Find Academic Supervisors

Truong NghiemView profile

Associate Professor

• Intelligent cyber-physical systems
• Digital twins
• Scientific and physics-informed machine learning
• +5 more

Truong Nghiem is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), part of the College of Engineering and Computer Science. He holds a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania and previously served as an Assistant and Associate Professor at Northern Arizona University (2018–2024). His research focuses on intelligent cyber-physical systems, digital twins, physics-informed machine learning, and distributed optimization/control, with applications in smart buildings, autonomous vehicles, and robotics. He leads the intelligent Cyber-Physical Systems (iCPS) Lab, advancing foundational research in these areas. Notable awards include the NSF CAREER Award (2023) for composite physics-informed learning and the NSF ERI Award (2022) for building HVAC system research. He is a Senior Member of the IEEE and a member of the ACM. Recent work emphasizes safe machine learning for control systems, communication-efficient optimization algorithms, and physics-constrained motion prediction for autonomous systems. His publications span journals like IEEE Robotics and Automation Letters and conferences such as the American Control Conference. Grants and research initiatives include projects on HVAC system modeling, distributed trajectory planning for multi-vehicle systems, and adaptive sampling strategies for mobile sensor networks. Collaborations involve industry and academic partners in energy systems, robotics, and control engineering. The iCPS Lab’s work integrates theory, simulation, and real-world validation to address complex cyber-physical challenges.

Irina PaciView profile

Professor

• Self-assembly on solid surfaces
• Monte Carlo methods
• Multi-scale computational methods
• +3 more

Dr. Irina Paci is a Professor of Chemistry at the University of Victoria, specializing in theoretical and computational chemistry. Her research focuses on understanding molecular self-assembly processes, particularly at solid surfaces, and developing multi-scale computational methods to model materials' dynamic behavior under external fields. She holds a PhD from Queen's University and completed postdoctoral research at Queen's and Northwestern University. Her work integrates Monte Carlo simulations, density functional theory (DFT), and quantum chemistry to study surface adsorption mechanisms, catalytic reactions, and nanocomposite dielectric properties. Key areas include cysteine adsorption on gold surfaces, palladium-catalyzed cross-coupling reactions, and the impact of solvent effects on reaction pathways. Recent studies explore aluminum oxide degradation in perovskite solar cells and quantitative reactivity models for nucleophilic aromatic substitution. Teaching interests span physical chemistry, quantum mechanics, and computational methods. Dr. Paci leads the Paci Research Group, which includes graduate students like Archita Adluri and Natalie Stubb, focusing on advancing computational methodologies for energy materials and surface science. Her lab's work bridges fundamental theory with applications in nanotechnology and sustainable energy systems.

Delphine Périé-CurnierView profile

Professor

• Biomedical Technology
• Biomechanics
• Solid Mechanics
• +6 more

Delphine Périé-Curnier is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal and Director of Graduate Studies. Her research focuses on developing quantitative MRI techniques for non-invasive characterization of living tissue mechanical properties, particularly in cardiotoxicity detection and musculoskeletal mechanobiology . She leads the Bioperformance Analysis and Innovation Laboratory (LIAB) and contributes to the Institute of Biomedical Engineering. Education: Ph.D. from Paul Sabatier University, Toulouse, France Her work bridges medical imaging , biomechanical modeling , and finite element analysis to predict disease progression through pathomechanism understanding. Key projects include exercise-induced cardiac changes in childhood cancer survivors and spinal biomechanics in scoliosis. Recent publications (2023-2024) emphasize cardiovascular MRI for childhood cancer survivorship and hemodynamic modeling in left ventricle analysis. She supervises 26 graduate students, with completed theses spanning topics like doxorubicin cardiotoxicity , knee replacement stability , and spatial cardiac MRI protocols . Teaching includes graduate courses in biomedical design , advanced biomechanics , and modeling techniques .

• Semiconductor Devices
• Millimeter-Wave Electronics
• Electronics Engineering
• +3 more

Prof. Colombo R. Bolognesi is a Full Professor of Millimeter-Wave Electronics at ETH Zürich's Department of Information Technology and Electrical Engineering. He holds a PhD in Electrical Engineering from the University of California, Santa Barbara, and has held academic positions at Simon Fraser University (1995-2006) before joining ETH in 2006. His research focuses on III-V semiconductor devices, particularly InP/GaAsSb and InP/GaInAsSb double heterojunction bipolar transistors (DHBTs), with applications in millimeter-wave and terahertz electronics. His work emphasizes device physics, high-frequency performance, and optoelectronic integration, including uni-traveling carrier (UTC) photodiodes and compact modeling. He has authored over 175 publications and holds prestigious awards such as IEEE Fellow (2008) and the Frontiers in Research Award (2001). Education: Bachelor of Electrical Engineering, McGill University (1987) Master of Electrical Engineering, Carleton University (1989) PhD in Electrical Engineering, University of California, Santa Barbara (1994) Research Interests: Millimeter-wave transistors, semiconductor device physics, high-frequency electronics, terahertz applications, UTC photodiodes, and compact modeling. His recent work includes record-breaking power-added efficiency (PAE) in InP/GaAsSb DHBTs at 170 GHz, advanced UTC photodiodes for 300 Gbps communications, and terahertz characterization of DHBTs. He leads a research group at ETH Zurich focused on pushing the boundaries of III-V semiconductor device performance for next-generation RF and optoelectronic systems. Awards: IEEE Fellow (2008), IEEE Senior Member (2003), Frontiers in Research Award (2001), GaAs IC Symposium Best Paper Award (1999), IEE Electronics Letters Premium Award (1998). Advising & Grants: While specific student names are not listed, his research involves collaboration with PhD/Master's students and postdoctoral researchers. His work is supported by grants from the Swiss National Science Foundation and industry partnerships in semiconductor technology. Key projects include developing UTC photodiodes and DHBT-based power amplifiers for 5G/6G applications. Labs/Teams: His research is conducted within ETH Zurich's Millimeter-Wave Electronics group, leveraging state-of-the-art fabrication facilities and characterization tools for III-V semiconductor devices.

Michael C. GurnisView profile

Professor

• Computational Geodynamics
• Mantle Dynamics
• Plate Tectonics
• +12 more

Michael C. Gurnis is the John E. and Hazel S. Smits Professor of Geophysics at the California Institute of Technology and has served as Director of the Seismological Laboratory (2009-2024) and Schmidt Academy for Software Engineering (2019-). With a B.S. from the University of Arizona (1982) and a Ph.D. from Australian National University (1987), he has held faculty positions at Caltech since 1994, becoming full Professor in 1996 and Smits Professor in 2005. His research spans computational geodynamics, mantle convection, and plate tectonics. California Institute of Technology Division of Geological and Planetary Sciences Seismological Laboratory Schmidt Academy for Software Engineering Research Interests: Gurnis focuses on computational geodynamics to link mantle convection to surface tectonics, using forward/inverse models to study plate motion dynamics , subduction zone mechanics , and deep mantle structures . His work bridges present-day geophysics with historical models through 4D Earth modeling , integrating seismic tomography, mineral physics, and paleogeography via the GPlates consortium. Recent projects include dynamic emergence of continents , plume interactions , and impact-driven subduction . Publications: Gurnis' research spans high-resolution global models of mantle flow, subduction zone rheology, and cratonic basin dynamics. Key trends include multi-physics coupling , Bayesian inversion techniques , and planetary-scale tectonic drivers . His 2024 papers address nonlinear mantle viscosity , shear zone weakening , and impact origins of tectonics . Scientific Awards: Gordon Bell Prize Finalist (2008, 2010) Springer CSE Prize (2011) for plate tectonics simulation work Advising: Mentors graduate students in computational geodynamics, including Jiaqi Fang, Erin Hightower, Yida Li, and Ojashvi Rautela. Collaborates with postdocs and international institutions like ETH-Zurich and National University of Mexico on mantle wedge dynamics and seismic geography. Labs & Teams: Leads the Seismological Laboratory and co-founded the Schmidt Academy for Software Engineering , driving innovations in geodynamic simulations and computational tools.

Benjamin TurnbullView profile

Associate Professor

• Cyber Security
• Simulation
• Digital Forensics
• +6 more

Associate Professor Benjamin Turnbull is Deputy Head of School at UNSW Canberra's School of Systems & Computing. His research focuses on cybersecurity, simulation, and emerging technologies, aiming to develop decision support systems for cyber resilience. He leads large-scale research projects funded by organizations like CSIRO, Australian Defence Force, and the US Naval Research Laboratory, totaling over $3 million AUD in grants. Teaching contributions include co-creating the Bachelor of Cyber Security program and directing multiple postgraduate cybersecurity offerings. His educational design emphasizes industry-relevant courses, from undergraduate to executive programs like the UNSW Online Cyber Security Masters. He has extensive experience in curriculum design, content development, and online education. Research interests span cyber situation awareness, secure simulation platforms, and automated learning from big datasets. Notable projects include the UNSW-MG24 microgrid cybersecurity dataset and frameworks like OQFL (quantum-based federated learning) for intelligent transportation systems. His work bridges theory and practice, with outputs including software tools and policy recommendations for cyber resilience in critical infrastructure. Key collaborations include the Cyber Cooperative Research Centre and DST Group. He has authored over 100 peer-reviewed publications, focusing on IoT security, blockchain-enabled learning, and cyber-physical-social systems resilience. His grants emphasize applied research with direct societal impact, such as mitigating supply chain cyber risks and enhancing microgrid cybersecurity.

Mingyan LiView profile

Research Fellow

• Imaging
• Sensing
• Biomedical Engineering
• +3 more

Dr. Mingyan Li is an Adjunct Research Fellow at The University of Queensland's School of Electrical Engineering and Computer Science. Their research focuses on advanced imaging and sensing technologies with applications in biomedical engineering, particularly in MRI system development, RF coil design, and medical signal processing. They hold a PhD from The University of Queensland (2015). Research interests include high-field MRI systems, rotating RF coil technologies, MRI-Linac integration, and electrical properties tomography (EPT). Key contributions include innovations in MRI-Linac distortion correction, RF shielding for SAR reduction, and deep learning approaches for cardiac arrhythmia classification. Publications span MRI hardware optimization, image reconstruction algorithms, and biomedical signal analysis. Collaborations include work on metamaterial-inspired RF shielding and multi-modal antenna systems for body MRI.

• Signal Processing
• Computer Vision
• Machine Learning
• +6 more

Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.

Paul FranzonView profile

Professor

• Quantum Information Science
• Machine Learning & AI Hardware
• 3D Integration
• +4 more

Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)

• Control Theory
• Applied Mathematics
• Computational Mechanics
• +7 more

Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.

• Advanced Manufacturing Technologies
• Computer Aided Design (CAD)
• Computer Aided Manufacturing (CAM)
• +7 more

Prof. Songlin Ding is a Professor of Manufacturing Engineering at RMIT University's School of Engineering. He joined RMIT in 2005 as a Lecturer, progressing to Senior Lecturer (2009), Associate Professor (2015), and Professor (2020). He currently manages the Master of Engineering (Manufacturing) program. His research focuses on advanced manufacturing technologies, including CAD/CAM, geometric modeling, and machining of difficult materials like synthetic diamonds and titanium alloys using CNC and non-traditional methods. He pioneered the 'adaptive iso-planar' machining strategy, now widely adopted in CAD/CAM software. His work on high-speed machining of ultra-hard materials and additive manufacturing has been supported by ARC, CRCs, Victorian Government, and industry. Teaching interests include advanced manufacturing technologies and supervision of projects in areas like electrical discharge machining, additive manufacturing, and robotic applications. He coordinates over 20 courses and has published >100 papers in manufacturing, mechanical, and control engineering. Key contributions include developing post-processing techniques for additive manufacturing biomedical components and creating novel cutting tools for robotic bone tumor excision. His research emphasizes industry impact, particularly in aerospace and medical applications.

Stephen LaneView profile

Teaching Professor

• Computer Graphics
• Computer Animation
• Virtual Reality
• +5 more

Stephen H. Lane is a Teaching Professor at the University of Pennsylvania's School of Engineering and Applied Science, Department of Computer and Information Science. He serves as Director of the Computer Graphics and Game Technology (CGGT) Master's Program and teaches courses such as Computer Animation (CIS462/562), Advanced Topics in Computer Graphics and Animation (CIS660), and Game Design and Development (CIS564). He also supervises the Game Design Practicum (CIS568) capstone course. Education: B.S. in Mechanical and Aerospace Engineering from Cornell University (1980) M.S. in Systems Engineering from UCLA (1982) Ph.D. in Mechanical and Aerospace Engineering from Princeton University (1988) Dr. Lane's research focuses on the intersection of robotics, physically-based character animation, embodied intelligent agents, and virtual reality user interfaces. His work integrates control theory, artificial intelligence, and computer animation techniques to develop advanced simulation and training systems. His publications since 1987 cover topics such as inverse kinematics, neural networks for motion control, B-spline receptive fields, robotic skill acquisition, and gesture recognition systems. His recent work (2010-2011) emphasizes sensor fusion for gesture recognition and immersive training interfaces. Scientific Awards: Co-inventor of four US patents related to robotic animation and motion control systems Contributions to hybrid controller hierarchies and neural network training methods As founder of soVoz, Inc., Dr. Lane commercializes behavioral animation technology for virtual environments. His academic-industry collaboration includes contracts with Microsoft, Disney, and the US Army. He has developed tools like ProScena™ to integrate interactive 3D simulation capabilities into gaming and training applications.

Dong NgoduyView profile

Associate Professor

• Connected and Autonomous Vehicles
• Traffic Flow Theory and Simulation
• Data Fusion
• +4 more

A/Professor Dong Ngoduy is the Head of Transport Engineering and an Associate Professor in Transport Engineering at Monash University's Department of Civil and Environmental Engineering. He holds a PhD in Traffic Flow Theory from TU Delft (Netherlands) and an MSc from Linköping University (Sweden). His research focuses on Connected and Autonomous Vehicles (CAVs), Traffic Flow Theory, Data Fusion, and Urban Network Optimization. He received the EPSRC Advanced Fellow Award (2011-2016) and chairs the Connected Traffic Systems Lab. His current work aims to develop smart city platforms for multi-modal transport infrastructure management. Education: MSc in Traffic Engineering, Linköping University PhD in Traffic Flow Theory, Delft University of Technology Research Expertise: Traffic Flow Theory Data Fusion Techniques Urban Network Optimization CAV Dynamics Grants & Awards: EPSRC Advanced Fellow Award (2011-2016) EU Horizon 2020 Project (~$1.2M) Australian Research Council (ARC) Assessor Teaching: Courses include Transport and Traffic Engineering (CIV2282) and Autonomous Vehicle Systems (CIV4100). Labs & Teams: Leads the Connected Traffic Systems Lab and collaborates on projects like Digital Twins for urban networks.

Ahmad IbrahimView profile

Associate Professor

• Internet of Things
• Fog Computing
• Cloud Computing
• +7 more

Dr. Ahmad Ibrahim is an Associate Professor in Computer Science at the University of Birmingham Dubai, where he serves as the Undergraduate Programme Director and Year 2 Coordinator for BSc students. He is also the School Outreach Coordinator for the Computer Science program at the Dubai campus. Educational Background: Postgraduate Certificate in Higher Education (PGCHE), University of Birmingham, 2021 Fellow of the Higher Education Academy (FHEA), University of Birmingham, 2021 PhD in Computer Science, University of Pisa, Italy, 2016 MSc in Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea, 2009 BSc in Computer System Engineering, G.I.K Institute of Engineering Sciences and Technology, Pakistan, 2005 Dr. Ibrahim's research focuses on the Internet of Things, Quality of Service (QoS) prediction, Web services, and Cloud and Fog computing. His teaching interests include Object-oriented programming, Full Stack application development, Security and Networks, Operating Systems and Systems Programming, and Advanced Networking. His recent publications demonstrate a strong trend in fog and edge computing, with particular emphasis on QoS optimization, cost-effective deployment, and resource management in distributed environments. His work bridges theoretical modeling with practical application deployment strategies across cloud-edge continua. Scientific Awards and Recognition: Fellow of the Higher Education Academy (FHEA), 2021 Postgraduate Certificate in Higher Education (PGCHE), University of Birmingham, 2021 Dr. Ibrahim has supervised and contributed to numerous research projects in distributed systems and cloud computing, particularly through collaborations on European initiatives like SeaClouds. He has secured recognition for his teaching excellence and is actively involved in curriculum development and academic outreach.

Pascal FuaView profile

Professor

• Computer Vision
• 3D Reconstruction
• Shape Modeling
• +6 more

Professor Pascal Fua is a distinguished faculty member at EPFL (Swiss Federal Institute of Technology) in the School of Computer and Communication Science. He joined EPFL in 1996 and currently serves as Head of the Computer Vision Laboratory (CVLAB). His extensive research spans multiple cutting-edge areas in computer vision and geometric deep learning, with applications ranging from 3D reconstruction to medical imaging and aerodynamic optimization. Dr. Fua's research interests encompass Computer Vision, 3D Reconstruction, Shape Modeling, Geometric Deep Learning, Medical Image Analysis, Augmented Reality, Motion Recovery, Surface Mesh Processing, and Aerodynamic Shape Optimization. His work demonstrates a remarkable ability to bridge theoretical computer vision with practical applications across diverse domains. His research has evolved from traditional geometric computer vision techniques to incorporating deep learning approaches for 3D modeling, with recent focus on differentiable rendering, implicit surface representations, and applications in medical imaging and engineering design. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on differentiable iso-surface extraction, geometric deep learning for aerodynamic shape optimization, and novel approaches to 3D reconstruction. His work spans both theoretical advances in computer vision algorithms and practical applications in medical imaging, autonomous driving, and computational fluid dynamics. IEEE Fellow Multiple ERC Grants recipient Associate Editor of IEEE Transactions for Pattern Analysis and Machine Intelligence Throughout his career, Professor Fua has mentored numerous PhD students who have gone on to make significant contributions in computer vision and related fields. His laboratory has established collaborations across multiple disciplines, including medical imaging, aerospace engineering, and neuroscience, demonstrating the broad applicability of his research. His current work continues to push the boundaries of geometric deep learning and 3D vision, with particular emphasis on making these techniques more practical and applicable to real-world engineering and medical problems.