Find Academic Supervisors

Jeremiah van OostenView profile

Lecturer

• Game Engine Development
• Graphics Programming
• Parallel Computing
• +3 more

Jeremiah van Oosten serves as a Lecturer at the Academy for AI, Games & Media in Breda, Netherlands, focusing on game engine development and graphics programming within the institution's Frontier building location. His research spans critical areas in interactive technology: Game Engine Architecture and Optimization GPU-Accelerated Parallel Computing (CUDA) Cross-Platform Graphics Rendering (OpenGL) Mathematical Foundations for 3D Systems Immersive Therapy Applications (VR/AR) Van Oosten's influential 2012 publications on 3dgep.com established comprehensive resources for game engine programming, emphasizing practical implementation of GPU techniques, real-time rendering pipelines, and mathematical frameworks. His work bridges theoretical computer graphics with industry-ready development practices, particularly benefiting independent game developers. Scientific recognition includes: No major awards or fellowships documented in current sources He actively contributes to healthcare innovation through research collaborations: ARET Project (2022-2024): Co-Investigator for Augmented Reality Exposure Therapy targeting Alcohol Use Disorder RECOVRY Project (2019-2021): Co-Investigator developing Virtual Reality solutions for alcohol addiction treatment VIBE Project (2018-2022): Co-Investigator creating virtual human systems for economic applications These EU and nationally funded initiatives demonstrate his commitment to applying game technology in therapeutic contexts through interdisciplinary teams spanning computer science, psychology, and healthcare.

Andrei FrolovView profile

Assistant Professor

• Cosmology
• Theoretical Physics
• Gravitation
• +5 more

Andrei Frolov is an Assistant Professor in the Department of Physics at Simon Fraser University, Canada. His research focuses on theoretical cosmology, gravitational physics, and black hole dynamics. He holds a PhD from the University of Alberta and has held postdoctoral positions at Stanford University and the Canadian Institute for Theoretical Astrophysics (CITA). Frolov’s work spans cosmological models of inflation, observational tests of early universe theories, and numerical relativity simulations. He is affiliated with the Cosmology Group at SFU and has contributed to major international projects like the Planck mission, earning awards such as the 2019 Cocconi Prize and Gruber Cosmology Prize. His research group advises PhD and MSc students exploring topics like black hole evaporation, cosmic microwave background (CMB) foregrounds, and gravitational lensing. Education: BSc (Honors) in Physics from Moscow State University and University of Alberta; PhD in Physics from University of Alberta (2000). Research Themes: Early universe physics, modified gravity, numerical relativity, CMB analysis, and astrophysical foreground modeling. Awards: 2019 ESA Planck Mission Contribution Award, 2018 Gruber Cosmology Prize, and Marcel Grossmann Award (2018). Grants & Collaborations: Involved in Simons Observatory and CMB-S4 initiatives, focusing on galactic science and instrumentation. Labs/Teams: Leading the Cosmology Group at SFU, collaborating internationally on CMB analysis and gravitational physics.

• High-Energy Physics
• Particle Physics
• Proton-Proton Collisions
• +5 more

Muammer Altan Çakır is a Professor in the Department of Physics Engineering at Istanbul Technical University, actively engaged in high-energy physics research with significant contributions to the CMS Collaboration at CERN. His work focuses on proton-proton collisions, Higgs boson physics, and top quark studies using Large Hadron Collider data. His research interests span experimental particle physics with emphasis on proton-proton collisions , Higgs boson characterization , lepton physics , and transverse momentum analysis . Recent work explores charm baryon production, Drell-Yan processes, and muon identification techniques using advanced multivariate methods in the CMS detector. Analysis of his 15 most recent publications reveals a strong focus on precision measurements in high-energy collisions, particularly in Higgs physics (3 publications), top quark physics (2), and detector optimization (2). His work consistently addresses fundamental questions in the Standard Model through advanced statistical techniques and large-scale data analysis. Doktora sonrası araştırma bursu (2010) Landes Baden Württenberg Doktorarbeit Stipendium (2006) LPC Fall Guest Visitor Faculty (2017) Multiple Performans ödülü awards (2012, 2013, 2015) Professor Çakır has supervised 15 students and leads multiple research projects including GPU-based distributed computing platforms for big data processing, real-time predictive maintenance systems, and deep learning applications for top quark spin correlation studies. His current projects integrate generative AI with domain-specific physics research while developing scalable open-source big data architectures for industrial applications. His laboratory work centers on CMS detector data analysis, with recent focus on online data visualization tools and multivariate techniques for particle identification. Current projects explore GPU-accelerated computing frameworks and generative AI applications for physics-informed design systems.

• Sustainable Energy Systems
• Numerical Optimization
• Deep Learning
• +3 more

Dr. Manuel Dahmen serves as Head of Department at the Institute of Climate and Energy Systems (ICE-1) within the Research Center Jülich. His research focuses on designing sustainable and cost-efficient energy systems through numerical optimization and deep learning techniques. He leads efforts in advancing energy system technology, particularly in optimizing renewable energy integration, process network analysis, and the co-design of fuels and engines. His work emphasizes innovative applications of machine learning, such as physics-informed neural networks and reinforcement learning, to address challenges in energy system design and operation. Key areas include reducing greenhouse gas emissions in industrial processes, optimizing energy networks, and developing data-driven models for dynamic process control. Dr. Dahmen’s contributions span algorithm development (e.g., MUSE-BB decomposition algorithms), energy system scenario generation, and robust design methodologies under uncertainty. His research bridges computational methods with practical energy solutions, aiming to achieve decarbonization in industries like copper production and transportation. Publications highlight advancements in renewable energy integration, fuel design for spark-ignition engines, and the application of graph neural networks for molecular property prediction. His interdisciplinary approach fosters collaboration across chemical engineering, computer science, and energy economics to tackle global sustainability challenges.

Jonathan SartonView profile

Lecturer

• Scientific Visualization
• Volume Rendering
• GPU Programming
• +3 more

Jonathan Sarton is a Lecturer in Computer Science at the University of Strasbourg and a Researcher at the ICube laboratory. He holds an affiliation with the Geometric and Graphics Computing (IGG) team within the UFR of Mathematics and Computer Science. His primary research focuses on scientific visualization, volume rendering, and GPU programming. Education includes a PhD (2018) from the University of Reims Champagne-Ardenne titled 'High-performance interactive visualizations of massive volumetric data: an out-of-core multiresolution approach based on GPUs' , and a Master's in Visualization, Imaging, and Performance (2014) from the University of Orléans. He has held roles including Temporary Teaching and Research Associate (ATER) at the University of Reims (2018-2019) and a doctoral researcher at CReSTIC (2015-2018). Current research emphasizes interactive visualization of large unstructured meshes from numerical simulations, supported by the ANR LUM-Vis project. His technical work includes GPU-based out-of-core architectures for handling AMR time series data and distributed visualization systems. Professional activities include teaching computer science courses at undergraduate and graduate levels, focusing on 3D graphics, parallel programming, and algorithms. Professional contact: Office C118 at ICube, sarton@unistra.fr .

Lauri SaviojaView profile

Professor

• Room Acoustics
• Virtual Reality
• Auditory Displays
• +5 more

Prof. Lauri Savioja is a full-time Professor and Head of the Department of Computer Science at Aalto University. His research focuses on room acoustics, virtual reality, and auditory displays, conducted within the Aalto Acoustics Lab—a multidisciplinary facility for sound-related research. He holds Fellowships from the Audio Engineering Society and Acoustical Society of America, and is an IEEE Senior Member. His work contributes to UN Sustainable Development Goals through advancements in audio engineering and immersive technologies. Education: Doctoral degree in Engineering and Technology (1999), Helsinki University of Technology Licentiate degree in Engineering and Technology (1995), Helsinki University of Technology Master's degree in Engineering and Technology (1991), Helsinki University of Technology Research Interests: Room acoustics simulation and auralization Acoustic radiance transfer and reverberation modelling Virtual reality audio and spatial perception Neural network applications in wave equation solutions Finite-difference time-domain (FDTD) methods Awards: Fellow of the Audio Engineering Society (2013) Fellow of the Acoustical Society of America (2018) Board of Governors Award, AES (2015) Best Doctoral Thesis Award in Technology (1999) Grants & Advising: Supervised 7 theses and actively participates in editorial boards (e.g., Acta Acustica ). Engages in conference organizing, international collaborations, and academic outreach, including roles in the Finnish Academy of Science and Letters. Labs & Collaborations: Leads the Aalto Acoustics Lab, collaborating globally on projects like hybrid acoustic modelling, GPU-based real-time simulations, and spatial audio systems for virtual environments.

Lin TianView profile

Senior Lecturer

• Multi-scale modeling
• Aerosol transport
• Fluid dynamics
• +6 more

Dr. Lin Tian is a Senior Lecturer in the School of Engineering at RMIT University, Melbourne, Australia. She holds expertise in multi-scale modeling of fluid dynamics and micro/nano particles, with a focus on aerosol transport, inhalation toxicity, and drug delivery in respiratory systems. Her research integrates computational methods like Lattice Boltzmann simulations to solve multiphase flow problems. Current projects include developing risk assessment platforms for inhaled carbon nanotubes and hydrodynamic modeling for underwater vehicle systems. Her academic journey includes roles as a Research Fellow at RMIT (2015–2018) and Assistant Professor at SUNY Canton, USA (2008–2014). She leads funded projects such as the DECRA-supported multi-scale risk assessment platform and DSTG-funded underwater vehicle hydrodynamics research. Teaching interests span advanced thermo-fluids, mechanics of solids, and computational fluid dynamics. Research highlights include nasal airflow modeling, fiber transport dynamics, and HVAC system optimization for pathogen control. Awards include the DECRA grant (2018), and her work addresses critical areas like occupational exposure, drug delivery efficacy, and sustainable building systems. Dr. Tian is open to supervising Masters and PhD students in computational fluid dynamics and multiphase flow applications.

• Applied Artificial Intelligence (AI)
• Computer Vision
• Disaster Management
• +4 more

Dr. Umair Iqbal is a Researcher in the School of Engineering at the University of Newcastle, specializing in Applied Artificial Intelligence (AI) with a focus on Computer Vision and Edge-Computing solutions for real-world challenges like disaster management and water resource optimization. He holds a PhD from the University of Wollongong and is an NVIDIA DLI Ambassador, delivering workshops on deep learning and AIoT technologies. His research emphasizes AI-driven video analytics for infrastructure monitoring (e.g., culvert blockage detection), public safety (e.g., violence detection in transit systems), and environmental applications (e.g., flood risk mitigation). His work combines interdisciplinary approaches, leveraging tools like TensorFlow, NVIDIA frameworks, and edge devices (Jetson platforms). Key contributions include developing an AIoT solution for pipeline protection, a real-time edge-computing system for public transport safety, and bibliometric reviews on drones and computer vision in flood management. Dr. Iqbal has secured $114,814 in grants, including a 2023 project on floodborne debris identification and a 2025 initiative for kerbside waste management. He actively supervises graduate students and leads projects like StopBlock (culvert blockage detection) and iMOVE (transport safety analytics). Research interests span AIoT frameworks, generative AI, and geotechnical applications. His publications (23+ journal articles, 3 conference papers, 2 book chapters) address hydraulic infrastructure resilience, machine learning in civil engineering, and disaster response technologies. He collaborates with industry partners like Remondis and fosters innovation through workshops and course coordination at undergraduate/postgraduate levels.

• Computer-Aided Drug Design
• Molecular Dynamics Simulations
• Structural Biology
• +3 more

Jacob Durrant is an Associate Professor in the Department of Biological Sciences at the University of Pittsburgh. His research focuses on computer-aided drug design (CADD), molecular dynamics simulations, and structural biology. He leads the Durrant Lab, which develops innovative computational tools for drug discovery, such as AutoGrow4 and ProteinVR. The lab investigates infectious diseases, cancer, and neurological disorders, emphasizing small-molecule ligand identification and molecular visualization. Key affiliations include the Molecular Biophysics and Structural Biology Graduate Program, the Department of Computational & Systems Biology, and the Joint CMU-Pitt PhD Program in Computational Biology. Durrant collaborates on projects like the RECOVERY trial, exploring treatments for severe COVID-19. His work integrates machine learning, cloud computing, and GPU acceleration to advance drug discovery. Research highlights include developing open-source tools (e.g., MolModa, FPocketWeb) and studying protein dynamics via molecular simulations. The lab’s ProteinVR project enables immersive virtual reality visualization of molecular structures for educational and collaborative purposes.

Aristidou AndreasView profile

Associate Professor

• Human Motion Analysis
• Computer Graphics
• Machine Learning
• +3 more

Aristidou Andreas is an Associate Professor at the Department of Informatics, University of Cyprus. His academic journey includes a BSc from National and Kapodistrian University of Athens (2005), MSc from King's College London (2006), and a PhD from the University of Cambridge (2011). He has held postdoctoral positions at multiple institutions and served as a visiting professor at the University of Nicosia. His research focuses on human motion analysis, computer graphics, cultural heritage digitization, and geometric algebra applications. He is a senior member of IEEE, ACM, and Eurographics, and serves on editorial boards for journals like The Visual Computer. Research highlights include the FABRIK algorithm (widely used in game engines) and motion capture innovations for cultural heritage preservation. Awards include the DIDAKTOR scholarship, Erasmus Mundus Grant, and NVIDIA GPU grant. He leads projects funded by EU initiatives (e.g., ITN-DCH, SCHEDAR) and collaborates internationally on digital twins, virtual museums, and AI-driven motion synthesis. Key contributions span 3D motion analysis, motion reconstruction from sparse data, and emotion recognition in theater performances. His work bridges computational methods with cultural preservation, biomedical applications, and interactive VR systems.

Anthony DanalisView profile

Academic

• High-Performance Computing (HPC)
• Performance Analysis and Optimization
• System Benchmarking
• +4 more

Anthony Danalis is a Research Assistant Professor at the University of Tennessee's Innovative Computing Laboratory (ICL), affiliated with the Department of Electrical Engineering and Computer Science. His research focuses on high-performance computing (HPC), including performance analysis, system benchmarking, compilers, MPI, and accelerators. He holds a Ph.D. and two M.S. degrees in Computer Science from the University of Delaware and the University of Crete, along with a B.S. in Physics from the University of Crete. Education: Ph.D. in Computer Science, University of Delaware M.S. in Computer Science, University of Delaware M.S. in Computer Science, University of Crete B.S. in Physics, University of Crete Research Interests: High-Performance Computing (HPC) Performance Analysis and Optimization System Benchmarking Compilers and Runtime Systems Parallel Programming Models (e.g., MPI) Accelerator Technologies Publications Highlight Trends in: Advancements in the PAPI library for exascale computing Dataflow-based execution for scientific applications Hardware performance counter analysis Compiler-driven optimizations Scientific Awards: Best Research Poster Finalist at SC 2017 Advising and Grants: No formal advisees listed Contributions to ICL's leadership and collaborative projects Labs and Teams: Innovative Computing Laboratory (ICL) Participation in projects like DAGuE, DPLASMA, and SHOC

• Biophysics
• Chemistry
• Intrinsically Disordered Proteins
• +3 more

Wenwei Zheng is an Associate Professor at Arizona State University (ASU), affiliated with the School of Applied Sciences and Arts within the CISA initiative. He holds a PhD in Chemistry from Rice University (2017), followed by postdoctoral research at the NIH. His research focuses on computational methods to study intrinsically disordered proteins (IDPs) and their roles in membraneless organelles and pathological fibril formation. Key areas include liquid-liquid phase separation and protein interaction networks. Dr. Zheng leads the Zheng Biophysics Lab, leveraging grants from NIH-NIGMS, NSF, and NVIDIA GPU resources. He teaches courses in chemistry and scientific computing at ASU. Research interests emphasize biophysical modeling of IDPs, with recent work exploring salt effects on conformation, phosphorylation impacts on phase separation, and drug discovery targeting disordered regions. Over 50 publications span journals like PNAS , ACS Central Science , and Journal of Physical Chemistry . Collaborations include projects on FUS protein toxicity and estrogen receptor disorder. His lab's 2024 advancements include studies on α-synuclein aggregation and transient protein interactions. Grants include a 5-year NIH R35 MIRA award (2022) and NSF support for biodegradation studies. Notable achievements include Anton2 supercomputing allocations and NVIDIA GPU donations. The lab actively recruits students interested in computational biophysics and protein dynamics.

Kyle E. NiemeyerView profile

Associate Professor

• Combustion Modeling
• Computational Fluid Dynamics
• Chemical Kinetics
• +5 more

Kyle E. Niemeyer is an Associate Professor at Oregon State University's School of Mechanical, Industrial, and Manufacturing Engineering. He leads the Niemeyer Research Group focused on computational modeling of combustion, reactive flows, and environmental systems. He also serves as Associate Head for Undergraduate Programs and Associate Editor-in-Chief of the Journal of Open Source Software (JOSS). Ph.D. in Mechanical Engineering, Case Western Reserve University (2013) M.S. and B.S. in Aerospace Engineering, Case Western Reserve University (2010, 2009) His research spans combustion modeling, GPU acceleration for chemical kinetics, ocean biogeochemical simulations, and open science practices. Key contributions include: Developing open-source tools like pyMARS and MC/DC Advancing GPU-based chemical kinetics integration algorithms Modeling smoldering combustion in biomass fuels Applying combustion-derived model reduction to ocean systems Recent publications focus on exascale Monte Carlo neutron transport, wildfire fuel modeling, and Cantera software optimization. Awards include AAAS Science & Technology Policy Fellowship and Welty Faculty Fellowship. AAAS Science & Technology Policy Fellow (2022–2023) Welty Faculty Fellow (2021–2024) Better Scientific Software Fellow (2019) His group actively engages in open-source software development and computational methods research for energy, aerospace, and environmental applications.

Abbey WaldronView profile

Lecturer

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

Dr Abbey Waldron is a Lecturer in Particle Physics at Queen Mary University of London, affiliated with the School of Physical and Chemical Sciences and the Centre for Fundamental Physics. Her research focuses on neutrino oscillations, matter-antimatter asymmetry, and neutrino-nucleus interactions, with a strong emphasis on experimental techniques and machine learning applications in detector systems. She contributes to major international experiments such as DUNE and ProtoDUNE, advancing liquid argon time projection chamber (LArTPC) technologies. Her work includes cross-section measurements, detector performance optimization, and high-energy physics collaborations. Education: PhD in Physics (focus on neutrino physics). Her research interests span experimental neutrino physics, including neutrino oscillations, scattering cross-section measurements, and detector development. She explores how machine learning enhances neutrino detection precision and addresses fundamental questions like the origin of matter-antimatter imbalance in the universe. Her recent publications analyze neutrino interactions in diverse materials (e.g., argon, hydrocarbon), detector performance in high-pressure environments, and algorithmic improvements for event reconstruction. She leads efforts in DUNE’s far detector technology and contributes to international collaborations like T2K and NOvA. Grants: £12,000 Royal Society grant (2024–2026) for machine learning in neutrino interactions. £291,349 STFC grant (2023–2025) supporting experimental PPRC exploitation at QMUL. She collaborates with global teams on detector development and neutrino physics, contributing to cutting-edge projects like the DUNE experiment’s vertical drift technology and ProtoDUNE’s pixelated LArTPC simulations.

Jürgen BrehmView profile

Adjunct Professor

• Computer Architecture
• Parallel Processing
• E-Learning
• +6 more

Jürgen Brehm is an Adjunct Professor at the Faculty of Electrical Engineering and Computer Science of Leibniz University Hannover . He holds a venia legendi in Computer Engineering after completing his habilitation in 2000. Education: Diploma in Computer Science (1986), Doctorate in Engineering (1991), Habilitation (2000) Research interests span computer architecture , parallel processing , performance analysis , and e-learning . His work explores ubiquitous computing , communication architectures , and optimization algorithms . Recent publications highlight trends in parallel computing , optimization , and interactive systems , including works on swarm intelligence , particle swarm optimization , and open content integration. Scientific award : Feodor Lynen Fellowship (1994) Teaching includes core courses like Basics of Computer Architecture , Operating Systems , and Parallel Processing . He also designed two multimedia-equipped computer science lecture halls and managed large-scale DFG projects for e-learning and HPC computing.