جست‌وجوی استاد و پژوهشگر

• Sustainable Aerospace Engineering
• Conceptual Aircraft Design
• Propulsion Systems
• +۳ مورد دیگر

Dr Albert Stevan van Heerden is a Lecturer in Aerospace Engineering at the James Watt School of Engineering, University of Glasgow. Previously, he was a Research Fellow at Cranfield University's Rolls-Royce University Technology Centre and Centres for Aeronautics and Propulsion and Thermal Power Engineering. He holds a PhD in Aerospace Engineering from Cranfield University, an MS in Aeronautics from California Institute of Technology (Fulbright scholar), and a BEng in Mechanical Engineering from the University of Pretoria. His research focuses on conceptual aircraft design , airframe and propulsion systems development , and technical/economic assessment of sustainable aerospace technologies . He employs deterministic and non-deterministic design methods, with a unique emphasis on evolvable aircraft family design for long-term relevance. His work spans traditional civil transport aircraft, electric aircraft , and hydrogen-powered aircraft systems. Key publications include advancements in set-based design techniques margin allocation strategies hydrogen propulsion systems thermal management frameworks uncertainty allocation methods . Scientific recognition includes Fellow of the Higher Education Academy (FHEA) Member of the Royal Aeronautical Society (MRAeS) . He also serves as Academic Adviser for the Commonwealth Scholarship Commission and Academic Ambassador for the James Watt School of Engineering. His teaching portfolio includes Thermodynamics 2, Aircraft Design 3, and Fluid Mechanics (University of Glasgow Singapore), reflecting his expertise in core aerospace disciplines.

Frans Kaashoekمشاهده پروفایل

استاد

• Computer Security
• Operating Systems
• Distributed Systems
• +۴ مورد دیگر

Frans Kaashoek is the Charles Piper Professor in MIT's Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Parallel and Distributed Operating Systems (PDOS) group, focusing on secure systems, formal verification, and distributed computing. His work emphasizes crash-safe systems, concurrent programming, and cryptographic security. Education: PhD in Computer Science from Vrije Universiteit Amsterdam (1992), thesis on group communication in distributed systems under Andy Tanenbaum. Research interests include operating systems, networking, programming languages, and computer architecture. Notable projects: FSCQ (verified crash-safe file system), Perennial (framework for verifying concurrent systems), and Noria (high-performance web backend). Awards: ACM SIGOPS Mark Weiser Award (2001), ACM Prize in Computing (2010), National Academy of Engineering membership (2006), and American Academy of Arts and Sciences membership (2012). Publications: Over 150 papers on systems software, verification, and security. Authored textbooks like Principles of Computer System Design: An Introduction and xv6 commentary.

Jiong Tangمشاهده پروفایل

استاد

• System Dynamics
• Control
• Sensing and Automation
• +۷ مورد دیگر

Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.

Pouria Sarhadiمشاهده پروفایل

مدرس ارشد

• Control Theory
• Autonomous Vehicles
• Adaptive Control
• +۳ مورد دیگر

Pouria Sarhadi is a Senior Lecturer at the University of Hertfordshire's School of Physics, Engineering & Computer Science. He has held roles as a Research Fellow at Queen's University Belfast (2021-2022) and the University of Surrey (2019-2021), and served as an Adjunct Professor at Babol Noshirvani University of Technology (2016-2018). He is a Chartered Engineer (CEng), Member of the IET, and recognized as UK 'Global Talent' by UKRI. His research focuses on control theory applications, adaptive control, autonomous vehicles, and systems engineering. He has contributed to over 50 peer-reviewed publications and led projects such as HertsLynx CAM On-Demand (UKRI-funded). He is actively involved in editorial roles for Applied Ocean Research and advises on IFAC Technical Committees. Research interests include: Autonomous systems (ground, marine, aerial) Machine learning in control systems Adaptive control and system identification Autonomous vehicle safety and navigation Key awards include CEng, MIET, and UK Global Talent recognition. He has advised on UKRI and Horizon Europe projects and consults for industries. Current projects include hybrid navigation safety simulators and cooperative control of multi-AUV systems.

Sebastian Sardinaمشاهده پروفایل

استاد

• Artificial Intelligence
• Automated Planning
• Knowledge Representation
• +۴ مورد دیگر

Professor Sebastian Sardina is a Professor in Artificial Intelligence at RMIT University's School of Computing Technologies. He holds a Bachelor's from South National University (Argentina) and a PhD from the University of Toronto (Canada). His research focuses on AI for dynamic systems, including automated planning, knowledge representation, and agent-oriented programming. He has contributed to enhancing agent programming languages with learning capabilities and advanced AI planning techniques. His work frequently appears in top AI venues like IJCAI and AAAI, with notable best paper nominations. Teaching interests include foundational CS courses such as Theory of Computation and Intro to AI. He actively promotes computational thinking through workshops for youth and educators, including roles in Victorian curriculum development (VCE Algorithmics). Supervision projects span hand gesture recognition, autonomous vehicle safety, and goal recognition in path-planning. His research has been presented globally and applied across domains like aviation safety, manufacturing systems, and healthcare. Recent trends in his publications emphasize goal recognition techniques (e.g., process mining applications), agent behavior modeling, and interdisciplinary AI applications in healthcare and automotive engineering. He has collaborated with industry and academic partners internationally, contributing to both theoretical advancements and practical AI solutions. Scientific Recognition: Multiple best paper nominations in AI conferences. Community Engagement: MAV conference presenter, VCAA Algorithmics curriculum panel member (2023). Supervision: Active mentor for 4+ research projects in AI planning and recognition.

Ceren Vardar Acarمشاهده پروفایل

استادیار

• Stochastic Processes
• Extreme Values and Risk Management
• Financial Mathematics/Statistics
• +۳ مورد دیگر

Ceren Vardar Acar is an Assistant Professor in the Department of Statistics at the Middle East Technical University (METU), Ankara, Turkey. She holds a PhD in Mathematics and Statistics from Bowling Green State University, USA (2002-2008), and prior degrees from METU, including a BSc and MSc in Statistics. Her professional experience includes roles as Assistant Professor at TOBB ETU (2011-2015) and instructorships at various institutions in Turkey and the USA. Her research focuses on Stochastic Processes , particularly their path properties, extreme value analysis, and applications in financial mathematics, risk management, and population dynamics. Key areas include fractional Brownian motion modeling, Lévy processes, and the analysis of maximum loss/drawdown phenomena. She has also contributed to methodological advancements in parameter estimation and Monte-Carlo simulation techniques. Academic leadership roles include serving as Co-Head of the Department of Mathematics at TOBB ETU (2013-2015) and coordinating service courses for non-mathematics departments (2010-2015). She secured a TÜBİTAK grant (2011-2014) exploring maximum loss distributions in fractional Brownian motion and Lévy processes. Her work bridges theoretical probability with applied domains like finance and healthcare, evidenced by publications in journals such as Extremes , Journal of Computational and Applied Mathematics , and Statistics and Probability Letters . She maintains active collaborations in interdisciplinary research, including dental education simulation and population modeling with delays.

• Nanophotonics
• Quantum Technologies
• Computational Optics
• +۵ مورد دیگر

Dr. Sven Burger is a leading Researcher at the Zuse Institute Berlin (ZIB) within the Modeling and Simulation of Complex Processes department. His work focuses on Nanophotonics , Quantum Technologies , and Optical Resonance Computation , particularly in photonic crystals, plasmonic systems, and quantum light sources. Key projects: NanoLab GRIPS 2024 , MATH+ TES QT , MATH+ PaA-1 (perovskite solar cells), Colour Impression of Solar Cells Collaborations: MATH+ , BIFOLD , Research Campus MODAL His research spans Bayesian optimization for quantum systems, quasinormal mode expansions , chiral plasmonics , and terawatt-scale photovoltaics . Recent work emphasizes RPExpand software for resonance analysis and AAA algorithm applications in photonic design. He contributes to quantum key distribution via plug&play single-photon sources, hot carrier dynamics in plasmonic nanocrystals, and high-efficiency light extraction for deep-UV LEDs. His computational methods address non-Hermitian systems , exceptional points , and self-interference nanoparticle tracking .

• Nuclear reactor stability analysis
• Neutron noise analysis
• Boiling water reactors (BWR)
• +۵ مورد دیگر

Dr. Carsten Lange is a faculty member at the Chair of Hydrogen and Nuclear Energy within the Institute of Process Engineering and Environmental Technology at Technische Universität Dresden . Since 2010, he has led the Reactor Dynamics workgroup and has served as Head of the nuclear training reactor AKR-2 since 2015. His research focuses on nonlinear stability analysis of boiling water reactors (BWR) , model order reduction techniques , neutron noise analysis , and non-invasive reactor monitoring . Dr. Lange earned his PhD in 2009 from Technische Universität Dresden with a dissertation titled Advanced nonlinear stability analysis of boiling water nuclear reactors . He has contributed to projects like GRE@T-PIONEER and international initiatives such as the OECD/NEA Zero Power Reactors Task Force . His work includes experimental reactor physics , nuclear safety , and reactor instrumentation development. His research spans nuclear reactor stability , neutron imaging , and advanced simulation techniques . Key publications analyze PWR power fluctuations , coupled fuel assembly vibrations , and reduced-order models for online monitoring . Dr. Lange actively mentors students in reactor physics and reactor training assignments.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۵ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the CMAP (Applied Mathematics Department). His research focuses on computational statistics, machine learning, and stochastic methods for Bayesian inference and generative models. Key areas include Monte Carlo methods, stochastic optimization, and Riemannian geometry in optimization. He has contributed to theoretical advancements in Markov processes and their applications in sampling algorithms. Affiliations: École Polytechnique (CMAP), ENS Paris-Saclay (teaching roles), and other academic collaborations. Teaching: Courses on mathematical statistics, stochastic methods, probability theory, and optimization at ENS Paris-Saclay and École Polytechnique. Research Interests: Development of Monte Carlo algorithms, stochastic approximation schemes, and theoretical analysis of Markov chain convergence. Specializes in Bayesian statistics, generative models, and high-dimensional inference. His work bridges theory and application, with notable contributions to Langevin dynamics, nonreversible MCMC methods, and federated learning. He has received a Best student paper award at ICASSP 2020 for collaborative research on Bayesian computation using Sliced-Wasserstein distances. Alain’s publications span prestigious journals like the Annals of Applied Probability and SIAM, with a focus on algorithmic innovation and rigorous mathematical foundations.

Theodor Misiakiewiczمشاهده پروفایل

استادیار

• Theory of Deep Learning
• Kernel and random feature methods in high-dimension
• Non-convex optimization, implicit regularization, landscape analysis
• +۲ مورد دیگر

Theodor Misiakiewicz serves as an Assistant Professor in the Department of Statistics and Data Science at Yale University, based in Room 1049 of Kline Tower, New Haven, Connecticut. His research bridges statistics, machine learning, probability theory, and computer science with a core focus on developing theoretical foundations for deep learning algorithms. His academic background includes: PhD in Statistics from Stanford University under Prof. Andrea Montanari M.Sc. in Theoretical Physics from Ecole Normale Superieure de Paris B.Sc. in Mathematics from Ecole Normale Superieure de Paris Dr. Misiakiewicz's work centers on fundamental theoretical questions in machine learning, particularly examining deep learning through the lenses of kernel methods, random feature models, non-convex optimization landscapes, and high-dimensional probability. His approach integrates rigorous mathematical analysis with practical machine learning challenges to establish provable guarantees for modern algorithms. His publication record (2019-2025) demonstrates consistent contributions to understanding generalization in kernel methods, SGD dynamics in neural networks, and dimension-free bounds for deep learning models. These works collectively advance the mathematical framework for analyzing learning algorithms in high-dimensional regimes using tools from random matrix theory and statistical physics. He actively recruits PhD students through Yale's program and plans to hire postdoctoral scholars starting Summer 2025, while also welcoming undergraduate and graduate visitors to his research group. His mentorship focuses on training the next generation of theoretical machine learning researchers. Dr. Misiakiewicz leads a dedicated research team within Yale's Statistics and Data Science department that investigates the mathematical principles underlying artificial intelligence, maintaining an active seminar series and collaborative projects with international researchers.

• Software Engineering
• Model Checking
• System Modeling
• +۵ مورد دیگر

Seung Yeob Shin serves as a Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where he contributes to the Software Verification and Validation (V&V) Lab under Prof. Lionel Briand. His active participation in major software engineering conferences includes serving on ASE 2025's Research Papers Program Committee and authoring multiple journal-first publications presented at premier venues like ICSE and ESEC/FSE. Shin earned his PhD in 2016 from the Laboratory for Advanced Software Engineering Research (LASER) at the University of Massachusetts Amherst's College of Information and Computer Sciences. His research expertise centers on applying formal methods to complex software systems, with particular emphasis on model-based verification techniques. His current research program bridges theoretical modeling and practical system validation across critical domains. Key focus areas include developing simulation frameworks for software-defined networks, creating model-checking approaches for cyber-physical control loops, and establishing probabilistic methods for real-time system verification. This work consistently targets reliability challenges in safety-critical infrastructure through rigorous engineering methodologies. Recent publications demonstrate a cohesive trajectory in applying formal verification to emerging system paradigms. The 2024 journal-first papers reveal increasing sophistication in handling non-deterministic behaviors across networking, embedded systems, and requirements engineering domains, with notable emphasis on failure induction and probabilistic safety guarantees. No scientific awards were documented in the available materials. While conference contributions indicate significant scholarly engagement, no information regarding student advising or research grants appears in the provided documentation. As a core member of the V&V Lab at SnT, Shin contributes to Luxembourg's national cybersecurity research infrastructure. The lab specializes in developing mathematical frameworks for system validation, with current projects addressing verification challenges in autonomous systems, critical infrastructure, and secure communications protocols through model-driven approaches.

• Statistical Physics
• Complex Systems
• Percolation Theory
• +۷ مورد دیگر

Professor Peter Grassberger is a distinguished researcher at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre, one of Germany's leading national research institutions. His work spans multiple decades in theoretical physics and computational science, with a particular focus on complex systems and statistical mechanics. His research has significantly advanced our understanding of critical phenomena and phase transitions in various physical systems. Grassberger's research interests center around statistical physics and complex systems, with specializations in percolation theory, self-organized criticality, and random walk models. His work explores the universal properties of phase transitions, critical phenomena, and the behavior of complex networks. His theoretical contributions have provided fundamental insights into how simple local rules can lead to complex global behavior in physical systems. His recent work continues to push boundaries in understanding extreme-value statistics, entropy estimation methods, and the dynamics of interface models. Through his extensive publication record spanning over 40 years, Grassberger has established himself as a leading authority in computational statistical physics. His work shows consistent focus on understanding universal properties of critical systems while developing innovative computational methods to analyze complex phenomena. His research bridges theoretical physics with practical computational approaches, making significant contributions to both fundamental understanding and methodological development in the field. Grassberger's scientific impact is evident through his numerous influential publications and his development of important computational techniques like the Grassberger-Procaccia algorithm for estimating fractal dimensions. His work on self-organized criticality, particularly his 2022 review "Self-Organized Criticality, Three Decades Later," demonstrates his enduring contribution to this important field. His recent publications continue to explore cutting-edge questions in statistical physics, showing active engagement with contemporary research challenges.

• Optimization and Optimal Control of PDEs
• Nonsmooth Optimization in Function Spaces
• Inverse Problems and Mathematical Imaging
• +۶ مورد دیگر

Karl Kunisch is University Professor at the Department of Mathematics and Scientific Computing, University of Graz , and simultaneously Scientific Director of the Radon Institute (RICAM) of the Austrian Academy of Sciences in Linz. A SIAM Fellow and recipient of the 2021 W.T. and Idalia Reid Prize, he leads the ERC Advanced Grant OCLOC and heads the research group “Optimization and Optimal Control”. Education: Dipl.-Ing. (1975), Dr. techn. (1978) and Habilitation (1980), Graz University of Technology Research Interests: His work centres on optimization and optimal control of partial differential equations , nonsmooth optimisation in function spaces , inverse problems and mathematical imaging , together with advanced numerical analysis . Current emphases are life-science applications , closed-loop control and machine-learning based feedback design. Publications Profile: With over 400 papers and two monographs, his recent output is dominated by high-impact studies on infinite-horizon optimal control , feedback stabilisation of semilinear parabolic and Navier–Stokes systems, risk-averse and data-driven control , and sparse control strategies . A clear trend is the fusion of rigorous PDE analysis with cutting-edge machine-learning techniques. Scientific Awards & Distinctions: W.T. and Idalia Reid Prize (2021) SIAM Fellow (2017) ERC Advanced Grant Horizon 2020 (2015) Alwin Walther Medaille (2008) ICM Invited Lecture, Hyderabad (2010) SIAM Outstanding Paper Prize (2006) Christian Doppler Laboratory Fellowship (1992) Fellowship of the Japanese Society for the Promotion of Science (1990) Max Kade Scholarship (1982/83) Fulbright Travel Grants (1979/80, 1985) Theodor-Körner-Fonds Research Award (1979) Pro Scienta Scholarship (1974–1977) Grants & Leadership: Principal Investigator, ERC Advanced Grant “ OCLOC – From Open to Closed Loop Control ” Scientific Director, Radon Institute (RICAM), Austrian Academy of Sciences Head of Research Group “Optimization and Optimal Control”, RICAM Co-Speaker, International Research Training Group IGDK Former member/consultant: MATHEON Scientific Advisory Board, Weierstrass Institute Scientific Advisory Board, Christian Doppler Forschungsgesellschaft Senate, DFG and INRIA evaluation boards Laboratory & Team: Prof. Kunisch currently leads the “Optimization and Optimal Control” group at RICAM, comprising post-docs, doctoral researchers and international visitors, focusing on interdisciplinary projects at the interface of PDE control, numerical optimisation and life sciences.

Titus von der Malsburgمشاهده پروفایل

استادیار

• Psycholinguistics
• Cognitive Modeling
• Eye-Tracking
• +۱۲ مورد دیگر

Titus von der Malsburg is a tenure-track Junior Professor of Psycholinguistics and Cognitive Modeling at the Institute of Linguistics, University of Stuttgart . His research focuses on incremental sentence comprehension, implicit gender biases, scanpath analysis, and computational modeling of language processing. He employs experimental methods like eye-tracking, ERP, and Bayesian statistics to investigate how humans integrate linguistic and cognitive cues during reading. Education : PhD in Cognitive Science (University of Potsdam), with postdoctoral positions at UC San Diego, University of Oxford, and University of Potsdam. His work spans psycholinguistics, cognitive science, and natural language processing, with a strong emphasis on reproducibility and open-source software development. He has contributed to debates on agreement attraction, semantic parsing, and the role of conversational principles in cognitive biases. His lab uses advanced eye-tracking equipment and promotes technical rigor in research practices. Recent publications highlight his contributions to understanding memory decay in syntactic dependencies, semantic attraction effects, and scanpath regularity as a predictor of reading comprehension. The lab actively seeks collaborators aligned with its focus on technical quality and open science. Notably, he clarifies his name’s correct formatting to avoid common misattributions.

• Ergodic Theory
• Dynamical Systems
• Probability Theory
• +۶ مورد دیگر

Professor Ian Melbourne is a Professor of Mathematics at the University of Warwick's Mathematics Institute. His research focuses on ergodic theory, dynamical systems, and their links with probability and stochastic analysis. He holds editorial roles at *Ergodic Theory and Dynamical Systems* and *Probability Theory and Related Fields*. His work explores complex systems such as fast-slow dynamics, Lévy processes, mixing rates, billiards, and statistical limit laws. Research Interests: Ergodic theory, dynamical systems, stochastic processes, Lévy processes, mixing rates, billiards, and statistical laws. His contributions span theoretical frameworks for deterministic homogenization, superdiffusive limits, and large deviations in non-equilibrium systems. Publications highlight advancements in understanding systems with singularities, such as Lorentz gases and intermittent maps. His recent work emphasizes convergence to Lévy processes and polynomial decay of correlations in nonpositively curved surfaces. He has advised numerous PhD students (not listed here) and contributes to the academic community through editorial work and collaborative research. No specific grants or labs are detailed in the provided texts.