Find Academic Supervisors

Sungyong JungView profile

Professor

• Integrated Circuits
• Embedded Systems
• Radio Frequency Design
• +3 more

Sungyong Jung is the Department Head and Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology. His research focuses on integrated circuits and embedded systems for bio/chemical sensing, radio frequency integrated circuit design, analog/mixed-signal design, and VLSI systems. He directs the Integrated Sensing Circuits and Systems Laboratory at SDSU. Academic responsibilities include editorial roles as Associate Editor for Frontiers in Sensors , IEEE Access , and IEEE Sensors Journal , and Senior Editor for IEEE Access . His research interests span cutting-edge topics such as chemical/bio sensing systems, RF IC innovations, and VLSI architectures. His work bridges theoretical circuit design with practical applications in sensing technologies. Office located at Daktronics Engineering Hall 214, Brookings, SD 57007.

• Culture and technology
• Media and Cultural Studies
• Media archaeology
• +10 more

Dr. Margie Borschke is a Senior Lecturer in the Discipline of Media and Communications at the University of Sydney . A Canadian-Australian academic, her work bridges Media Archaeology , Cultural Studies , and Digital Humanities , focusing on the cultural implications of copying, circulation, and curation in digital networks. BA (Hons), McGill University MA, University of Toronto PhD, University of New South Wales Her research interests include: Culture and Technology Media Archaeology Piracy and Copyright Authenticity in Digital Contexts Environmental Humanities Data Visualization Recent publications explore underground media practices , NFTs and blockchain imaginaries , and the history of computing . She has been a Visiting Professor at the University of Toronto (2018) and is actively involved in PhD supervision . Professional associations : Association for Cultural Studies Society for the History of Authorship, Reading and Publishing Internet Researchers' Association International Association for the Study of Popular Music

Sara CarenaView profile

Senior Lecturer

• Structural Geology
• Active Tectonics
• 3-D Modeling and Visualization
• +5 more

Sara Carena is a Senior Lecturer and Senior Scientist at the Department of Earth and Environmental Sciences at Ludwig-Maximilians-Universität München (LMU). As part of the Faculty of Geology, she specializes in Structural Geology, Active Tectonics, and 3D Modeling. Her role includes managing the Geological Remote Sensing Facility and Analog Modeling Laboratory. Education: 2014: Habilitation in Geology, LMU Munich 2003: Ph.D., Princeton University, USA 1995: Laurea (M.Sc.), State University of Milano, Italy Research Interests: Her research focuses on understanding active tectonic processes, particularly in regions like Taiwan, the Eastern California Shear Zone, and the Makran subduction zone. Key areas include: 3D fault modeling using seismic and remote-sensing data Coulomb stress evolution and earthquake interactions Dynamic topography and its links to mantle flow Long-term vertical motion patterns in continental regions Teaching and Innovation: She has pioneered virtual field trips (VFTs) for geoscience education, including dual-language trips to Spain and the US. These VFTs use photogrammetry and 3D modeling to enhance student learning. She also created the 'Creating 3D Hand Samples' webinar, recognized with the 2021 LMU Lehrinnovationspreis. Labs and Facilities: As manager of the Geological Remote Sensing Facility and Analog Modeling Laboratory, she supports advanced geophysical and structural analysis tools like SKUA-GOCAD for 3D modeling.

Veit Dominik KunzView profile

Professor

• Electrical Engineering
• Renewable Energy
• Wind Energy
• +4 more

Prof. Veit Dominik Kunz is a Professor of Electrical Engineering and Renewable Energy at the Department of Process Engineering, Hamburg University of Applied Sciences. His academic role includes teaching and research in advanced electrical systems, power electronics, and renewable energy technologies. He is affiliated with the Faculty of Engineering and Computer Science and leads research projects such as FLEDERWIND (focusing on bat detection in wind parks) and Drones4Bats (wildlife impact assessment). His expertise spans semiconductor device design, nanotechnology, and strategic product development. Research interests include optimizing renewable energy systems, mitigating environmental impacts of wind energy, and advancing semiconductor technologies for high-performance electronics. His work bridges academic research with practical applications, particularly in energy management and sustainable technologies. Publications highlight contributions to vertical MOSFET design, renewable energy systems, and product development strategies. He is also involved in academic governance as head of the examination committee for his department.

• Engineering Design
• Creativity in Engineering Education
• Innovation Strategies
• +3 more

Anthony Nix is a Lecturer in the Department of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University’s College of Engineering. He specializes in engineering design, creativity methodologies, and innovation strategies. His research focuses on ontology development for customer needs analysis, design education curriculum advancement, and cognitive processes in design thinking. He is affiliated with the Sch Mech/Indust/Manufact Engr 316 Rogers Hall in Corvallis, OR. Dr. Nix’s work explores interdisciplinary design education, TRIZ-based innovation tools, and divergent thinking techniques. His contributions include studies on mental metadata in analogy formation and repository systems for engineering creativity. He has published extensively on topics such as provocative stimuli in innovation, function-based design approaches, and alternative uses tests for creativity assessment. Contact: anthony.nix@oregonstate.edu | Phone: 1-541-737-9117

Katherine FuView profile

Associate Professor

• CAE and Design Engineering
• Design Theory and Methodology
• Computational Design Tools
• +6 more

Katherine Fu is an Associate Professor of Mechanical Engineering at the University of Wisconsin-Madison and holds an adjunct role as Adjunct Associate Professor at the Georgia Institute of Technology's Woodruff School of Mechanical Engineering. Her research focuses on design cognition, computational design tools, and fostering innovation through analogy-based methods. She has a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2012), joined Georgia Tech in 2014, and moved to UW-Madison in 2021. Education: Ph.D., Mechanical Engineering, Carnegie Mellon University, 2012 M.S., Mechanical Engineering, Carnegie Mellon University, 2009 B.S., Mechanical Engineering, Brown University, 2007 Research interests emphasize cognitive studies of design processes, computational tools for creativity, and design-by-analogy methodologies. Notable contributions include frameworks for design heuristics extraction, functional analogy search algorithms, and studies on analogical distance effects. Awards include the 2019 GT LGBTQIA Leadership Award, 2018 ASME Reviewer of the Year, and 2015 ASME Early Career Engineer of the Year. Her work bridges engineering education and design science, with a focus on fostering ethical and socially impactful innovation. Labs and Teams: Active leadership in the Design Research Lab (EDRL) and collaborations with institutions like Jet Propulsion Laboratory (JPL). Teaching contributions include curriculum development in GD&T and ethics integration in design education.

• In-Memory Computing
• AI Hardware Acceleration
• Edge Computing
• +5 more

Shaahin Angizi is an Assistant Professor in the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). His research focuses on next-generation computing systems, emphasizing energy-efficient architectures, in-memory computing, and secure AI hardware. He leads multiple National Science Foundation (NSF)-funded projects, including initiatives on robotic vision systems, in-cache AI acceleration, and edge computing. His work spans hardware security, emerging memory technologies, and interdisciplinary applications of AI in healthcare and IoT. Notable projects include developing secure imaging sensors (SenGuard/iSEW), adversarial attack defenses for neural networks, and AI-driven accelerator design automation using large language models (LLM-IMC/TPU-Gen). Angizi has authored over 136 publications and secured 4 active NSF grants between 2022-2027. His research bridges device-level innovations (e.g., magnetoelectric FETs) with system-level designs, aiming to reduce computational carbon footprint while enhancing reliability. Key Projects: Infrared Retinomorphic Vision (2024-2027) Toward Opportunistic In-Cache AI Acceleration (2023-2025) Integrated Sensing & Normally-off Computing (2022-2026) Research Themes: Processing-in-Memory Architectures Edge Intelligence Hardware Hardware Security & Trust Non-Volatile Memory Systems Angizi’s contributions include pioneering sensor-embedded watermarking (iSEW), adversarial attack analysis on LLMs, and energy-efficient in-sensor processing frameworks (PISA/HyperSense). His work frequently explores synergies between photonics, analog computing, and AI to create sustainable high-performance systems.

Austin MooreView profile

Senior Lecturer

• Dynamic Range Compression
• Music Production Techniques
• Distortion in Audio
• +2 more

Austin Moore is a Senior Lecturer in music production and sound engineering at the University of Huddersfield, where he leads courses in Sound Engineering and Creative Music Production. His research examines distortion, compression, and non-linear signal processing in music production. His work analyzes sonic signatures of audio hardware, perceptual effects of dynamic range compression, and spatial audio techniques. He investigates how distortion shapes musical aesthetics across genres from rock to electronic music. He co-edited Distortion in Music Production: The Soul of Sonics (Routledge 2023) and co-edits the Journal of Music Production Research. He teaches modules on sound engineering, mixing, synthesis, and electronic music production.

• Neural Networks
• Automatic Control Systems
• Analog VLSI Integrated Circuits
• +3 more

Chrysostomos Kasimis is a Lecturer in the Electronics and Computers Department at the University of Patras, affiliated with the School of Sciences. He holds a Master's Degree (2007) and a PhD (2013) in Electronics from the University of Patras. His primary roles include teaching laboratory courses such as Physics Laboratory IV/V, Computer Programming I/II, and Analog Electronics Laboratory. He is a member of the Laboratory Teaching Staff (EDIP) in the Electronics Laboratory. His research focuses on neural networks for control systems and analog VLSI integrated circuits , with contributions to adaptive filter design, low-power circuits, and bio-inspired neural network applications. His work spans hyperbolic function-based filters, zeroing neural networks for optimization, and companding techniques in biomedical signal processing. Publications highlight innovation in analog circuit design (e.g., sinh-domain filters, OTA-based systems) and interdisciplinary applications in finance through neural networks. His career demonstrates expertise in both theoretical and applied electronics, with a strong emphasis on practical laboratory instruction.

Qingjin PengView profile

Professor

• Product Design and Manufacturing
• Digital Manufacturing
• Design Methodology
• +5 more

Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.

Matt JonesView profile

Professor

• Human Learning
• Computational Modeling
• Reinforcement Learning
• +5 more

Matt Jones is a Professor in the Department of Cognitive Psychology and Neuroscience at the University of Colorado, affiliated with the Institute of Cognitive Science. He holds a PhD from the University of Michigan (2003). His research focuses on human learning mechanisms, computational modeling of cognitive processes, and applications in clinical and occupational settings. Key interests include reinforcement learning, perceptual knowledge, and the development of algorithms inspired by human cognition. His work spans interdisciplinary domains such as neurocognitive interventions for irritability, safety in construction via VR/AR technologies, and robust machine learning systems. Notable areas of exploration include Bayesian methods for online learning, neuroplasticity in behavioral therapy, and the dynamics of pain perception. Recent publications highlight contributions to algorithmic robustness, human-AI interaction, and computational psychiatry. His lab investigates how temporal structure influences learning and applies these insights to improve training protocols and clinical outcomes. No scientific awards are explicitly listed, though his work includes collaborations on grants related to cognitive modeling and safety engineering. Matt leads the Jones Laboratory, focusing on bridging theoretical models with practical applications in education, healthcare, and occupational safety. His research integrates behavioral experiments with advanced computational techniques, emphasizing translational impact across disciplines.

Justin BurtonView profile

Associate Professor

• Experimental condensed matter physics
• Soft matter physics
• Non-equilibrium physics
• +3 more

Justin Burton is an Associate Professor and Winship Distinguished Research Professor at Emory University, with a focus on experimental condensed matter physics, soft matter physics, and geophysics. His research explores fluid and granular flow dynamics, non-equilibrium systems, and ice mélange behavior. He leads the Burton Research Lab, located in the Math & Science Center, Atlanta. He holds a Ph.D. from the University of California, Irvine (2006), and has received prestigious awards including the NSF CAREER Award (2015) and the 2024 American Physical Society Fellowship. Research interests include ice mélange rheology, electrostatic interactions in biological systems, and granular physics. His work bridges experimental and theoretical approaches, such as continuum models for ice dynamics and dusty plasma studies using 3D tomography. Recent projects involve glacier instability modeling and atmospheric charge dynamics of particles. Awards : Fellow of the American Physical Society (2024) Winship Distinguished Research Professor (2024) Gordon and Betty Moore Experimental Physics Investigator (2023) Sustainability Innovator Award (2018) His lab investigates phenomena across scales—from atomic fluid mechanics to geophysical glacial systems. Ongoing studies explore ice mélange's role in Greenland calving and electrostatic effects in parasitic nematode attachment. The lab also pioneers experimental ice analogs to study glacial instabilities.

Milan R. DincicView profile

Associate Professor

• Metrology and Measurement Technology
• Signal Processing
• Data Compression
• +3 more

Milan R. Dincic is an Associate Professor at the University of Niš, Faculty of Electronic Engineering, Department of Telecommunications. He holds a PhD in Metrology and Measurement Technology (2017) and a Master’s degree in Telecommunications (2012), both from the same institution. His academic career includes research on quantization techniques, signal processing, and metrology applications. Dr. Dincic’s research focuses on optimizing quantizers for measurement signals, including Gaussian and Laplacian distributions. He has contributed to adaptive quantization, lossless coding algorithms, and hybrid systems integrating codecs like ITU-T G.711. His work bridges theoretical signal processing with practical applications in telecommunications and sensor systems. His publications (26 journal articles) emphasize quantizer design, image sampling, and compression techniques. He co-authored the textbook "Sensors in Vehicles" (2014). Current projects include national/international collaborations in metrology and measurement technology. His lab focuses on advancing signal processing methodologies for precision measurement systems.

Ben WuView profile

Associate Professor

• Optical Communication
• Neuromorphic Computing
• Photonics
• +3 more

Ben Wu, Ph.D., is an Associate Professor in the Department of Electrical & Computer Engineering at Rowan University’s Henry M. Rowan College of Engineering. He holds a B.S. from Nankai University (2008) and a Ph.D. from Princeton University (2015). His research focuses on photonics, including photonic neuromorphic computing, free-space optical communication, and optical imaging. Dr. Wu’s work emphasizes high-speed, low-latency photonic systems for applications in communication and artificial intelligence. He leads the Ben Wu Lab, which explores photonic neural networks, lidar signal processing, and secure optical communication systems. His research has been supported by grants from organizations like the National Science Foundation and NEC Laboratories America. Notable awards include the 2018 Inspira Health Award. Dr. Wu actively advises graduate students and postdoctoral researchers, with openings for scholars interested in optics, photonics, and wireless communication. His lab collaborates on projects such as noninvasive 3D medical imaging and secure key distribution systems.

Liyi LiView profile

Assistant Professor

• Formal Methods
• Programming Languages
• Software Engineering
• +4 more

Liyi Li is an Assistant Professor in the Department of Computer Science at Iowa State University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign, where his research focused on compiler verification and formal methods. After completing his postdoctoral work at the University of Maryland, he expanded his research to include quantum computing, software engineering, and compiler optimization. Education: B.S. in Computer Science, University of Illinois at Urbana-Champaign (2012) M.S. in Computer Science, University of Illinois at Urbana-Champaign (2014) Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2020) Research Interests: Liyi Li’s work bridges formal methods, programming languages, and quantum computing. Key areas include quantum program verification, compiler correctness, memory-safe dialects (e.g., Checked C), and distributed quantum systems. He emphasizes applying formal techniques to ensure software reliability and security. Awards: 2020 UMD Victor Basili Postdoctoral Fellowship 2019 UIUC Spring Outstanding Teaching Assistant Award 2012 UIUC University Honor (Bronze Tablet) Grants & Advising: Co-PI for NSF Grant NQVL:QSTD (2024–2025) focusing on quantum analog pathways PI for NSF Grant CCF-2422127 (2024–2027) on Just-in-Time Verification Advises over 13 students, including PhD candidates at Iowa State, University of Maryland, and William & Mary Collaborations: Liyi Li collaborates with institutions such as the University of Maryland and works with researchers like Mingwei Zhu and Xiaodi Wu on quantum verification and compiler optimization tools.