جست‌وجوی استاد و پژوهشگر

Charalampos Psillakisمشاهده پروفایل

استادیار

• Adaptive Control
• Nonlinear Control
• Intelligent Control
• +۷ مورد دیگر

Charalampos (Haris) Psillakis is an Assistant Professor at the Division of Signals, Control, and Robotics within the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). He holds a Ph.D. and Diploma in Electrical & Computer Engineering from the University of Patras. Previously, he served as an Adjunct Lecturer at multiple institutions and worked at Hellenic Electricity Distribution Network Operator (HEDNO) S.A. His research focuses on adaptive control, nonlinear control, robotics, multi-agent systems, power systems, and distributed control. Education: Ph.D., Electrical & Computer Engineering, University of Patras, 2006 Diploma in Electrical Engineering, University of Patras, 2000 Research Interests: Adaptive Control Nonlinear Control Intelligent Control Robotics and Automation Power Systems Control Multi-Agent Systems Stochastic Systems Uncalibrated Visual Servoing Publications: His work emphasizes distributed control algorithms for multi-agent systems, nonlinear control methodologies, and applications in robotics and power systems. Recent trends include consensus protocols under communication delays, adaptive neural network control, and robustness in dynamic network topologies. Teaching: He teaches courses on automatic control, nonlinear control systems, and advanced control methods at both undergraduate and graduate levels. Labs/Teams: His research is associated with the Signals, Control, and Robotics division at NTUA, focusing on robotics, power systems, and distributed control systems.

Tobias Glasmachersمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۶ مورد دیگر

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

Leilei Zengمشاهده پروفایل

استاد

• Biostatistics
• Health Sciences
• Longitudinal Data Analysis
• +۵ مورد دیگر

Leilei Zeng is a Professor and Associate Chair (Research) at the University of Waterloo's Department of Statistics and Actuarial Science. She holds the Graham Trust Chair in Health Statistics. Her research focuses on statistical methodologies applied to health sciences, including event history analysis, longitudinal data modeling, and clinical trial design. She collaborates with researchers in aging, dementia, rheumatology, and environmental health. Dr. Zeng earned her Ph.D. in Biostatistics from the University of Waterloo in 2005. She previously served as an Assistant Professor at Simon Fraser University before joining Waterloo in 2011 as an Associate Professor. Her work addresses complex data challenges such as multivariate processes, incomplete observations, and measurement error in health-related studies. Her research interests span statistical methodology development for clinical and epidemiological studies, including the design of cancer trials, analysis of progression-free survival, and the integration of auxiliary information in longitudinal datasets. She has contributed to studies on maternal cortisol profiles, cognitive reserve in dementia, and the impact of environmental factors on health outcomes. Dr. Zeng’s publications emphasize rigorous statistical approaches to address real-world health data complexities. Her recent work highlights advancements in mitigating bias in intermittent data measurements and improving cohort study designs for disease progression analysis.

Liang Hongمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• Network Security
• +۲ مورد دیگر

Liang Hong is a Professor in the Department of Electrical and Computer Engineering at Tennessee State University. He earned his Ph.D. in Electrical Engineering from the University of Missouri-Columbia in 2002, following industry experience at Siemens Corporate Research. His research encompasses wireless communications, signal processing, network security, and embedded systems. Hong has led multiple federally funded projects including a $599,800 Office of Naval Research initiative on embedded system security and a $310,000 Air Force Research Laboratory program on spectrum communication. His extensive publications demonstrate consistent focus on cognitive radio networks, beamforming techniques, cybersecurity, and networked control systems. Recent work emphasizes AI applications for threat detection, cloud security, and adaptive resource management in wireless environments. Hong received the TSU Faculty Research Award (2005) and was nominated for Teacher of the Year (2010). He has chaired technical committees for IEEE conferences and guided award-winning student research.

• Applied Mathematics
• Bayesian Inference
• Monte Carlo Methods
• +۵ مورد دیگر

Simon Cotter is a Professor of Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, stochastic modeling, and computational methods, with applications in biological systems (e.g., tendon mechanics, placental development) and financial modeling. He contributes to the university’s Digital Futures research beacon and collaborates across disciplines including biomechanics, computational biology, and data science. Research Interests: Bayesian data assimilation and parameter estimation Monte Carlo methods (e.g., multi-index, adaptive importance sampling) Multiscale stochastic systems and reaction networks Inverse problems in material science and biological systems Recent work highlights include developing NuZZ (a numerical Zig-Zag algorithm for general models) and advancing hierarchical Bayesian methods for data selection. His projects address challenges in debt recovery forecasting, placental development modeling, and cell cycle heterogeneity analysis. He co-leads a multi-modal pregnancy research project aimed at understanding stillbirth mechanisms and is actively involved in initiatives promoting gender equity in academia.

Kevin Linمشاهده پروفایل

استادیار

• Single-cell Biology
• Statistical Genetics
• Biostatistics
• +۳ مورد دیگر

Kevin Lin is an Assistant Professor in the Department of Biostatistics at the University of Washington, joining in Fall 2023. His research focuses on developing statistical methods for analyzing single-cell data to uncover cellular mechanisms in diseases like Alzheimer’s and immune resistance. He holds a PhD from Carnegie Mellon University and completed postdoctoral training at the University of Pennsylvania. His work bridges computational methods with biological questions, emphasizing matrix factorization, network modeling, and changepoint detection. Education: PhD in Statistics & Data Science (Carnegie Mellon University, 2020) Previous Role: Postdoctoral Researcher at University of Pennsylvania (Wharton Statistics) Research interests include high-dimensional data analysis, statistical genetics, and single-cell RNA-Seq. Notable contributions include methods like Tilted-CCA for multimodal data integration and eSVD-DE for cohort-wide differential expression analysis. His work has been recognized with awards such as the Wikimedia Foundation Research Award (2023). Lin’s lab collaborates on projects involving endolysosomal dysfunction in Alzheimer’s, yeast cell division dynamics, and lineage-aware machine learning. Key achievements include over 20 publications in journals like Nature Biotechnology , PNAS , and Biometrics . He advises on grants related to single-cell technologies and serves on the EDI subcommittee for mental health initiatives at UW. Outside academia, Lin enjoys zumba and cooking.

Mikko Juusolaمشاهده پروفایل

استاد

• Systems Neuroscience
• Neurophysiology
• Sensory Processing
• +۴ مورد دیگر

Professor Mikko Juusola is a leading academic in Systems Neuroscience at the University of Sheffield's School of Biosciences, leading the Drosophila Vision Laboratory. He holds concurrent roles as a Visiting Professor at Beijing Normal University’s Cognition in Small Brains Institute and has been recognized as a High-End Foreign Expert by China’s National Recruitment Program (2015–2017). His academic journey includes positions as a Royal Society University Research Fellow (1999–2007) and Jane and Aatos Erkko Research Fellow (2013–2017). Education: He earned a Ph.D. (1993) and M.D. (1993) in Neurophysiology from the University of Oulu, Finland, followed by postdoctoral training at institutions including the University of Cambridge and University of Alberta. Research Interests: His work focuses on neural mechanisms underlying sensory processing in Drosophila, including visual system dynamics, efficient coding strategies, and cognition in small brains. He employs experimental and computational approaches to study how sensory signals are encoded and transmitted across neural circuits. Recent studies highlight microsaccadic sampling in fly vision and social learning in bees. Awards: Notable honors include the Jane and Aatos Erkko Fellowship and Royal Society University Research Fellowship. Grants and Advising: As PI of multiple labs and fellowships, he has driven interdisciplinary research collaborations. Advising details are not explicitly listed here. Labs/Teams: Directs the Drosophila Vision Laboratory and collaborates internationally with the Beijing-based Cognition in Small Brains Institute.

Johann Ostmeyerمشاهده پروفایل

پژوهشگر

• Condensed Matter Theory
• Lattice Gauge Theory
• Quantum Computing
• +۴ مورد دیگر

Johann Ostmeyer is a post-doctoral researcher at the Helmholtz Institute for Radiation and Nuclear Physics at the University of Bonn. He holds a PhD and MSc in Physics from the University of Bonn (2021 and 2018, respectively). His research focuses on Condensed Matter Theory , Lattice Gauge Theory , and Quantum Computing . Key interests include developing Monte Carlo algorithms to address the fermion sign problem , studying quantum spin chains , and simulating organic molecular semiconductors . He also explores Hamiltonian lattice formulations and algorithmic optimization for high-precision simulations. Recent work includes advancements in radial updates for ergodicity, Fourier acceleration for autocorrelation reduction, and high-precision studies of charge transport in organic materials. His methods bridge theoretical frameworks with computational efficiency, addressing challenges in quantum many-body systems. Awards: Mario Markus Prize for Ludic Science (2022) Pineapple Science Award in Physics (2021) Initiativ-Preis der Universitätsgesellschaft Bonn (2021) His GitHub repositories showcase contributions to quantum Monte Carlo , lattice field theory , and machine learning applications . Collaborations include work on the Thirring model with domain wall fermions and Trotter decomposition optimizations for classical/quantum computing.

Henry P. Schriemerمشاهده پروفایل

استاد

• Photovoltaics
• Solar Energy
• Smart Grid
• +۸ مورد دیگر

Henry P. Schriemer is a Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa's Faculty of Engineering. His career bridges fundamental physics, photonics innovation, and applied renewable energy systems, with significant contributions to both academic research and industrial photonics development. His educational foundation includes a B.Sc. in Mathematics (1987) and Ph.D. in Physics (1997), both from the University of Manitoba. Early career milestones featured postdoctoral research at Queen's University and a prestigious FOM Postdoctoral Fellowship at Amsterdam's van der Waals-Zeeman Institute. Schriemer's research spans Nanophotonics, Optoelectronics, and Complex Systems, with current emphasis on Photovoltaics and Smart Grid technologies. His photovoltaic work integrates multiphysics simulation, spectral irradiance modeling, and system reliability testing, while his smart grid research pioneers blockchain-secured demand response and game-theoretic energy management. This dual focus addresses critical challenges in renewable energy integration and grid stability. Analysis of his 2020-2024 publications reveals a decisive shift toward solar energy systems and grid modernization. His team develops predictive spectral models incorporating weather dynamics, advanced demand response frameworks using Stackelberg game theory, and novel instrumentation like the CanSIM network for solar resource assessment. These works consistently bridge atmospheric science, power systems engineering, and cybersecurity. His scientific achievements include: FOM Postdoctoral Fellowship (1998) for nanophotonics research at van der Waals-Zeeman Institute NCIT Research Fellowship in Photonics (2003-2005) at University of Ottawa Industry recognition as innovator of strain-engineered planar lightwave circuits Schriemer has authored over 50 refereed publications in Science, Physical Review, and IEEE journals. His industrial contributions include foundational work in diffusive acoustic wave spectroscopy and optical scattering techniques, while his academic leadership manifests through the CanSIM network and IEEE 2030/2030.5 standard implementations. He maintains active collaborations across atmospheric science, power systems, and semiconductor physics domains. His laboratory operations focus on photovoltaic testbeds, solar spectral irradiance instrumentation, and smart grid simulation platforms. The CanSIM network provides critical northern-climate solar data, while his module-level power electronics research directly addresses rapid cloud-shading challenges in real-world PV deployments.

• Algorithm Design
• Algorithmic Economics
• Game Theory
• +۶ مورد دیگر

Richard J. Cole is a Silver Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences, part of the Faculty of Arts and Science. His research focuses on algorithm design, algorithmic economics, game theory, and parallel computing. He holds a Ph.D. in Computer Science from Cornell University (1982) and a B.A. in Mathematics from Oxford University (1978). Education: Ph.D., Computer Science, Cornell University, USA, 1982 B.A., Mathematics, Oxford University (University College), United Kingdom, 1978 His research spans algorithmic economics (market theory, mechanism design), parallel algorithms, and foundational areas like string matching and graph algorithms. Recent work emphasizes stable matching, non-quasi-linear agent mechanisms, and fair resource allocation. He has advised notable Ph.D. students including Yun Kuen Cheung and Vasilis Gkatzelis. Teaching includes undergraduate and graduate courses on algorithms, computational theory, and algorithmic aspects of the internet. His 15+ years of publications reflect contributions to theoretical computer science and interdisciplinary applications in economics. His work on parallel computing and resource-oblivious algorithms addresses multicore efficiency, while contributions to market equilibrium analysis and mechanism design highlight algorithmic solutions to economic challenges.

Pierre Nyquistمشاهده پروفایل

دانشیار

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +۷ مورد دیگر

Pierre Nyquist is an Associate Professor in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. Previously, he held a position at KTH Royal Institute of Technology. His research focuses on probability theory, mathematical statistics, and applied mathematics, with an emphasis on large deviations, stochastic numerical methods, and statistical learning theory. He is supported by grants from the Swedish Research Council, Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish e-Science Research Center (SeRC). Education: Ph.D. in Applied and Computational Mathematics (2014, advisor Henrik Hult), postdoc at Brown University (2014–2016), and assistant professor at TU Eindhoven (2016–2020). Research interests include large deviations theory, stochastic processes, gradient flows, and applications to machine learning and computational statistics. His work spans theoretical developments and practical methodologies, such as Monte Carlo algorithms and uncertainty quantification tools. Recent activities include organizing seminars, supervising PhD students, and collaborating with industry partners. He has been awarded membership in the Young Academy of Sweden (2024–2029) and serves as a scientific ambassador for EURANDOM. Key publications address topics like large deviations in stochastic approximations, Metropolis-Hastings algorithms, and neural entropy estimation. His teaching includes courses on statistical learning, probability, and Monte Carlo methods.

Eleni Vasilakiمشاهده پروفایل

استاد

• Bioinspired Machine Learning
• Neuromorphic Computing
• Computational Neuroscience
• +۳ مورد دیگر

Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.

Emanuele Viterboمشاهده پروفایل

استاد

• Error Control Coding
• Communications
• Lattice Codes
• +۵ مورد دیگر

Professor Emanuele Viterbo is a faculty member in the Department of Electrical and Computer Systems Engineering at Monash University, where he has served as Professor since 2010 and Associate Dean (Graduate Research) from 2012 to 2020. His research focuses on error control coding, lattice codes, and algebraic coding theory with applications to wireless communications. He holds a PhD and Laurea in Electrical Engineering from Politecnico di Torino. Key research areas include lattice codes for fading channels, algebraic space-time coding (e.g., Golden Code), and OTFS modulation. He has received prestigious awards such as the IEEE Fellow designation (2011) and the Australia-India Fellowship (2012-13). His work on polar codes and coding for NAND flash memories has advanced practical coding schemes for modern communication systems. Major grants include projects on blockchain coding, high-mobility wireless systems, and NAND flash memory reliability. He has supervised numerous PhD students and contributed to foundational research in information theory. His affiliations include leadership roles in IEEE committees and visiting research positions at institutions worldwide. Scientific achievements include pioneering contributions to space-time coding and lattice-based modulation. Current projects explore OTFS modulation, error control for blockchains, and advanced modulation techniques for 5G/6G networks.

Matteo Papiniمشاهده پروفایل

استادیار

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Matteo Papini is an Assistant Professor at Politecnico di Milano, affiliated with the Department of Electronics, Information, and Bioengineering. He is a member of the Artificial Intelligence and Robotics Lab and an active researcher in reinforcement learning. His work focuses on sequential decision-making under uncertainty, policy optimization, and safe exploration. Education: PhD in Information Technology (cum laude, 2021) from Politecnico di Milano; M.Sc. and B.Sc. in Computer Engineering from the same institution. Research Interests: Reinforcement Learning Policy Gradient Methods Contextual Bandits Safe and Efficient Exploration Key Awards: NeurIPS 2018 Oral Presentation, Juan de la Cierva-Formación Grant, and multiple travel awards. He has authored over 20 peer-reviewed papers in top conferences like NeurIPS, ICML, and COLT. Teaching: Currently teaching Algorithms and Advanced Deep Learning. Served as a Teaching Assistant for multiple AI and machine learning courses. Service: Local Co-chair of ALT 2025, Area Chair for NeurIPS and ICML, and Action Editor for TMLR. Labs/Teams: Member of the Artificial Intelligence and Robotics Lab at Politecnico di Milano and part of the ELLIS network.

Neal Madrasمشاهده پروفایل

استاد

• Probability Theory
• Discrete Mathematics
• Mathematical Modeling in the Sciences
• +۵ مورد دیگر

Neal Madras is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. His research focuses on probability theory, discrete mathematics, and mathematical modeling in the sciences. He explores applications in statistical mechanics, theoretical computer science, communications networks, and biological systems. Notable interests include self-avoiding walks, polymer behavior modeling, and disease progression mechanisms. Madras has contributed to interdisciplinary studies, such as HIV dynamics and mosquito population modeling under climate influences. He is also actively involved in mathematics competitions like the Canadian Mathematical Olympiad. His work bridges theoretical rigor and practical applications, with a focus on stochastic processes, combinatorial problems, and computational methods. Publications span topics from graph theory and knot invariants to epidemic control strategies and polymer adsorption. Madras maintains an active research program, engaging with both foundational mathematical questions and real-world problem-solving.