surveying at the University of Portsmouth. She leads the Microplastics Research Group and the 'Evaluating change across the plastics lifecycle' theme for the Revolution Plastics Institute, focusing on pollution sources, interventions, and collaborations with industry. She holds editorial roles at Cambridge Prisms: Plastics and peer reviews for multiple journals and funding bodies. Education: BSc in Marine Biology (Queen's University Belfast), PhD in Biogeochemistry (funded project on Strangford Lough), postdoctoral research at the University of Plymouth. Research Interests: Contaminants' fate, microplastics' environmental and health impacts, nutrient dynamics, heavy metals, and organic pollutants like PAHs. Her work integrates pure science with civil engineering solutions. Publications span 2007–2025, emphasizing microplastic toxicity, soil-oil interactions, and pollution mitigation strategies. Over 27 peer-reviewed articles highlight her contributions to environmental science. Advising & Grants: Supervises MSc/PhD students and collaborates with companies like Southern Water. Active in STEM outreach and UK research policy, including the EU's HR Excellence in Research accreditation process. Labs/Teams: Microplastics Research Group, Revolution Plastics Institute, and interdisciplinary teams in environmental technology and resilience.

• Cement and concrete technology
• Alkali-activated materials
• Geopolymer science
• +۳ مورد دیگر

Professor John L Provis is a leading expert in cement materials science at the University of Sheffield 's School of Chemical, Materials and Biological Engineering. He also holds a Visiting Professor position at Luleå University of Technology's Building Materials division. PhD in Chemical Engineering (University of Melbourne) 2013 RILEM Robert L'Hermite Medal 2015 Honorary Doctorate from Hasselt University Editor-in-Chief of Materials and Structures His research focuses on alkali-activated materials and geopolymer binders for sustainable construction, with key themes in chemical speciation , waste immobilization , and novel cement systems for nuclear applications. Recent publications highlight 15 representative articles spanning topics: Radiation-resistant cementitious matrices Geopolymer synthesis for heavy metal containment Thermodynamic modeling of binder systems Corrosion mechanisms in alkali-activated concretes Low-carbon cement alternatives Microstructural analysis via advanced spectroscopy Scientific Recognition : RILEM Robert L'Hermite Medal recipient Hasselt University Honorary Doctorate Editorial leadership roles in major journals Contact: j.provis@sheffield.ac.uk | Sir Robert Hadfield Building, University of Sheffield

Michael Klasenمشاهده پروفایل

استاد

• Particle Physics
• Quantum Chromodynamics
• Physics beyond the Standard Model
• +۳ مورد دیگر

Prof. Dr. Michael Klasen is a leading theoretical physicist at the Institute of Theoretical Physics at the University of Münster, where he heads his eponymous research group. His work bridges nuclear and particle physics, with significant contributions to quantum chromodynamics and physics beyond the Standard Model. His research focuses on Particle Physics , Quantum Chromodynamics , and Physics beyond the Standard Model , with particular emphasis on understanding the quark-gluon structure of atomic nuclei and dark matter phenomena. His innovative approach connects microscopic quark-gluon dynamics with nuclear binding phenomena, creating a crucial bridge between nuclear and particle physics. Prof. Klasen's recent work analyzing nucleon binding at the quark-gluon level was recognized as a "Breakthrough of the Year 2024" by Physics World. His research group's publication in Physical Review Letters demonstrated how quarks and gluons behave differently in nucleon pairs than in free nucleons, fundamentally advancing our understanding of nuclear binding. Breakthrough of the Year 2024 from Physics World Leadership of Research Training Group 2149 "Strong and weak interactions - from hadrons to dark matter" Supervision of award-winning doctoral research including the Infineon Dissertation Prize 2025 Prof. Klasen has successfully mentored numerous PhD students, with 20 of his group's graduates continuing their academic careers at prestigious institutions including CERN and Stanford University. His research has been supported by major funding bodies including the German Research Foundation (DFG), the Helmholtz Alliance for Astroparticle Physics, and BMBF collaborative research programs. The Klasen working group maintains active collaborations with international research networks including CTEQ, DM@NLO, and RESUMMINO.

Simon Makمشاهده پروفایل

استادیار

• Statistical Modeling
• Bayesian Statistics
• Gaussian Process
• +۵ مورد دیگر

Simon Mak is an Assistant Professor of Statistical Science at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences. His educational background includes: Ph.D. in Statistics, Georgia Institute of Technology (2018) M.S. in Statistics, Georgia Institute of Technology (2018) B.S. in Statistics, Simon Fraser University (2013) Dr. Mak's research focuses on advanced statistical methodologies for complex scientific problems. His expertise spans statistical modeling , Bayesian inference , Gaussian process emulation , and uncertainty quantification . He applies these methods to nuclear physics (heavy-ion collisions), engineering (engine control systems), and music information retrieval, emphasizing scalability and interpretability in scientific computing. Analysis of his 2023-2025 publications reveals dominant trends in scalable Gaussian process methods for massive datasets and multi-fidelity simulations, particularly applied to high-energy physics and engineering systems. He has pioneered innovations in Bayesian optimization for expensive simulators and developed novel frameworks for online change-point detection in streaming data, demonstrating exceptional cross-disciplinary impact. Dr. Mak leads multiple significant research initiatives: Collaborative Research: Cost-Efficient and Confident Sampling for Modern Scientific Discovery (2023-2026) Science-Integrated Predictive modeLing (SCINPL) for scalable scientific computing (2022-2025) The X-SCAPE collaboration for statistically advanced nuclear collision modeling (2020-2025) These projects fund his development of statistical frameworks for scientific discovery in complex systems. He actively contributes to the JETSCAPE collaboration, developing multi-stage frameworks for studying jet quenching in heavy-ion collisions, and applies statistical methods through the Duke Institute for Brain Sciences to advance neuroscience research.

Graham Sanderمشاهده پروفایل

استاد

• Soil Erosion
• Hydrology
• Environmental Science
• +۳ مورد دیگر

Professor Graham Sander is a Professor of Hydrology at Loughborough University, affiliated with the School of Civil and Building Engineering. His research focuses on mathematical modeling of soil erosion, unsaturated soil flow, contaminant transport dynamics, and nonlinear diffusion-convection equations. He leads the NERC-funded project on multi-dimensional soil erosion and chemical transport, collaborating with Lancaster University’s Department of Environmental Science. His academic qualifications include a BSc (Hons) and PhD. Recent research emphasizes integrating particle size-selective models to predict sediment and contaminant delivery to water bodies, supported by lab and field experiments. He has also pioneered pseudospectral methods for infiltration modeling and explored flood risk management through nature-based solutions like leaky barriers. Key contributions include advancing understanding of rainfall-driven erosion dynamics, including splash effects and rock fragment coverage impacts. His work spans experimental hydrology, numerical simulation, and interdisciplinary approaches to environmental challenges. He co-edits hydrology journals and advocates for rigorous scientific communication in the field. Grants: NERC-funded soil erosion project, Australian Research Council project on unsaturated soil flow. Labs/Teams: Collaborator with Lancaster University’s Environmental Science Department.

• Medical Physics
• Artificial Intelligence in Medicine
• Radiation Therapy Treatment Planning
• +۷ مورد دیگر

Dan Nguyen, Ph.D., is a faculty member in the Department of Radiation Oncology at UT Southwestern Medical Center, where he is part of the Division of Medical Physics and Engineering. He is a founding member of the Medical Artificial Intelligence and Automation (MAIA) Laboratory, collaborating closely with Dr. Steve Jiang to advance AI applications in radiotherapy. His work focuses on deep learning for treatment planning, dose prediction, auto-segmentation, and adaptive radiotherapy. Ph.D. in Biomedical Physics, University of California, Los Angeles (UCLA), 2017 Mentor: Dr. Ke Sheng Faculty appointment at UT Southwestern since 2017 Dr. Nguyen’s research is centered on applying artificial intelligence to solve critical challenges in radiation oncology. His primary interests include deep learning-based dose prediction, auto-segmentation of anatomical structures, optimization of treatment plans, and real-time adaptive radiotherapy. He has pioneered work in direct aperture optimization, 4π radiotherapy, and uncertainty quantification in AI models. His research bridges the gap between AI innovation and clinical implementation, with a focus on improving plan quality, reducing planning time, and enhancing accessibility for less experienced clinicians. The most recent publications (2023–2025) demonstrate a consistent trend in developing fast, accurate, and robust deep learning models for radiotherapy. Key themes include dose prediction with transfer and meta-learning, adaptive segmentation using test-time optimization, uncertainty assessment in AI predictions, and mathematical modeling of radiotherapy-immunotherapy synergy. These works span high-impact journals in medical physics, AI, and oncology, reflecting interdisciplinary innovation. While no specific scientific awards are listed, Dr. Nguyen’s leadership in the MAIA Lab and extensive publication record in top-tier journals indicate significant recognition in the field of medical physics and AI in medicine. Dr. Nguyen has co-authored numerous studies involving mentoring and collaborative research, particularly with trainees and junior faculty in the MAIA Lab. His work is supported by institutional and likely federal funding, given the scale and scope of AI deployment studies. He has contributed to large-scale collaborative efforts such as OpenKBP-Opt, involving international teams evaluating knowledge-based planning pipelines. The MAIA Laboratory is a multi-investigator research group focused on innovating, developing, and applying artificial intelligence technologies to empower clinicians—especially those with less experience or limited resources—for improved patient care. The lab’s work spans machine learning, deep learning, reinforcement learning, and mathematical modeling in radiation oncology.

Jorge Piekarewiczمشاهده پروفایل

استاد

• Nuclear Theory
• Neutron Stars
• Dense Matter
• +۶ مورد دیگر

Prof. Jorge Piekarewicz is a Professor of Physics at Florida State University (FSU), affiliated with the College of Arts and Sciences. He earned his Ph.D. in theoretical nuclear physics from the University of Pennsylvania in 1985, followed by postdoctoral research at Caltech and Indiana University. Since 1990, he has been a faculty member at FSU. Education: Ph.D. in Theoretical Nuclear Physics, University of Pennsylvania (1985) Postdoctoral Fellowships: California Institute of Technology and Indiana University Research Interests: His work focuses on extreme-density nuclear matter in neutron stars, bridging terrestrial experiments and astrophysical observations. Key areas include: Neutron star structure and equation of state Weak interaction probes (e.g., parity-violating electron scattering) Multi-messenger astronomy insights from neutron star mergers Covariant energy density functionals and symmetry energy constraints Articles Trends: Recent work emphasizes refining the nuclear equation of state using PREX-CREX experiments, gravitational wave data (e.g., GW170817), and Bayesian methods. Key themes include neutron skin thickness, crust-core interactions, and symmetry energy sensitivity. Service & Outreach: Nuclear Science Advisory Committee (2012–2015) FRIB Theory Alliance Director (2017–2021) INT National Advisory Committee Chair (2018–2020) OLLI Lecturer on stellar evolution and neutron stars Collaborations: Active involvement with CERN, FRIB, JLab, and the Facility for Rare Isotope Beams (FRIB). Research leverages facilities like the Relativistic Heavy Ion Collider and the National Superconducting Cyclotron Laboratory.

Yong Zhangمشاهده پروفایل

استاد

• Hydrogeology
• Hydrology
• Environmental Geochemistry
• +۴ مورد دیگر

Yong Zhang is a Professor in the Department of Geological Sciences at the University of Alabama, serving as Undergraduate Program Director. His research focuses on stochastic hydrology, contaminant transport in soil/water/aquifers (including heavy metals, PFAS, microplastics, and antibiotics), and surface water-groundwater interaction. He has held postdoctoral positions at the University of California, Davis; Desert Research Institute; and Colorado School of Mines. Current research includes the 'Groundwater 2070' project in Baldwin County, Alabama, addressing climate change impacts and seawater intrusion. Education: BS in Hydrogeology and Geo-Engineering (Nanjing University, 1993), PhD in Hydrology and Water Resources (Nanjing University, 1998). He teaches courses like GEO 101, GEO 306, and specialized topics such as Fractional Calculus and Hydrogeophysics. His work integrates fractional calculus with hydrogeology, yielding models for non-Fickian transport and pollutant source identification. Students under his advisement include Jonathan Frame, Chaloemporn Ponprasit, and Hossein Gholizadeh. Research outputs emphasize environmental geochemistry, numerical modeling, and groundwater sustainability. Notable collaborations involve Dr. Geoff Tick on co-advised PhD students.

Beata Gorczycaمشاهده پروفایل

استاد

• Environmental Engineering
• Water and Wastewater Treatment
• Solid/Liquid Separation
• +۵ مورد دیگر

Dr. Beata Gorczyca is a Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. Her research focuses on potable water treatment, with expertise in solid/liquid separation, chlorine disinfection by-products control, and fractal analysis of materials. She leads a research group collaborating with Canadian water utilities like Portage la Prairie and Pembina Valley Water Co-op. Education: PhD in Chemical Engineering (2000, University of Toronto), M.Sc. in Civil Engineering (1992, University of Toronto), B.Sc. in Geological Engineering (1986, AGH University, Poland). Active roles: Member of the Particle Specialist Group at the International Water Association, keynote speaker at conferences. Research interests include water purification processes, membrane filtration, and bioremediation. Her work addresses challenges in high-DOC and high-hardness water treatment, with contributions to nanofiltration fouling mechanisms and microbial remediation solutions. She has supervised numerous graduate students and is involved in advancing water treatment technologies through interdisciplinary collaborations.

Martha Constantinouمشاهده پروفایل

دانشیار

• Quantum Chromodynamics (QCD)
• Theoretical/Computational Nuclear Physics
• Lattice Quantum Chromodynamics
• +۴ مورد دیگر

Martha Constantinou is an Associate Professor of Physics at Temple University, specializing in Theoretical/Computational Nuclear Physics with a focus on Lattice Quantum Chromodynamics (QCD). Her research addresses fundamental questions in hadron structure, including nucleon spin content and proton radius puzzles, leveraging supercomputing resources. She leads a group conducting advanced numerical simulations at major computational facilities. Constantinou holds a Ph.D. in Theoretical Computational Physics (University of Cyprus, 2008) and a BS in Physics (University of Cyprus, 2003). Her work aligns with the upcoming Electron-Ion Collider (EIC) at Brookhaven National Lab, aiming to explore nucleon structure and dark matter connections. Key research areas include generalized parton distributions (GPDs), axial form factors, and high-performance computing applications. Notable awards include the US Department of Energy Early Career Award (2019) and the Selma Lee Bloch Brown Professorship (2020). Her publications (15 most recent listed) emphasize Lattice QCD advancements, with contributions to GPDs, quark-gluon momentum partitioning, and EIC theory. She actively promotes STEM outreach and public engagement through collaborative initiatives.

Sourav Sarkarمشاهده پروفایل

دانشیار

• Probability Theory
• KPZ Universality Class
• Random Growth Models
• +۱۰ مورد دیگر

Sourav Sarkar is a University Associate Professor in Probability at the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge (since July 2024) and a Fellow of Trinity Hall. Previously, he held positions as an Assistant Professor at DPMMS (2021–2024), Postdoctoral Fellow at the University of Toronto (2019–2021), and completed his Ph.D. in Statistics at UC Berkeley (2019) under Prof. Alan Hammond. His education includes a BSc (2013) and MSc (2015) in Statistics from the Indian Statistical Institute, Kolkata, advised by Prof. Parthanil Roy. Research Interests : Probability theory with a focus on KPZ universality class, random growth models (e.g., last passage percolation, exclusion processes), Coulomb gas, and percolation theory. His work bridges stochastic processes, mathematical physics, and statistical mechanics. Teaching : Teaches advanced courses in Probability and Stochastic Calculus at the University of Cambridge, including Part II courses on Applied Probability and Probability & Measure. Previously taught at UC Berkeley, where he received the Outstanding Graduate Student Instructor Award (2019). Publications : Focuses on KPZ fixed point, geodesic properties, and phase transitions in interacting particle systems. Recent work includes studies on the directed landscape, stable random fields, and competitive erosion dynamics. Awards : Recognized for exceptional teaching at Berkeley and sustained contributions to probability theory research.

Huazhen Fangمشاهده پروفایل

دانشیار

• Systems and Control
• Advanced Battery Management
• Energy Storage Systems
• +۵ مورد دیگر

Huazhen Fang is an Associate Professor in the Department of Mechanical Engineering at the University of Kansas School of Engineering, where he joined in 2014. He leads the Information & Smart Systems Laboratory (ISSL) and holds a courtesy appointment in the Department of Electrical Engineering & Computer Science. His research focuses on enabling intelligence for complex systems through information-driven approaches. Dr. Fang received his Ph.D. in Mechanical Engineering from the University of California, San Diego in 2014, following an M.Sc. from the University of Saskatchewan and a B.Sc. in Computer Science & Technology from Northwestern Polytechnic University in China. He was a Visiting Faculty Fellow at Mitsubishi Electric Research Laboratories in 2022. His research interests span Systems and Control, Advanced Battery Management, Energy Storage Systems, and Robotics, with particular focus on system modeling, estimation, control design, machine learning and numerical optimization. Dr. Fang's work has significant applications in energy management, cooperative robotics, and environmental observing systems. His research has been supported by the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. His extensive publication record shows a clear trend toward increasingly sophisticated integration of physics-based modeling with machine learning approaches, particularly in battery management systems and autonomous vehicle control. Recent work demonstrates a growing emphasis on Bayesian inference methods, distributed control architectures, and safety-critical applications of intelligent control systems. Faculty Early Career Award from National Science Foundation (2019) University Scholarly Achievement Award (2024) Miller Professional Development Award (2022) Miller Faculty Scholar Award (2018, 2019, 2023) Wesley G. Cramer Outstanding Mechanical Engineering Faculty Award (2016) Big XII Faculty Fellowship (2015) IEEE Transactions on Transportation Electrification Prize Paper Award (2024) Dr. Fang has successfully mentored numerous graduate students through the Information & Smart Systems Laboratory, with many receiving awards for their research. His research has attracted significant funding from prestigious organizations including the National Science Foundation, Department of Energy, Army Research Laboratory, and Mitsubishi Electric Research Laboratories. He currently serves as an Associate Editor for multiple prestigious journals including Information Sciences, IEEE Transactions on Industrial Electronics, and IEEE Control Systems Letters. The Information & Smart Systems Laboratory (ISSL) under Dr. Fang's leadership has established itself as a center for cutting-edge research in information-driven smart systems. The lab focuses on pushing the frontiers of information extraction, analysis and exploitation for dynamic systems to deal with system complexity and enable system intelligence. The lab actively collaborates with industry partners and local communities, emphasizing research that serves societal needs.

Olaf Kaczmarekمشاهده پروفایل

پژوهشگر

• Quantum Chromodynamics (QCD)
• Lattice Field Theory
• High Energy Physics
• +۳ مورد دیگر

Olaf Kaczmarek is a researcher at the Faculty of Physics , Bielefeld University , specializing in Lattice Quantum Chromodynamics (QCD) and Strongly Interacting Matter . He leads projects related to QCD thermodynamics , quark-gluon plasma , and heavy quark transport . Principal Investigator in TRR 211/2 Subproject A06: Hadronic Excitations and Spectral Functions in the Medium (2025) Co-PI in TRR 211/2 Subproject Z02: Software Development Center (2025) Contributor to GPUHEP2014 and LATTICE2024 symposia Research Focus: Thermal QCD phase transitions, heavy quark diffusion , transport coefficients , lattice simulations , and quarkonium spectroscopy . His work bridges theoretical physics and high-performance computing , particularly in Multigpu Systems for QCD calculations. Recent Publications explore topics like the chiral crossover , spatial string tension , and thermal photon production , with keywords spanning Quantum Chromodynamics , Lattice Gauge Theory , and High Temperature Physics . Teaching: Offers courses in Lattice Field Theory , GPU Computing , and Gradient Flow for graduate students. Contributes to collaborative seminars in the CRC-TR211: Strong-interaction matter under extreme conditions .