Find Academic Supervisors

Cem YukselView profile

Associate Professor

• Computer Graphics
• Real-Time Rendering
• Physically-Based Simulations
• +5 more

Cem Yuksel is an Associate Professor at the Kahlert School of Computing, University of Utah, and founder of Cyber Radiance LLC. His research focuses on computer graphics, including real-time rendering, physically-based simulations, global illumination, and hair/cloth modeling. He holds a PhD from Texas A&M University and has been a postdoc at Cornell University. Education: PhD in Computer Science (2010, Texas A&M), MS in Computer Engineering (2003, Bogazici University), BS in Physics (2000, Bogazici University). Research interests include Hair Meshes for real-time hair rendering, high-performance polynomial solvers, mesh color textures, and wearable 3D knitting. He has authored over 100 peer-reviewed papers and received awards such as the Wolfgang Straßer Best Paper Award (2022) and multiple Dean’s commendations for teaching. Courses taught include Introduction to Computer Graphics and Rendering with Ray Tracing. Key projects: Hair Meshes (SIGGRAPH 2024), Stochastic Lightcuts (HPG 2019), Mesh Color Textures (2020), and high-performance polynomial solvers (SIGGRAPH 2022). His work has been implemented in commercial software like Hair Farm for 3ds Max. Grants and service roles include steering committee member for High-Performance Graphics (HPG) and organizing SIGGRAPH conferences. He runs a graphics research lab at the University of Utah.

• Computational Fabrication
• Computer Graphics
• Textile Design Automation
• +3 more

Jenny Han Lin is an Assistant Professor at the University of Utah's Kahlert School of Computing. Her research focuses on computational fabrication, particularly leveraging knot theory and low-level computer representations to advance textile manufacturing and real-world phenomenon modeling. She completed her PhD in Computer Science at Carnegie Mellon University under James McCann, preceded by a B.E. in Computer Science and Molecular Biology from MIT. Her work bridges computer graphics, robotics, and mathematics, with notable contributions to machine knitting compilers, braid group theory applications, and crochet representation. She has been a postdoctoral scholar at the University of Utah and taught courses at CMU and the University of Washington. Lin actively promotes textile fabrication through initiatives like Knitout Office Hours and served as SIGBOVIK organizing committee chair (2019-2022), creating an award-winning conference website. Lin's research has been recognized with a finalist award at ICRA 2021. She collaborates widely, mentoring students in interdisciplinary projects at the intersection of computing and physical fabrication.

Taku KomuraView profile

Professor

• Computer Graphics
• Physical Simulation
• Character Animation
• +6 more

Roles & Affiliations: Professor of Computer Graphics at the Department of Computer Science, University of Hong Kong (since 2020). Previously at University of Edinburgh (2006-2020), City University of Hong Kong (2002-2020), and RIKEN (2000-2002). Holds a BSc, MSc, and DSc in Information Science from the University of Tokyo. Research Interests: Focuses on physically-based animation, character animation, 3D modeling, cloth animation, and robotics. Recent work emphasizes machine learning integration into animation synthesis. Notable projects include MotionNet (3D motion reconstruction), neural state machines for character interactions, and fracture simulation with material points. Publications: Over 100 peer-reviewed papers spanning SIGGRAPH, Eurographics, and IEEE journals. Recent work emphasizes deep learning applications in animation, medical imaging analysis, and physics-based simulations. Key trends include neural networks for motion synthesis, transformer-based models for 3D gestures, and topology-aware shape reconstruction. Awards: Royal Society Industry Fellowship (2014), Google AR/VR Research Award (2017). Recognized for contributions to physically-based animation and medical imaging AI. Labs & Collaborations: Leads research groups in computer graphics and AI at HKU. Collaborates with institutions on robotics, VR/AR, and medical imaging projects. Active in organizing SIGGRAPH and Eurographics conferences.

Karel MatousView profile

Professor

• Predictive Computational Science and Engineering
• Multiscale Modeling
• Computational Mechanics
• +4 more

Dr. Karel Matous is a Professor of Aerospace and Mechanical Engineering at the University of Notre Dame and Director of the Center for Shock-Wave Processing of Advanced Reactive Materials (C-SWARM). He holds a Ph.D. in Theoretical and Applied Mechanics from the Czech Technical University in Prague. His research focuses on predictive computational science and engineering, multiscale modeling, and high-performance computing, with applications in materials science and energetic systems. Education: Ph.D. in Theoretical and Applied Mechanics (Czech Technical University, 2000), M.S. in Theoretical and Applied Mechanics (Czech Technical University, 1998). His work bridges applied mathematics, computational science, and materials science, emphasizing data-driven modeling and experimental-computational synergy. Key contributions include multiscale models for heterogeneous materials, adaptive wavelet algorithms, and computational homogenization at extreme scales. His research has been funded by agencies like the DOE, NSF, and industry partners, with over $12M in grants for the C-SWARM center. Publications span topics such as solid propellant modeling, particle debonding in elastomers, and microstructure-property relations. Awards include ASME Fellow, Melosh Medal (student award), and Rector's Award. He collaborates with institutions globally and teaches advanced mechanics courses at Notre Dame. Labs/Teams: Directs C-SWARM, leading teams in multiscale simulations and shock-wave processing of materials. Active in computational physics and materials research groups.

Hannes RaddatzView profile

Researcher

• Embedded Systems
• Industrial Internet of Things (IIoT)
• OPC UA Protocols
• +4 more

Dr. Hannes Raddatz is a researcher at the University of Rostock, focusing on embedded systems and industrial communication protocols. He works within the Department of Embedded Systems, contributing to projects involving OPC UA, MQTT-SN, and Time-Sensitive Networking (TSN) for Smart Manufacturing and IIoT applications. His expertise spans protocol evaluation, secure communication frameworks, and virtual prototyping of distributed systems. PhD in Embedded Systems Research Interests: Industrial IoT, Smart Factories, Cybersecurity His recent work includes protocol integration for sensor networks and optimizing real-time communication in industrial environments. He collaborates with institutions like the University of Rostock and researchers such as Prof. Christian Haubelt and Dr. Frank Golatowski. Key publications address OPC UA server evaluation, MQTT-SN binding for IIoT, and secure frameworks for Smart Home systems. He has presented at conferences like INDIN, ETFA, and WF-IoT. Scientific Contributions: Best Paper Award Nominee at PATMOS 2019 Key roles in projects: GenerIoT, SUSTAIN Dr. Raddatz contributes to advancing secure and efficient communication protocols in industrial and embedded contexts, with applications in logistics, crane systems, and smart manufacturing environments.

Konrad PolthierView profile

Professor

• Discrete Differential Geometry
• Applied Geometry
• Mathematical Visualization
• +2 more

Prof. Dr. Konrad Polthier is a full professor of mathematics at Freie Universität Berlin since 2005, leading the Mathematical Geometry Processing Group. He holds roles as chair of the Berlin Mathematical Society and co-chair of the Berlin Mathematical School. His research focuses on discrete differential geometry, applied geometry, and mathematical visualization, with contributions to geometry processing, computer graphics, and educational media. He has authored books like A Mathematical Picture Book and produced award-winning films such as MESH – A Journey through Discrete Geometry . His work bridges academic research and public engagement, including the MathFilm Festival and the platform math.berlin . Polthier's scientific accolades include international film awards for his videos and funding for projects like QuadCover. He has served on editorial boards for journals and conferences, including co-editing Computer Aided Geometric Design .

Jin YangView profile

Assistant Professor

• Viscoelastic Materials
• Experimental Mechanics
• Full-Field Deformation Measurements
• +3 more

Dr. Jin Yang is an Assistant Professor in the Department of Aerospace Engineering & Engineering Mechanics at the University of Texas at Austin. He joined the faculty in Fall 2022 after completing a postdoctoral position at the University of Wisconsin-Madison and a PhD from the California Institute of Technology (Caltech). His research focuses on developing experimental techniques and analytical tools to study viscoelastic materials under extreme loading conditions, particularly leveraging machine learning and advanced imaging methods like digital image correlation (DIC) and laser-induced cavitation. Key areas include material characterization of hydrogels, biological tissues, and foams at ultra-high strain rates (10 -4 to 10 7 s -1 ). Education: B.En. in Engineering Mechanics, Tsinghua University (2013) Ph.D. in Engineering Mechanics, California Institute of Technology (2019) Research Interests: Dynamic instability and failure mechanisms in viscoelastic materials Machine learning-driven material characterization Laser-induced microcavitation for rheometry Development of high-speed full-field deformation measurement techniques (DIC, DVC) Notable Awards: National Science Foundation CAREER Award (2025) Haythornthwaite Initiation Grant (ASME, 2022) ICTAM Fellowship (International Congress of Theoretical and Applied Mechanics) Grants and Initiatives: Academic Development Funds for enhancing aerospace materials labs (2024-25) Funding for high-speed imaging equipment (Shimadzu HPV-X2 camera) Labs & Teams: Active in the Yang Research Group at UT-Austin, focusing on cutting-edge mechanics of solids and biomaterials.

• Computer Vision
• Mathematical Morphology
• Deep Learning
• +3 more

Rick Groenendijk is a researcher in the field of computer vision and mathematical morphology at the University of Amsterdam's Computer Vision Lab. His work focuses on integrating morphological operators into deep learning frameworks, particularly in neural network architectures. He holds a PhD in Computer Vision & Mathematical Morphology from the University of Amsterdam, supervised by Dr. Leo Dorst and Prof. Theo Gevers, which he successfully defended in October 2023. His research interests include advancing hybrid neural network architectures, such as HaarNet and MorphPool, which combine linear and morphological operations for tasks like semantic segmentation and depth estimation. Recent contributions address challenges in RGB-D data processing and adversarial training. He has also explored applications in photogrammetry and social robotics learning. Publications span top venues including IEEE TPAMI, arXiv, and the British Machine Vision Conference. His code repository for the Geometric Back-Propagation paper is publicly available on GitHub under the MIT license. Rick has collaborated with institutions on multi-loss optimization techniques and climate action analysis through machine learning. While no formal awards are listed, his work demonstrates impactful contributions to computer vision and deep learning methodologies.

Zackory EricksonView profile

Assistant Professor

• Human-Robot Interaction
• Rehabilitation and Health Care Robotics
• Robot Learning
• +13 more

Zackory Erickson is an Assistant Professor at Carnegie Mellon University's Robotics Institute, leading the Robotic Caregiving and Human Interaction (RCHI) Lab. His work focuses on healthcare robotics, particularly empowering older adults and people with disabilities through assistive technologies. Research Interests: At the intersection of physically assistive robotics , robot learning , physics simulation , and haptic perception , Erickson develops methods for robotic caregivers to intelligently interact with humans. Key areas include physical human-robot interaction , deep learning , rehabilitation robotics , and deformable object manipulation . Notable trends in his recent publications include large language model integration for assistive robotics, electromyography control systems, and sim-to-real frameworks for human mesh recovery. His work emphasizes safe, adaptive human-robot collaboration in healthcare settings. Scientific Awards: Best Paper Nomination at HRI 2025 Best Paper Award at HRI 2024 He advises both PhD and Masters students in projects ranging from robotic feeding systems to wearable interfaces for mobility assistance . Teaching responsibilities include graduate courses on Mechanics of Manipulation and Human-Robot Interaction .

Alan Cheng HolunView profile

Senior Lecturer

• Computational Geometry
• Parametric Surfaces
• Computer Graphics
• +2 more

Alan Cheng Holun is a Senior Lecturer in the Department of Computer Science at the National University of Singapore, specializing in computational geometry, computer graphics, and bio-geometric modeling. He received his Ph.D. in Computing Science from the University of Illinois at Urbana-Champagne in 2002 under Herbert Edelsbrunner. Education: B.Sc. (Computing Science, Chinese University of Hong Kong) M.Phil. (Computing Science, Hong Kong University of Science and Technology) Ph.D. (Computing Science, University of Illinois at Urbana-Champaign) Research Interests: Computational Geometry Parametric Surfaces Computer Graphics Visualization Bio-geometric Modeling Courses Taught: CS1010E Programming Methodology CS2040C Data Structures and Algorithms CS3241 Computer Graphics IT5001 Software Development Fundamentals Scientific Contributions: Focus on skin surface meshing, molecular modeling, and mesh deformation algorithms Developed dynamic skin triangulation methods with applications in bio-geometry Pioneering work in quality mesh generation for molecular structures Published extensively in conferences like IEEE Visualization, SODA, and ISAAC Awards: Best Teaching Assistant Award (HKUST, 1996) Collaborations: Former visiting scholar at Duke University's BioGeometry Center Contact: Email: hcheng@comp.nus.edu.sg Office: AS6-05-03, NUS Consultation Hours: Thursday 2:00-4:00pm

Adam KolkiewiczView profile

Professor

• Financial Mathematics
• Risk Management
• Stochastic Modeling
• +3 more

Dr. Adam Kolkiewicz is a Professor Emeritus in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on financial mathematics, risk management, and computational methods for actuarial science. He develops stochastic models for pricing financial derivatives like Bermudan options and reverse mortgages. His work includes numerical techniques for high-dimensional problems in finance, such as low-discrepancy mesh methods and Monte Carlo simulations. Dr. Kolkiewicz maintains consulting activities with financial institutions and has professional recognition as a Chartered Professional Engineer. His publications appear in Quantitative Finance, Insurance Mathematics and Economics, and Journal of Computational Finance.

Gustaaf JacobsView profile

Professor

• Computational Fluid Dynamics
• Multiphase Flow
• Aerodynamic Design
• +7 more

Professor Gustaaf Jacobs is a Full Professor in the Department of Aerospace Engineering at San Diego State University (SDSU), within the College of Engineering. He holds a M.Sc. in Aerospace Engineering from Delft University of Technology (1998) and a Ph.D. in Mechanical Engineering from the University of Illinois at Chicago (2003). His research focuses on computational multiphase and multi-scale flow physics, particularly in particle-laden flows, flow separation in complex geometries, and plasma-based flow control for applications in combustion optimization and drag reduction. Jacobs has received the AFOSR Young Investigator Award (2009) and became an Associate Fellow of AIAA (2013). His academic journey includes roles as a Visiting Assistant Professor at Brown University (2003–2006) and Postdoctoral Fellow at MIT (2003–2006). At SDSU, he advanced from Assistant Professor (2006) to Associate Professor (2010) before becoming Full Professor in 2013. Research interests span high-order numerical methods, shock wave interactions, and aerodynamic design, with a strong emphasis on experimental validation and computational modeling. Jacobs’ recent work includes studies on synthetic surface generation for additive manufacturing, turbulence transition on airfoils, and stochastic modeling of particle dynamics. His contributions address challenges in fluid-structure interactions, flow control, and predictive simulation techniques for aerospace systems. He collaborates on projects involving high-fidelity CFD, experimental aerodynamics, and data-driven modeling to advance engineering applications.

Gang LiView profile

Assistant Professor

• Renewable Energy Technologies
• Control Theory
• Condition Monitoring Algorithms
• +5 more

Gang Li is an Assistant Professor in the Department of Mechanical Engineering at Mississippi State University , affiliated with the Michael W. Hall School of Mechanical Engineering. He holds dual PhDs from the University of Maryland, Baltimore County (2017) and the University of Shanghai for Science and Technology (2017), alongside a Master's (2013) and Bachelor's (2010) in Mechanical Engineering from Shanghai. His research focuses on renewable energy systems, particularly tidal and wind energy harvesting, control theory, and mechatronics. He has contributed to advancements in gearbox technologies, condition monitoring, and life cycle assessments for sustainable energy systems. Dr. Li’s work integrates interdisciplinary approaches to address challenges in energy efficiency and system reliability. Notable achievements include developing novel control strategies for tidal energy converters and advancing wear prediction models using machine learning. He has been recognized with the 2022 Best Paper Award in Mechatronics (ASME) and the NSF I-Corps Best Performance Award (2020) . His academic journey includes roles as a Postdoc Research Associate (2017–2022) and Assistant Research Professor (2022–2023) at UMBC. His teaching emphasizes system dynamics and innovative curriculum design, fostering student engagement in applied engineering problems. Current research initiatives involve digital twin technologies for marine turbines and federated learning for decentralized wind systems.

Po ChenView profile

Associate Professor

• Seismology
• Computational Geophysics
• 3D Seismic Tomography
• +3 more

Po Chen is an Associate Professor in the Department of Geology and Geophysics at the University of Wyoming , affiliated with the College of Engineering and Physical Sciences and the School of Energy Resources . His research bridges computational methods with seismology for advanced Earth structure and earthquake source modeling. Education : PhD in Seismology (University of Southern California, 2005), BS in Geophysics and Economics (Beijing University, 2000). Po Chen's research focuses on Full-3D Waveform Tomography (F3DT) , leveraging high-performance computing to solve nonlinear structural inversion problems. He develops and applies numerical methods for seismic wave propagation, fault-plane ambiguity resolution, and ground-motion estimation. Recent work integrates machine learning for real-time earthquake detection (e.g., RED-PAN, RockNet) and landslide parameter inversion. His publications highlight advancements in seismic source inversion, GPU-accelerated algorithms, ambient-noise tomography, and critical zone imaging. Key collaborations include tools like UCVM (3D velocity model querying) and pSIN (seismic interferometry). Teaching emphasizes active learning and the synergy between research and education, with graduate advisees contributing to projects in seismic imaging and hazard analysis.

Bijan JabbariView profile

Professor

• Wireless Networks
• 5G and LTE Systems
• IoT
• +5 more

Bijan Jabbari is a Professor in the Department of Electrical and Computer Engineering at George Mason University, affiliated with the Volgenau School of Engineering. He holds a PhD from Stanford University and has dual MS degrees in Electrical Engineering and Engineering Economics from Stanford, along with a BS from Arya-Mehr University. His research focuses on wireless networks, IoT, machine learning applications in networking, and cognitive networks. Notable contributions include work on LTE/5G systems, edge computing, and spectrum management. He has led projects funded by the National Science Foundation, Naval Research Laboratory, and KDDI Corporation. Jabbari is a Fellow of IEEE and IET, recipient of the IEEE Third Millennium Medal, and has pioneered initiatives like the Heart’s Delight charity event. Jabbari teaches graduate courses including ECE 528 (Random Processes), ECE 642 (Computer Network Design), and ECE 629 (Wireless Networks). He directs the Communications and Networks Laboratory and collaborates with Telecom ParisTech. His work emphasizes resilient network design, cross-layer protocols, and machine learning-driven optimization in telecommunications. Research Grants: Secure MAC Layer Protocols (NRL, 2016–2018) Cross-Layer Resilient Networking (NRL, 2012–2015) Network Virtualization (KDDI, 2010) Awards: Washington DC Engineer of the Year Award GMU Outstanding Faculty Research Award Labs/Teams: Communications and Networks Lab (CNL) at GMU Collaborations: Telecom ParisTech (France)