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Weiwen JiangView profile

Assistant Professor

• Quantum Computing
• AI Accelerators
• Hardware-Software Co-Design
• +3 more

Weiwen Jiang is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University (GMU), affiliated with the College of Engineering and Computing (CEC). He leads the JQub lab, focusing on hardware/software co-design for computing systems, spanning classical (FPGAs, ASICs) and quantum computing applications in AI-driven fields like medical imaging and geophysics. Prior to GMU, he held a postdoctoral position at the University of Notre Dame and earned his PhD in Computer Science from Chongqing University with a joint PhD in Electrical and Computer Engineering from the University of Pittsburgh. His research emphasizes quantum computing, AI accelerators, and domain-specific computing. Notable achievements include the 2025 NSF CAREER Award, ACM Sigda Meritorious Service Award (2024), and IEEE QuantumWeek Best Paper Award (2023). His work is funded by NSF, DoE, ARO, Meta, and Leidos. He co-chaired IEEE QuantumWeek (2023–2025) and created workshops like StableQ at ESWEEK 2023. Key contributions include developing frameworks like QuPAD for quantum learning and JQub's AI-driven geophysical and medical imaging tools. His lab graduated Dr. Yi Sheng (now at University of South Florida) and Dr. Zhepeng Wang (Amazon Applied Scientist). Current research explores quantum machine learning, noise mitigation, and fairness in AI for edge devices.

Liping LiuView profile

Associate Professor

• Machine Learning
• Data Science
• Deep Learning
• +3 more

Liping Liu is an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering. He holds a Ph.D. from Oregon State University and has held postdoctoral positions at Columbia University and Tufts. His research focuses on machine learning, generative models, graph learning, and their applications in biochemical data analysis and fluid dynamics simulation. His work on graph generative methods earned the NSF CAREER Award. Education: Ph.D. (Oregon State University, 2016), M.Sc. (Nanjing University, 2009), B.S. (Hebei University of Technology, 2006). Research Interests: Machine Learning, Deep Learning, Generative Models, Time Series, Graph Learning. Dr. Liu's research emphasizes probabilistic modeling and neural networks, addressing challenges in graph generation, data-driven physics simulation, and biochemical analysis. His recent work includes advancements in graph-based recommendation systems, turbulence modeling, and enzymatic reaction prediction. His publications span top AI conferences like NeurIPS, ICML, and ICLR. He has secured grants totaling over $9 million, including the NSF CAREER Award and NIH funding for metabolomics and enzymatic promiscuity studies. His teaching includes courses on generative models, deep learning, and machine learning for graph analytics. Awards: NSF CAREER Award (2023), NIH grants, DARPA ACT-NOW project (2019). Service: NSF panelist, program committee member for AAAI, NeurIPS, and IJCAI.

Jianxi GaoView profile

Associate Professor

• Network Science
• Artificial Intelligence
• Control Theory
• +3 more

Jianxi Gao is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research focuses on network science, particularly network resilience, robustness, and control, integrating network theory, control theory, statistical physics, and operations research. He also explores the intersection of network science and AI, including applications of AI to network analysis and vice versa. His work aims to understand, predict, and control the resilience of complex systems against cascading failures. Key research areas include network resilience in transportation systems, quantum networks, and biological systems, with applications to pandemic response and infrastructure optimization. Gao's contributions span theoretical frameworks and computational tools, such as the NuRsE MATLAB package for network resilience analysis. His GitHub repositories (e.g., NuRsE and NON) showcase his open-source contributions to network science and computational methods. His recent publications address topics like AI-driven network analysis, quantum network percolation, and pandemic-induced healthcare system stress. He actively collaborates on interdisciplinary projects, emphasizing real-world applications of network science principles.

David MouldView profile

Professor

• Computer Graphics
• Computer Games
• Non-Photorealistic Rendering
• +4 more

David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.

William OatesView profile

Professor

• Solid mechanics of active materials
• Smart material device design
• Nonlinear control
• +6 more

William S. Oates is the Cummins, Inc. Professor of Engineering in the Department of Mechanical Engineering at Florida A&M / Florida State University. He holds affiliations with the Mechatronics and Energy Center and the Florida Energy Systems Consortium (FESC). His research focuses on solid mechanics of multifunctional materials, quantum-informed continuum modeling, and applications in robotics, aerospace, and energy systems. He has advised over 20 graduate students and holds awards including ASME Fellow (2018) and NSF CAREER Award (2011). Education: Ph.D. from Georgia Institute of Technology. Research spans smart materials, fractal media mechanics, and quantum computing for material modeling. Key projects include high-temperature sapphire pressure sensors, photomechanical polymers, and Bayesian uncertainty quantification in materials science. Notable awards include DARPA Young Faculty Award (2009) and FSU Guardian of the Flame Teaching Award (2010). His lab collaborates with the National High Magnetic Field Lab and Challenger Learning Center for K-12 outreach. Current research includes quantum algorithm implementation for engineering applications and fractal-based viscoelastic models.

Fei LiuView profile

Assistant Professor

• Robotics
• Surgical Robotics
• Medical Robotics
• +3 more

Fei Liu is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His research focuses on surgical robotics, medical robotics, and control systems. He holds a PhD in Robotics from the University of Lyon (INSA de Lyon), France, an MSc in Control Systems and Automation Engineering from INSA de Lyon, and a BSc in Control Systems and Automation Engineering from Northwestern Polytechnical University, China. Fei's research interests include autonomous robotic systems, deformable object manipulation, and perception frameworks for surgical applications. His work emphasizes bridging real-world and simulation environments through advanced modeling and control techniques. Recent projects involve optimizing robotic actions using multi-modal demonstrations, improving tool-tissue interaction tracking, and developing frameworks for boundary parameter estimation in surgical settings. His articles highlight contributions to surgical robotics, including real-to-sim matching of deformable tissues, autonomous suturing, and trajectory optimization for wound care. He has also explored applications in haptic training systems and medical telerobotics. Fei's work often combines machine learning, physics-based simulation, and real-time control to address challenges in robotic surgery. Fei is affiliated with the Tickle College of Engineering and maintains an active research profile with collaborations in robotics and medical engineering domains. His lab focuses on advancing robotic autonomy in healthcare environments through interdisciplinary approaches.

Roman SamulyakView profile

Professor

• Computational Fluid Dynamics
• Magnetohydrodynamics
• Plasma Physics
• +2 more

Roman Samulyak is a Professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He holds a Ph.D. from NJIT in Applied and Computational Mathematics with specializations in Hydro- and Electrodynamics. His research develops advanced numerical algorithms for modeling complex physical systems in high-energy physics and fusion energy. Research spans computational methods for magnetohydrodynamics, plasma physics, nuclear fusion/fission systems, and particle accelerator design. Current applications include disruption mitigation in tokamaks and laser-driven particle acceleration. Recent publications demonstrate strong focus on plasma-based accelerators and fusion reactor modeling, particularly pellet ablation dynamics, laser wakefield acceleration, and MHD simulations of tokamak plasmas. Research utilizes high-performance computing resources for large-scale simulations. Office location is Math Tower 1-108 at Stony Brook University.

• Hardware Security
• Machine Learning
• 3D IC Design
• +4 more

Wafi Danesh is an Assistant Professor in the Department of Engineering Programs at SUNY New Paltz, part of the School of Science & Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Missouri Kansas City (2022). Prior to academia, he served as a Senior Engineer I - Design at Microchip Technology Inc. (2022-2023). His research focuses on hardware security, leveraging machine learning for FPGA Trojan detection and secure 3D IC design. Teaching interests include System-on-Chip Design, Digital Logic Fundamentals, and Computer Architecture. Education: PhD in Electrical and Computer Engineering, University of Missouri Kansas City, 2022 Research Interests: Dr. Danesh explores cutting-edge methods to enhance hardware security, including AI-driven approaches for IoT device protection and thermal management in 3D integrated circuits. His work bridges machine learning and physical hardware vulnerabilities, emphasizing FPGA security and PUF-based solutions for wireless systems. Publications Trends: His articles span FPGA Trojan detection via NLP and unsupervised learning, thermal challenges in 3D ICs, and neuromorphic computing innovations. Recent work highlights automated security tools and multi-valued computing for energy efficiency. Awards: None explicitly listed in the provided materials. Advising & Grants: No formal advisees or grants are mentioned. His professional activities center on research and teaching.

Nikolaos NikiforakisView profile

Professor

• High Performance Computing
• Numerical Algorithms
• Computational Fluid Dynamics
• +4 more

Prof Nikolaos Nikiforakis is a Professor at the University of Cambridge, leading the Laboratory for Scientific Computing at the Cavendish Laboratory. He holds roles including Director for Academic Programmes of the Centre for Scientific Computing, Course Director of the MPhil in Scientific Computing, and Deputy Director of the EPSRC Centre for Doctoral Training in Computational Methods for Materials Science. He is also a Fellow and Director of Studies in Mathematics at Selwyn College, Cambridge. He directs The Gianna Angelopoulos Programme for Science Technology and Innovation. He holds a BSc in Aeronautical Engineering from the University of Manchester, followed by an MSc in Aerospace Propulsion and a PhD in 'Evolution of Detonation Waves' from Cranfield Institute of Technology. His postdoctoral research at the University of Cambridge’s Department of Chemistry focused on computational models for stratospheric ozone depletion. He later founded the Laboratory of Computational Dynamics at the Department of Applied Mathematics and Theoretical Physics before joining the Cavendish Laboratory in 2008. His research focuses on numerical algorithms and High Performance Computing for multi-physics simulations involving complex systems of nonlinear PDEs. Applications span detonation dynamics, plasma physics, and materials science, with industry collaborations for software development. His work addresses multi-scale, multi-physics problems previously deemed intractable, with practical applications in aerospace, energy, and environmental fields. He leads academic programmes in scientific computing and supervises doctoral research through the EPSRC CDT. His contributions bridge fundamental science and industrial innovation, emphasizing computational methods for materials and fluid dynamics.

Charles G. SmithView profile

Professor

• Quantum Physics
• Nanotechnology
• Semiconductor Devices
• +7 more

Professor Charles G. Smith is a prominent academic in quantum physics and nanotechnology at the University of Cambridge's Cavendish Laboratory, affiliated with the Ray Dolby Centre for Quantum Information and Control. His research focuses on semiconductor nano-devices, quantum transport, and hybrid superconductor-semiconductor systems. He pioneered work on GaAs quantum dots, single-electron charge measurement techniques, and cryogenic scanning probe methods. Smith has developed novel low-temperature measurement tools and contributed to carbon-based nanoelectronics and graphene research. He leads major grants, including EPSRC projects on quantum integrated circuits and many-body localization. He founded Cavendish-Kinetics and Cambridge Lab on Chip, leveraging his nano-mechanical and microfluidic innovations. His work bridges fundamental physics with scalable quantum technologies. Education & Research Expertise: PhD in Physics (Implied by career trajectory) Extensive post-1985 contributions to nano-device physics Research Highlights: Quantum dot arrays and cryogenic multiplexing for high-throughput analysis Hybrid superconductor-semiconductor devices for quantum applications Graphene-based electronics and magnetoresistance phenomena Scalable quantum integrated circuits and error mitigation strategies Grant Leadership & Industry Impact: EPSRC Grant EP/S019324/1: Scaling quantum devices Program Grant EP/R029075/1: Non-Ergodic Quantum Manipulation Two spin-out companies commercializing nano-technology Labs & Facilities: Active in Cavendish Laboratory’s cutting-edge nanofabrication and cryogenics facilities, supporting large-scale quantum device integration.

Peter MolnarView profile

Professor

• Hydrology
• Geomorphology
• Sediment Dynamics
• +7 more

Peter Molnar is a Professor at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, where he leads research in hydrology, geomorphology, and environmental engineering. His work spans river basin dynamics, sediment transport, precipitation modeling, and climate-water interactions, with field sites focused on Alpine systems. His research interests center on river basin water and sediment dynamics , precipitation analysis and stochastic modeling , fluvial systems including river networks and riparian zones , and geomorphic processes in mountain streams . Key projects include the Maggia River Experimental Site, basin sediment cascade modeling, and hydroclimatic analysis of rainfall intensity-temperature relationships. His publication record shows consistent high-impact work in journals including Nature , Nature Climate Change , and Water Resources Research , with recent focus on climate change impacts, sensor development, and sustainable agriculture. Golden Owl Award for best teaching (2011) Organizer of international workshops on geomorphology complexity and urban precipitation Patron of CometX initiative Molnar actively mentors PhD students including current candidates working on droughts in Ethiopia, Alpine Rhine sediment budgets, and mountain ecohydrology. His research is supported by Swiss National Science Foundation grants, ETH funding, and international collaborations. He leads experimental work at the Maggia River site and develops warning systems for rainfall-triggered landslides.

Bhuvana SrinivasanView profile

Professor

• Fusion Energy
• Plasma-based Propulsion
• High-Energy-Density Plasmas
• +5 more

Bhuvana Srinivasan is a Professor in the Department of Aeronautics and Astronautics at the University of Washington, directing the PLASMAWISE Laboratory. Previously, she held the rank of Associate Professor and served as Director of the Plasma Dynamics Computational Laboratory at Virginia Tech, supported by the Crofton Faculty Fellowship. Her research focuses on fusion energy, plasma-based propulsion, and computational plasma physics, with an emphasis on plasma-material interactions and instabilities across diverse plasma regimes. She has authored over 30 peer-reviewed publications and secured grants from the NSF, DOE, and AFOSR. Education: Ph.D. in Aeronautics and Astronautics, University of Washington (specializing in computational plasma physics) M.S. in Aeronautics and Astronautics, University of Washington B.S. in Aerospace Engineering and Mechanical Engineering, Illinois Institute of Technology Research Interests: Her work spans fusion energy concepts, plasma propulsion systems, high-energy-density plasma instabilities, and ionospheric dynamics. Key areas include plasma-surface interactions in fusion devices, magnetic field effects on plasma mixing, and algorithm development for fluid-kinetic models. She emphasizes high-fidelity multi-fluid simulations using discontinuous Galerkin methods. Awards & Recognition: NSF CAREER Award (2019-2024) Crofton Faculty Fellow (Virginia Tech, 2021-2023) Dean’s Outstanding Assistant Professor (Virginia Tech, 2017) Amelia Earhart Fellowship (Zonta International, 2007-2009) Advocacy & Leadership: She chairs DEI initiatives in academic departments and serves on national committees including the DOE Fusion Energy Sciences Advisory Committee and the APS Division of Plasma Physics Executive Board. Her work bridges computational plasma physics with societal impact, including fusion energy democratization and space exploration propulsion systems.

Emily WhitingView profile

Associate Professor

• Computer Graphics
• Computational Fabrication
• Architectural Geometry
• +4 more

Emily Whiting is an Associate Professor of Computer Science at Boston University and Director of the Shape Design & Computation Lab. She also serves as Director of PhD Admissions and Co-Director of the BU Computer Graphics Lab. Her research focuses on computational fabrication, architectural geometry, and computer-aided design, bridging digital geometry processing, engineering mechanics, and rapid prototyping. She holds a PhD from MIT (2012), an SM in Design & Computation from MIT (2006), and a BASc in Engineering Science from the University of Toronto (2004). Previously, she was faculty at Dartmouth and a Marie Curie Postdoctoral Fellow at ETH Zurich. Her research interests include 3D printing optimization, structural design for fabrication, and tools for functionally-valid object creation. Notable projects include work on elastic garments, climbing experience replication, and print-wind instrument design. Her work has been featured on TEDx and PBS NOVA, and she has received awards such as the NSF CAREER Award and Sloan Research Fellowship. Education: PhD (MIT), SM (MIT), BASc (University of Toronto) Labs: Shape Design & Computation Lab, BU Computer Graphics Lab Key Projects: Knitting 4D garments, Environment-Scale Fabrication, Thermal-comfort casts Recent professional activities include program committee roles at SIGGRAPH 2025 and UIST 2024, and serving as Program Co-Chair for Pacific Graphics 2024. She advises a team of PhD and MS students, with alumni now in academia and tech industries.

Ahmed HemaniView profile

Professor

• Computer Engineering
• Embedded Systems
• Hardware Acceleration
• +5 more

Professor Ahmed Hemani is a faculty member at the Division of Electronics and Embedded Systems, KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty. He holds the role of PI for the project 'New Chip Architectures for Industrial Vision' and leads research in reconfigurable computing, memristor-based systems, and hardware acceleration for AI and edge computing. His work bridges theoretical computer science with practical VLSI design and embedded systems development. He actively contributes to cross-disciplinary initiatives at Digital Futures, a joint center with Stockholm University and RISE Research Institutes of Sweden focused on digital innovation. His research emphasizes scalable FPGA/HPC architectures, low-power neuromorphic systems, and optimization techniques for custom silicon solutions. Current projects include a Lego-inspired edge AI framework and memristor-driven MIMO acceleration. Teaching responsibilities span advanced courses in SOC design, digital system verification, and embedded systems. He supervises advanced-level degree projects across computer engineering and ICT innovation specializations, emphasizing hands-on hardware-software co-design methodologies. Recent publications highlight innovations in memristor applications, FPGA-based acceleration, and reconfigurable architectures for neural networks and bioinformatics. His work addresses challenges in dark silicon utilization, energy-efficient computation, and high-performance embedded systems.

Martin NorgrenView profile

Professor

• Electromagnetic Inverse Problems
• Material Characterization
• Biomedical Imaging
• +3 more

Martin Norgren is a Professor at KTH Royal Institute of Technology, leading the Department of Electromagnetic Fusion Physics. His research focuses on electromagnetic inverse problems, including material characterization, biomedical imaging (e.g., brain current sources), environmental monitoring (e.g., snow and avalanche prediction), and smart grid technologies. He specializes in reconstructing object properties using electromagnetic measurements and has contributed to applications in healthcare, energy systems, and environmental science. His work involves advanced analytical and numerical methods such as mode-matching techniques, perturbation theory, and convex optimization. Notable projects include noncontact current measurement in power grids and transformer diagnostics using microwave radiation. Norgren teaches courses in electromagnetic field theory and electrical engineering design, emphasizing practical applications and interdisciplinary collaboration. Recent research trends highlight advancements in glide/twist symmetry-based metamaterial design, waveguide analysis, and inverse scattering techniques. His studies bridge fundamental physics with applied engineering, addressing challenges in energy infrastructure and medical diagnostics. As a department head, he oversees educational and research programs at KTH, fostering innovation in electromagnetism and fusion physics. His contributions to curriculum development include project-based courses integrating theory and hands-on design.