جست‌وجوی استاد و پژوهشگر

Anke Wieseمشاهده پروفایل

دانشیار

• Stochastic Systems
• Stochastic Differential Equations
• Computational Finance
• +۳ مورد دیگر

Anke Wiese is an Associate Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University, within the Actuarial Mathematics & Statistics department. Prior to her academic roles, she worked in risk management in the financial services industry and held positions at the University of Hamburg and the University of Karlsruhe (now KIT). She earned her PhD from the University of Karlsruhe, Germany. Her research focuses on stochastic systems, particularly developing methods for solving stochastic differential equations (SDEs) while preserving qualitative characteristics. Key areas include algebraic structures of SDEs, integration methods for SDEs with jumps, and applications in computational finance. She bridges mathematical disciplines such as stochastic analysis, algebra, and quantum stochastics. Recent publications explore Grassmannian flows in coagulation systems, integrable equations via Pöppe triples, and efficient inversion techniques in the Heston model. Her work emphasizes numerical methods, stochastic processes, and interdisciplinary applications. Dr. Wiese actively supervises PhD students and welcomes inquiries for doctoral applications. Her research has been published in journals like Physica D, SIAM Journal on Financial Mathematics, and the Proceedings of the Royal Society A.

Michael Reimerمشاهده پروفایل

دانشیار

• Quantum Optics
• Quantum Photonics
• Semiconductor Nanowires
• +۳ مورد دیگر

Michael Reimer is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC). His primary role involves advancing quantum photonic technologies through experimental and theoretical research. He is a full-time faculty member with expertise in semiconductor nanowire-based devices, quantum dot systems, and their integration with atomic ensembles for quantum communication applications. His research interests focus on developing high-efficiency single-photon sources and detectors, metamaterials for photonic applications, and leveraging cold atoms in hollow-core fibers to enhance quantum state manipulation. He also explores hybrid quantum-classical systems, particularly in optimizing signal-to-noise ratios (SNR) and mitigating nonlinear impairments in optical networks. His work bridges fundamental quantum optics with practical implementations in quantum computing and secure communication systems. Recent articles highlight trends toward achieving near-unity absorption in metamaterials, on-demand photon generation with quantum dots, and probabilistic constellation shaping for optical networks. While no scientific awards are explicitly mentioned, his contributions to quantum photonic devices are notable. No specific grants or advisees are detailed in the provided text, but his affiliation with IQC suggests involvement in collaborative research projects. He is affiliated with the Institute for Quantum Computing, a leading academic unit at the University of Waterloo.

• Partial Differential Equations
• Nonlinear Analysis
• Elliptic Equations
• +۳ مورد دیگر

Craig Cowan is an Associate Professor in the Department of Mathematics at the University of Manitoba, located in Winnipeg, Manitoba. He is affiliated with the Faculty of Science and focuses on research in Analysis of Partial Differential Equations, particularly elliptic equations with nonlinear gradient terms and critical advection. His work explores existence, regularity, and symmetry properties of solutions to supercritical problems, often involving mixed local/nonlocal operators and Neumann boundary conditions. Research interests include: Elliptic systems, Fourth-order equations, Supercritical growth phenomena, and Nonlinear advection-diffusion models. His studies frequently address questions of regularity of extremal solutions, stability of entire solutions, and Liouville-type theorems. Publications since 2021 reflect a focus on advancing methodologies for supercritical problems via variational principles and fixed-point arguments, with applications to nonradial domains and perturbed geometries. Recent work examines singular solutions, Neumann problems with mixed diffusion, and Hénon-type equations. No academic awards or formal advisees are listed in the provided materials. His research has consistently addressed challenges in nonlinear PDEs across diverse domains, emphasizing both theoretical developments and technical estimates for gradient-dependent systems.

Stephen Yurkovichمشاهده پروفایل

استاد

• Systems Engineering
• Control Systems
• Automotive Engineering
• +۵ مورد دیگر

Stephen Yurkovich is the Louis Beecherl Jr. Distinguished Chair Professor and Director of the Center for Control Science and Technology at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He holds a PhD in Electrical Engineering from the University of Notre Dame (1984) and a B.S. in Engineering Science from Rockhurst University (1978). His research focuses on system identification, nonlinear control systems, automotive control systems, energy storage, robotics, and aerospace systems. He leads initiatives to advance interdisciplinary engineering education and industry collaboration. Key Roles: Department Head of Systems Engineering, Director of Center for Control Science and Technology. Notable Awards: 2008 John R. Ragazzini Award, IEEE Fellow (2001), Honda Partnership Award (2004). His publications span energy storage systems, automotive engine control, and advanced control methodologies. He has held visiting professorships at institutions like Université Catholique de Louvain and directed programs such as the Honda-OSU Partnership. Current lab affiliations include the Energy Storage Systems Lab, advancing innovations in power systems and control technologies.

Christof Büskensمشاهده پروفایل

استاد

• Optimization
• Optimal Control
• Control Systems
• +۵ مورد دیگر

Prof. Dr. Christof Büskens is a Professor of Technomathematics at the University of Bremen, leading the AG Optimierung und Optimale Steuerung (Optimization and Optimal Control Group) within the Faculty of Mathematics and Computer Science . His research focuses on Optimization, Optimal Control, and their applications in industrial and real-time systems. He holds leadership roles in interdisciplinary projects such as BESTVILLE and Safety Control Center for autonomous vehicle systems, and has contributed to maritime navigation, renewable energy management, and agricultural robotics. Büskens has supervised numerous PhD and master's students, advancing topics like autonomous exploration, neural architecture search, and trajectory optimization. His work integrates advanced numerical methods with practical applications, emphasizing real-world problem-solving in dynamic systems. Key affiliations include the ZeTeM (Center for Industrial Mathematics) and collaborations with industry partners. He has led over 20 projects since 2020, addressing challenges in autonomous systems, energy systems, and robotics. His educational contributions include courses on numerical analysis and optimal control, fostering interdisciplinary training for future researchers. Büskens' expertise bridges theoretical optimization and applied engineering, with over 100 publications and contributions to software tools like the WORHP solver.

• Nonlinear Potential Theory
• Partial Differential Equations
• Mathematical Analysis
• +۳ مورد دیگر

Tero Kilpeläinen is a Professor and Head of the Department of Mathematics and Statistics at the University of Jyväskylä, affiliated with the Faculty of Mathematics and Science. His research focuses on nonlinear potential theory and partial differential equations, particularly exploring the interplay between these fields and their applications in modeling nonlinear phenomena. He has contributed extensively to areas such as p-Laplacian equations, Hardy-Orlicz spaces, and boundary behavior of harmonic functions. Key publications include works on lattice properties of p-admissible weights, harmonic Hardy-Orlicz spaces, and maximal regularity for elliptic systems involving measures. His research group, Nonlinear Partial Differential Equations, addresses fundamental questions in nonlinear analysis and geometric measure theory. Kilpeläinen collaborates widely, often with co-authors like Pekka Koskela and Nageswari Shanmugalingam. While no specific grants or awards are listed, his work reflects deep engagement with foundational mathematical theory and its analytical applications. He advises within the Department of Mathematics and Statistics, contributing to both research and academic leadership roles at the University of Jyväskylä.

• Applied Econometrics
• Applied Statistics
• Housing Economics
• +۳ مورد دیگر

Ranjodh Singh is a Senior Lecturer at the School of Accounting, Economics and Finance within the Faculty of Business and Law at Curtin University. He holds a BSc (Hons) and PhD in disciplines relevant to his research focus. His teaching responsibilities include undergraduate and postgraduate courses in Data Analytics and Econometrics, and he supervises Honours, Masters, and PhD students in these areas. Ranjodh’s research interests center on Applied Econometrics , Applied Statistics , and Housing Economics . His work explores topics such as housing market dynamics, policy impacts on mental wellbeing, Indigenous mobility, and nonlinear econometric modeling. Recent studies include analyzing rental subsidy effectiveness, cannabis-tobacco consumption linkages, and post-pandemic housing demand shifts. His publications span leading journals like Housing Studies , International Journal of Housing Policy , and Journal of Population Economics . He frequently collaborates with researchers across disciplines, including economists, statisticians, and sociologists. His research has addressed policy-relevant issues such as rental affordability, remote infrastructure planning, and pandemic-driven economic shifts. Ranjodh’s contributions extend to major research initiatives like the AHURI Final Reports series, focusing on housing stress and Indigenous community needs. His methodologies blend traditional econometrics with machine learning advancements, as seen in his work on nonlinear models and forecast combinations.

Carlos A. Ocampo Martínezمشاهده پروفایل

دانشیار

• Control Engineering
• Model Predictive Control
• Constrained Control Systems
• +۱۲ مورد دیگر

Carlos A. Ocampo Martínez is an Associate Professor in the Department of Automatic Control (ESAII) at the Universitat Politècnica de Catalunya (UPC), BarcelonaTech, Spain. He is affiliated with the Institut de Robòtica i Informàtica Industrial, CSIC-UPC, a joint research center between UPC and the Spanish National Research Council. He has been with UPC since 2011 and served as Deputy Director of IRI from 2014 to 2018. Education: PhD in Control Engineering, Universitat Politècnica de Catalunya, 2007 MSc in Industrial Automation, National University of Colombia, 2003 BSc in Electronics Engineering, National University of Colombia, 2001 His research is centered on model predictive control (MPC) , particularly constrained and distributed MPC, with applications in energy, water, and smart manufacturing. He investigates large-scale systems management, partitioning strategies, and non-centralized control architectures. His work integrates IoT frameworks for smart industrial systems. Key domains include renewable hydrogen production, fuel cell vehicles, microalgae bioreactors, and solar thermal plants. The recent publications reflect a strong trend in applying control theory to sustainable energy systems and environmental management. There is a clear focus on integrating game theory, population dynamics, and optimization into MPC frameworks for distributed and coalitional control. Applications span hydrogen infrastructure, solar energy, water irrigation, and transportation electrification, demonstrating interdisciplinary impact. Scientific Awards: Juan de la Cierva Research Fellow Dr. Ocampo-Martínez actively supervises PhD students and leads research projects such as MASHED , which focuses on digitalized energy systems with hybrid storage. He has advised students on topics including alkaline electrolyzers, hydrogen production control, and alcohol steam reformers. His grant involvement emphasizes renewable integration, smart grids, and sustainable transport. He collaborates with researchers across Europe and contributes to high-impact journals and IFAC conferences. He is a key member of the Automatic Control research group at ESAII and contributes to the strategic direction of the Institut de Robòtica i Informàtica Industrial. His team integrates control theory, optimization, and real-world industrial applications, particularly in energy and water systems.

• Automation
• Robotics
• Control Systems
• +۵ مورد دیگر

Prof. Igor Krčmar is a full professor at the Faculty of Electrical Engineering of the University of Banja Luka, specializing in Automation and Robotics . He holds a position in the Department of Automation and teaches courses at both undergraduate and graduate levels. His research focuses on control systems, neural networks, electrical drives, and adaptive control methodologies. He has authored several textbooks including Upravljanje u realnom vremenu (Real-Time Control) and Senzori i Aktuatori (Sensors and Actuators). His recent work emphasizes sensorless motor control, PI controller optimization for industrial systems, and machine learning applications in occupancy estimation. Over 25 years, he has published extensively in journals like IEEE Transactions on Industrial Electronics and conferences such as INFOTEH-JAHORINA and IcETRAN. His work bridges theoretical advancements with practical implementations in industrial automation and energy efficiency. Key contributions include: Development of discrete rotor flux estimators for high-speed drives Adaptive neural control algorithms for nonlinear systems Integration of fuzzy logic with hydrodynamic process control Optimization of PMSM drives for energy efficiency He collaborates internationally on EU-funded projects and serves on editorial boards of electrical engineering journals.

Serena Ngمشاهده پروفایل

استاد

• Econometrics
• Empirical Macroeconomics
• Time Series Analysis
• +۷ مورد دیگر

Serena Ng is the Edwin W. Rickert Professor of Economics at Columbia University and an Affiliated Faculty member in the Department of Statistics. Her research spans econometrics, empirical macroeconomics, time series analysis, and big data methods, with a focus on factor models, missing data, and macroeconomic forecasting. She has developed influential datasets such as FRED-MD and FRED-QD, widely used in macroeconomic research. Her research interests include: High-dimensional econometric modeling Factor analysis and principal components Missing data and matrix completion Dynamic modeling of disasters and climate shocks Macroeconomic forecasting and nowcasting Structural vector autoregressions and DSGE identification Her recent publications (2021–2025) reflect a strong trend toward integrating machine learning and computational methods into econometric modeling, particularly in handling large datasets, imputing missing values, and analyzing the macroeconomic impact of climate and disaster shocks. She has also contributed to foundational work in uncertainty measurement and time-varying parameter models. Her scientific contributions are recognized through extensive publication in leading journals. While no specific awards are listed, her editorial and collaborative roles (e.g., with the Journal of Econometrics) indicate high standing in the profession. She advises doctoral students in economics and statistics, though no names are publicly listed. She has received funding from major institutions including the National Science Foundation and NIH for interdisciplinary research. Her work bridges econometrics with environmental and health economics, particularly in projects related to climate adaptation and disaster impacts. She maintains a laboratory-like research group focused on macroeconometric modeling and big data analysis, contributing to the development of tools for real-time economic monitoring and policy analysis.

• Probability Theory
• Large Deviations
• Harris Recurrent Markov Chains
• +۱۲ مورد دیگر

Jeffrey F. Collamore is a full Professor at the Department of Mathematical Sciences , University of Copenhagen, specializing in Insurance and Economics (work area: IE). His research focuses on advanced probability theory and its applications in insurance mathematics and quantitative risk management. University of Copenhagen (2002-present) ETH Zurich (2000-2002) EURANDOM (1999-2000) Lund University (1998-1999) University of Illinois (1996-1998) Research Focus : Large deviations and rare event estimation Stochastic fixed point equations and random matrices Harris recurrent Markov chains Multidimensional ruin problems in insurance Stochastic simulation techniques Education : Ph.D. and MA in Mathematics, University of Wisconsin, Madison (Advisor: Peter Ney) BS in Physics and Mathematics, University of California, San Diego

• Quantum Control Systems
• Quantum Information Processing
• Quantum Reservoir Computing
• +۵ مورد دیگر

Dr. Hendra Nurdin is a Senior Lecturer in the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), where he has been employed since 2012. His academic journey began with a Sarjana Teknik (equivalent to a Bachelor of Engineering) in Electrical Engineering from Institut Teknologi Bandung, Indonesia, followed by an MSc in Engineering Mathematics from the University of Twente in the Netherlands, and culminated with a PhD in Engineering and Information Science from the Australian National University in 2007. His educational background includes: PhD in Engineering and Information Science, Australian National University, 2007 MSc in Engineering Mathematics, University of Twente, The Netherlands Sarjana Teknik (ST, equivalent to Bachelor of Engineering) in Electrical Engineering, Institut Teknologi Bandung, Indonesia Dr. Nurdin's research lies at the intersection of control engineering and systems theory with quantum physics and energy systems. He has made significant contributions to quantum control systems, quantum information processing, and microgrid control. His work combines theoretical advances in quantum stochastic processes with practical applications in quantum computing and renewable energy systems. He has developed novel approaches to quantum reservoir computing, quantum parameter estimation, and control of distributed energy resources. His recent publications reveal a strong focus on quantum reservoir computing, non-Markovian quantum systems, and the intersection of quantum information with machine learning. There's a clear trend toward practical implementations of quantum information processing systems, particularly exploring how quantum systems can enhance computational capabilities. His work bridges fundamental quantum theory with engineering applications, demonstrating how quantum phenomena can be harnessed for practical computing and sensing tasks. Dr. Nurdin has received recognition including an ARC APD Fellowship (2009-2011). His research has resulted in numerous publications in top-tier journals including Nature Communications, Physical Review series, and IEEE Transactions. He has successfully supervised multiple PhD students to completion, including Dr. Jiayin Chen (2022), Dr. Jiacheng Li (2021), Dr. Muhammad Ali (2021), and Dr. Zhan Shi (2016). Currently, he is supervising Mr. Wen Liu as a PhD candidate. His research is supported by various funding mechanisms including Sydney Quantum Academy scholarships and UNSW research grants, enabling him to pursue cutting-edge research in quantum systems and control. Dr. Nurdin is actively involved with the Sydney Quantum Academy, supervising research in quantum systems and control. His work contributes to Australia's growing quantum technology ecosystem, collaborating with researchers across multiple institutions to advance quantum information processing and quantum engineering applications.

• Electric Machines
• Permanent Magnet Motors
• Reluctance Motors
• +۷ مورد دیگر

Lale Ergene is a Professor in the Department of Electrical Engineering at Istanbul Technical University (ITU), College of Engineering. Her research focuses on advanced electric machine design and control systems for industrial and automotive applications. She is actively involved in motor drive innovation, particularly in permanent magnet and reluctance motor technologies. Research Interests: Dr. Ergene specializes in electric machines, with emphasis on Permanent Magnet Synchronous Motors (PMSM), Interior Permanent Magnet (IPM) motors, and Permanent Magnet Assisted Synchronous Reluctance Motors (PMaSynRM). Her work spans sensorless control, field weakening techniques, finite element analysis, and motor optimization for electric vehicles and home appliances. She applies intelligent control methods such as neuro-fuzzy systems and real-time diagnostics. Recent Research Trends: Her recent publications (2023–2024) show a strong focus on improving motor efficiency and control robustness, especially in EV traction systems and white goods. Key themes include voltage distortion reduction, flux weakening enhancement, real-time parameter estimation using FFT, and lean sensorless control at zero/low speeds. Her work bridges theoretical modeling with industrial applications. Scientific Awards: Best Poster Paper AWARD (2016) Graduation Design and Project Competition 2nd Prize (2015) ITU 2014 Best Doctoral Thesis Award (2015) Advising and Grants: Dr. Ergene has supervised or is currently supervising 25 theses, indicating a strong mentoring role. She has led multiple funded projects, including TÜBİTAK and ITU BAP grants, focusing on FPGA-based neural network control, three-level inverter design, real-time model diagnostics, and sensorless control for washing machines and EVs. Her projects demonstrate sustained research funding and applied engineering impact. Labs and Research Teams: While specific lab names are not mentioned, her projects imply leadership in a motor control and electric machines research group at ITU, likely involving FPGA, real-time simulation, and embedded control systems. Her collaborations with researchers like A.F. Ergenc, M. Yilmaz, and A. Tap suggest an active, multidisciplinary team focused on next-generation motor drives.

• Computer Vision
• Pattern Recognition
• Facial Analysis
• +۵ مورد دیگر

Jorge Manuel M. C. Pereira Batista is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, Portugal. He serves as a senior researcher at the Institute for Systems and Robotics (ISR-UC) and leads the Computer Vision Group. His career spans academic roles, research coordination, and industry collaboration. Academic Affiliation: University of Coimbra (Electrical & Computer Engineering Department) Research Institute: Institute for Systems and Robotics (ISR-UC) His research focuses on Computer Vision , Pattern Recognition , and applications of Differential Geometry in these fields. Key subdomains include facial analysis, visual surveillance, real-time vision systems, and machine learning integration. From 2010–2023, his recent publications highlight advancements in probabilistic models , transformer architectures , multi-branch learning , and biomimetic robotics . He has coordinated multiple funded projects such as: STORK : Avian protection systems NeuroCity : Intelligent street lighting 4D Facial Dynamics : Identity recognition iTRAFFIC : BRISA highway traffic monitoring His work bridges theoretical innovation (e.g., Riemannian manifold applications) with practical deployments in transportation, energy, and healthcare sectors.

Rolf Findeisenمشاهده پروفایل

استاد

• Model Predictive Control (MPC)
• Cyber-Physical Systems
• Battery Systems Optimization
• +۵ مورد دیگر

Prof. Rolf Findeisen is a Professor in the Department of Control and Cyber-Physical Systems (CCPS) at Technische Universität Darmstadt. His work focuses on advancing control theory and its applications in cyber-physical systems, autonomous systems, and energy storage systems. Key areas include model predictive control (MPC), battery management systems, machine learning integration into control frameworks, and optimization of crystallization processes. He leads research on safety-critical systems, data-driven control methods, and interdisciplinary applications in robotics and biotechnology. His research spans theoretical advancements in MPC stability, stochastic control, and Gaussian process modeling, alongside practical implementations in autonomous vehicles, lithium-ion battery systems, and bioprocess optimization. Notable contributions include frameworks like HILO-MPC for integrating machine learning with control systems, and methodologies for safe exploration in autonomous navigation. Prof. Findeisen's publications emphasize energy-efficient trajectory planning, fault detection in battery systems, and real-time optimization of manufacturing processes. His work bridges academic theory with industrial applications, addressing challenges in scalability, safety, and computational efficiency. His lab collaborates on national projects like IN-Fly-Tec and INFLIGHT, focusing on innovative flight control systems and sensor technologies. Current research trends include hybrid intelligent optimization, cybergenetic control of microbial systems, and safe reinforcement learning for control systems.