جست‌وجوی استاد و پژوهشگر

Manuel Moralesمشاهده پروفایل

دانشیار

• Financial Mathematics
• Actuarial Science
• Machine Learning Applications in Finance
• +۵ مورد دیگر

Manuel Morales is an Associate Professor in the Department of Mathematics and Statistics at the University of Montreal since 2005. He holds a Ph.D. in Mathematics (2003) from Concordia University, an M.Sc. in Statistics (2000) from Concordia, and a B.Sc. in Mathematics (1996) from the National Autonomous University of Mexico. His research focuses on Financial and Actuarial Mathematics, particularly in Ruin Theory, Lévy processes, and High-Frequency Finance. He leads applied projects integrating Machine Learning in Banking and ESG Investment, and has pioneered AI governance frameworks at the National Bank of Canada as their Chief AI Scientist. Education: Ph.D. Mathematics, Concordia University, 2003 M.Sc. Statistics, Concordia University, 2000 B.Sc. Mathematics, National Autonomous University of Mexico, 1996 Research Interests: His work spans theoretical and applied directions, including non-Gaussian option pricing, regime-switching models, Limit Order Book dynamics simulation, and AI applications in finance. He emphasizes responsible investment and ESG factors through alternative data analysis. Advising & Partnerships: Supervises Master’s/Ph.D. students in Insurance and Financial Mathematics. Leads the FinML Network (since 2018) and collaborates with industry partners like the National Bank of Canada on AI-driven financial projects. His grants and contracts enable applied research in high-frequency market surveillance and model governance. Labs & Teams: Directs the FinML Network and oversees the National Bank’s AI initiatives, focusing on AI governance and algorithmic trading strategies.

CHENG Shih-Fenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Decision Making & Optimization
• +۵ مورد دیگر

CHENG Shih-Fen is an Associate Professor of Computer Science at Singapore Management University (SMU) and a Principal Research Scientist at Amazon. He holds a PhD in Industrial and Operations Engineering from the University of Michigan and a BSE in Mechanical Engineering from National Taiwan University. His research focuses on modeling and optimization of complex systems in urban computing, decision-making, and transportation, with notable contributions to taxi fleet management, ride-hailing systems, and sustainable logistics. Research interests include Artificial Intelligence , Decision Optimization , Machine Learning , and Urban Sustainability . Notable achievements include prestigious awards from CIKM, AAMAS, and INFORMS. He has advised students such as Qian Shao and Pang Jin Tan, who received SMU Presidential Doctoral Fellowships. Key contributions include the Driver Guidance System (DGS) for taxis and patented taxi demand prediction models. Publications span top venues like IJCAI, AAAI, and Transportation Science. He is a Senior Editor of Electronic Commerce Research and Applications and actively contributes to professional communities like INFORMS and AAAI.

• Renewable Energy Systems
• Thermal Energy Storage
• Building Energy Efficiency
• +۴ مورد دیگر

Professor Zhenjun Ma is a Professor and Deputy Director of the Sustainable Buildings Research Centre (SBRC) at the University of Wollongong. He holds a PhD from The Hong Kong Polytechnic University and has expertise in renewable energy systems, thermal energy storage, and building energy efficiency. His research focuses on advancing sustainable HVAC solutions, building control optimization, and demand flexibility in energy systems. He has received prestigious awards including the World Society of Sustainable Energy Technologies Innovation Award and Excellence in HVAC&R Research from AIRAH. Education: BEng and MSc from Xian Jiaotong University; PhD from Hong Kong Polytechnic University. Current academic roles include editorial board memberships in journals like Renewable Energy and Energy Conversion and Management , and leadership in initiatives like the NSW Decarbonisation Innovation Hub. Research Interests: Renewable energy integration in buildings, thermal storage technologies, data-driven building analytics, and grid-to-building energy systems. Active in over 40 funded projects, including grants from Australia's Department of Industry and the NSW Government. Awards: Invitational Fellowship from Japan Society for the Promotion of Science (2025), Fellow of AIRAH (2021), and multiple best paper awards. Supervises over 30 PhD/Master’s students on topics like energy flexibility optimization and net-zero building systems. Labs/Teams: Leads SBRC’s energy efficiency and sustainability research clusters. Collaborates with industry partners like BlueScope Steel on solar energy solutions.

• Thermal Analysis of Rocket Engines
• Sustainable Building Systems
• Solar Energy
• +۴ مورد دیگر

Dr. Mohammad Naraghi is a Professor in the Department of Mechanical Engineering at Manhattan University, specializing in thermal analysis of rocket engines, sustainable building systems, and radiative heat transfer. His research focuses on rocket thermal evaluation (RTE), solar energy optimization, and crystal growth processes. He holds a PhD from the University of Akron, MS from the University of Wales, and BS from the University of Tehran. Research areas include: Thermal modeling of regeneratively cooled rocket engines Radiative heat transfer in enclosures and aerospace systems Solar energy systems optimization (panel orientation, photovoltaic plants) Energy dynamics of green buildings and data centers CFD analysis of fluid flow and heat transfer in propulsion systems His 30+ years of publications span advanced thermal modeling techniques, including RTE software development and stochastic methods. Key contributions include NASA-recognized rocket engine thermal models and a patented seasonally selective building façade. Grants include NASA-funded rocket thermal research and ARPA/AFOSR crystal growth projects. Awards include ASME Fellow, AIAA Associate Fellow, and multiple NASA/ASEE fellowships. Teaching includes courses on solar energy systems, fluid mechanics, and green building energy dynamics. Advises graduate students in mechanical engineering and contributes to industry partnerships through applied thermal research.

Lamine M. Miliمشاهده پروفایل

استاد

• Power Systems
• Signal Processing
• Robust Estimation Theory
• +۷ مورد دیگر

Lamine M. Mili is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His expertise spans power systems, signal processing, and robust estimation theory. He holds an IEEE Fellowship (2016) for contributions to robust state estimation in power systems. Mili's research focuses on advancing methodologies for power system reliability, control, and integration of renewable energy sources. His work includes studies on dynamic state estimation, nonlinear dynamics, bifurcation theory, and quantum computing applications. He has contributed extensively to resilience engineering and computational social science in power systems. Mili’s recent articles address challenges in smart grids, quantum circuit error prediction, and multifractal signal analysis in EEG. His research often combines advanced statistical techniques with real-world grid data, emphasizing robustness and adaptability in dynamic environments. Education: Ph.D., University of Liège, 1987 M.S., University of Tunis, 1983 B.S., Swiss Federal Institute of Technology, Lausanne, 1976 Research Interests: Power system stability and control State estimation and robust filtering Quantum computing for power systems Resilience and cyber-physical-social systems Nonlinear dynamics and bifurcation analysis His recent publications reflect a focus on hybrid power systems, probabilistic methods, and data-driven approaches for grid optimization. The 2025 articles highlight advancements in photovoltaic state estimation, quantum error prediction, and robust modulation techniques. Mili’s work often bridges theoretical models with practical grid applications, emphasizing uncertainty quantification and real-time monitoring.

• Econometrics
• Macroeconomics
• Policy Analysis
• +۳ مورد دیگر

Professor Adrian Pagan holds the position of Professor of Economics at the University of Sydney's School of Economics. His research focuses on macro-econometric modeling, policy analysis, and business cycle theories. He has held visiting appointments at prestigious institutions including Oxford University and Princeton University. Key achievements include: Fellowships with the Academy of Social Sciences, Econometric Society, and Journal of Econometrics Medallist Fellow of the Modelling and Simulation Society of Australia and New Zealand Distinguished Fellow of the Economic Society of Australia Centenary Medal recipient (2001) His work emphasizes structural macroeconomic modeling, particularly in analyzing recurrent economic events and policy impacts. Recent research explores business cycle synchronization, financial frictions, and shock decomposition in macroeconomic systems. Publications span over four decades, with notable contributions to journals like Journal of Econometrics , Macroeconomic Dynamics , and European Economic Review . He has authored the influential 2016 book The Econometric Analysis of Recurrent Events in Macroeconomics and Finance .

Milton Boydمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Derivatives
• +۷ مورد دیگر

Milton Boyd is a Professor in the Department of Agribusiness and Agricultural Economics at the University of Manitoba, Faculty of Agricultural and Food Sciences. He also serves as an Adjunct Professor in Risk Management and Actuarial Science at the Asper School of Business. His research and teaching focus on quantitative finance, risk management, derivatives, and international business. PhD, Purdue University, United States MA, Washington State University, United States BA, Seattle Pacific University, United States His research interests center on quantitative finance and agricultural risk management , with a strong emphasis on derivatives, portfolio strategies, and market behavior. He applies statistical and financial modeling to real-world agricultural and commodity markets, often integrating international and policy perspectives. His work bridges academic rigor with practical application in agribusiness and government sectors. His recent publications reflect a consistent focus on financial risk modeling, commodity pricing, and market efficiency. Themes include hedging strategies, volatility analysis, and the role of futures markets in price discovery and income stability. His work spans both developed and emerging markets, with increasing attention to Asia and global trade dynamics. Two University of Manitoba Awards for Research and Outreach Fellow of Seattle Pacific University Best Conference Presentation Award Outstanding Journal Paper Award Dr. Boyd has advised numerous graduate students and contributed to academic service through editorial roles, conference committees, and keynote presentations. He has secured research funding through university and external grants, though specific grants are not listed. His outreach includes media commentary, industry training, and policy advising, particularly in commodity market development. He has been actively involved in professional development and public service, including teaching short courses at the Winnipeg Commodity Exchange (ICE), Canadian International Grains Institute, and Canadian Securities Course. He has served as a Policy Advisor for the Frontier Center for Public Policy and as a columnist for Grainews , contributing to public understanding of financial and commodity markets.

Mircea R. Stanمشاهده پروفایل

استاد

• AI Hardware
• Processing in Memory
• Low Power Design
• +۷ مورد دیگر

Mircea R. Stan is a Professor of Electrical and Computer Engineering at the University of Virginia, serving as Director of Computer Engineering and Virginia Microelectronics Consortium (VMEC) Professor. He leads the High-Performance Low-Power (HPLP) lab and is an associate director of the Center for Automata Processing (CAP). His research focuses on AI hardware, Processing in Memory, Low Power Design, Cyber-Physical Systems, and Spintronics. Education: Ph.D. (1996) and M.S. (1994) from UMass Amherst; Diploma (1984) from Politehnica University, Bucharest. Research interests include energy-efficient computing architectures, IoT systems, and emerging technologies like magnetic skyrmions and memristors. He has pioneered work on asynchronous stochastic computing, thermal-aware microarchitecture, and microfluidic cooling for 3D-ICs. Key awards include the 2024 A. Richard Newton Technical Impact Award, 2018 ISCA Influential Paper Award, and IEEE Fellow (2014). He has held editorial roles at IEEE TVLSI, IEEE TNano, and IEEE Design & Test. Notable contributions include the HPLP lab’s advancements in low-power logic computing, the VCRFID framework for Industry 4.0, and thermal-aware design tools like Hot-LEGO and Cool-3D.

Charles Wangمشاهده پروفایل

عضو هیئت علمی

• General Relativity
• Quantum Gravity
• Modified Gravity
• +۱۳ مورد دیگر

Dr. Charles Wang is a Reader in the Department of Physics at the University of Aberdeen, within the School of Natural and Computing Sciences. He has held this position since 2005 and is also an Honorary Cruickshank Lecturer in Astronomy at the same institution. His academic journey began with a BSc in Physics from National Taiwan University, followed by a Certificate of Advanced Study in Mathematics from Cambridge, and culminated in a PhD in Mathematical Physics from Lancaster University. Education: BSc (National Taiwan University), CASM (Cambridge), PhD (Lancaster) Current Position: Reader, Department of Physics, University of Aberdeen Additional Role: Honorary Cruickshank Lecturer in Astronomy Dr. Wang's research lies at the intersection of theoretical physics and experimental gravity, focusing on general relativity, quantum gravity, modified gravity, astrophysics, and cosmology. He is a pioneer in quantum gravity phenomenology, particularly through atom interferometry techniques. His work extends into applied mathematics, including differential geometry, Clifford algebra, and the Cosserat theory of rods. He actively collaborates with industry and research institutions on quantum sensing, gravity gradiometry, and precision measurement applications. His recent publications reveal a strong trajectory in quantum aspects of gravity, including Unruh radiation, gravitational decoherence, quantum sensing of spacetime fluctuations, and loop quantum gravity formulations. These works are published in high-impact journals such as Physical Review D, Classical and Quantum Gravity, and the European Physical Journal C. Scientific honors include being a Fellow of the STFC Centre for Fundamental Physics. He has secured significant research funding from sources like MoD/Dstl, EPSRC, UKSA, and BP for projects related to quantum gravity experiments and gravity sensing technologies. STFC Centre for Fundamental Physics Fellow Dr. Wang has supervised PhD students and contributed to major international collaborations such as STE-QUEST (ESA mission candidate), GG-TOP (with Birmingham), and CERN-related supernova research. He has also served in professional roles including Grampian Regional Organiser for the Institute of Physics in Scotland and as a Series Editor for Springer Briefs in Physics. He is affiliated with research groups including the Plasma Science Research Group (PSRG) and contributes to interdisciplinary initiatives such as the Advanced Centre for Energy and Sustainability (ACES).

Philip Brabazonمشاهده پروفایل

مدرس ارشد

• Operational Research
• Logistics
• Mass Customization
• +۵ مورد دیگر

Dr. Philip Brabazon is a Senior Lecturer at the University of Portsmouth, affiliated with the School of Organisations, Systems and People and the Centre for Operational Research & Logistics. His work focuses on operational research, logistics, and mass customization in industries such as automotive and manufacturing. He has published extensively on topics including order fulfillment systems, supply chain management, and production economics. Dr. Brabazon is currently accepting PhD students and has supervised 7 theses. His research explores themes like automotive order-to-delivery processes, virtual build-to-order systems, and the application of simulation in operational systems. He has contributed to understanding mass customization strategies, risk analysis in offshore operations, and safety case frameworks. His work bridges theoretical models with practical industry applications, particularly in optimizing supply chains and production systems. Notable publications include analyses of automotive logistics, long-tail retail dynamics, and Markovian approximations in fulfillment systems. His research has been cited over 100 times in key journals like the European Journal of Operational Research and Production and Operations Management.

Lars BEEXمشاهده پروفایل

پژوهشگر

• Computational Mechanics of Solids
• Bayesian Inference
• Multiscale Methods
• +۵ مورد دیگر

Lars BEEX is a Senior Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine, Department of Engineering. He holds the right to supervise PhD students and has directed five to completion. His research focuses on computational mechanics of solids, including Bayesian inference, multiscale methods, and quasicontinuum approaches, with applications to materials like textiles, foams, and medical devices. His academic journey includes a PhD from Eindhoven University of Technology (2008-2012), supervised by Marc Geers and Ron Peerlings, as well as MSc and BSc degrees from the same institution. **Research Interests:** - Computational mechanics of solids - Bayesian inference and uncertainty quantification - Multiscale modeling (quasicontinuum method) - Mechanical modeling of fibrous and discrete materials - Phase-field damage models - Contact mechanics and elastoplasticity **Awards:** - Biezeno Solid Mechanics Award 2013 (Best PhD thesis in solid mechanics, Netherlands) - Cum laude distinction for both MSc and BSc degrees **Industrial Collaborations:** - SISTO Armaturen - IEE - Kiswire International **Teaching:** - Numerical methods for continuous optimization - Courses for Computer Science, Mathematical Modelling, and Engineering students **Lab/Affiliations:** - Legato Team (part of the University of Luxembourg's engineering research cluster)

Kristopher Kleinمشاهده پروفایل

دانشیار

• Solar and Heliospheric Physics
• Plasma Turbulence
• Kinetic Plasma Theory
• +۶ مورد دیگر

Kristopher Klein is an Associate Professor at the Lunar and Planetary Laboratory (LPL), University of Arizona, within the Department of Planetary Sciences, College of Science. He earned his Ph.D. from the University of Iowa in 2013 and has been a faculty member at LPL since 2017. His research focuses on theoretical and computational plasma physics in the context of solar and heliospheric systems. Education: Ph.D., 2013, University of Iowa Years with LPL: 2017–present Dr. Klein's research centers on fundamental plasma phenomena in the heliosphere, particularly turbulent heating, energization mechanisms, and departure from thermodynamic equilibrium in collisionless plasmas like the solar wind. He employs analytic theory, numerical simulations (e.g., AstroGK, HVM, gkeyll), and spacecraft data from missions such as Parker Solar Probe and HelioSwarm. He is a co-developer of the Arbitrary Linear Plasma Solver (ALPS), an open-source tool for dispersion analysis. His recent publications (2019–2025) reveal a strong focus on plasma turbulence, wave-particle interactions, kinetic instabilities, and solar wind heating mechanisms. The work frequently combines Parker Solar Probe observations with theoretical modeling to understand energy transfer at kinetic scales. Themes include ion and electron heating, stochastic heating, cyclotron damping, and multi-scale turbulence characterization. Scientific Awards: 2024 AAS Harvey Prize 2022 Landau-Spitzer Award for Outstanding Contributions to Plasma Physics Dr. Klein advises graduate students including Niranjana Shankarappa and Waverly Gorman, with former student Teddy Broeren (Ph.D., 2023). He leads major NASA-funded research projects related to the HelioSwarm and Parker Solar Probe missions. His involvement includes being Deputy Principal Investigator for HelioSwarm and Co-Investigator and Project Scientist for the SWEAP instrument on Parker Solar Probe. These roles involve significant grant leadership and collaboration with interdisciplinary teams. He is actively involved in instrumentation and data analysis, particularly through quasi-thermal noise spectroscopy and wave-particle correlation techniques. His work bridges theory, simulation, and observational data to advance understanding of space plasma physics.

Daniel W. Apleyمشاهده پروفایل

استاد

• Industrial and engineering statistics
• Statistical learning and data mining
• Predictive analytics
• +۷ مورد دیگر

Daniel W. Apley is Professor of Industrial Engineering and Management Sciences at the McCormick School of Engineering and Applied Science, Northwestern University, where he has served since 2003. He is Editor-in-Chief-Elect of Technometrics and previously Editor-in-Chief of the Journal of Quality Technology . He is also affiliated with Northwestern’s Master of Science in Machine Learning and Data Science Program. Education PhD Mechanical Engineering, University of Michigan, Ann Arbor MS Electrical Engineering, University of Michigan, Ann Arbor MS Mechanical Engineering, University of Michigan, Ann Arbor BS Mechanical Engineering, University of Michigan, Ann Arbor Research Interests Professor Apley is an industrial statistician whose work sits at the intersection of engineering modeling, statistical analysis, and predictive analytics. His major thrusts include statistical modeling of complex engineering and enterprise systems, machine learning for manufacturing data, quality engineering and Six Sigma methodologies, and computer-experiment–based design optimization under uncertainty. Recent applications span healthcare risk modeling, credit-risk analytics, materials microstructure prediction, and autonomous process control. Scientific Awards NSF CAREER Award IIE Transactions Best Paper Award (Quality & Reliability) Wilcoxon Prize for best practical application paper in Technometrics Teaching & Advising At Northwestern he teaches undergraduate courses in Statistical Methods for Quality Improvement, Introductory Statistics, and Statistical Tools for Data Mining, as well as graduate courses in Predictive Analytics, Engineering Applications of Data Mining, and Intermediate Statistics. His research has been supported by numerous industrial partners and federal agencies, underscoring a strong record of funded graduate and post-doctoral advising. Leadership & Service Beyond editorial roles, Professor Apley has chaired the Quality, Statistics & Reliability Section of INFORMS and served as Director of the Manufacturing and Design Engineering Program at Northwestern, shaping interdisciplinary curriculum and research initiatives.

• Energy Informatics
• Building Energy Simulation
• Multi-Scale Energy Modelling
• +۴ مورد دیگر

James O'Donnell is an Associate Professor in the School of Mechanical and Materials Engineering at University College Dublin (UCD). He joined UCD in 2013 after working at Lawrence Berkeley National Laboratory. His research focuses on multi-scale energy modeling, interoperable BIM solutions, and sustainable building design. He contributes to buildingSMART’s Energy Modelling Expert Panel and teaches modules on building physics and energy analysis. Education: B.E. Civil and Environmental Engineering, University College Cork Ph.D. in Holistic Building Performance Analysis, University College Cork Professional Diploma in University Teaching & Learning, University College Dublin Research Interests: James specializes in energy informatics, building energy simulation, and semantic web technologies for the built environment. His work bridges BIM integration with energy modeling to optimize urban-scale performance. Grants & Funding: PROBONO (2022–2026): Integrator-centric approach for energy-efficient buildings NexSys (2022–2026): Next-Generation Energy Systems ALIVE (2020–2024): Indoor Environmental Quality in Energy-Efficient Buildings Teaching & Editorial Roles: James coordinates courses like Energy Systems in Buildings and serves as an Associate Editor for Journal of Building Engineering and editorial board member of Advanced Engineering Informatics . Professional Affiliations: Member of Engineers Ireland, International Building Performance Simulation Association (IBPSA), and European Council on Computing in Construction.

Narendra Goelمشاهده پروفایل

استاد

• Computational Modeling
• Computer Simulation
• Remote Sensing
• +۲ مورد دیگر

Narendra Goel is Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research spans computational modeling of physical, biological, and engineering systems, with applications in computer graphics and remote sensing of vegetation. Research interests focus on: Simulation frameworks for ecological and biological systems Remote sensing algorithms for vegetation analysis Theoretical foundations of computer modeling Publications demonstrate long-standing expertise in radiative transfer modeling (2000-2004) and computational biology (1972-2013). Current teaching includes Introduction to Computer Science (CSC1000), Fundamental Structures in Computer Science (CSC1500), and Introduction to Theoretical Computer Science (CSC4500). Contact at aa2671@wayne.edu or Google Scholar profile.