Find Academic Supervisors

• Thermodynamics
• Fluid Mechanics
• Probabilistic Inference
• +6 more

Associate Professor Robert Niven is a distinguished academic at UNSW Canberra's School of Engineering and Information Technology, where he has established himself as an internationally recognized research leader in thermodynamics, probabilistic inference, and environmental contaminants. With a career spanning over two decades, he has secured significant research funding and developed impactful technologies with real-world applications. His educational background includes a BSc(Hons) and The University Medal in Chemistry and Geology from UNSW Sydney (1991) and a PhD in Civil and Environmental Engineering from UNSW Sydney (1998). Niven's research interests span three primary areas: (a) Thermodynamics and fluid mechanics, particularly entropic insights into dimensional analysis, generalized conservation laws, and Lie symmetries; (b) Probabilistic inference in data science, including maximum entropy analysis and Bayesian inference applications; and (c) Environmental contaminants and impacts, especially PFAS contaminants and related policy. His work bridges theoretical foundations with practical applications in environmental remediation and engineering systems. Analysis of his recent publications reveals a strong focus on applying entropy-based methods to fluid dynamics problems, developing Bayesian approaches for environmental assessment, and creating innovative solutions for PFAS contamination. His research demonstrates consistent integration of theoretical frameworks with practical engineering applications, particularly in water treatment technologies. Churchill Fellowship (1998) Fulbright Fellowship (2003) JSPS Fellowship (2006) Marie Curie Incoming International Fellowship, Denmark (2007-08) Endeavour Fellowship (2010) Isaac Newton Institute Fellowship (2010) Editorial Board Member of Entropy (since 2007) Editorial Board Member of Stat (since 2019) Niven has secured over A$4.501 million in research funding from diverse sources including ARC Discovery Project grants, an ARC SRI PFAS grant (A$1.364 million), and industry partnerships. He has successfully supervised numerous undergraduate thesis, Masters, and PhD students while also hosting visiting scholars. His PFAS remediation project has led to significant commercialization, with 34 water treatment plants manufactured and exported globally. He leads a major research initiative on PFAS contaminants across UNSW Canberra and Macquarie University, with manufacturing facilities established in Australia and the USA. His work bridges academic research with industrial application, demonstrating strong translational impact in environmental engineering.

Yao HaoView profile

Associate Professor

• Medical Physics
• Radiation Oncology
• Adaptive Radiotherapy
• +7 more

Yao Hao is an Associate Professor in the Division of Medical Physics at the Washington University School of Medicine and a key member of the Siteman Cancer Center. His career since joining the faculty in 2019 has focused on advancing radiation oncology through innovative medical physics research. PhD in Medical Physics from University of Massachusetts (2016) Residency at Washington University (2019) Bachelor's and Master's in Physics/Logic from Shanxi University, China (2004 & 2007) Research interests span adaptive radiotherapy , brachytherapy , nanoparticle-aided radiation therapy , boron neutron capture therapy , and automated linac commissioning . His work integrates deep learning methods and deformable image registration to improve precision in radiation delivery. Recent publications highlight advancements in 4D-CBCT motion reconstruction , low-dose CT for proton therapy , and global radiotherapy implementation . Collaborations extend to institutions in sub-Saharan Africa and Europe, focusing on health equity in cancer care. Active in clinical innovation at the Siteman Cancer Center , he contributes to developing proton therapy systems and machine learning applications in medical imaging. His work remains centered on improving radiation therapy accuracy and accessibility through technical and algorithmic advancements.

Thomas MazurView profile

Associate Professor

• Radiation Oncology
• Medical Physics
• Image-Guided Radiation Therapy
• +3 more

Thomas Mazur , PhD, serves as Associate Professor of Radiation Oncology at Washington University School of Medicine . He joined the faculty in 2016 after completing a medical physics residency at the same institution. His academic journey began with a BS in Physics and Mathematics (2007) from Union College, followed by a PhD in Experimental Atomic Physics (2014) at The University of Texas at Austin. Key Research Areas: Image-Guided Radiation Therapy (MR Guidance) Adaptive Treatment Delivery Systems Radiomics and Belief Function Theory Optically Stimulated Luminescence Dosimetry Stable Isotope Enrichment for Radioisotopes Selected Scientific Awards: RO-ILS Physics Safety Star Award (2024) Mentor of the Year – Radiation Oncology (2020) Springer Theses Prize (2015) UT Austin Outstanding Dissertation Award (2014) Dr. Mazur's publications (55 total) demonstrate expertise in tumor motion prediction, radiomics-based outcome modeling, and automated treatment planning systems. Collaborative projects include copper complex anticancer studies and storage phosphor imaging development.

• Particle Physics
• Neutrino Physics
• Experimental Physics
• +3 more

Dr Rhiannon Jones serves as a Lecturer in Experimental Particle Physics within the School of Mathematical and Physical Sciences at the University of Sheffield. She is an active member of the Particle Physics and Particle Astrophysics Research Cluster (PPPA), contributing to major international collaborations including the Deep Underground Neutrino Experiment (DUNE) and Short-Baseline Near Detector (SBND) project. Her work focuses on advancing liquid argon time projection chamber (LArTPC) technologies for neutrino detection, with responsibilities spanning detector development, data analysis, and physics interpretation across multiple experimental frameworks. Her research program centers on experimental neutrino physics, specifically targeting neutrino interaction measurements, detector response optimization, and background mitigation in LArTPC systems. Key investigation areas include scintillation light characterization for particle identification, reconstruction of low-energy electron events, simulation of detector performance using GPU-accelerated computing, and analysis of cross-section uncertainties affecting supernova neutrino studies. Her work bridges particle physics, nuclear physics, and computational science to address fundamental questions about neutrino properties, oscillation parameters, and potential sterile neutrino signatures. Analysis of her publication record from 2018-2024 reveals consistent contributions to neutrino experiment infrastructure and physics analysis. Early work established foundations in joint oscillation analyses (VALOR) and SBND detector construction, while recent efforts have driven DUNE Phase II development, vertical drift detector technology, and innovative data processing techniques using deep neural networks. Her publications demonstrate growing leadership in technical design reporting, white paper authorship, and cross-collaboration studies addressing sterile neutrino phenomenology and detector performance optimization. The Particle Physics and Particle Astrophysics Research Cluster (PPPA) provides Dr Jones's primary institutional framework at Sheffield, connecting her to a multidisciplinary team engaged in detector development, data acquisition systems, and physics analysis for both accelerator-based experiments and astroparticle physics. Within PPPA, she contributes specifically to the experimental neutrino program, working on near-detector calibration systems for SBND and far-detector technologies for DUNE, with strong operational ties to Fermilab and international partner institutions.

• Dark Matter Detection
• Particle Astrophysics
• Liquid Xenon Detectors
• +3 more

Dr. Adam Softley-Brown is a Research Fellow in Particle Physics at the University of Sheffield 's School of Mathematical and Physical Sciences. His work focuses on experimental particle astrophysics and direct dark matter searches using liquid-xenon detectors within the LZ collaboration and XLZD collaboration . Operates at Sanford Underground Research Facility (USA) Develops next-generation multi-tonne liquid xenon detectors for dark matter and neutrino physics Advocates for UK hosting of future experiments at Boulby Underground Laboratory His research involves minimizing radiation backgrounds through material selection and veto systems to enhance sensitivity for dark matter and neutrinoless double-beta decay detection. Recent publications highlight advancements in signal processing , calibration techniques , and detector design for liquid xenon time projection chambers. Key collaborations include XENON and DARWIN projects. Contact: adam.m.brown@sheffield.ac.uk

• Machining Simulation
• Manufacturing Processes
• Metal Forming
• +1 more

Milan Rakita is an Associate Professor of Practice in the School of Engineering Technology at Purdue University. His office hours are held in Knoy 140 on Mondays (10:30–12:30), Tuesdays (10:30–12:30), Wednesdays (10:30–12:30), and Thursdays (1:30–3:30), with additional availability by appointment. He joined Purdue in 2014 and has held roles as Clinical Assistant Professor and Visiting Instructor. Prior to Purdue, he was a researcher at the Vinca Institute of Nuclear Sciences in Serbia and held tenure-track positions at the University of Novi Sad. Education PhD , Mechanical Engineering Technology, Purdue University (2013) MSc , Mechanical Engineering, University of Novi Sad, Serbia (2005) MEng , Mechanical Engineering, University of Novi Sad, Serbia (2000) Research Focus Rakita's work centers on optimizing industrial manufacturing processes, with emphasis on: Machining simulation and troubleshooting Metal forming techniques Solidification processing Ultrasonic-assisted manufacturing (e.g., shot peening, extraction) Die-casting efficiency (lubrication, cooling) His research bridges computational modeling, experimental validation, and industrial implementation to enhance sustainability and efficiency. Publications & Recognition Rakita has co-authored over 30+ publications spanning materials science, manufacturing, and engineering education. Key trends include ultrasonic processing (40% of recent works), die-casting optimization (30%), and educational pedagogy (15%). He received the Best Paper Award from the American Foundry Society (2013) for work on magnesium alloy solidification. Industry & Consulting He has led projects with major automotive firms including: Stellantis : Cryogenic machining for aluminum/steel tool life (2020–2021) Fiat Chrysler : Ultrasound-assisted die casting, pulsed lube systems (2015–2017) 30+ projects via Purdue Technical Assistance Program (2007–2010) covering stress analysis, tool design, and process optimization. Professional Affiliations American Foundry Society American Welding Society

Pete ChapmanView profile

Professor

• Nuclear Engineering
• Particle Physics
• Machine Learning Applications in Nuclear Detection
• +2 more

Pete Chapman (COL) is a United States Military Academy graduate (1998) with a PhD in Nuclear Engineering from North Carolina State University. Currently serving as Department Head at West Point since 2024, he has held academic roles including course director (2010-2014) and core physics program director (2019). BS Engineering Physics (West Point) MS Nuclear Science and Engineering (MIT) PhD Nuclear Engineering (NC State) COL Chapman's research focuses on nuclear material detection , particle identification methodologies , and machine learning applications in nuclear security contexts. His recent work explores advanced algorithms for radiation sensor optimization and fissile material imaging. Publications trend analysis reveals consistent focus on machine learning integration in nuclear detection systems (2019-2022), with increasing emphasis on multi-detector array optimization and unsupervised classification techniques. Scientific Awards: Legion of Merit Bronze Star (with two oak leaf clusters) Meritorious Service Medal (with two oak leaf clusters) Combat Infantry Badge Parachutist Badge Air Assault Badge Ranger Tab Sapper Tab

• Neuroscience
• Traumatic Brain Injury
• Neurosurgery
• +5 more

Neil G Harris is a Professor in the Department of Neurosurgery at the University of California, Los Angeles (UCLA) School of Medicine . His research focuses on traumatic brain injury (TBI) , neuroimaging , and rehabilitation strategies , with particular emphasis on biomarker development , functional connectivity , and metabolic interventions . Current NIH-funded projects: R01NS116383 (Co-PI), UH3NS106945 (PI), UH3NS106945 (PI) Key collaborations: Richard Staba (UCLA), Fernando Gomez-Pinilla (UCLA), Paul Vesna (UCLA) Research interests span neurotrauma , neuroplasticity , and advanced neuroimaging techniques . His work integrates multicenter preclinical studies through consortia like TOP-NT and EpiBioS4Rx . Recent publications highlight MRI harmonization , BDNF mimetics , and epileptogenesis biomarkers . Grants include continuous NIH support since 2007, with leadership roles in U01NS082320 and R01NS055910 . He contributes to neurotrauma common data elements and preclinical standardization .

• Environmental Systems Engineering
• Flood Risk Management
• Remote Sensing
• +5 more

Dr Monica Rivas Casado is a Reader in Environmental Systems Engineering at the Cranfield Environment Centre , School of Water, Energy and Environment, Cranfield University. With a PhD in applied geostatistics and an MSc in Environmental Water Management, she specializes in UAV-based environmental monitoring systems for flood risk, climate disasters, and ecosystem services quantification.

• Atomic-scale simulations of irradiation damage in metallic alloys
• ab initio methods
• molecular dynamics
• +7 more

Charlotte BECQUART is Professor at Centrale Lille Institute, University of Lille, France, and member of the Physical Metallurgy and Materials Engineering team within the Materials and Transformations Unit (UMET, CNRS UMR 8207). Since 1993 she has conducted research on atomic-scale modeling of irradiation damage in metallic alloys using ab initio, molecular dynamics and kinetic Monte Carlo methods. Education & Background Metallurgy and thermodynamics applied to development, modeling and surface treatments. Working at École Nationale Supérieure de Chimie de Lille since 1993. Research Focus Her work centers on understanding irradiation damage at the atomic scale, particularly embrittlement mechanisms in Pressurized Water Reactor (PWR) vessel steels and materials for fusion reactors such as tungsten divertor components. She employs advanced simulation techniques including ab initio calculations, molecular dynamics and kinetic Monte Carlo methods to study deformation, phase change and damage phenomena. Research Trends Recent publications demonstrate a strong emphasis on multi-scale modeling of irradiation effects in metallic systems, statistical analyses of displacement cascades, nanocavity diffusion in tungsten, radiation-induced segregation at grain boundaries, and development of machine-learned interatomic potentials for complex alloys. The research spans nuclear materials, computational materials science and advanced alloys for energy applications. PhD Supervision She has successfully supervised 12 defended theses since 2012 and currently co-directs 3 ongoing doctoral projects (2022–2025) focusing on mesoscopic scale modeling of irradiated steels, multi-scale modeling of shape memory alloys and oxide-cladding interface characterization for future nuclear fuels. Affiliations & Teams UMET – Materials and Transformations Unit, CNRS UMR 8207, University of Lille. Physical Metallurgy and Materials Engineering research team. Collaborates with EDF, CEA and international fusion programs.

• Earthquake Engineering
• Seismic Design
• Steel Structures
• +2 more

Lydell Andree Wiebe is a Professor in the Department of Civil Engineering at McMaster University's Faculty of Engineering. He specializes in earthquake engineering, seismic design of steel structures, and resilient infrastructure systems. His academic credentials include a BASc (2005) and PhD (2013) in Civil Engineering from the University of Toronto, and an MSc in Earthquake Engineering (2008) from the ROSE School in Pavia, Italy. His research focuses on developing innovative structural systems such as controlled rocking braced frames and masonry walls to enhance seismic resilience. Key areas include performance-based design, structural dynamics, and risk assessment of infrastructure under seismic loads. He actively contributes to professional committees, including the CSA S16 Technical Committee (Steel Structures) and the Canadian Association for Earthquake Engineering. Dr. Wiebe's recent publications (2020–2025) emphasize seismic loss mitigation, optimization of controlled rocking systems, and machine learning applications in structural engineering. His work integrates experimental testing with advanced computational models to improve design methodologies for earthquake-resistant structures. Awards & Honors: GJ Jackson Fellowship Award (2005) HA Krentz Research Award (2015) McMaster Faculty of Engineering Teaching Excellence Award (2017) McMaster Students Union Teaching Award (2014, 2017) He teaches courses including Steel Structures , Seismic Design and Analysis , and Introduction to Civil Engineering , reflecting his commitment to education and mentorship.

Polly FordyceView profile

Associate Professor

• Microfluidics
• Biophysics
• Biochemistry
• +5 more

Polly Fordyce is an Associate Professor at Stanford University with joint appointments in the Departments of Bioengineering and Genetics . She is a Chan Zuckerberg Biohub Investigator , Sarafan ChEM-H Institute Scholar , and member of interdisciplinary institutes including Bio-X , Wu Tsai Neurosciences Institute , and SPARK at Stanford . Ph.D. in Physics from Stanford University (2007) B.A. in Physics and Biology from University of Colorado at Boulder (2000) Postdoctoral work in Biophysics at University of California San Francisco (2014) Her research focuses on microfluidic platform development for quantitative biophysics and biochemistry studies. Key projects include: SPARKfold for protein unfolding kinetics STAMMPPING to map activation domain-coactivator interactions MRBLEs for spectral multiplexing in assays Dropception for high-throughput droplet sorting Her 15 most recent publications span protein stability analysis , transcription factor specificity , droplet microfluidics , organoid phenotyping , and plant biosynthesis pathway discovery . These works frequently employ machine learning and multiplexed assays to address biophysical questions. Scientific recognition includes: 2024 President's Award for Excellence Through Diversity 2023 Eli Lilly Award in Biological Chemistry 2023-2028 NIH Pioneer Award (DP1) 2017-2022 Chan Zuckerberg Biohub Investigator 2016-2021 NIH New Innovator Award (DP2) She advises 33 doctoral students and postdoctoral researchers, and leads the Stanford Microfluidics Foundry (2015-present).

• Material characterization
• Petrophysics
• Enhanced oil recovery
• +2 more

Son Dang is an Assistant Professor of Petroleum and Geological Engineering at the University of Oklahoma , focusing on advanced energy systems and subsurface fluid dynamics. His research bridges experimental and computational techniques to address critical challenges in energy production and carbon neutrality. Education: B.S., Petroleum Engineering, University of Oklahoma, 2012 M.S., Petroleum Engineering, University of Oklahoma, 2015 Ph.D., Petroleum Engineering, University of Oklahoma, 2019 Research Interests span material characterization, petrophysics, enhanced oil recovery (EOR), geo-storage, and mass transport in nanoporous media. His work emphasizes hydrogen storage, CO2 sequestration, and unconventional reservoir optimization. Scientific Contributions include pioneering studies on hydrogen diffusivity in depleted reservoirs, CO2 injection-induced geomechanics, and machine learning applications for rock typing. He employs nuclear magnetic resonance (NMR) spectroscopy and molecular simulations to unravel fluid-rock interactions at microstructural scales. Publications reveal a focus on energy transition technologies, with 15 recent works covering topics from supercritical CO2 fracturing to NMR-based diffusion analysis. These studies highlight his expertise in integrating experimental data with computational models to improve subsurface energy systems.

Jess SnedekerView profile

Professor

• Orthopedic Biomechanics
• Tendon Disease and Healing
• Cell-Biomaterial Interactions
• +2 more

Prof. Dr. Jess Snedeker is a Professor at ETH Zurich and the University of Zurich, where he holds joint chairs in the Department of Health Sciences and Technology (ETH) and the Department of Orthopedics (UZH). He leads the Laboratory for Orthopedic Biomechanics, focusing on interdisciplinary research in musculoskeletal health. His research spans three primary domains: (1) mechanical and biological mechanisms of tendon disease and healing, (2) micro-scale cell-biomaterial interactions for therapeutic applications, and (3) clinical biomechanics for innovating orthopedic implant design. This work integrates engineering principles with medical science to address complex challenges in tendon repair and regenerative medicine. Snedeker's recent publications (2023–2025) emphasize tendon biomechanics , bioprinting , surgical implants , and AI-driven pathology analysis , reflecting a strong focus on translational solutions for musculoskeletal disorders. Recurring themes include growth factor therapies, biomaterial scaffolds, and computational modeling.

Liu YangView profile

Assistant Professor

• Multiscale/Multiphysics Modeling and Simulation
• Uncertainty Quantification
• Heterogeneous Data Analysis
• +1 more

Liu Yang is an Assistant Professor in the Department of Nuclear Engineering at Texas A&M University, affiliated with the Scientific Machine Learning for Advanced Reactor Technologies (SMART) Lab. His academic work focuses on multiscale modeling, uncertainty quantification, and intelligent control systems for nuclear reactors. Ph.D., North Carolina State University (2018) M.S., China Institute of Atomic Energy (2013) B.S., Tsinghua University (2010) His research interests emphasize advanced computational methods for reactor systems, data-driven engineering decision-making, and machine learning applications in nuclear technology. While his primary affiliation is in nuclear engineering, his Google Scholar publications suggest interdisciplinary work spanning skeletal biology and molecular signaling pathways. Recent publications highlight trends in developmental biology, including studies on Notch signaling, cartilage homeostasis, and genetic factors in scoliosis. These works span both computational and experimental approaches, with applications in disease modeling and tissue repair mechanisms. American Nuclear Society American Society of Mechanical Engineers