جستجوی استاد راهنمای دانشگاهی

• Operations Research
• Optimization
• Combinatorial Optimization
• +۶ مورد دیگر

Michael Trick is the Senior Associate Dean for Faculty and Research and Higgins Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University. His research focuses on combinatorial optimization, sports scheduling, constraint programming, and operations research applications. He has contributed to seminal work on sports timetabling (e.g., scheduling college basketball conferences) and the traveling tournament problem. Trick's work bridges theoretical advancements with practical applications, including integer programming, branch-and-price methods, and stochastic dynamic programming. He has led initiatives in operations research education and served as President of INFORMS, emphasizing the field's societal impact. His research also spans voting systems, optimization for newspaper zoning, and algorithmic design for complex scheduling problems. Trick's expertise spans academic leadership, research methodology, and cross-disciplinary applications. His articles address topics like auction design for spectrum allocation, bike-sharing logistics, and robust scheduling strategies. He collaborates across academia and industry, contributing to both theoretical advancements and real-world operational challenges. His work often highlights the practical utility of mathematical programming techniques in diverse domains.

Xiao-Ping Zhangمشاهده پروفایل

استاد

• Electrical Power Systems
• Smart Grids
• High-Voltage Direct Current (HVDC)
• +۱۵ مورد دیگر

Professor Xiao-Ping Zhang is a full-time Professor of Electrical Power Systems at the Department of Electronic, Electrical and Systems Engineering, School of Engineering, University of Birmingham, UK. He holds leadership roles as Director of Smart Grid at the Birmingham Energy Institute and Co-Director of the Birmingham Energy Storage Centre (sponsored by EPSRC), and leads the Electrical Power & Control Systems Group. PhD in Electrical Engineering, 1993 MSc in Electrical Engineering, 1990 BEng (Hons) in Electrical Engineering, 1988 His research focuses on the transformation of modern power systems, with core interests in Smart Grids, HVDC and FACTS technologies, renewable energy integration (wind and wave), control of PHEVs in power grids, energy markets using game theory, smart metering, distributed energy management, and wide-area grid awareness. He pioneered the concepts of 'Global Power & Energy Internet' and 'Energy Union,' the latter adopted by the European Commission, and envisioned the Midlands as the UK’s 'Energy Valley,' now realized through national initiatives. His recent publications reflect a strong trend in advancing HVDC/FACTS control, stability analysis in renewable-rich grids, energy storage integration, smart grid optimization, and market modeling. The articles span high-impact journals such as IEEE Transactions on Power Systems and IEEE Transactions on Smart Grid, demonstrating sustained leadership in electrical power engineering. IEEE Fellow IET Fellow Alexander-von-Humboldt Fellow Foreign Fellow of Chinese Society for Electrical Engineering (CSEE) Professor Zhang has secured substantial research grants from UK Government (Science City Initiative), EPSRC, FP7 EU Smart Energy Network Programme, and industry partners. He advises doctoral students in key areas of power systems and smart grids. He established two advanced laboratories at the University of Birmingham: the Smart Power Grid Laboratory and the Real-time Power Grid Simulation, Measurement, Protection and Control Laboratory. He also initiated the UK-China Smart Grid Workshop series, leading to 13 joint research projects. He is actively involved in professional leadership, serving as Editor for IEEE Transactions on Smart Grid and IEEE Transactions on Power Systems, IEEE PES Distinguished Lecturer, Secretary of IFAC Technical Committee on Power and Energy Systems Control, and Co-Chair of the IEEE PES Working Group on Test Systems for Economic Analysis. He has contributed to UK national energy policy, including reports for the Prime Minister’s Council for Science and Technology.

Frank Kellyمشاهده پروفایل

استاد

• Random processes
• Networks
• Optimization
• +۳ مورد دیگر

Professor Frank Kelly holds the Professor of the Mathematics of Systems at the University of Cambridge , affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS). His work bridges theoretical insights with practical applications in random processes , networks , and optimization . Academic Rank: Professor Department: DPMMS Research Themes: Stochastic systems, network resource allocation, mathematical modeling Kelly’s research focuses on stochastic networks , including congestion control in communication systems, fair bandwidth sharing , and electricity grid optimization . His work often incorporates proportional fairness principles and Markov models to analyze complex systems. Recent publications reveal a trajectory in network dynamics and resource allocation , with applications to electric vehicles, satellite routing, and virtualized networks. His work shows interdisciplinary impact across computer science , telecommunications , and energy systems . Kelly has contributed foundational works like Stochastic Networks (2014) and pioneered models for loss networks and congestion control . He maintains active collaborations and has advised on policy matters, including spectrum auctions and network regulation.

Leila Moslemi Naeniمشاهده پروفایل

مدرس ارشد

• Project Management
• Fuzzy Methods
• Mathematical Modelling
• +۵ مورد دیگر

Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.

Lacra Pavelمشاهده پروفایل

استاد

• Game Theory
• Control Theory
• Optimization
• +۷ مورد دیگر

Lacra Pavel is a Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, Faculty of Applied Science and Engineering. She joined the department in August 2002 after industry experience at Nortel Networks and Solinet Systems, and remains active in the System Control Group and Photonics Group. Her educational background includes: Diploma of Engineering (with distinction) in Automatic Control, Technical University Gh. Asachi of Iasi, Romania (1989) PhD in Electrical and Computer Engineering, Queen's University at Kingston (1996) Research focuses on integrating game theory, control theory, and optimization within networked systems. She pioneered applications in noncooperative/evolutionary game theory for network control, nonlinear/robust control frameworks, and energy-efficient optical/transportation networks. Current work develops mathematical foundations for learning in games via control-theoretic approaches to enable autonomous multi-agent network optimization. Recent publications (2019-2013) reveal dominant trends: distributed Nash equilibrium seeking using passivity-based and operator-splitting methods, stability analysis for optical network power control with time-delays, and extensions to transportation systems like railway timetabling. Key subfields include graphical games, ADMM algorithms, and Lyapunov-based boundary control for distributed parameter systems. Scientific recognition includes: Fellow of the IEEE (2025) for contributions to game theory, control, and optimization for network systems Connaught New Staff Award, University of Toronto (2003) New Opportunities Infrastructure Award, CFI/OIT (2003) Award for Innovation, Solinet Systems (2001, 2002) Inventor Recognition Award, Nortel Networks (2000) She has advised over 20 graduate students including current PhD candidates and former students now at MIT, Princeton, Amazon, and Ciena. Major grants include the CFI/OIT New Opportunities Infrastructure Award (2003). Her research bridges theoretical game control with practical implementations in optical and transportation networks. As co-director of the System Control Group and member of the Photonics Group, she leads teams developing algorithms for autonomous network optimization. Current projects focus on stochastic approximation methods for multi-agent learning and energy-efficient network design.

• Applied Stochastic
• Stochastic Control
• Differential Equations
• +۳ مورد دیگر

Fatima Zahra Lahbiri is a researcher affiliated with the Chair of Applied Stochastics at FernUniversität in Hagen . Her work focuses on stochastic control systems, boundary feedback mechanisms, and mathematical modeling of random processes. Research Interests : Stochastic control systems Boundary noise analysis Delay-differential equations Semigroup approaches to stochastic models Mathematical systems theory Publication Trends : Recent works emphasize stochastic systems with boundary conditions, including controllability analysis, delay equations, and parabolic models driven by boundary white-noise. Collaborations with S. Hadd dominate her contributions. Labs & Teams : Active member of the Chair of Applied Stochastics research group at FernUniversität in Hagen.

Lina von Sydowمشاهده پروفایل

استاد

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +۲ مورد دیگر

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

• Operations Research
• Market Design
• Algorithmic Game Theory
• +۳ مورد دیگر

Pengyu Qian serves as an Assistant Professor in the Department of Operations and Technology Management at Boston University's Questrom School of Business, where he conducts research at the intersection of operations research, economics, and algorithmic systems design. Education PhD, Graduate School of Business, Columbia University, 2021 B.Sc., Peking University, 2015 Research Interests Dr. Qian specializes in theoretical and applied problems within matching markets, dynamic resource allocation, and queueing network optimization. His work develops algorithmic solutions for market inefficiencies using tools from game theory, stochastic modeling, and mechanism design. Current investigations focus on partner competition dynamics in two-sided markets, incentive structures for resource pooling, and blind control policies in closed networks where system states remain partially observable. Publication Trends His 2018-2024 publications demonstrate consistent output in top-tier venues including Management Science and ACM Economics & Computation conferences, with increasing emphasis on real-world market applications. The research trajectory shows progression from foundational queueing network control to sophisticated matching market analyses, maintaining strong theoretical rigor while addressing practical constraints like information asymmetry and dynamic partner competition. Scientific Awards No scientific awards, fellowships, or major honors are documented in the available profile information. Advising and Grants While specific student advisees and grant funding details are not disclosed in the current materials, Dr. Qian's publication record suggests active collaboration with leading researchers including Yash Kanoria and Itai Ashlagi, indicating participation in significant research initiatives within operations management and market design. Labs and Teams Though no dedicated laboratory is specified, his research profile aligns with Boston University's operations management research groups at Questrom, particularly those focused on algorithmic market design and stochastic optimization within business contexts.

Matt Castleمشاهده پروفایل

پژوهشگر

• Modelling for Food Security
• Plant Disease Modelling
• Infectious Diseases and Food Security
• +۷ مورد دیگر

Dr. Matt Castle is a Researcher affiliated with the University of Cambridge , specializing in interdisciplinary approaches to food security and plant sciences. He serves as the Head of Bioinformatics Training and Head of PSLS Biostatistics Initiative within the Department of Plant Sciences . His work spans multiple domains, including plant disease modelling, infectious disease dynamics, and global governance of food systems. Research Themes: Modelling for Food Security, Plant Disease Modelling, Food Landscapes, Political Economy of Hunger His recent publications highlight expertise in ecological and agricultural modelling, with a focus on: Aflatoxin contamination in maize Urban forest pest dynamics Climate-driven range expansions Size-structured ecosystem resilience While his work intersects with food supply chains, land resources, and health, no specific scientific awards or student advisement details were listed in available public records.

Emese Lazarمشاهده پروفایل

استاد

• Risk Measurement
• Model Risk
• Financial Econometrics
• +۴ مورد دیگر

Professor Emese Lazar serves as Professor of Finance and Deputy School Director of Teaching and Learning at the ICMA Centre, Henley Business School, University of Reading. She joined the institution in 2005 and is an active member of the Econometrics with Data Science research cluster, focusing on quantitative finance applications. Her educational background includes: PhD in Finance from the University of Reading BSc in Finance and Banking from the University of Economic Studies, Bucharest BSc in Computer Science from the University of Bucharest Research interests span risk measurement and management , model risk , financial econometrics , derivatives pricing , green finance , climate risk in finance , and machine learning applications . Her work bridges theoretical finance with practical risk management challenges, particularly in climate-related financial risks and algorithmic risk modeling. Recent publications reveal a pronounced shift toward integrating climate risk metrics with traditional financial models and developing neural network-based forecasting for tail risk measures. Her research demonstrates consistent innovation in volatility modeling, model risk quantification, and climate finance applications across top-tier journals. Professor Lazar teaches postgraduate modules in Market Risk and Climate Change and Risk Management, supervising PhD students in her specialized research areas. She actively contributes to the Econometrics with Data Science research cluster, fostering interdisciplinary collaboration between finance, data science, and climate risk modeling.

Neil Lawrenceمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Uncertainty Quantification
• +۳ مورد دیگر

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge's Department of Computer Science and Technology. He also holds positions as a Senior AI Fellow at The Alan Turing Institute and a Visiting Professor of Machine Learning at the University of Sheffield. After three years as Director of Machine Learning at Amazon, Lawrence recently returned to academia, bringing extensive industry experience to his academic work. Lawrence's research focuses on the intersection of machine learning with the physical world, particularly in uncertainty quantification and end-to-end solutions for real-world applications. His work was initially inspired by deploying machine learning systems in African contexts, where comprehensive solutions are often required. His technical expertise spans over two decades in machine learning methods, with a growing interest in public understanding of machine learning, policy decisions, and data governance implications. His recent publications reveal a diverse research portfolio spanning climate science, healthcare applications, systems engineering for AI deployment, data governance, and theoretical machine learning. Lawrence's work demonstrates a consistent theme of bridging theoretical machine learning with practical applications across multiple domains, with particular attention to uncertainty quantification and the societal implications of AI systems. Lawrence serves on the board of the AISTATS conference and the ELLIS Foundation, and acts as the founding and series editor for the Proceedings of Machine Learning Research. He is also the co-host of the Talking Machines podcast, demonstrating his commitment to public engagement with machine learning concepts. At Cambridge, Lawrence teaches Advanced Data Science (Part II) and Machine Learning and the Physical World (MPhil ACS, Part III), contributing to both undergraduate and postgraduate education in computer science. His work with the Accelerate Programme for Scientific Discovery and the Data Trusts Initiative positions him at the forefront of developing frameworks for responsible and effective AI deployment in scientific and societal contexts.

Zijin Zhangمشاهده پروفایل

استادیار

• Operations Management
• Data-Driven Decision Making
• Algorithm Design
• +۴ مورد دیگر

Zijin Zhang is an Assistant Professor of Business Analytics at the Carroll School of Management, Boston College. She holds a Ph.D. in Technology & Operations from the University of Michigan's Ross School of Business and a B.S. in Mathematics and Statistics from Nanjing University. Her office is located in Fulton Hall 454B at Boston College. Her research centers on data-driven decision-making in operations management, organized around three pillars: Engineering value : Designing algorithms for real-time decisions under uncertainty Economic value : Optimizing data acquisition costs versus decision impact Social value : Examining ethical implications of data practices and technology regulation Applications span retail operations, AI markets, and public-sector resource allocation. Recent publications focus on algorithmic decision-making in operations management, with recurring themes of data optimization, fairness in resource allocation, and policy impact analysis. Methodologies combine optimization, game theory, and machine learning. Awards & Honors: Rackham Doctoral Intern Fellowship (2024) Michigan Ross China Research Award (2023) First Prize, National Olympiad Informatics (2013) Thomas William Leabo Fellowship (2022-2023) DSI Doctoral Research Showcase Finalist (2025) Teaching includes Operations Management (TO 313) at University of Michigan with a 4.9/5.0 evaluation score. She has received multiple research grants including Rackham Research Grant (2024) and Ross School Doctoral Grant (2022). Engages in academic service as INFORMS conference session co-chair (2024) and PhD program panels. Maintains industry connections through past internships at Oracle and CITIC Group.

Didier Raboissonمشاهده پروفایل

استاد

• Animal Health Economics
• Ruminant Population Medicine
• Bioeconomic Modeling
• +۴ مورد دیگر

Didier Raboisson is a Full Professor in Ruminant Population Medicine and Animal Health Economics at the National Veterinary School of Toulouse (ENVT), France. Holding a DVM, MSc, PhD, and Diplomate status in the European College of Bovine Health Management (ECBHM), his career spans from clinical practice (2004-2006) to academic leadership at ENVT since 2006, progressing from contractual assistant to full professor in 2019. His educational credentials include a Doctor in Veterinary Medicine (2003), ruminant clinical internship (2005), MSc in Agricultural Socio-economics (2007), PhD in Institutional Economics (2011), and HDR accreditation (2017). His research pioneered the DairyHealthSimulator® (DHS®), a stochastic dynamic bioeconomic model optimizing dairy herd management under constraints like antimicrobial use. Key contributions address veterinary workforce shortages, disease cost analytics, and veterinary business models through innovative frameworks like the DODforD herd health approach. Raboisson's publication portfolio exceeds 75 international peer-reviewed papers, with recent work focusing on antimicrobial reduction strategies, lameness economics, and dairy cooperative development. His research demonstrates consistent interdisciplinary integration of veterinary science, economics, and stochastic modeling, particularly evident in the shift from descriptive disease cost analysis to utility-optimized decision frameworks. Scientific recognition includes: ECBHM Diplomate status (2011) HDR accreditation (2017) DHS® and App Qost® patents Academic editor roles at PlosOne and Frontiers in Veterinary Sciences As supervisor of 8 PhD students and 6 post-doctorates, Raboisson leads the VetEconomics research group while directing France's continuous training program in bovine population medicine. His grant portfolio exceeds €1.4 million from INRAE, Ministry for Agricultural Sovereignty, ANR, and EU programs. International collaborations span Cornell, Liverpool, and Indian institutions through CEFIPRA and Prezode initiatives, with policy impact evidenced by DGAL working group contributions on veterinary shortages and biosecurity.

• Hardware Security
• Embedded Systems
• Energy-Efficient Computing
• +۵ مورد دیگر

Mahdi Fazeli is an Associate Professor at the School of Information Technology, Halmstad University, Sweden, specializing in hardware security and trust, energy-efficient computing, and embedded and cyber-physical systems. His academic journey began with a Ph.D. in Computer Engineering from Sharif University of Technology, Iran, in 2011. His career progression includes positions as Associate Professor at Bogazici University (2019-2021) and Iran University of Science and Technology (2016-2019), and Assistant Professor at the same institution (2011-2016). His research interests focus on hardware security and trust, reliable VLSI circuits and systems, energy-efficient computing, and dependable embedded systems. His work bridges the gap between theoretical security concepts and practical implementations in real-world systems, particularly in IoT and embedded environments. He has established himself as a leading researcher in Physical Unclonable Functions (PUFs), hardware trojans detection, and energy-efficient security solutions for resource-constrained devices. His publication record shows a clear progression and deepening expertise in hardware security, with recent work focusing on cutting-edge applications in edge computing, vehicular networks, and IoT security. His 2023-2025 publications demonstrate significant contributions to magnetic memory-based security primitives, anomaly detection systems, and energy-efficient security mechanisms. Throughout his career, Fazeli has led multiple research initiatives including the Dependable Systems and Architecture Lab (DSA) and the Networked and Embedded Systems Lab at Iran University of Science and Technology. His leadership extends to heading the Hardware Group and serving as Vice Chair for Educational Affairs, demonstrating his commitment to both research excellence and academic administration.

Robert Moserمشاهده پروفایل

استاد

• Turbulence physics
• Direct numerical simulation and spectral methods
• Large-eddy simulation
• +۲ مورد دیگر

Dr. Robert D. Moser is a Professor at the University of Texas at Austin and holds the W.A. "Tex" Moncrief, Jr. Chair in Computational Engineering and Sciences I. He is affiliated with the Thermal and Fluid Systems program, the Institute for Computational Engineering and Sciences (ICES), and serves as Director of the DOE-funded Center for Predictive Engineering and Computational Sciences (PECOS). Ph.D. in Mechanical Engineering from Stanford University (1984) His research focuses on computational methods for turbulence modeling, cardiovascular fluid mechanics, and uncertainty quantification in complex physical simulations. He develops large-eddy simulation techniques for aerospace applications and biological flow analysis, while pioneering methods to characterize uncertainties in reentry vehicle simulations and turbulence modeling. Dr. Moser leads interdisciplinary research at PECOS and ICES, combining computational engineering with biomedical applications. His work spans theoretical turbulence physics, numerical methods for Navier-Stokes equations, and practical implementations for aerodynamic and medical device design.