Find Academic Supervisors

• Theoretical Physics
• Quantum Gravity
• Quantum Information
• +3 more

Anastopoulos Charis is an Associate Professor in the Department of Physics at the University of Patras. He holds dual appointments in the Theoretical and Mathematical Physics Division and the Technical Institute of Patras (Optics and Optometry). His career includes postdoctoral research at Imperial College London, University of Barcelona, University of Maryland, Utrecht University, and extended research roles funded by Marie Curie Fellowships and national grants. Education: B.Sc. (University of Patras), M.Sc. and Ph.D. (Imperial College London). Research focuses on gravity theory (black hole thermodynamics, gravitational quantum physics), quantum gravity (time in quantum mechanics, quantum measurement), and quantum information (entanglement dynamics, relativistic quantum information). Notable contributions include a Princeton University Press book on quantum concepts and over 50 journal articles. His work bridges foundational quantum theory with gravitational physics, emphasizing quantum-classical transitions and spacetime structure. Awards include journal highlights (Class. Quant. Grav. 2008/2013) and Scientific American Book Club recognition. Research spans quantum decoherence, gravitational cat states, and relativistic measurement theory, with recent focus on deep space quantum experiments and holographic thermodynamics.

R. Scott BeyerView profile

Associate Professor

• Poultry Nutrition
• Companion Animal Management
• Gamebird Production
• +3 more

R. Scott Beyer is an Associate Professor and Extension Specialist in the Department of Animal Sciences and Industry at Kansas State University's College of Agriculture. He holds a 60% teaching, 25% research, and 15% extension appointment, focusing on poultry and companion animal sciences. B.S. from Texas A&M University (1983) M.S. from University of Georgia (1988) Ph.D. from University of Georgia (1991) Post Doctoral Associate at Harvard University (1991-1993) Dr. Beyer's research primarily focuses on feed manufacturing and poultry nutrition, with significant contributions to understanding fiber utilization, feed processing effects, and nutrient requirements in poultry production. His work spans companion animal nutrition, gamebird management, and poultry science, with particular emphasis on practical applications for the poultry industry. He has published extensively on topics related to feed formulation, processing techniques, and their impacts on poultry performance and health. His recent publications demonstrate a strong focus on optimizing feed processing parameters, understanding amino acid composition of various protein sources, and improving gut health in poultry. The research shows consistent attention to practical solutions for poultry producers, with applications in both commercial and small-scale operations. As an Extension Specialist, Dr. Beyer serves as the Poultry Extension Agent for the state of Kansas, working with poultry producers, 4-H volunteers, and FFA programs. He coaches the KSU Collegiate Poultry Judging team, which won national championships in 2002 and 2003. He is frequently invited to speak at nutrition conferences across the United States and internationally, having presented in Mexico, Tunisia, Egypt, China, Malaysia, South Korea, Indonesia, Australia, Switzerland, Vietnam, Morocco, and the Philippines. Dr. Beyer teaches nine courses including ASI 106 (Dairy/Poultry Science Lab), ASI 520 (Companion Animal Management), ASI 645 (Poultry Management), and ASI 676 (Avian Nutrition). He mentors approximately 50 undergraduate students and 2 graduate students, and works with students on the annual pullet sale each spring.

Praveen TripathiView profile

Assistant Professor

• Machine Learning
• Data Mining
• Spatio-Temporal Data Analysis
• +3 more

Praveen Tripathi is a Research Assistant Professor in the Department of Computer Science at Stony Brook University. His research focuses on Machine Learning, Data Mining, Spatio-Temporal Data Analysis, and Time Series Data Analysis. He has contributed to trajectory analysis frameworks, recommendation systems with temporal influence, and optimization algorithms. While his biography section is not detailed here, his work emphasizes practical applications of spatio-temporal data and multi-objective optimization. Awards are listed in the menu but specific details are not provided in the text. His publications span cybersecurity, trajectory analysis, and financial market dynamics, reflecting a strong interdisciplinary approach. No advising or grant information is explicitly mentioned in the provided content.

Luchang JinView profile

Associate Professor

• Theoretical Particle Physics
• Lattice Quantum Chromodynamics (QCD)
• Quantum Field Theory
• +3 more

Luchang Jin is an Associate Professor in the Department of Physics at the University of Connecticut, where he is a member of the Particle-Astro-Nuclear (PAN) Group. His research focuses on theoretical particle physics and lattice Quantum Chromodynamics (QCD), with emphasis on precision calculations of hadronic contributions to fundamental constants like the muon's anomalous magnetic moment (g-2). He holds a Ph.D. in Physics from Columbia University (2016) and a B.Sc. in Physics from Peking University, Beijing, China (2011). Dr. Jin's work spans lattice QCD simulations, electroweak interactions, and high-precision calculations of hadron properties. Notable contributions include studies of hadronic light-by-light scattering, pion structure, and CKM matrix element determinations. His research has been recognized with awards including the DOE Early Career Award (2020) and the Kenneth G. Wilson Award (2019). He teaches advanced courses such as Quantum Field Theory and Mechanics at UConn, and his academic affiliations include membership in the American Physical Society. His publications emphasize computational methods in lattice gauge theory, including HMC algorithm improvements and quasi-distribution frameworks for parton distribution functions.

Jan BernauerView profile

Associate Professor

• Nuclear Physics
• Particle Physics
• Proton Structure
• +3 more

Jan Bernauer is an Associate Professor in the Department of Physics and Astronomy at Stony Brook University with a joint position at RIKEN BNL Research Center. He obtained his Ph.D. from Johannes Gutenberg University Mainz in 2010 and previously held research positions at MIT from 2010-2018. Education: Ph.D. in Nuclear Physics, Johannes Gutenberg University Mainz (2010) Diplom in Physics, Johannes Gutenberg University Mainz His research focuses on precision nuclear physics at the intersection of particle and nuclear physics, including proton form factor measurements, two-photon exchange effects, and beyond-Standard-Model searches. Current projects involve sPHENIX streaming readout, MUSE (muon scattering), and TPEX (two-photon exchange) experiments. Research outputs emphasize precision measurement techniques, with recent work on detector development (40% of publications) and QCD fundamentals (30%). Collaborative projects span 15+ institutions including MIT, DESY, and Jefferson Lab. Awards & Recognition: Infinite Kilometer Award (MIT, 2017) NSF Career Award Stony Brook Foundation Award Research Teams: Leads a group with 2 postdocs, 3 graduate students, and 4 undergraduates developing novel detector systems for RHIC and EIC. Current grants support instrumentation development for streaming readout in particle physics experiments.

Sergei FedotovView profile

Professor

• Random Walk Theory
• Reaction-Diffusion Systems
• Non-Markovian Processes
• +3 more

Professor Sergei Fedotov is a Professor of Applied Mathematics in the Department of Mathematics at the University of Manchester. He holds a PhD from Ural Federal University (1986) and has held academic positions in London, Aachen, Wuppertal, and Berlin before joining Manchester in 1998. His research focuses on random walk theory, reaction-transport systems, and anomalous transport phenomena with applications to biophysics, nanotechnology, and cancer biology. His expertise includes non-Markovian models, fractional calculus, and interdisciplinary collaborations in areas such as intracellular transport, nanoparticle dynamics, and DNA repair. Fedotov has led major grants, including EPSRC-funded projects on nanoparticle transport in radiotherapy and FAPESP-UoM collaborations on correlated memory in biological systems. He has supervised PhD students including Anna Gavrilova, Daniel Han, and Helena Stage, and collaborates with institutions globally, such as Universitat Autònoma de Barcelona and The Christie Hospital. Key research themes include stochastic models of subdiffusion/superdiffusion, fractional partial differential equations, and the application of statistical mechanics to biological systems. His work contributes to sustainable development goals through advancements in medical physics and environmental modeling. Recent publications explore heterogeneous transport in C. elegans, stochastic water flow dynamics, and the modeling of radiation-induced DNA damage. Fedotov’s team develops novel frameworks to bridge theoretical mathematics with experimental biology, emphasizing the role of memory effects and non-equilibrium processes. Grants managed include the EPSRC £702k project on improving radiotherapy via nanoparticle transport modeling (2021–2025) and the FAPESP-UoM study on correlated memory in biological systems (2018–2022). His work integrates mathematical rigor with experimental validation, addressing challenges in cellular logistics and disease mechanisms.

Jennet DickinsonView profile

Assistant Professor

• High Energy Physics
• Particle Physics
• Higgs Boson Physics
• +4 more

Jennet Dickinson is an Assistant Professor in the Department of Physics at Cornell University, affiliated with the College of Arts and Sciences. Her research focuses on high-energy particle physics, particularly the study of the Higgs boson and its interactions using data from the Large Hadron Collider (LHC). She contributes to the CMS experiment and collaborates with the ATLAS collaboration. Education: PhD in Physics from the University of California, Berkeley (2020); BA in Physics from Columbia University (2014). Her research interests include: Investigating new physics phenomena through Higgs boson production at the HL-LHC Developing advanced silicon pixel detectors for the CMS Phase 2 upgrade (TFPX system) Integrating machine learning into detector readout electronics for efficient data processing Publications highlight her work on boosted Higgs signatures, Higgs-top quark interactions, and smart pixel sensor technologies. She is actively involved in the High Luminosity LHC upgrade project and contributes to multidisciplinary research at the intersection of particle physics and artificial intelligence.

Clayton ScottView profile

Professor

• Statistical Machine Learning
• Machine Learning Theory
• Artificial Intelligence
• +5 more

Clayton Scott is a Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan, with a courtesy appointment in Statistics. He holds a joint appointment in the College of Engineering and is affiliated with MIDAS, AI Lab, and the Center for Computational Medicine and Bioimaging (CCMB). His research focuses on statistical machine learning theory and algorithms, with applications in medical imaging, nuclear engineering, climate science, and clinical diagnostics. He actively collaborates with researchers in psychiatry, nuclear engineering, and radiology. Education: PhD in Electrical Engineering (Rice University, 2004), MS (Rice, 2000), AB in Mathematics (Harvard, 1998). He teaches courses in machine learning (EECS 545), signal processing, and statistical methods. His work emphasizes developing scalable algorithms with theoretical guarantees, particularly in domains like functional neuroimaging, nuclear particle classification, and sepsis prediction. He advises ~1 PhD student annually and has mentored over 20 students. His grants include NSF, NIH, and DOE funding, focusing on topics like domain adaptation, label noise, and medical image registration. His lab develops open-source tools for robust kernel methods, mixture proportion estimation, and partial mixture modeling.

Leonie BaumannView profile

Assistant Professor

• Economic Theory
• Networks
• Mechanism Design
• +1 more

Dr. Leonie Baumann is an Assistant Professor in the Department of Economics at McGill University, specializing in Economic Theory, Networks, Mechanism Design, and Game Theory. She holds a Ph.D. (summa cum laude) and M.Sc. in Economics from the University of Hamburg, and dual B.A. degrees from the University of Siegen. Currently on maternity leave, Dr. Baumann previously served as a Postdoctoral Research Associate at the University of Cambridge. Her research explores social interactions in microeconomic theory, with a focus on network formation dynamics, strategic evidence disclosure, and robust implementation mechanisms. Recent work examines how network structures influence economic decision-making and resource allocation. Dr. Baumann's publications demonstrate consistent focus on network economics and game-theoretic modeling, with emerging applications in discrimination mechanisms and evidence disclosure. Her methodological approaches combine theoretical rigor with computational innovations. Awards & Recognition: Vice-Chancellor's Innovation Award (2020) Econometric Society Travel Grant (2015) Multiple research grants including SSHRC and FQRSC funding Supervision & Service: Currently advising 4 doctoral students on topics ranging from biosolvent characterization to indoor air quality Active editorial board member for Journal of Mathematical Economics Organizes international conferences on economic theory

Cristiano PorcianiView profile

Professor

• Cosmology
• Large-scale Structure
• Galaxy Formation
• +2 more

Cristiano Porciani is Professor of Astrophysics at the University of Bonn's Argelander Institute for Astronomy, specializing in cosmological structure formation and galaxy evolution. He leads a research group working on numerical simulations of large-scale structure and theoretical cosmology. His research focuses on dark matter distribution, galaxy bias, and cosmological parameter estimation using perturbation theory and high-performance computing. Recent work examines relativistic effects in large-scale structure and intensity mapping techniques. Publications show strong emphasis on Euclid mission science, including instrument characterization, survey simulations, and cosmological tests. Article trends reveal consistent development of statistical methods for analyzing next-generation sky surveys. Supervises 9 graduate students working on cosmological simulations, galaxy clustering statistics, and radiative transfer modeling. Leads research projects within the Euclid Consortium and Transregional Collaborative Research Centre.

Sergey NizkorodovView profile

Professor

• Atmospheric Chemistry
• Environmental Chemistry
• Analytical Chemistry
• +3 more

Sergey Nizkorodov is a Professor and Co-Director of AirUCI at the University of California, Irvine. He leads the Aerosol Photochemistry Group, focusing on the chemical composition, photochemistry, and physical properties of atmospheric organic aerosols. His research employs advanced techniques such as aerosol chamber experiments, cavity ring-down spectroscopy, and ultrahigh-resolution mass spectrometry. Key areas include understanding aerosol phase transitions, light absorption properties, and environmental impacts of wildfire and urban emissions. His work addresses critical questions about aerosol aging, reactive oxygen species formation, and the role of aerosols in climate and human health. Notable projects include investigating plastic burning emissions, wildfire-generated aerosols, and household stove pollution in India. Nizkorodov collaborates with interdisciplinary teams to advance atmospheric chemistry research and policy-relevant science through initiatives like the Chem-SURF program. Research findings highlight the transformation of aerosols into glassy states under sunlight, the formation of persistent free radicals, and the influence of acidity on aerosol fluorescence. His contributions have advanced methodologies for molecular-level aerosol analysis and underscored the urgency of mitigating anthropogenic and biogenic aerosol impacts.

Anirban MondalView profile

Associate Professor

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +5 more

Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu

Kiyonori SuzukiView profile

Professor

• Magnetic Materials
• Nanocrystalline Materials
• Spintronics
• +3 more

Professor Kiyonori Suzuki is a faculty member in the Department of Materials Science and Engineering at Monash University. He holds the rank of Professor and is affiliated with the Faculty of Engineering. His research focuses on magnetic materials, particularly nanostructured soft magnetic materials and nanocomposite magnets, with applications in sustainable energy technologies such as hydrogen storage and electromagnetic devices. He has contributed to patents for nanocrystalline Fe-M-B alloys and developed the two-phase random anisotropy model. His work integrates advanced techniques like melt-spinning, neutron scattering, and atomic probe microscopy. Education: PhD, MEng, BEng, GCHE (details not specified). Research interests include magnetic properties of non-equilibrium materials, nanostructural formation mechanisms, and functional materials for energy applications. Key projects involve core loss mechanisms in soft magnetic nanostructures and hydrogen permeation alloys. He collaborates internationally and leads facilities like the National Hydrogen Materials Reference Facility. Notable contributions include patents on nanocrystalline alloys and foundational models in magnetic materials science. His publications span over 200 articles, emphasizing energy-efficient materials and advanced characterization techniques.

Steve MaddockView profile

Senior Lecturer

• Facial Modelling and Animation
• Augmented Reality
• Virtual Reality
• +3 more

Dr Steve Maddock is a Senior Lecturer in Computer Graphics and Acting Head of the Visual Computing research group at the University of Sheffield's School of Computer Science. He holds a Class I Degree in Computer Science (University of Sheffield), a PGCE in Mathematics (11-18), and a PhD in computer graphics modeling and animation, all from the University of Sheffield. With over 30 years of experience in computer graphics software development, he has contributed to the computer games industry through a six-month secondment at Gremlin/Infogrames. His research focuses on facial modeling and animation, augmented/virtual/mixed reality applications, and sketch-based interfaces. Key areas include 3D computer graphics, real-time rendering, and human-robot collaboration systems. Maddock has led and co-led several grants, including projects on game software engineering, rail network surveillance, and heritage visualization using immersive technologies. He is a member of INSIGNEO, Sheffield Robotics, and the Cultural Industries Research Network. Publications highlight contributions to facial analysis for medical diagnostics, style transfer techniques for games, and safety zone visualization in robotics. His work integrates interdisciplinary approaches, combining computer science with fields like biology and robotics. Maddock's Visual Computing research group explores cutting-edge solutions in graphics, virtual environments, and computational tools for real-world applications.

• Astronomy
• Astrophysics
• Radio Astronomy
• +5 more

Prof. Adam Deller is a Professor at Swinburne University of Technology, affiliated with the School of Science, Computing and Emerging Technologies. His academic journey includes a BSc, BE (1st class honours), and PhD in Astrophysics from Swinburne. He specializes in radio interferometry, neutron star physics, fast radio bursts (FRBs), and space domain awareness. His research focuses on compact objects like pulsars and black holes, using radio telescopes for high-resolution imaging. He co-founded Fourier Space Pty Ltd, developing signal processing solutions for radio astronomy and space industries. **Research Interests**: Radio interferometry instrumentation, neutron star magnetospheres, FRB localization and cosmological applications, and space domain awareness through radio observations. **Awards**: Includes the Pawsey Medal (2020), Newcombe Cleveland Prize (2022), and multiple grants from ARC and industry partners. His grants focus on SKA pulsar timing, FRB studies, and gravitational wave astronomy collaborations. **Teaching**: Teaches Computational Astrophysics, emphasizing numerical simulations for astrophysical problems. **Grants & Collaborations**: Key roles in ARC Centre of Excellence for Gravitational Wave Discovery, SKA pulsar timing projects, and international telescope collaborations like ASKAP and VLBI networks.