Find Academic Supervisors

Hassan Saeed KhanView profile

Assistant Professor

• Urban Climatology
• Building Energy Management
• Renewable Energy Technologies
• +4 more

Dr. Hassan Saeed Khan is a Lecturer (Assistant Professor) at Central Queensland University and an Adjunct Lecturer at the University of New South Wales . He holds a Ph.D. in Built Environment from UNSW (2018-2022), a Master of Science in Architectural Engineering from Politecnico di Milano (2010-2012), and a Bachelor of Science in Building and Architectural Engineering from UET Lahore (2003-2008). His research focuses on zero-energy buildings , radiative cooling materials , and urban heat mitigation . Ph.D. in Built Environment, UNSW Australia (2018-2022) MSc in Architectural Engineering, Politecnico di Milano (2010-2012) BSc in Building and Architectural Engineering, UET Lahore (2003-2008) Dr. Khan specializes in urban climatology , energy-efficient building systems , and climate-responsive urban design . His work includes fluorescent radiative coolers for urban surfaces, seasonal performance modulation of cooling materials, and microclimate evaluation in coastal regions. His research spans thermal performance of buildings, extreme heat event analysis , and renewable energy integration . His recent publications highlight scalable radiative cooling solutions, synoptic weather interactions with urban heat, and green infrastructure benefits in subtropical environments. Key projects include ARC Discovery grants on fluorescent materials and consultancy with BlueScope and Dulux for material performance optimization. HDR Completion Scholarship (UNSW, 2022) Data61 CSIRO Ph.D. Top-up (2019-2022) ADA HDR Research Output Awards (2021, 2022) UniverLecco Gold Merit Scholarship (Politecnico di Milano, 2010-2012) Award of Distinction (KFUPM, 2016-2017) Dr. Khan has supervised research in 4011 Environmental Engineering , 3302 Building , and 3304 Urban Planning . His interdisciplinary approach bridges building science , materials engineering , and climate adaptation . He employs tools like ENVI-met for microclimate analysis and DesignBuilder for energy modeling.

Curt von KeyserlingkView profile

Associate Professor

• Quantum Many-Body Systems
• Topological Phases of Matter
• Quantum Information Theory
• +4 more

Dr. Curt von Keyserlingk is a Reader (equivalent to Associate Professor) in theoretical physics at King's College London, based in the Theory & Simulation of Condensed Matter Group within the Department of Physics, Faculty of Natural, Mathematical & Engineering Sciences. His research focuses on understanding complex quantum systems through both analytical and numerical approaches. His educational background includes an MMath from the University of Cambridge, followed by DPhil studies at the University of Oxford under Professor Steve Simon. Prior to his current position at King's, he held a postdoctoral research fellowship at the Princeton Center for Theoretical Science and was a lecturer at the University of Birmingham. Dr. von Keyserlingk's research centers on interacting quantum systems, studying exotic phenomena such as superconductivity, topological order, localization, and time crystallinity. His work bridges the gap between fundamental quantum mechanics and practical applications in quantum computing. He develops both analytical frameworks and numerical tools to understand how quantum systems evolve and behave under various conditions, with particular emphasis on non-equilibrium dynamics, quantum information processing, and topological phases of matter. His recent publications reveal a strong focus on quantum many-body systems, with particular attention to topological phases in three dimensions, operator dynamics in quantum systems, and the interplay between dissipation and quantum information. His work spans from fundamental theoretical questions about quantum thermalization to practical applications in quantum error correction and quantum computing architectures. Dr. von Keyserlingk is the recipient of a prestigious UKRI Future Leaders Fellowship, which supports his research on robust many-body quantum phenomena. His current projects include 'Robust Many-body Quantum Phenomena Through Driving And Dissipation' (2025-2028) and 'Robust many-body Quantum phenomena through Driving and Dissipation' (2022-2025). He actively supervises PhD students and runs the physics intercollegiate programme between King's College London and Royal Holloway, University of London. His research group focuses on developing new theoretical frameworks to understand quantum systems that could potentially be harnessed for quantum computing applications.

Florian BociortView profile

Assistant Professor

• Optical System Design
• Gradient-Index Optics
• Artificial Intelligence in Lens Design
• +3 more

Florian Bociort is an Assistant Professor at the Optics Research Group , Delft University of Technology (Faculty of Applied Sciences). He holds a PhD in Physics from TU Berlin (1994) and has dedicated his career to optical system design, gradient-index optics, and computational methods in lens design. Research Interests Bociort’s research focuses on design landscapes of optical systems , where he pioneered the use of saddle points to escape local minima in optimization. His work spans Gradient-index optics (conversion of homogeneous lenses to GRIN media) Artificial intelligence in lens design Optics education (simulation-driven learning) Academic Contributions He has supervised multiple PhD theses on topics like: A. M. Boyd (2025): Generalized gradient-index lens optimization Z. Hou (2023): Systematic lens design searches Y. Shao (2021): Imaging coherence and optimization M. Strauch (2020): Tunable optics M. Mout (2019): Ray-based diffraction simulation Recent Publications His 2025-2018 publications show a trajectory from classical optical design to modern computational approaches, including simulation-driven education, gradient-index conversions, and high-NA diffraction modeling. The 2024 paraxial reconstruction and 2025 simulation-education articles exemplify this evolution. Patents & Expertise He co-invented two ASML-related patents in lithographic design and served as expert witness in the 2018 ASML-Nikon patent lawsuit. His personal webpage details his networks of local minima and fractal basins in optimization.

• Computational Chemistry
• Density Functional Theory
• Quantum Chemistry
• +6 more

Dr. Freija De Vleeschouwer is a postdoctoral researcher and teaching faculty member in the Department of Chemistry at Vrije Universiteit Brussel (VUB) in Brussels, Belgium. With an ORCID identifier 0000-0003-0563-1509, she has established herself as a prominent researcher in computational chemistry with 927 citations and a 15 h-index. Her academic journey includes multiple FWO postdoctoral fellowships and a Research Professor appointment in Basic, Nature & Applied Sciences (2020). Dr. De Vleeschouwer's research focuses on the application of computational quantum chemical methods to solve complex problems in molecular design and materials science. Her work spans several key areas including density functional theory, molecular reactivity, self-healing polymers, and nonlinear optical materials. She employs a multidisciplinary approach combining computational predictions, molecular dynamics simulations, and experimental validation to advance understanding in these fields. Her recent research output demonstrates a strong trend toward computational-experimental integration, particularly in the development of self-healing polymer networks through Diels-Alder chemistry. She has also made significant contributions to understanding hexaphyrin compounds and their optical properties using explainable machine learning approaches. This work bridges traditional computational chemistry with modern data science techniques. Scientific Awards: FWO postdoctoral fellowship (2010) for molecular design using conceptual DFT FWO postdoctoral fellowship (2013) for inverse molecular design in radical chemistry Poster prize at the 15th International Congress of Quantum Chemistry (2015) Research Professor in Basic, Nature & Applied Sciences (0.1 ZAP) (2020) Dr. De Vleeschouwer actively supervises graduate students and has served on PhD committees. Her research is supported by multiple competitive grants including FWOTM and SRP projects. She organizes international conferences, including the 19th International Conference on Density Functional Theory and its Applications (2022), and participates in international collaborations such as research stays at Palacky University Olomouc. She leads several active research projects through 2026-2027, including FWOTM1148 on accelerating Diels-Alder kinetics in self-healing polymers and SRP73 on molecular and material property prediction using combined quantum chemical approaches.

Pavel V. TsvetkovView profile

Professor

• Nuclear Engineering
• System Analysis & Optimization Methods
• Complex Engineered Systems
• +6 more

Professor Pavel V. Tsvetkov is a faculty member at Texas A&M University , holding the rank of Professor of Nuclear Engineering and serving as the Director of the Graduate Program in Nuclear Engineering . He is also an Affiliated Faculty member of the Multidisciplinary Engineering program . His office is located in the AIEN M205B building, and he can be contacted at tsvetkov@tamu.edu . Educational Background: Ph.D. in Nuclear Engineering, Texas A&M University (2002) M.S. in Theoretical & Experimental Reactor Physics, Moscow State Engineering Physics Institute (1995) Research Interests: Professor Tsvetkov's research spans a wide range of advanced nuclear engineering topics. His primary focus includes system analysis and optimization methods , complex engineered systems , and symbiotic nuclear energy systems . He is deeply involved in waste minimization and sustainability , particularly through the development of high-temperature gas-cooled reactors (HTGRs) and molten salt reactors (MSRs) . His work also explores direct nuclear energy conversion systems and the integration of AI and deep learning into nuclear reactor control and monitoring systems. Research Trends in Publications: Over the past few years, Professor Tsvetkov has published extensively on the application of machine learning and deep learning in nuclear engineering. His recent works focus on autonomous reactor control , reactor dynamics simulation , and remote monitoring systems using satellite data and AI. He has also contributed to the design and analysis of microreactors for space applications and molten salt reactor dynamics . Scientific Awards: George Armistead, Jr ’23 Faculty Excellence Teaching Award Advising and Grants: As Director of the Graduate Program in Nuclear Engineering, Professor Tsvetkov plays a key role in mentoring and advising graduate students. While specific student names are not listed, his leadership in the program and extensive research output suggest active involvement in student research and training. Labs and Teams: Professor Tsvetkov is associated with the Nuclear Power Engineering group at Texas A&M University, contributing to both academic and applied research in nuclear systems design and safety.

• Dark Matter
• Particle Astrophysics
• Cosmology
• +2 more

Pearl Sandick is a Professor in the Department of Physics and Astronomy and Interim Dean in the College of Science at the University of Utah. She has previously served as Associate Chair of the Department of Physics and Astronomy and Associate Dean for Faculty and Research in the College of Science. Her academic journey at the University of Utah began in 2011 as an Assistant Professor, progressing to Associate Professor in 2017, and achieving the rank of Professor in 2022. Her educational background includes: BA in Mathematics from New York University (2003) PhD in Physics from the University of Minnesota (2008) Sandick is a theoretical particle physicist whose research focuses on physics beyond the Standard Model, with particular emphasis on dark matter. Her work spans theoretical modeling, connections to astrophysical observations, and implications for experimental detection. She investigates various dark matter candidates and their potential signatures in current and future experiments, including collider searches, direct detection experiments, and indirect detection through astrophysical observations. Her research also extends to connections between particle physics and cosmology, including early universe phenomena and implications for cosmic structure formation. She has developed computational tools like MADHAT for dark matter analysis and has made significant contributions to understanding how stellar evolution can constrain axion physics. Her scholarly contributions have been recognized with several prestigious awards: University of Utah Early Career Teaching Award (2016) University of Utah Distinguished Mentor Award Linda K. Amos Award for Distinguished Service to Women University of Utah Presidential Scholar Sandick has been actively involved in mentoring graduate students, as evidenced by her teaching of PhD thesis research and Master's research courses. She has secured significant research funding from the National Science Foundation and other agencies to support her work on dark matter, dark energy, and new physics. Her grant portfolio includes projects on theoretical particle physics, connections to astrophysical observations, and studies on graduate education reform following a departmental tragedy. She is an active member of the American Physical Society, having served as Chair of the regional Four Corners Section in 2021-2022, demonstrating her commitment to the broader physics community and leadership in her field.

• Remote Sensing
• Atmospheric Physics
• Meteorology
• +4 more

Alessandro Battaglia is an Associate Professor at the Department of Environmental, Land and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin. He specializes in microwave remote sensing of clouds and precipitation, with expertise in Doppler radar, cloud and snow microphysics, and microwave radiometer technology. His research spans atmospheric physics, meteorology, and climate science with applications in Earth observation from space. Dr. Battaglia's research interests focus on remote sensing of atmospheric phenomena, particularly using advanced radar technologies. His work encompasses cloud microphysics, precipitation measurement, and wind observation from space. He is particularly known for his contributions to the development of spaceborne Doppler radar systems for measuring in-cloud winds, which represents a significant advancement in atmospheric observation capabilities. His research bridges engineering, physics, and meteorology to improve our understanding of Earth's atmospheric processes and climate systems. His recent publications demonstrate a strong focus on the WIVERN (Wind Velocity Radar Nephoscope) mission, with research spanning cloud microphysics, snowfall measurement, wind field reconstruction, and innovative radar signal processing techniques. These works highlight the interdisciplinary nature of his research, connecting atmospheric science, engineering, and computational methods to advance space-based Earth observation capabilities. NASA Group Achievement Award (2015) Fellow of the National Center for Earth Observation, UK (2014-present) Dr. Battaglia actively mentors several PhD students including Marco Coppola, Francesco Manconi, Riccardo Rabino, Susmitha Sasikumar, Aida Galfione, and Paolo Martire across Civil and Environmental Engineering and Aerospace Engineering programs. He serves as Principal Investigator for multiple research projects funded by ESA (3 projects), UK-NERC (1 project), UK-NCEO (1 project), and the US Department of Energy (1 project). His current research focuses on the WIVERN mission, EarthCARE mission, and NASA's INCUS mission, with particular emphasis on developing algorithms for spaceborne Doppler radar systems. He leads research teams working on cutting-edge remote sensing technologies for atmospheric observation, with particular focus on developing the next generation of spaceborne instruments capable of measuring in-cloud winds—a capability that has been missing from Earth observation systems until now.

Daniel FremontView profile

Associate Professor

• Formal Methods
• Automated Reasoning
• Cyber-Physical Systems
• +6 more

Daniel Fremont is an Associate Professor of Computer Science and Engineering at the University of California, Santa Cruz, where he conducts research at the intersection of formal methods and autonomous systems. His work focuses on developing mathematical techniques to improve the reliability of software, hardware, and cyber-physical systems through precise specification, formal verification, automatic synthesis, and principled testing approaches. Dr. Fremont's research interests center on applications of logic in computer science, particularly using automated reasoning to enhance system reliability. His work spans formal methods for cyber-physical systems (CPS), especially autonomous systems that incorporate machine learning. Key research areas include algorithmic improvisation for creating systems with controlled randomness, probabilistic programming through the Scenic language for environment modeling, and formal verification techniques applicable to safety-critical autonomous systems. His group has successfully applied these methods to autonomous vehicles, aircraft systems, and robotics, with significant contributions to both theoretical foundations and practical implementations. The publication record reveals a strong trajectory from theoretical foundations of control improvisation toward practical applications in autonomous systems verification. Early work established the theoretical framework of control improvisation, while recent publications focus on applying these techniques to real-world challenges in autonomous driving, aircraft systems, and AI-based autonomy. A consistent theme across his research is the integration of formal methods with machine learning to address the verification challenges posed by complex, learning-based systems operating in uncertain environments. Best Paper Award at IoTDI 2016 for 'Control Improvisation with Probabilistic Temporal Specifications' Dr. Fremont leads a research group focused on formal methods for autonomous systems, with significant contributions to the development of tools like Scenic (a probabilistic programming language for scenario specification) and VerifAI (a toolkit for formal design and analysis of AI-based systems). His work bridges theoretical computer science with practical engineering challenges in safety-critical autonomous systems, receiving funding from various sources supporting research at the intersection of formal methods and artificial intelligence. The group's approach combines theoretical algorithm development with practical implementation and testing, often collaborating with industry partners working on autonomous vehicle technology. The research group maintains active development of several open-source tools, including the Scenic language for scenario specification and VerifAI for formal analysis of AI systems. They have demonstrated applications across multiple domains including autonomous vehicles, aircraft systems, and robotics, with particular emphasis on simulation-based testing and verification approaches that can provide formal guarantees about system behavior.

• Food Engineering
• Thermal Engineering
• Biophysics
• +3 more

Thijs Defraeye is a Senior Scientist at Empa (Swiss Federal Laboratories for Materials Science and Technology) and Adjunct Professor at Dalhousie University. He holds a PhD in Building Physics from KU Leuven (2011) and a Master's in Civil Engineering (2006). His work focuses on optimizing food supply chains through multiphysics simulations and digital twins, addressing challenges in refrigerated transport, postharvest quality preservation, and energy-efficient food processing. He leads the SimBioSys group, developing solutions for perishable goods logistics and electrohydrodynamic technologies. Research interests include: Biophysics of food systems Digital twin applications in agriculture Electrohydrodynamic drying Thermal management in cold chains Sustainable food technologies Recent work emphasizes reducing food loss through physics-based modeling of refrigerated containers, ventilated packaging optimization, and scalable evaporative cooling systems. His studies bridge engineering principles with biological processes, aiming to enhance global food security and environmental sustainability.

Florian BörgelView profile

Researcher

• Climate Science
• Oceanography
• Computational Modeling
• +3 more

Dr. Florian Börgel is a Senior Research Scientist and leader of the Junior Research Group Large-Scale Circulation and North European Climate Variability at the Leibniz Institute for Baltic Sea Research (IOW) in Warnemünde. He holds a Dr. rer. nat. in Physical Oceanography (2020) from IOW, an M.Sc. in Engineering Physics from the University of Oldenburg (2017), and a B.Eng. from the University of Applied Sciences Münster (2014). His research focuses on large-scale ocean-atmosphere interactions, regional climate dynamics in Northern Europe and the Baltic Sea, and the application of machine learning to improve climate models and river runoff forecasting. Key areas include Atlantic Multidecadal Variability (AMV), teleconnections between the North Atlantic and regional climates, and the impacts of climate drivers on marine environments. Notable achievements include the Outstanding Early Career Scientist Award (EGU 2023) and membership in expert groups such as the UN’s World Ocean Assessment III and the CMIP7 Data Analysis Working Group. He co-leads the Baltic Earth Teleconnections Working Group, focusing on North Atlantic-Baltic Sea linkages. Awards: EGU Early Career Award (2023), Fulbright Scholarship (2015-16) Grants/Positions: IOW appointee to Deutsches Klima-Konsortium (DKK), Parental Leave (2019-2020) Labs/Teams: Leads the Large-Scale Circulation group; contributes to the Baltic Sea Model Intercomparison Project (BMIP) and Validator tool development

Stefan KlusView profile

Lecturer

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +8 more

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

• Computer Architecture
• Accelerated Machine Learning
• Computer Science Education
• +3 more

Thomas Yeh is an Assistant Professor of Teaching in the Department of Computer Science at the University of California, Irvine. His academic background includes a Ph.D. in Computer Science from UCLA and a BS in Electrical Engineering and Computer Science from UC Berkeley. Prior to academia, he gained industry experience across research, architecture, design, verification, marketing, and management roles. His educational credentials: Ph.D. in Computer Science, UCLA BS in Electrical Engineering and Computer Science, UC Berkeley Dr. Yeh's research spans computer architecture, accelerated machine learning, and computer science education. In architecture, he pioneers error-tolerant physics simulation and heterogeneous computing. His ML work focuses on adaptive precision techniques for energy-efficient acceleration. In education, he develops interactive tools for novice programmers and experiential learning frameworks for computer architecture. His cross-disciplinary approach bridges hardware-software co-design with pedagogical innovation. Publication trends reveal consistent focus on computational efficiency across physics simulation, ML acceleration, and educational technology. His work connects real-time systems optimization with emerging AI applications, particularly in interactive environments and physics-based animation. No scientific awards are documented in the provided materials. While advising details and grant funding specifics are absent from available information, his industry-academia transition informs practical research directions. Teaching responsibilities include core courses like Introduction to CS, Data Structures, and Efficient ML Computing. Research infrastructure details remain unspecified, though his publications suggest collaborations in physics simulation and heterogeneous computing environments.

Ed PickeringView profile

Senior Lecturer

• Metallurgy
• Materials Science
• Nuclear Engineering
• +9 more

Ed Pickering is a Senior Lecturer in Metallurgy and Materials Engineering at the University of Manchester. He has held roles since 2015, advancing to Reader in 2023. His affiliations include the Henry Royce Institute (Research Area Lead for Advanced Metals Processing), the Advanced Metallics System CDT and Fusion CDT Management Boards, and industrial technical advisory panels. Ed’s work bridges academic and industrial collaboration with Rolls-Royce, UKAEA, Airbus, EDF, and Sheffield Forgemasters. Ed completed his undergraduate studies (2011) and PhD (2014) in Materials Science at the University of Cambridge, followed by a Research Associate role in Cambridge’s Rolls-Royce UTC. His academic trajectory includes: Senior Lecturer (2019–present) Reader (2023–present) Ed’s research focuses on phase transformations, microstructural characterization, and alloy development for nuclear (fission/fusion) and aerospace applications. Key themes include optimizing processing routes to enhance material properties while minimizing waste and environmental impact. His studies frequently address steel, high-entropy alloys, and novel refractory alloys, emphasizing their service performance under extreme conditions. His scientific contributions span structural integrity assessment of welded joints, machine learning applications in metallurgy, and material flow uncertainties in forging. He has also advanced heat treatment optimization for reactor steels and explored cobalt-free hardfacing alloys. Frank Fitzgerald Medal (2017) Grunfeld Memorial Medal (2021) In advising and grants, Ed leads the Materials Performance Centre (MPC) and co-leads the NEWAM project on wire-additive manufacturing. He supervises research across these initiatives and collaborates with over 30 PGR students in interdisciplinary teams. His work also involves managing technical facilities like the Advanced Metal Processing platform. Ed’s laboratory affiliations include the MPC and WAAM-based Engineering and Process Metallurgy groups, where he explores sustainable materials solutions for energy and aerospace industries.

Xiajun JiangView profile

Assistant Professor

• Adaptive AI Computing
• Physics-Informed Deep Learning
• AI for Healthcare
• +3 more

Xiajun Jiang is an Assistant Professor in the Department of Computer Science at the University of Memphis, joining in Fall 2024. He holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2024), an M.S. in Computer Science from the University of Southern California (2018), and a B.S. in Electrical Engineering and Automation from Zhejiang University (2016). His research focuses on adaptive AI computing, physics-informed deep learning, and their applications in healthcare, particularly in medical imaging and cardiac simulation. Key contributions include hybrid neural state-space modeling for electrocardiographic imaging and physics-informed frameworks for bi-ventricular electrophysiological simulations. Education: PhD, Rochester Institute of Technology, 2024 M.S., University of Southern California, 2018 B.S., Zhejiang University, 2016 Research Interests: Machine learning for healthcare Adaptive computing in AI models Physics-informed deep learning His work bridges machine learning and biomedical engineering, with applications in cardiac imaging and electrophysiology. Recent articles highlight advancements in hybrid models for ECGI and meta-learning approaches for personalized cardiac simulations. He has reviewed for top conferences like ICLR, NeurIPS, and MICCAI, and contributed to projects like the Computational Biomedical Lab (CBL).

• Primary Science Education
• Early Years Science
• Teacher Professional Learning
• +2 more

Associate Professor Christine Preston at the University of Sydney's Faculty of Education and Social Work specializes in primary science education with a focus on early childhood development. She maintains active classroom experience through her kindergarten science teaching and leads research on science diagrams, toy-based learning, and teacher professional development. Her work integrates embodied learning and representational pedagogies to enhance conceptual understanding in STEM subjects. Dr. Preston's research examines innovative approaches including citizen science integration, digital technologies, and adaptive curriculum design. She currently lectures in primary science and technology education, while supervising doctoral candidates exploring computational thinking and marine biology education. Her scholarly contributions emphasize practical classroom applications and teacher support resources. Award recognition includes multiple Excellence in Teaching awards (2007, 2016) and the 2001 NSW Quality Teaching Award. She maintains active membership in international science education associations including ASTA, ESERA, and NSTA, serving on editorial boards for science education journals.