Find Academic Supervisors

Jean-François ArguinView profile

Professor

• Experimental Particle Physics
• High Energy Physics
• Supersymmetry
• +5 more

Prof. Jean-François Arguin is a Full Professor at the University of Montreal's Department of Physics, affiliated with the Faculty of Arts and Sciences. He leads research in experimental particle physics through the ATLAS experiment at CERN's Large Hadron Collider (LHC). His work focuses on discovering new physics phenomena, including supersymmetry, dark matter, and top quark studies using advanced machine learning techniques. He has held leadership roles in ATLAS, coordinating top quark and pixel detector research. Education: Ph.D. 2005, University of Toronto (Top Quark Mass Measurement) M.Sc. 2000, Université de Montréal (Particle Physics) B.Sc. 1998, Université de Montréal (Physics-Mathematics) His research explores the LHC's tera-electron-volt energy frontier, targeting discoveries like supersymmetry and dark matter. He employs machine learning for data analysis and detector optimization. Key projects include ATLAS upgrades and silicon pixel detector development. Publications highlight ATLAS collaborations' findings on supersymmetry, top quark physics, and detector performance. Awards include the Tanaka Prize and recognition from the American Physical Society. Awards: Tanaka Prize (APS), Association of American Universities Award Grants: Multiple NSERC and Canadian Innovation grants for ATLAS research, detector upgrades, and machine learning applications. He advises over a dozen graduate students on topics like electron identification, supersymmetry searches, and detector mechanics. His lab focuses on advancing particle physics through cutting-edge experimental and computational methods.

Zhanqing LiView profile

Professor

• Atmospheric Science
• Environmental Science
• Climate Science
• +4 more

Zhanqing Li is a Distinguished University Professor at the University of Maryland, specializing in atmospheric science and environmental studies. His research focuses on aerosol dynamics, planetary boundary layer processes, and the interaction between clouds, land surfaces, and human activities. He has pioneered advanced remote sensing techniques, including satellite-based aerosol retrievals and machine learning models for air quality analysis. His work integrates interdisciplinary approaches, such as using lidar data for boundary layer height estimation and exploring the impacts of urbanization on convective systems. Key contributions include studies on aerosol-cloud interactions, pollution mitigation strategies, and the development of high-resolution global PM2.5 and ozone mapping systems. Recent studies emphasize the role of aerosols in extreme weather events, lightning dynamics, and the health implications of particulate matter. Li's methodologies bridge observational data with advanced computational models, addressing critical gaps in understanding climate change and environmental policy.

Javad KomijaniView profile

Research Fellow

• Particle Physics
• Theoretical Physics
• Lattice Quantum Chromodynamics (QCD)
• +5 more

Dr. Javad Komijani is a Research Fellow in Computational Physics at the Institute for Theoretical Physics , ETH Zürich. His work focuses on precision calculations in quantum chromodynamics (QCD) and electroweak interactions, particularly in the context of lattice gauge theory and computational methods. Research interests: Lattice QCD simulations for hadronic vacuum polarization (HVP) and baryon mass calculations Electromagnetic effects in QCD+QED frameworks GPU-accelerated algorithms and numerical techniques for large-scale simulations Machine learning applications to quantum field theories Publications emphasize cutting-edge advancements in precision physics, including contributions to the muon g-2 anomaly and isospin symmetry-breaking corrections. Collaborative efforts with experimental initiatives like MUonE highlight his bridge between theoretical and experimental physics. No scientific awards or formal advisees are listed in available records. His research is anchored at ETH Zürich's Institute for Theoretical Physics, where he contributes to both foundational and applied theoretical physics projects.

Nguyen TruongView profile

Lecturer

• Data Privacy
• Security
• Trust
• +4 more

Dr. Nguyen Truong is a Lecturer in Cybersecurity at the School of Computing Science, University of Glasgow, UK. Previously, he held a Research Associate position at the Data Science Institute, Imperial College London (2018–2022). He earned his PhD, MSc, and BSc from Liverpool John Moores University (2018), Pohang University of Science and Technology (2013), and Hanoi University of Science and Technology (2008), respectively. Before academia, he worked as a Software Engineer at DASAN Networks (2012–2015). His research focuses on Data Privacy, Security, Trust, Personal Data Management, Distributed Systems, Blockchain, and Privacy in Federated Learning. Notable contributions include GDPR-compliance frameworks using blockchain and defeasible logic reasoning, as well as surveys on Ethereum pseudonymity and federated learning privacy. Teaching responsibilities include COMPSCI5079 Cryptography and Secure Development, COMPSCI4062/5063 Cyber Security Fundamentals, and COMPSCI5110 Emerging Topics in Cyber Security. He advises PhD student Omodolapo Victor Babalola on Robust Privacy-Preserving Federated Learning for securing IoT. His work spans over 25 peer-reviewed publications, including articles in IEEE Transactions and Elsevier journals. Key themes include blockchain-based trust systems, privacy preservation in distributed environments, and trust evaluation mechanisms in IoT and smart city applications.

Sho TakatoriView profile

Assistant Professor

• Bioengineering
• Materials & Interfaces
• Modeling
• +3 more

Sho Takatori is an Assistant Professor in the Department of Chemical Engineering at the University of California, Santa Barbara (UCSB). His research focuses on the nonequilibrium statistical mechanics and fluid dynamics of soft materials, integrating experimental, theoretical, and computational approaches to study complex systems such as active colloids, bacterial biofilms, and cell membrane interfaces. The Takatori Lab aims to bridge materials science, physics, and bioengineering to advance understanding of complex fluid dynamics. Education Bachelor of Science in Chemical Engineering, University of California, Berkeley (2012) Doctor of Philosophy in Chemical Engineering, California Institute of Technology (Caltech) (2017) Research Interests Dr. Takatori explores emergent properties in soft materials, including active matter systems, membrane biophysics, and colloidal self-assembly. His work emphasizes the interplay between microscopic dynamics and macroscopic material behavior. Key areas include: Active colloidal suspensions and their collective behavior Membrane domain organization and actin network mechanics Control strategies for active matter using physics-informed models Awards & Honors Miller Research Fellowship, UC Berkeley (2017) Milton and Francis Clauser Doctoral Prize, Caltech (2017) NSF Graduate Research Fellowship, Caltech (2015) Gates Millennium Scholar, Caltech (2012) Lab & Collaborations The Takatori Lab at UCSB develops novel experimental tools and computational frameworks to study complex fluids. Current projects involve: Neural network-augmented control of active particles Dynamic surfactant-driven colloidal assembly Membrane domain regulation via active surface flows

Chiara AmorinoView profile

Assistant Professor

• Statistical inference for stochastic differential equations
• High-frequency data analysis
• Malliavin calculus
• +7 more

Chiara Amorino is an Assistant Professor at the Department of Economics and Business, Universitat Pompeu Fabra (Barcelona, Spain), starting April 2024. Prior to this role, she was a postdoctoral researcher at the University of Luxembourg (2020–2024), working under Prof. Mark Podolskij. She holds a PhD in Mathematics from Université Paris-Saclay (2020), supervised by Prof. Arnaud Gloter. Her research focuses on statistical inference for stochastic differential equations (SDEs), with particular emphasis on high-frequency data, Malliavin calculus, volatility estimation, and minimax theory. She also investigates McKean-Vlasov equations, Hawkes processes, and local differential privacy. Her work bridges theoretical statistics, probability, and applications in mathematical finance and data science. Amorino has taught courses such as 'Probability and Statistics' at UPF and 'Continuous Time Models in Mathematical Finance' at the University of Luxembourg. She has supervised multiple Master's and Bachelor's theses, including projects on kernel density estimation and stochastic processes. Her recent publications (2023–2025) emphasize parameter estimation in complex stochastic systems, leveraging techniques like Malliavin calculus and deconvolution. She actively contributes to academic conferences, including the Bachelier World Congress and the European Young Statisticians Meeting, and reviews for journals like Annals of Statistics and Stochastic Processes and Applications. Amorino collaborates with institutions globally and leads research initiatives on topics like nonparametric estimation in McKean-Vlasov SDEs and privacy-aware statistical methods.

• Computational Mechanics
• Fluid-Structure Interaction
• Solid Dynamics
• +12 more

Professor Antonio Gil is a faculty member at Swansea University within the Faculty of Science and Engineering , specifically in the Department of Civil Engineering . He graduated magna cum laude from the University of Granada (Spain) in 1999 and earned his PhD in computational analysis of non-linear structural membranes at Swansea University in 2005, winning the UK Society for Computational Mechanics best PhD paper in 2004. Education: Ingeniero de Caminos, Canales y Puertos (University of Granada, 1999); MSc in Computational Mechanics; PhD in Computational Mechanics (Swansea University, 2005) Research Interests span Computational Mechanics , Fluid-Structure Interaction , Finite Strain Modeling , and Soft Robotics , with a focus on physics-informed machine learning and multi-physics simulations. His work addresses challenges in electromechanical systems , cardiac mechanics , and high-strain dynamics . Recent Publications highlight advancements in SPH algorithms , topology optimization , and hyperelastic metamodels , reflecting his leadership in numerical methods and material modeling . These contributions have been recognized by prestigious awards including the Philip Leverhulme Award (2011) and the Olgierd Cecil Zienkiewicz Award (2016) . Scientific Awards: UK Philip Leverhulme Award (2011) Olgierd Cecil Zienkiewicz Award (2016) 1st National Award (Spanish Ministry of Education, 2000) Grants include coordination of the Erasmus Mundus PhD Program and leadership in H2020 Marie Curie ETN projects with budgets exceeding €6M. His supervision covers PhD projects in soft robotics , computational fracture , and MRI scanner design .

Yanzhi ZhangView profile

Professor

• Fractional Partial Differential Equations (PDEs)
• Scientific Computing
• Numerical Analysis
• +6 more

Dr. Yanzhi Zhang is a Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (Missouri S&T), where she has held academic positions since 2010. She earned a Ph.D. in Mathematics from the National University of Singapore and a B.Sc. in Applied Mathematics (with a Computer Science minor) from Chongqing University. Her research spans computational and applied mathematics, focusing on fractional PDEs, nonlocal models, machine learning applications in materials science and business, Bose-Einstein superfluids, and seismic wave inversion. Dr. Zhang has been awarded the Faculty Excellence Award from Missouri S&T and other accolades. Her work is supported by grants from the National Science Foundation, including projects on fractional viscoacoustic wave equations, Bose-Einstein superfluids, and dispersive wave equations. She advises Ph.D. students in interdisciplinary research, such as Yumeng Wang (machine learning for eye-tracking data) and Yixuan Wu (seismic inversion). Her research integrates mathematical rigor with computational innovation, addressing real-world challenges in geophysics, materials science, and business analytics. Collaborations with industry and academia enhance her contributions to data-driven modeling and open-source strategies for digital transformation.

Simone AlioliView profile

Associate Professor

• High-Energy Physics
• Theoretical Physics
• Fundamental Interactions
• +3 more

Simone Alioli is an Associate Professor at the Department of Physics "Giuseppe Occhialini" at the University of Milano-Bicocca. His research focuses on high-energy theoretical physics, particularly in collider phenomenology and Monte Carlo event generator development. Role: Associate Professor Institution: University of Milano-Bicocca Department: Department of Physics "Giuseppe Occhialini" Research Interests: Theoretical physics of fundamental interactions, precision calculations for hadron colliders, resummation techniques, and Monte Carlo simulations. His work bridges quantum field theory with practical event generation tools for experiments like the LHC and future colliders. High-Energy Physics Quantum Chromodynamics Collider Phenomenology Monte Carlo Methods Effective Field Theories Future Collider Design Article Trends: Recent publications emphasize improving event generator accuracy through NNLO matching, resummation methods for top-quark and Higgs boson production, and feasibility studies for future colliders like FCC. Collaborative works often involve experimental design, software validation, and beyond-Standard-Model investigations. Grants & Projects: P4: Precise Predictions for Particle Phenomenology (2019, MIUR) Improved simulations for the LHC and future colliders (2017, CARIPLO Foundation) REINVENT: Resummation-Improved Monte Carlo Event Generator (2017, European Commission)

Pavlidou VasilikiView profile

Professor

• High-energy astrophysics
• Cosmology
• Polarimetry of high-energy sources
• +5 more

Professor Vasiliki Pavlidou holds a faculty position in the Department of Physics at the University of Crete. She is also Director of the Institute of Astrophysics at FORTH and Skinaka Observatory. Her career includes postdoctoral roles at the University of Chicago and Caltech, followed by visiting research at the Max-Planck Institute. She has led major initiatives like the RoboPol and PASIPHAE projects, focusing on blazar polarimetry and Galactic magnetic field mapping. Her research integrates high-energy astrophysics, cosmology, and particle physics probes through cosmic rays. Key achievements include securing an ERC Synergy Grant (2024) and receiving the L’Oréal-UNESCO Women in Science Prize (2010). She has pioneered studies on cosmic birefringence, milli-lensing, and turnaround radius cosmology. Education: BSc in Physics (Aristotle University of Thessaloniki, 1999); PhD in Physics (University of Illinois Urbana-Champaign, 2005) Positions: Deputy Director of Institute of Astrophysics-FORTH (2021–); Associate Editor of Astronomy & Astrophysics (2023–) Research Interests: Her work spans cosmic microwave background polarization studies via LiteBIRD, galactic magnetic field tomography, and ultra-high-energy cosmic ray deflection modeling. She investigates blazar variability mechanisms, dark matter/dark energy probes through galaxy clusters, and synergies between particle physics and astrophysical observations. Recent focus includes applying machine learning to cosmological structure analysis and developing novel polarimetric instrumentation. Impact: Over 70 peer-reviewed publications since 2005, including seminal works on CMB lensing forecasts, magnetic field reconstruction algorithms, and blazar polarimetric monitoring. Her ERC-funded MW-Atlas project aims to create a 3D galactic map. She also contributes to astronomy education through teacher training programs (ABATAC project). Grants: ERC Synergy Grant (2024), L’Oréal-UNESCO Prize (2010), Einstein Fellowship (2008–2011) Leadership: Directs two major facilities and oversees international collaborations like PASIPHAE (pan-European optical polarimetry survey)

Or HenView profile

Associate Professor

• Nuclear Physics Experiment
• Particle Physics Experiment
• Quantum Chromodynamics (QCD)
• +4 more

Or Hen is an Associate Professor of Physics at the Massachusetts Institute of Technology (MIT), affiliated with the MIT School of Science and the Department of Physics. He holds a joint appointment at the Laboratory for Nuclear Science. His research focuses on QCD effects in nuclear media, short-range correlated nucleon pairs (SRCs), and the development of the Electron-Ion Collider (EIC). He has been actively involved in experiments at the Thomas Jefferson and Fermi National Accelerators, and co-led the ECCE consortium for EIC detector design. Hen received his PhD from Tel-Aviv University and joined MIT in 2017 after a Pappalardo Fellowship. **Education**: Bachelor's in Physics and Computer Engineering, Hebrew University PhD in Experimental Physics, Tel-Aviv University **Research Interests**: Hen explores the interplay between nuclear structure and QCD dynamics via high-energy scattering experiments. His work bridges low-energy nuclear physics with high-energy quark-gluon interactions, studying SRCs' role in nuclear EMC effects and astrophysical phenomena. Recent efforts include experimental leadership for the upcoming EIC at Brookhaven National Lab. **Awards and Recognition**: 2022 APS Fellow (nuclear physics) 2019 IUPAP Young Scientist Prize 2018 Stuart Jay Freedman Award Multiple MIT fellowships and DOE awards **Grants & Collaborations**: Hen leads the ePIC collaboration and chairs the EIC Users Group. His work is supported by the DOE Early Career Program, Sloan Foundation, and MIT grants. He has pioneered methods to measure nucleon modifications in correlated pairs through electron/nucleus collisions. **Lab Affiliations**: Hen Lab (MIT) MIT Laboratory for Nuclear Science

• AI in Manufacturing
• Chemical Engineering Modelling
• Data-Driven Process Design
• +3 more

Dr. Javier Cardona Amengual is a Chancellor’s Fellow - Senior Lecturer in Chemical and Process Engineering at the University of Strathclyde’s Faculty of Engineering. His research focuses on revolutionizing chemical process design through AI-powered multi-sensor systems and multi-scale predictive modeling, addressing challenges in Industry 5.0 and digital manufacturing. He leads a multi-disciplinary team integrating physics-based and data-driven models to enhance process understanding and decision-making in chemical and pharmaceutical manufacturing. Key collaborations include the EPSRC MediForge Hub and CEDAR Centre for Doctoral Training, advancing resilient medicines manufacturing and cyber-physical systems. His work emphasizes sustainable engineering solutions, leveraging partnerships with global industries and academic institutions to drive real-world impact. Current projects include developing open-access crystal image databases (OpenCrystalData) and AI-driven parameter estimation for industrial processes. Research interests span AI applications in manufacturing, multi-scale modeling, pharmaceutical process optimization, and data-driven approaches for in-line monitoring. His publications highlight advancements in particle characterization, neural networks, and Industry 5.0 integration. He actively contributes to conferences and workshops, promoting knowledge exchange in digital manufacturing and sustainable engineering practices.

Marcella BonaView profile

Associate Professor

• Flavour Physics
• Dark Matter Search
• Machine Learning
• +2 more

Dr. Marcella Bona is a Reader in Particle Physics at Queen Mary University of London (QMUL), affiliated with the School of Physical and Chemical Sciences. She leads research in Flavour Physics, Dark Matter searches, and Machine Learning applications, contributing to the ATLAS and Belle II collaborations. She co-founded the UTfit phenomenological group and the SAPIENS pollution modeling project. As an Associate Professor, she holds roles including Director of the Artificial Intelligence and Machine Learning in Science MSc Programme and Deputy Leader of the QMUL ATLAS Level 1 Calorimeter Trigger team. Education: PhD in Particle Physics, Postgraduate Certificate in Academic Practice. Fellowships include the Royal Society Leverhulme Trust Senior Research Fellowship (2021–2023). Research Interests: Focus on indirect and direct searches for physics beyond the Standard Model (e.g., Dark Matter, matter-antimatter asymmetry). Her work integrates global flavour fits via UTfit and pollution modeling using Machine Learning. She analyzes ATLAS data on rare B meson decays and explores semi-visible jet signatures for Dark Matter. Awards & Grants: MInstP (Institute of Physics), FHEA (Higher Education Academy). Active in grants including the Royal Society fellowship and collaborations with institutions like the Alan Turing Institute. Advising & Teams: Supervises current PhD students on pollution modeling (Mexico City), Dark Matter, and ATLAS analyses. Alumni include postdocs and industry professionals. Leads the SAPIENS project and UTfit collaboration.

• Particle Astrophysics
• High-Energy Neutrino Astronomy
• Radio Detection Techniques
• +2 more

Brian Clark is an Assistant Professor at the University of Maryland, specializing in particle astrophysics with a focus on high-energy neutrino astronomy. His research involves the IceCube Neutrino Observatory, the Askaryan Radio Array (ARA), and the Radio Neutrino Observatory in Greenland (RNO-G). He holds affiliations with the Joint Space-Science Institute and collaborates on projects like IceCube Diffuse Neutrino Group and ARA analysis coordination. Education: PhD from The Ohio State University (2019), NSF Graduate Research Fellow. Postdoctoral work at Michigan State University as an NSF Astronomy and Astrophysics Fellow. His experimental work spans data analysis, simulation, and detector development for neutrino detection techniques. Research emphasizes radio detection of ultra-high-energy neutrinos (>10 PeV), cosmic ray studies, and next-generation observatory design. Key projects include IceCube-Gen2 and RNO-G, focusing on improving sensitivity to astrophysical neutrinos and understanding cosmic accelerators. Recent work includes analyzing seasonal variations in atmospheric neutrino flux, optimizing RNO-G trigger algorithms, and exploring sterile neutrino parameters with IceCube data. His contributions advance radio and optical neutrino detection technologies in polar environments. Labs/Teams: Co-convener of IceCube Diffuse Neutrino Group, analysis co-coordinator for ARA. Involved in interdisciplinary efforts like the IceTop Enhancement and firn ice characterization for RNO-G.

• Clean Energy Technology
• Net-zero Industrial Processes
• Hydrogen Production
• +7 more

Professor Gus Nathan is a distinguished Professor in the Department of Mechanical Engineering at the School of Electrical and Mechanical Engineering, University of Adelaide, within the Faculty of Sciences, Engineering and Technology. He is a Fellow of the Academy of Technological Sciences and Engineering and of the Combustion Institute, and an ARC Discovery Outstanding Researcher Awardee. Nathan is the founding director of the Centre for Energy Technology and Research Director of the $215 million Heavy Industry, Low-carbon Transition Cooperative Research Centre. Professor Nathan's research focuses on clean energy technologies, particularly net-zero heavy industrial processes with hydrogen as a key enabler. His expertise spans high temperature processes and reactors involving combustion, concentrated solar thermal, and electrification, together with their hybrids. He has published approximately 300 journal articles, 200 conference papers, holds 17 patents, and has delivered over 80 invited presentations on clean energy technology. His recent publications demonstrate a strong focus on solar thermal technologies for industrial applications, hydrogen production, mineral carbonation, and advanced particle flow systems for concentrated solar power. The research shows a clear trajectory toward developing practical solutions for decarbonizing heavy industry through innovative thermal processes and renewable energy integration. Fellow of the Academy of Technological Sciences and Engineering Fellow of the Combustion Institute ARC Discovery Outstanding Researcher Awardee (Fellowship) Listed in top 2% of scientists worldwide by Elsevier and Stanford University Principal leader of Chief Design Team for Sydney Olympic Relay Torch Co-inventor of patented combustor for Athens Games torch Professor Nathan has led significant research initiatives including the $215 million Heavy Industry, Low-carbon Transition Cooperative Research Centre, the $82 million Australian Solar Thermal Research Initiative, and the $14 million ARENA funded program with Alcoa and Hatch to develop technology targeting 45% reduction in CO2 emissions from the Bayer Alumina process. He works in partnership with companies including Alcoa, Calix, and Hatch on low-carbon technologies. He is also a founding chair of the High Temperature Minerals Processing (HiTeMP) Forum and co-founder of the Hydrogen Production Technologies (HyPT) Forum.