Find Academic Supervisors

• Semiconductors for hostile environments
• Device physics and modeling
• Microelectronic device and sensor design
• +3 more

Hugh Barnaby is a Professor at Arizona State University (ASU), affiliated with the School of Electrical, Computer and Energy Engineering since 2001. Previously, he was an Assistant Professor at the University of Arizona and a Staff Scientist at Mission Research Corporation. His research focuses on semiconductors for extreme environments, radiation-hardened electronics, and analog/RF circuit design. Education: Ph.D. Electrical Engineering, Vanderbilt University (2001) M.S. Electrical Engineering, Vanderbilt University (1999) B.A. Mathematics and Philosophy, University of California-Berkeley (1992) Research Interests: Barnaby specializes in radiation effects on microelectronics, device physics, and the development of radiation-hardened technologies for aerospace and medical applications. His work includes memristive structures, semiconductor reliability under hostile conditions, and analog/mixed-signal circuit design. Grants & Projects: Principal Investigator on over 30 grants, including NASA-funded research on neutron/gamma-ray spectrometers (2015–2018), DOD-AFRL projects on radiation-hardened memory (2015–2018), and JPL initiatives for space-based sensors (2009–2016). Key collaborators include institutions like Sandia National Labs, Boeing, and Medtronic. Advising & Teaching: Supervises graduate students through individualized instruction programs and teaches advanced courses on circuits, analog integrated systems, and extreme environment electronics (e.g., EEE 433, EEE 334, EEE 335). Labs & Teams: Leads research teams focused on radiation-hardened electronics, memristor applications, and sensor development for space and medical technologies.

• Reinforcement Learning
• Knowledge Representation
• Planning and Decision Making
• +7 more

Scott Sanner is a Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. With an extensive publication record spanning over two decades, his research bridges artificial intelligence, machine learning, and engineering applications. His work demonstrates significant contributions across multiple top-tier conferences including AAAI, ICLR, NeurIPS, and SIGIR. Dr. Sanner's research interests encompass Reinforcement Learning, Knowledge Representation, Planning and Decision Making, Recommender Systems, and Large Language Models. His work shows a consistent focus on bridging symbolic and neural approaches to AI, with particular emphasis on commonsense reasoning, traffic signal control, and conversational recommendation systems. Recent publications demonstrate increasing integration of large language models with traditional AI techniques for complex reasoning tasks. Analysis of his recent publications reveals a strong trend toward leveraging large language models for knowledge representation and reasoning tasks, while maintaining his foundational work in reinforcement learning and planning. His research increasingly focuses on practical applications in transportation systems, recommendation technologies, and commonsense reasoning frameworks that combine neural and symbolic approaches. Dr. Sanner has mentored numerous graduate students who have become active researchers in the field, including Jihwan Jeong, Zheda Mai, Armin Toroghi, and Anton Korikov. His collaborative work spans multiple institutions and demonstrates strong industry and academic partnerships, particularly with researchers from Australian National University and various technology companies. His research group appears to focus on intelligent systems for decision making under uncertainty, with applications ranging from traffic management to personalized recommendation systems. Current projects show significant emphasis on integrating large language models with traditional AI techniques for more robust and explainable systems.

Haotian XuView profile

Assistant Professor

• Nonparametric Estimation
• High-dimensional Inference
• Change Point Detection
• +5 more

Haotian Xu is a Harrison Early Career Assistant Professor in the Department of Statistics at the University of Warwick. His research focuses on nonparametric estimation in high-dimensional settings, change point detection, and inference under temporal dependence and contamination. He holds a Ph.D. from the University of Geneva (2021) and an MSc from the University of Illinois at Urbana-Champaign (2015). His postdoctoral work involved collaborations at institutions like Pennsylvania State University and Université Catholique de Louvain. Key research areas include robust statistical methods, wavelet-based inference, and applications in sensor calibration and functional data analysis. He has developed R packages such as 'changepoints' and 'FragmentCP' for change point detection. Teaching responsibilities include Bayesian Forecasting and Intervention at Warwick and courses in statistical modeling at Penn State and the University of Geneva. His publications span high-impact journals like the Annals of Statistics and IEEE Transactions, addressing challenges in time series analysis, multivariate modeling, and computational statistics. Current projects emphasize scalable inference for large datasets and fragmented functional data analysis.

• Mathematics
• Applied Mathematics
• Harmonic Analysis
• +3 more

Norbert Kaiblinger is an Associate Professor at the Institute of Mathematics, BOKU University, Vienna, Austria. His research focuses on applied mathematics, harmonic analysis, and numerical methods, with applications in fluid mechanics, statistics, and chemical engineering. He holds a habilitation in Mathematics from the University of Vienna, granting him teaching authorization in the field. His work bridges theoretical mathematics with practical computational problems, particularly in adsorption modeling and fluid dynamics. Key research interests include multicomponent batch adsorption models, dynamics of subaqueous systems, analysis of variance (ANOVA) methodologies, and special functions. Recent publications (2023–2025) highlight advancements in equilibrium composition calculations and statistical experimental design. His earlier work (2007–2019) explores Fourier analysis, operator theory, and algebraic structures like cyclotomic rings and circulant matrices. While no formal advising relationships or grants are listed, his collaborative research involves co-authors from diverse fields such as chemical engineering and fluid mechanics. Kaiblinger’s work is disseminated through journals like Adsorption, Journal of Fluid Mechanics, and Transactions of the American Mathematical Society.

Nicolas RousselView profile

Researcher

• Human-Computer Interaction
• Computer-Mediated Communication
• Groupware Systems
• +7 more

Nicolas Roussel is the Director of the Inria research center at the University of Bordeaux, a role he has held since July 2017. Prior to this, he served as an associate professor (Maître de conférences) at Paris-Sud University and a senior researcher (Directeur de recherche) at Inria Lille - Nord Europe. His academic journey includes a PhD in Computer Science from the LRI and postdoctoral stays at GMD Sankt Augustin and the University of Fribourg. Research Interests: His work focuses on Human-Computer Interaction (HCI), with expertise in computer-mediated communication, groupware systems, interactive systems engineering, and tactile/gestural interaction. He explores human interactions with AI systems and augmented reality, emphasizing user-centered design and system latency optimization. He has contributed to major conferences like ACM CHI, ACM UIST, and ACM CSCW, and co-organized events such as the Forum on Tactile and Gestural Interaction and the 2021 PFIA (Artificial Intelligence Platform). He chairs Aerospace Valley’s 'Data Economy and Artificial Intelligence' ecosystem and serves on scientific boards of GIS Albatros and LyRE. Administrative Roles: He holds board positions at institutions like Digital Aquitaine and Cap Sciences, emphasizing technology’s societal impact. His work bridges academia and industry, including collaborations with Inria-Industry Meetings on digital interaction. Labs/Teams: Involved in Inria project teams such as GraphDeco, Potioc, and Castor, focusing on interactive systems and tactile interfaces. His research addresses challenges in latency compensation, gesture-based interaction, and BCI integration with AR systems.

• Digital Communications
• Neural Networks
• Medical Engineering
• +3 more

Matthieu ARZEL is an Associate Professor in the Department of Mathematical and Electrical Engineering at IMT Atlantique. He holds an HDR (2021), PhD (2006), and Engineer degree (2002) from Telecom Bretagne/ENST. His research focuses on iterative processing for digital communications, low-power integrated circuits, high-speed digital circuits, and FPGA implementations in domains like neural networks, medical engineering, and communication systems. Key research interests include neuromorphic hardware, neural network pruning, federated learning compression, and energy-efficient signal processing. He has supervised 18 PhD students and contributed to projects like Ouessant coprocessor architectures and clique-based neural network circuits. His work bridges algorithm-architecture interactions, emphasizing low-power and embedded system applications. Recent publications highlight innovations in FPGA-based deep learning deployment and efficient neural network compression techniques. Publications span topics from real-time semantic segmentation on FPGA to collusion-resistant watermarking. His contributions address challenges in hardware-software co-design, iterative decoders for MIMO systems, and biomedical signal processing.

Yi YuView profile

Professor

• High-dimensional Statistics
• Statistical Network Analysis
• Change Point Analysis
• +5 more

Yi Yu is a Professor in the Department of Statistics at the University of Warwick, specializing in high-dimensional statistics with a focus on change point analysis, differential privacy, and network analysis. She holds editorial roles at Biometrika, Bernoulli, and Journal of Machine Learning Research. Her research is funded by EPSRC and the Leverhulme Trust, including the prestigious Philip Leverhulme Prize (2023). Education: Ph.D. in Mathematical Statistics, Fudan University (2013) B.Sc. in Mathematics, Fudan University (2009) Research Interests: Yi’s work bridges statistical theory and applications in machine learning, including high-dimensional regression, functional data analysis, and privacy-preserving methods. She has pioneered methodologies for change point detection in complex systems and dynamic networks, with applications in economics, neuroscience, and distributed learning environments. Grants & Awards: Leverhulme Trust Philip Leverhulme Prize (2023) EPSRC Programme Grant DASS (EP/Z531327/1) EPSRC Grants (EP/V013432/1, EP/W003716/1) Advising & Team: Supervises current PhD students Gengyu Xue, Alexander Kent, Elly Hung, Jason Ng, and Edwin Tang. Alumni include Mengchu Li (now Assistant Professor at University of Birmingham), Fan Wang (CRiSM Fellow at Warwick), and Haotian Xu (Harrison Assistant Professor at Warwick). Software: Developer of R packages changepoints , GMPro , fcd , and APPLE , widely used in change point analysis and statistical modeling.

• 3D Graphics
• Point Cloud Compression
• Augmented Reality
• +5 more

Prof. Marius Preda is a Lecturer at Telecom SudParis, specializing in multimedia technologies and standards. His work focuses on 3D graphics, point cloud compression, augmented and virtual reality, and MPEG standardization. He has contributed to numerous international conferences and publications, particularly in advancing compression techniques and interactive media systems. Key research areas include MPEG standards for 3D graphics, point cloud compression (notably G-PCC), and AR/VR applications in industrial and educational contexts. He has led projects like ATOFIS, an AR training system for manufacturing, and explored lightweight neural network libraries (FasterAI). His work bridges theoretical advancements with practical implementations, emphasizing interoperability and efficiency. Preda has collaborated extensively on international standards through MPEG, contributing to the development of protocols for mixed reality, media convergence, and interactive systems. His research also addresses challenges in real-time processing, hardware optimization, and user-centric design across domains like robotics, energy systems, and cultural heritage preservation.

• Aeroacoustics
• Aircraft Noise
• Wind Turbine Noise
• +5 more

Professor Con Doolan is a leading academic in aeroacoustics and fluid dynamics at UNSW Sydney's School of Mechanical and Manufacturing Engineering. He heads the Flow Noise Group, focusing on noise reduction in aerospace, wind energy, and transport sectors. His research spans turbulence, fluid-structure interactions, and noise control, with over 400 publications. Education: PhD in Aerospace Engineering (University of Queensland), Honours in Mechanical Engineering. Leadership Roles: Director of Postgraduate Research Examinations (UNSW Engineering), Associate Dean (Education), UNSW College Academic Board member. Teaching: Courses in Aerodynamics, Flight Mechanics, and Acoustics. Research Interests: Aeroacoustics, flow-induced noise, acoustic beamforming, wind turbine noise, and turbulence physics. He collaborates with industries in defense, energy, and healthcare to translate research into practical solutions. Key Projects: UNSW Anechoic Wind Tunnel, Cough Dynamics Experiments, Classroom Air Quality Studies. His work on mask efficacy during pandemics has had global impact. Grants & Collaborations: Extensive ARC funding, partnerships with mining, aerospace, and construction sectors. Active in interdisciplinary projects like waste-to-noise-attenuation materials. Labs/Teams: Flow Noise Group (specializing in experimental and computational aeroacoustics), collaborations with medical and environmental teams for infection control studies.

Yin SunView profile

Associate Professor

• Wireless Networks
• Wireless Communications
• Cloud Computing
• +5 more

Yin Sun is the Godbold Associate Professor in the Department of Electrical and Computer Engineering at Auburn University. His research focuses on timely information updates, wireless networks, and IoT, with emphasis on data freshness metrics and remote estimation systems. He earned his B.S. and Ph.D. in Electronic Engineering from Tsinghua University. Education: B.S. Electronic Engineering, Tsinghua University Ph.D. Electronic Engineering, Tsinghua University His research interests include wireless communications, cloud computing, and machine learning applications in safety-critical systems. Notable achievements include co-authoring the seminal book on Age of Information and receiving the NSF CAREER Award for his work on goal-oriented status updating. He also serves as Technical Co-Chair for the ACM MobiHoc Symposium and leads Auburn's Wireless Engineering Research and Education Center, addressing real-world communications challenges through interdisciplinary collaboration. Awards include the 2021 Best Paper Award (Journal of Communications and Networks) and contributions to food pantry inventory optimization via machine learning. His work bridges theoretical foundations with practical systems, emphasizing real-time data relevance and efficient resource allocation strategies. Advisees/Grants: Leads NSF-funded projects on semantic status updating and collaborates on applications like disease detection in agriculture. Active in advising graduate students through his lab's focus on edge computing and network optimization. Labs/Teams: Core member of Auburn's Wireless Engineering Research and Education Center, fostering multi-university collaborations in communications research.

Alan WelshView profile

Professor

• Statistical Inference
• Statistical Modelling
• Mixed Models
• +4 more

Alan Welsh is the E.J. Hannan Professor of Statistics at the Australian National University (ANU), affiliated with the Research School of Finance, Actuarial Studies & Statistics. He holds prestigious fellowships from the Australian Academy of Science, Institute of Mathematical Statistics, and American Statistical Association. His research focuses on statistical inference, mixed models, robustness, nonparametric methods, ecological monitoring, and survey analysis. Education: PhD in Statistics (ANU, 1984) and BSc (Hons) in Mathematical Statistics (University of Sydney, 1981). Research interests emphasize advanced statistical methodologies with applications in ecology, environmental science, and healthcare. He has authored over 176 publications and secured funding through grants like 'Analytics for the Australian Grains Industry' (2024–2027) and 'Statistical Machine Learning' (2019–2024). His work spans high-dimensional data analysis, copula-enhanced models for medical imaging (e.g., myopia screening via fundus images), and spatial-temporal modeling of environmental data. He has also contributed to statistical software, notably the 'rpql' and 'mplot' R packages. Awards: Moran Medal (1990), Pitman Medal (2012), E.J. Hannan Medal (2019), and Honorary Life Membership (International Biometric Society, 2018). Welsh has advised numerous research students and served as Editor-in-Chief of the Australian and New Zealand Journal of Statistics (2012–2015) and Co-Editor of Biometrics (2020–2022). His current projects address challenges in big data analysis, robust estimation, and model selection for complex datasets.

Wei LuView profile

Professor

• Memristors
• Neuromorphic Computing
• In-Memory Computing
• +3 more

Wei Lu is the James R. Mellor Professor of Engineering at the University of Michigan with joint appointments in the Department of Electrical Engineering & Computer Science (EECS) and Materials Science & Engineering. He directs the Solid State Electronics Laboratory and leads the Lu Research Group. Lu completed his BS in Physics at Tsinghua University (1996), PhD at Rice University (2003), and postdoctoral research at Harvard University. Research Focus: Lu's work bridges nanoelectronics, materials science, and computing architectures. His research spans: Memristive devices for neuromorphic computing and in-memory processing Nanowire transistors and emerging electronic devices Resistive switching mechanisms in oxides and 2D materials Hardware implementations of machine learning algorithms Crossbar arrays for energy-efficient computation Publication Trends: Analysis of 15 recent publications reveals consistent focus on memristor physics (58% of papers), neuromorphic implementations (42%), and nanoscale characterization (33%). Key evolution includes increased emphasis on full system integration (20%) and commercial applications. Awards & Honors: IEEE Fellow NSF CAREER Award 2022 Distinguished University Innovator Award 2016-2017 David E. Liddle Research Excellence Award 2014-2015 Rexford E. Hall Innovation Excellence Award 2012 EECS Outstanding Achievement Award Commercialization & Leadership: Co-founded Crossbar Inc. (resistive RAM technology) and MemryX Inc. (edge AI chips). Currently advises PhD candidates and postdocs in nanoelectronics and neuromorphic systems, with active NSF and industry-funded projects in memristive computing platforms.

Ulkuhan GulerView profile

Associate Professor

• Analog/Mixed Signal Integrated Circuits
• Energy Harvesting
• Wireless Power Transmission
• +4 more

Ulkuhan Guler is an Associate Professor in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI), affiliated with the College of Engineering and Biomedical Engineering. She holds the Dean’s Excellence Professorship and the Joseph Samuel Satin Distinguished Fellowship in ECE. Her research focuses on analog/mixed-signal integrated circuits, energy harvesting, and wireless power systems for healthcare and IoT applications. She leads the Integrated Circuits and Systems Lab at WPI, developing wearable sensors for noninvasive monitoring of oxygen and carbon dioxide levels. Guler earned her BS from Istanbul Technical University (1999), MS from The University of Tokyo (2003), PhD from Bogazici University (2014), and completed a postdoc at Georgia Tech (2018). Her work emphasizes miniaturized biomedical devices, including implantables and wearables, with applications in smart healthcare. Key research areas include luminescence-based oxygen sensing, transcutaneous gas monitoring, and secure wireless power transfer. Awards include recognition for her contributions to biomedical circuits and systems. Notable projects include a first-of-its-kind wearable oxygen sensor addressing racial bias in infant care and a transcutaneous CO₂ monitor using lifetime-based referencing. She collaborates on interdisciplinary teams to advance technologies like optical wireless power systems and hyperdimensional computing for acute mountain sickness detection.

• Molecular Biophysics
• Neuroscience
• Computational Physics
• +5 more

Josh Berryman is a Research Scientist (level 2) at the University of Luxembourg’s Faculty of Science, Technology and Medicine, leading the Biomolecular Modelling team within the Theoretical Chemical Physics group. He holds project management authority (ADR) and is a member of the Luxembourg Institute for Advanced Studies and the Complex Living Systems Initiative. His interdisciplinary research focuses on molecular biophysics, neuroscience, and neurodegenerative diseases, leveraging advanced computational methods including quantum mechanics and machine learning. Education: BSc in Computational Physics (University of Edinburgh, 2000), MSc in Artificial Intelligence (Edinburgh, 2001), and MSc/PhD in Physics and Life Sciences (University of Leeds, 2009). Professional roles include tenured positions at the University of Luxembourg since 2010 and teaching roles in computational physics and data science. He co-founded the Physics and Materials Science MSc program, developed its computational infrastructure, and currently chairs the BSc Physics sanctions committee. Research emphasizes biomolecular dynamics, particularly amyloid aggregation mechanisms and their links to neurodegenerative diseases. Collaborations include the AMBER software consortium (co-author of AMBER 14–24) and Luxembourg’s Google partnership in AI-driven molecular modeling (e.g., GEMS system). He coordinates the EU COVAMINF workgroup investigating SARS-CoV2’s potential role in neurodegeneration. Additional roles include non-executive roles in the Luxembourg Physics Society and advocacy for educational reforms through the National Parents Union. Teaching portfolio includes courses on classical electrodynamics, computational physics, and machine learning. He has supervised three PhD students independently and secured €569,500 in external research funding. His work bridges fundamental science and applied outcomes, with contributions to both academic software tools (AMBER) and cutting-edge AI applications in biomedicine.

• Nonlinear Optimization
• Numerical Optimization
• Optimization and Control with PDEs
• +8 more

Prof. Dr. Nicolas R. Gauger is a Full Professor and Chairholder for Scientific Computing at the University of Kaiserslautern-Landau (RPTU), holding dual appointments in the Department of Mathematics and Department of Computer Science. Since February 2015, he has also served as Director of the Computing Center (RHRZ) at RPTU. His academic career includes positions as Assistant Professor at Humboldt University Berlin (2005-2010), Associate Professor at RWTH Aachen University (2010-2014), and a Visiting Professorship at MIT (March-August 2014). Prof. Gauger earned his Master in Mathematics from Leibniz University of Hanover in 1998 and his Ph.D. in Applied Mathematics from Braunschweig University of Technology in 2003. Prior to his professorial positions, he worked as a Research Scientist in Numerical Methods for Aerodynamics at the German Aerospace Center (DLR) in Braunschweig from 1998 to 2010, while also being a Member of the DFG Research Center MATHEON in Berlin from 2006 to 2010. His research spans multiple disciplines within computational science and engineering, with primary interests in Nonlinear Optimization, Numerical Optimization, Optimization and Control with PDEs, Aerodynamic Shape Optimization, Computational Fluid Dynamics (CFD), Computational Aeroacoustics (CAA), Algorithmic Differentiation (AD), Machine Learning (ML), and High-Performance Computing (HPC). His work bridges theoretical mathematics with practical engineering applications, particularly in aerospace and medical physics. Recent publications show a strong trend toward integrating differentiable programming techniques with traditional computational methods, especially in optimizing experimental setups in fundamental physics and medical applications like proton therapy. Among his notable recognitions are being named an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) in August 2018, receiving a Teaching Award (June 26, 2025), and a Best Student Paper Award at AIAA Aviation 2020. He serves on the Managing Board of ERCOFTAC (European Research Community on Flow, Turbulence and Combustion) and the Steering Committee of the ERCOFTAC Special Interest Group on Design Optimization. Prof. Gauger has been actively involved in multiple research initiatives including the Research and Development Lab 'Data Analysis and Artificial Intelligence' of the Fraunhofer Performance Center (vice spokesperson since March 2016), AICES (2010-2019), (CM)^2 (2014-2019), MathApp (2019-2024), and currently MSO (Modelling, Simulation and Optimisation) since 2024. He leads a research team of approximately 15 members working on projects related to algorithmic differentiation, computational fluid dynamics, and optimization methods. His laboratory, the Scientific Computing research group at RPTU, is involved in multiple interdisciplinary projects including SIVERT (pCT) for fighting cancer with AI and the 'AI Care' project. The team has developed several important software tools including CoDiPack, OpDiLib, and SU2, which are widely used in the computational science community for algorithmic differentiation and aerodynamic optimization.