Find Academic Supervisors

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +3 more

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Dynamical systems and gradient flows
• Analysis of partial differential equations
• Stochastic analysis
• +2 more

Arnulf Jentzen is a distinguished mathematician holding dual positions as Presidential Chair Professor at the School of Data Science and Shenzhen Research Institute of Big Data at The Chinese University of Hong Kong, Shenzhen, and as Full Professor at the Faculty of Mathematics and Computer Science at the University of Münster, Germany. His research spans multiple institutions with significant contributions across mathematical disciplines. His primary research interests include dynamical systems and gradient flows (particularly geometric properties, domains of attractions, blow-up phenomena), analysis of partial differential equations, stochastic analysis (including stochastic calculus and well-posedness analysis), machine learning (with focus on mathematics for deep learning and stochastic gradient descent methods), and numerical analysis (particularly computational stochastics and computational finance). His work demonstrates a strong interdisciplinary approach bridging pure mathematics with practical computational applications. Jentzen's publication record shows a clear trend toward machine learning applications in solving complex mathematical problems, particularly in overcoming the curse of dimensionality in high-dimensional PDEs through deep neural networks. His research group actively publishes on optimization methods like Adam, convergence analysis, and applications of deep learning to partial differential equations and optimal control problems. ICBS Frontier of Science Award in Mathematics (2024) Fellow, Lamarr Institute (2023) ERC Consolidator Grant (2022) Joseph F. Traub Prize for Achievement in Information-Based Complexity (2022) Felix Klein Prize, European Mathematical Society (EMS) (2020) Professor Jentzen advises numerous PhD students across both institutions and serves on multiple editorial boards including SIAM Journal on Numerical Analysis, Journal of Complexity, and Communications in Computational Physics. His research group at Münster and CUHK-Shenzhen focuses on developing mathematical foundations for machine learning with applications to scientific computing problems. He has received significant research funding including an ERC Consolidator Grant, supporting his interdisciplinary work at the intersection of mathematics and artificial intelligence.

• 5G/6G Systems
• Next-Generation Wi-Fi
• Wireless Internet of Things
• +4 more

Evgeny Khorov is a Full Professor and Deputy Chair at Moscow Institute of Physics and Technology (MIPT) and Head of the Wireless Networks Lab at the Institute for Information Transmission Problems of the Russian Academy of Sciences (IITP RAS) and the Telecommunication Systems Lab at Higher School of Economics (HSE). His research focuses on 5G/6G systems , next-generation Wi-Fi , Wireless IoT , and QoS-aware optimization. Ph.D. (2012) and D.Sc. (2022) in Telecommunications from IITP RAS and MIPT Visiting Research Fellow at King's College London (2015) His work includes mathematical modeling of networking protocols, Wi-Fi standardization (IEEE 802.11), and contributions to Wi-Fi 6 (802.11ax) and Wi-Fi 7 (802.11be) . He supervises students and has co-authored over 200 papers. Recent articles highlight advancements in Wi-Fi 7/8 , URLLC , 5G multi-connectivity , and machine learning for traffic classification . Scientific Awards: Best Demo Award, ACM Mobihoc (2022) Best Paper Awards: IEEE ISWCS (2012), Elsevier Computer Communications (2018), IEEE PIMRC (2019) Moscow & Russian Government Prizes for Young Scientists (2013, 2016) Scopus Award Russia (2018) Best Cooperation Project Leader (multiple times) He serves as Editor-in-Chief of Problems of Information Transmission (since 2024) and chairs major IEEE conferences (Globecom 2018 Workshop, BlackSeaCom 2019).

Aravindan VijayaraghavanView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning Theory
• Data Science
• +7 more

Aravindan Vijayaraghavan is an Associate Professor in the Department of Computer Science at Northwestern University (affiliated with McCormick School of Engineering). He also holds courtesy appointments in the Industrial Engineering and Management Sciences (IEMS) department. Research interests include theoretical computer science , machine learning algorithms , quantum information , and combinatorial optimization under non-worst-case paradigms. He leads IDEAL (Institute for Data, Economics, Algorithms and Learning) as Site Director at Northwestern and former Institute Director (2023-24). Academic Background : PhD in Computer Science from Princeton University (advisor: Moses Charikar ) Bachelor's Degree in Computer Science from Indian Institute of Technology Madras Postdoctoral work at Courant Institute (NYU) and Carnegie Mellon University via Simons Collaboration grants Research Contributions : Developed smoothed analysis frameworks for random matrices with dependent entries Created sum-of-squares certificates for anti-concentration beyond Gaussian distributions Advanced quantum entanglement certification algorithms for subspaces Improved weak-to-strong generalization theory with data distribution expansion properties Designed error-tolerant e-discovery protocols for legal document classification Scientific Recognition : NSF CAREER Award NSF AITF Award (CCF-1637585, CCF-2154100) Google Research Scholar Program grant Amazon Research Awards program support Simons Postdoctoral Fellowship Academic Leadership : General Chair for FOCS 2024 Co-organizer of Junior Theory Workshop and Northwestern QTW series Active in program committees for COLT , ICML , NeurIPS , and STOC conferences Teaching Portfolio : CS262: Mathematical Foundations of CS (Continuous Mathematics for Computer Science) CS212: Mathematical Foundations of Computer Science (multiple offerings since 2015) CS496: Graduate Algorithms (since 2016) CS396/496: Quantum Computation & Information (co-taught with S. Rao) CS497: Machine Learning Theory (Spring 2025 offering)

Simon SponbergView profile

Associate Professor

• Neuromechanics
• Biomechanics
• Insect Flight
• +4 more

Simon Sponberg is the Dunn Family Associate Professor at Georgia Institute of Technology, holding joint appointments in the School of Physics and School of Biological Sciences within the College of Sciences. He directs the Agile Systems Lab and serves as Physics & Biological Sciences Director. His research bridges physics, biology, and engineering to understand the principles of animal locomotion. Dr. Sponberg received his Ph.D. in Integrative Biology from UC, Berkeley and completed postdoctoral research at the University of Washington. His academic journey began with undergraduate studies at Lewis & Clark College, where he first explored biomechanics research focusing on gecko adhesion. Dr. Sponberg's research centers on neuromechanics - an integrative science examining how physics and physiology enable animals to achieve remarkable stability and maneuverability. His work specifically investigates insect flight mechanics, particularly in hawkmoths (Manduca sexta), exploring how nervous systems interact with muscle mechanics to produce locomotion. Key research areas include: Mechanisms of Maneuverability: How animals maintain stable flight during perturbations Sensing in Complex Environments: Multisensory integration of vision and mechanosensation Multiscale Physics of Muscle: How muscle structure relates to function across scales Evolution of Flight: Comparative studies of different insect flight strategies His publication record reveals a strong focus on the intersection of biomechanics, neuroscience, and physics, with recent work emphasizing resonant mechanics in insect flight, precise neural control of movement, and multisensory integration for robust performance across varying environmental conditions. A notable trend is the integration of experimental biology with computational modeling and robotics to extract general principles of movement. Dr. Sponberg's scientific achievements have been recognized with numerous awards and fellowships: Hertz Fellow (since 2002) National Science Foundation Fellowships American Physical Society Awards Society of Integrative and Comparative Biology Awards Woods Hole Marine Biological Institute Fellowships University of California Fellowships International Association of Physics Students Awards As an advisor, Dr. Sponberg mentors a diverse team of graduate students and postdoctoral researchers through the Quantitative Biosciences Graduate Program, Neuroscience and Neurotechnology Graduate Program, and Bioengineering Graduate Program. His lab has received significant funding, including an NSF-funded Biological Integration Institute (the Integrative Movement Sciences Institute) and a FLAP MURI grant. His mentoring philosophy emphasizes interdisciplinary collaboration and hands-on research experience, with over 120 undergraduate students having participated in his lab through Georgia Tech's Vertically Integrated Projects program. The Agile Systems Lab, housed in the Howey Physics Building at Georgia Tech, brings together researchers from physics, biology, engineering, and neuroscience to study the fundamental principles of movement. The lab features state-of-the-art equipment for high-speed videography, electrophysiology, robotic flower tracking systems, and X-ray diffraction studies of living muscle. Current collaborative projects include the NSF-funded Integrative Movement Sciences Institute, which explores movement across scales from molecules to organisms, and the FLAP MURI grant investigating resonant mechanics in flapping flight.

Thi Phuong Khanh NguyenView profile

Researcher

• Prognostics and Health Management (PHM)
• Predictive Maintenance
• Condition-Based Maintenance
• +8 more

Dr. Thi Phuong Khanh Nguyen is a researcher at the Ecole Nationale d'Ingénieurs de Tarbes (ENIT) , affiliated with the College of Engineering and Department of Systems . Her work focuses on Prognostics and Health Management (PHM) , predictive maintenance, and industrial data analytics, combining machine learning with physics-informed modeling to address uncertainty in system degradation. Teaching: Mathematics for engineers, Probability, Statistics, Operating safety Research: Health indicators, diagnostics, prognostics, multimodal data fusion Methods: Data mining, physical and data-driven models, decision support systems Tools: FAST, Petri nets, UML, HMM, RNN, CNN, Transformer architectures Her recent publications highlight advancements in explainable AI , physics-informed neural networks , and multimodal learning for fault detection, battery RUL prediction, and robotic inverse dynamics. She also explores blockchain and federated learning for decentralized prognostics.

Nick BassiliadesView profile

Professor

• Semantic Web
• Ontologies
• Linked Open Data
• +7 more

Nick Bassiliades is a Professor at the School of Informatics , Aristotle University of Thessaloniki , Greece. His academic roles include serving as President of the Digital Governance Committee and the Digital Transformation of Greek Universities Committee, as well as Director of the Web, Data, and Knowledge Engineering Sector. Education: B.Sc. in Physics, Aristotle University of Thessaloniki (1991) M.Sc. in Applied Artificial Intelligence, University of Aberdeen (1992) Ph.D. in Parallel Knowledge Base Systems, Aristotle University of Thessaloniki (1998) His research focuses on Semantic Web , Ontologies , Knowledge Graphs , and applications in Artificial Intelligence , eGovernment , and Intelligent Agents . Recent publications emphasize ontological frameworks for requirements engineering, explainable AI, and electric vehicle knowledge graphs. He actively contributes to scientific communities as a Senior Member of IEEE and ACM , and serves as Co-Editor-in-Chief for the International Journal of Artificial Intelligence in Business and Management . His work involves collaborations with the Intelligent Systems laboratory and projects like XR4DRAMA for disaster management.

Rebecca YangView profile

Visiting Professor

• Building Integrated Photovoltaics
• Solar Energy Applications in Buildings
• Construction Innovation
• +2 more

Rebecca Yang is a Visiting Professor at RMIT University's School of Property, Construction and Project Management, specializing in building, construction, and distributed renewable energy research. She integrates theoretical knowledge with cutting-edge technologies to advance sustainable urban development. Her research focuses on solar energy applications in buildings, construction innovation, and international energy policy frameworks through her leadership roles in the International Energy Agency's Photovoltaic Power Systems Programme (PVPS) Task 15 and Solar Heating and Cooling Programme (SHC) Task 66. She established RMIT's Solar Energy Application Lab and has 8 years of BIPV expertise. Notable achievements include: Australian representative in international BIPV standardization (IEC 63092) 2019 Facilitator Prize for BIPV Tool development She supervises research projects related to: Solar building envelope optimization Machine learning for energy systems Fire safety in BIPV installations Blockchain-enabled energy trading Circular economy for PV waste

Shaul DruckmannView profile

Associate Professor

• Neuroscience
• Computational Biology
• Neural Circuits
• +7 more

Shaul Druckmann is an Associate Professor at Stanford University in the departments of Neurobiology, Psychiatry and Behavioral Sciences, and by courtesy in Electrical Engineering. He is affiliated with Stanford Bio-X and the Wu Tsai Neurosciences Institute. Key Research Areas: Neural circuit dynamics, computational neuroscience, sensory-motor integration, and brain-wide information representation. Academic Roles: Advisor, mentor, and instructor for graduate and doctoral programs at Stanford. His work combines theoretical modeling, in vivo imaging , and genetic dissection to uncover how neural circuits process information and generate behavior. Recent trends in his research emphasize cross-species comparative analysis (Drosophila, rodents, C. elegans) and interdisciplinary applications in neuroprosthetics. 2023-2024 Awards: Award for Excellence in Graduate Teaching, Stanford University McKnight Scholar, McKnight Foundation Sloan Research Fellow, Sloan Foundation Advising & Teaching: Dr. Druckmann mentors doctoral students across Neurobiology, Psychiatry, and Applied Physics. He teaches foundational courses like Introduction to Mathematical Tools in Neuroscience and Neuroscience Computational Core , while supervising independent studies in bioengineering and physics. Laboratory: The Druckmann Lab at Stanford employs advanced imaging , computational modeling , and connectome analysis to bridge theoretical neuroscience with applications in medical devices and cognitive frameworks.

• Machine Learning
• Artificial Intelligence
• Bayesian Statistics
• +5 more

Dr Cuong Nguyen is a Lecturer in the Department of Mathematical Sciences at Durham University, specializing in Machine Learning, Artificial Intelligence, and Statistics. His research bridges theoretical foundations with practical applications, with particular expertise in Bayesian methods, transfer learning, and multimodal systems. His educational background includes a PhD in Computer Science or a related field (specific institution not mentioned in provided data), with research focusing on machine learning theory and applications. Nguyen has established himself as a researcher with publications spanning top conferences including NeurIPS, UAI, and ACM Web Conference. Research Interests: Nguyen's work centers on lifelong learning systems that overcome catastrophic forgetting, transferability estimation between tasks, and multimodal learning applications. His research integrates Bayesian principles with deep learning to create more robust and adaptable AI systems. Recent Trends: Analysis of his 15 most recent publications reveals a strong focus on practical applications of theoretical machine learning concepts, particularly in security (CAPTCHA systems), real-world problem solving (fake advertisement detection), and fundamental learning theory (transferability metrics). Dr Nguyen has made significant contributions to understanding the theoretical underpinnings of transfer learning and continual learning, with his work on LEEP providing a practical metric for transferability estimation. His research on CAPTCHA systems demonstrates both theoretical rigor and practical security implications. Advising: While specific students aren't listed in the provided data, his publications show collaborations with researchers across institutions, suggesting active supervision of PhD and Master's students. Research Groups: He is affiliated with the Statistics research center within Durham's Department of Mathematical Sciences, contributing to the university's strength in mathematical and computational research.

Johanna UllrichView profile

Professor

• Network Security
• Critical Infrastructure Security
• IPv6 Security
• +4 more

Johanna Ullrich is a Professor at the University of Vienna's Faculty of Computer Science and a Key Researcher at SBA Research in Vienna. She leads the Research Group Communication Technologies and serves as Head of the Networks and Critical Infrastructures Security Group at SBA Research. Her academic journey includes positions as Principal Investigator & Manager of Third Party Funded Projects at the University of Vienna and Post-Doctoral Researcher at the Christian Doppler Laboratory for Security and Quality Improvement in the Production System Lifecycle. Her educational background includes a PhD sub auspiciis praesidentis in Computer Science from TU Wien (2013-2016), an MSc in Automation Engineering from TU Wien (2010-2013), and a BSc in Electrical Engineering from TU Wien (2007-2010). She also holds a Venia Docendi for Computer Engineering from the University of Vienna. Ullrich's research focuses on the intersection of computer science and classical engineering, with particular emphasis on network security, IPv6 measurement experiments, and critical infrastructure protection. Her groundbreaking work demonstrated vulnerabilities in the IPv6 Privacy Extension that led to modifications in major client operating systems, protecting millions of users. She is renowned for her research on cyber-physical attacks against power grids, showing how coordinated load attacks can destabilize electrical infrastructure. Her work spans both theoretical security frameworks and practical implementations with significant real-world impact. Her publication record reveals a consistent trajectory from fundamental network security research toward increasingly complex interdisciplinary investigations at the boundary of computer science and physical infrastructure. Recent work emphasizes AI/ML applications for network security, power grid resilience, and socio-technical approaches to cybersecurity. Her research demonstrates a progression from protocol-level security (IPv6) to system-level security (cloud, IoT) and now to infrastructure-level security (power grids, critical national infrastructure). 2nd in the Faculty of Computer Science's Best-of-the-Best Ranking 2024 Category Third Party Funding Nomination for the Hedy Lamarr Prize 2019 and 2020 Scholarship of Excellence 2018 Research Prize of the Dr. Maria Schaumayer Foundation 2018 Promotio Sub Auspiciis Praesidentis 2017 Diploma Thesis Award of the City of Vienna 2013 Ullrich actively contributes to the academic community through extensive grant acquisition and service. She has secured numerous research projects including SPyCoDe (Semantic and Cryptographic Foundations of Security and Privacy by Compositional Design), DynAISEC (Adaptive AI/ML for Dynamic Cybersecurity Systems), and Q-Crit (Quantum-Safe Critical Infrastructure for Austria). Her leadership extends to committee roles including Program Committee Member of IEEE Symposium on Security and Privacy (S&P) 2024 and Technical Program Chair of Network Traffic Measurement and Analysis Conference (TMA) 2023. At SBA Research, she leads the Networks and Critical Infrastructures Security Group, which investigates security challenges at the intersection of digital networks and physical infrastructure. The group conducts both theoretical research on security frameworks and practical measurements of real-world systems. Their work combines network measurement techniques with power systems engineering to develop comprehensive security approaches for critical infrastructure.

Danesh TaftiView profile

Professor

• Computational Fluid Dynamics
• Fluid Mechanics
• Heat Transfer
• +7 more

Danesh Tafti is the William S. Cross Professor and Associate Department Head for Graduate Studies in the Department of Mechanical Engineering at Virginia Tech. He holds a PhD from Pennsylvania State University (1989) and has held roles at institutions including the University of Illinois at Urbana-Champaign and West Virginia Institute of Technology. His research focuses on computational fluid dynamics (CFD) and heat transfer, with applications in gas turbines, biomedical systems, and renewable energy. Key research areas include turbulence modeling, fluid-structure interaction, and biofluid mechanics, leveraging high-performance computing. His work spans aerodynamics of flapping flight, cardiovascular flows, and particle-laden flows. Tafti leads the High Performance Computational Fluid-Thermal Science and Engineering Lab, developing tools like GenIDLEST software for complex fluid-thermal systems analysis. Education: PhD (Penn State, 1989), MS (Texas Tech, 1983), BE (Bombay University, 1980) Awards: ASME Fellow (2014), Virginia Tech Dean’s Research Award (2012) Labs: High Performance Computational Fluid-Thermal Science and Engineering Lab Publications emphasize CFD advancements, machine learning integration, and multiphase flow modeling. Tafti’s work bridges computational methods with real-world applications in energy systems, aerospace, and biomedicine.

Shengwang DuView profile

Professor

• Atomic, Molecular, and Optical (AMO) Physics
• Quantum Optics
• Quantum Networks
• +5 more

Shengwang Du is a Professor of Physics and Astronomy and Professor of Electrical and Computer Engineering at Purdue University, serving as Deputy Director of the Purdue Quantum Science and Engineering Institute (PQSEI). He holds adjunct and visiting roles at institutions globally, including co-founding Light Innovation Technology (LiT) companies. His research focuses on quantum optics, quantum computing, optical neural networks, and bioimaging. Du earned his PhD in Physics from the University of Colorado Boulder in 2005, with prior degrees from Peking University and Nanjing University. Education: PhD, Physics (Atomic, Molecular and Optical Physics), University of Colorado Boulder, 2005 MS, Electrical Engineering, University of Colorado Boulder, 2002 MS, Physics, Peking University, 1999 BS, Electrical Engineering, Nanjing University, 1996 Research Interests: AMO Physics, Quantum Optics, Quantum Networks, Quantum Computing, Optical Neural Networks/AI, Optical Microscopy, Bioimaging. Professional Contributions: Associate Editor of Optics Express , Optica Fellow (2019), APS Fellow (2019), and key organizer of international quantum conferences. His publications span quantum optics, bioimaging, and photonics, with recent work emphasizing quantum networks and AI-driven microscopy. Awards: APS and Optica Fellowships (2019), Hong Kong Young Academy of Sciences membership (2020–2025), and the 2011 HKUST Research Award. Grants & Labs: Co-founded Light Innovation Technology (LiT) and NanoBioImaging (NBI) Ltd. Active in quantum engineering, optical instrumentation, and quantum sensing initiatives.

Javier F. MedinaView profile

Professor

• Neural Circuits
• Computational Neuroscience
• Prediction Machines
• +3 more

Javier F. Medina, Ph.D., is a Professor and Brown Foundation Chair in Neuroscience at Baylor College of Medicine. His research focuses on understanding how neural circuits implement supervised learning to make predictions, particularly in the context of motor control and cognitive functions. His laboratory combines experimental techniques such as electrophysiology, optogenetics, and two-photon imaging with computational modeling and artificial neural networks to uncover the computational principles underlying brain function. Education Postdoctoral Fellowship in Neuroscience, UCSF (2007) Ph.D. in Neuroscience, University of Texas, Houston (2000) Bachelor of Science in Computer Science and Physics, Drexel University, Philadelphia (1993) Research Interests Medina’s work integrates computational neuroscience with experimental approaches to study prediction and error correction in neural circuits. His lab investigates how the cerebellum encodes temporal information, processes sensory input, and adapts through learning. Key topics include supervised learning algorithms, neural coding in Purkinje cells, and the role of prediction errors in motor adaptation. Publications His research spans themes in cerebellar circuitry, motor learning, and computational modeling. Recent work includes deep learning strategies for cell-type identification and studies on cerebellar modules for predictive control. Grants & Labs Medina leads a lab at Baylor College of Medicine, focusing on experimental and theoretical neuroscience. His work has implications for motor disorders linked to learning deficits, such as ataxia and Parkinson’s disease.

• Structural Health Monitoring
• Structural Control
• Stochastic Fatigue
• +4 more

B. F. Spencer Jr. is the Nathan M. and Anne M. Newmark Endowed Chair in Civil Engineering at the University of Illinois at Urbana-Champaign, where he directs the Multi-Axial Full-Scale Sub-Structured Testing & Simulation Facility and the Smart Structures Technology Laboratory. He joined the university in 2002 after serving as Leo E. and Patti Ruth Linbeck Professor of Engineering at the University of Notre Dame (1985-2002). Education includes: Ph.D. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1985) M.S. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1983) B.S. in Mechanical Engineering, University of Missouri-Rolla (1981) His research focuses on pioneering innovations in structural health monitoring, stochastic mechanics, and smart sensor technologies. Key areas include development of wireless sensor networks for real-time infrastructure assessment, seismic hazard mitigation strategies, and AI-driven damage detection systems. His work bridges theoretical computational mechanics with practical civil engineering applications to enhance resilience against natural disasters. Recent publications emphasize digital twins, UAV-based structural inspection, machine learning for damage identification, and advanced sensor networks. Trends show strong integration of AI, 3D visualization, and edge computing for rapid post-disaster evaluation and predictive maintenance of critical infrastructure. Major scientific honors: ASCE Housner Medal (2015) J.M. Ko Medal (2014) Foreign Member of Polish Academy of Sciences (2005) Structural Health Monitoring Person of the Year (2011) JSPS Fellowships (1999, 2000) He leads significant infrastructure projects including NSF-funded facilities and industry collaborations. Laboratory initiatives involve full-scale testing of bridges, gates, and seismic mitigation systems. Educational outreach includes K-12 STEM programs like 'Shakes and Quakes' to inspire future engineers.