جست‌وجوی استاد و پژوهشگر

• Neuroscience
• Cognitive Sciences
• Neurobiology
• +۵ مورد دیگر

Massimo De Agrò is a Research Fellow at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) within the University of Trento, Italy. He holds a PhD in Psychological Sciences (2019) and a Master’s in Cognitive Neuroscience and Clinical Neuropsychology (2016), both from the University of Padua. His research focuses on brain simplification strategies in invertebrates, particularly jumping spiders, exploring how limited neural resources enable functional efficiency. This work aims to bridge biological insights with technological innovation. He has held postdoctoral positions at institutions including Harvard University (2020), Regensburg University (2021-2022), and the University of Florence (2022). His expertise spans neurobiology, cognitive sciences, and ethology, with a focus on visual systems, behavioral ecology, and animal cognition. Key projects include studying animacy perception in arthropods, REM-like sleep states in spiders, and developing tools like the SPiDbox for spider behavior analysis. His interdisciplinary approach integrates experimental biology, computational modeling, and robotics.

• Computer Science
• Information Engineering
• Deep Learning
• +۳ مورد دیگر

Di Zhang is affiliated with Guangdong Medical College's School of Information Engineering and holds a PhD in Synthetic Aperture Radar Image Interpretation from the University of Hamburg (2022). Their research focuses on interdisciplinary fields such as deep learning, remote sensing, optimization algorithms, and their applications in medical imaging, environmental science, and education technology. They have published extensively in top-tier journals like IEEE Access, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Remote Sensing. Key affiliations: University of Hamburg (PhD), Guangdong Medical College, and others listed in disambiguation entries. Research interests include AI-driven medical diagnostics, SAR image analysis, IoT data management, and educational assessment systems. Recent work emphasizes deep learning frameworks for image processing, algorithm optimization, and multimodal data fusion. Publications span diverse topics such as migraine diagnosis via radiomics, social support in online learning, and robust visual SLAM systems. Their work bridges theoretical advancements with practical applications in healthcare, robotics, and environmental monitoring.

Robert Woodمشاهده پروفایل

استاد

• Robotics
• Materials Science
• Mechanical Engineering
• +۷ مورد دیگر

Robert Wood is the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at Harvard University, affiliated with the School of Engineering & Applied Sciences. He holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. His research focuses on robotics, particularly bio-inspired systems and soft robotics applications in natural environments and biomedicine. He emphasizes interdisciplinary approaches, combining engineering with biological insights to advance robotic design and functionality. Wood teaches courses such as 'How Music Works: Engineering the Acoustical World,' 'Microrobotics and Bio-inspired Autonomous Robotic Systems,' and 'Advanced Topics in Robotics Research.' He advocates for hands-on learning and the value of iterative failure in education, stating, 'Making mistakes and learning from them is an absolutely essential part of the learning process.' His lab, the Microrobotics Lab, explores topics like compliant actuators, bioinspired locomotion, and soft robotic systems. While no formal scientific awards are listed, Wood’s contributions to robotics have been highlighted through his work on projects like RoboBee and the Cetacean Translation Initiative (Project CETI). He directs graduate studies in his department and has advised students across his research and teaching activities. His lab’s current and future work includes advancements in soft wearable sensors, modular robotic systems, and biomimetic mechanisms for medical and environmental applications.

Shuping Shiمشاهده پروفایل

استاد

• Financial Econometrics
• Applied Economics
• Bubble Detection
• +۲ مورد دیگر

Shuping Shi is a Professor in the Department of Economics at Macquarie University, affiliated with the Centre for Risk Analytics and Housing and Urban Research Centre. Her research focuses on financial econometrics with applications to real estate markets and bubble detection. Key research areas include: Development of PSY bubble monitoring technique used by central banks High-frequency data analysis for financial markets Risk assessment in global housing markets Causal change detection in economic systems Publications demonstrate strong focus on empirical finance methodologies, particularly volatility modeling, unit root testing, and bubble detection algorithms. Recent work advances statistical methods for real-time market monitoring. She has secured over $1 million in external funding including Australian Research Council grants: Discovery Early Career Researcher Award (2019-2021) and Discovery Project Awards (2015-2018, 2019-2021). Her bubble detection techniques have been adopted by institutions including Federal Reserve Bank of Dallas and Hong Kong Monetary Authority.

Paul Bartonمشاهده پروفایل

استاد

• Energy Systems Engineering
• Energy Efficiency
• Renewable Energy
• +۳ مورد دیگر

Paul Barton is a Professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), affiliated with the MIT Energy Initiative. His research focuses on energy systems engineering, particularly in energy efficiency, conventional and renewable energy technologies, and process optimization. He leads projects on liquid air energy storage (LAES), natural gas liquefaction processes, and hybrid polygeneration systems. His work integrates advanced mathematical modeling, nonsmooth analysis, and optimization techniques to address challenges in energy systems and chemical engineering. Recent research highlights include developing novel frameworks for energy storage feasibility analysis, optimizing LNG production processes, and advancing nonsmooth differential-algebraic equation (DAE) simulation tools. His projects often involve interdisciplinary collaborations with MIT’s Future Energy Systems Center and the U.S. Department of Energy. Barton has contributed to over 30 publications in 2021–2025 alone, covering topics from stochastic chemical systems to dynamic optimization of pharmaceutical manufacturing. His research portfolio demonstrates a strong emphasis on bridging theoretical advancements with practical industrial applications, particularly in decarbonization and sustainable energy infrastructure. Current work includes analyzing LAES economics in future electricity markets and improving decision-making under uncertainty for energy infrastructure investments.

• System Identification
• Stochastic Signal Processing
• Dynamic System Modelling
• +۴ مورد دیگر

Professor Brett Ninness is a faculty member at the University of Newcastle, Australia, holding the position of Professor in the School of Electrical Engineering and Computing. He has served in various administrative roles, including Assistant Dean (Research Training), Deputy Head of Faculty, and Acting Pro Vice Chancellor. His research focuses on dynamic system modeling, system identification, stochastic signal processing, and wireless communications. He has authored over 100 papers and held editorial roles in journals like Automatica and IEEE Transactions on Automatic Control. Education: Brett Ninness earned his BE, ME, and PhD in Electrical Engineering from the University of Newcastle. His teaching spans systems theory, signal processing, digital systems, and communications. Research Interests: He specializes in system identification, noise-robust signal processing, and applications in wireless communications. His work includes developing algorithms for state estimation, Kalman filtering, and nonlinear model identification. Recent projects involve collaborations with industry partners like Bell Labs and Agere Systems. Publications: His most recent works address smoothed state estimation, nonlinear system identification, and distributed Kalman filtering. Articles highlight advancements in stochastic processes, control systems, and Bayesian methods. Awards: Recipient of the 2001 Tall Poppy Award for outstanding scientific contribution. He is a member of editorial boards and international committees, including the IEEE Technical Committee on System Identification and IFAC. Grants & Supervision: Extensive grants in system identification and control theory. Supervised numerous PhD/Master’s students, though specific names are not listed here. Collaborations: Partnerships with institutions like Royal Institute of Technology (Sweden), Linköping University, and Vrije Universiteit Brussel. Active in global research networks through IEEE and IFAC committees.

• Control Engineering
• System Identification
• Bayesian Estimation
• +۵ مورد دیگر

Adrian Wills is an Associate Professor in the School of Engineering (Mechatronics) at the University of Newcastle, Australia. He leads the Mechatronics Engineering program and holds academic appointments since July 2015. His research focuses on Bayesian estimation, system identification, and control engineering, with applications in robotics and mechatronics. Wills has collaborated with institutions globally, including Linköping University, Uppsala University, and the University of British Columbia. He earned his B.E. (Elec.) and Ph.D. from the University of Newcastle in 1999 and 2003, respectively. Teaching expertise includes delivering advanced courses in estimation and optimization within the Mechatronics Engineering program. Administrative roles include program convenor for Mechatronics Engineering. Research highlights include contributions to state-space models, nonlinear system identification, and model predictive control. His work bridges theoretical advancements with practical applications in engineering systems and healthcare. Key collaborations involve Professors Lennart Ljung, Thomas Schön, and Bhushan Gopaluni, among others. His technical contributions span MATLAB toolboxes (e.g., UNIT), FPGA/ASIC implementations for control systems, and interdisciplinary projects in strain measurement using neutron diffraction.

Şule Şahinمشاهده پروفایل

مدرس ارشد

• Actuarial Science
• Stochastic Investment Models
• Mortality Modelling
• +۳ مورد دیگر

Dr. Şule Şahin is a Senior Lecturer in Actuarial Science at the University of York's School for Business and Society and Director of the Centre for Actuarial Compensation and Valuation of Life (CAVOL). She holds a PhD in Actuarial Mathematics from Heriot-Watt University and is a Fellow of the Institute and Faculty of Actuaries. Her affiliations include membership in the Ogden Working Party (developing injury compensation tables) and the AFIR-ERM board of the International Actuarial Association. Her research integrates actuarial mathematics, statistics, economics, and sociology, focusing on: Stochastic investment models for long-term applications Mortality modelling with transitory jumps (e.g., pandemics) Actuarial compensation systems for loss of earnings Longevity risk hedging and economic scenario generators Recent publications center on pandemic-influenced mortality modelling, actuarial compensation frameworks, and insurance pricing innovations, reflecting interdisciplinary applications across legal and socioeconomic contexts. Awards: SCOR Actuarial Award, Spain and Portugal (2023) Current Projects: Ogden Tables work-life expectancy estimation (UK government injury compensation) COVID-19 impact on public pension systems International actuarial compensation table development She leads CAVOL and collaborates globally, including research visits to Columbia University and the University of Witwatersrand funded by actuarial societies.

Robert Azencottمشاهده پروفایل

استاد

• Genomics
• Proteomics
• Medical Imaging
• +۹ مورد دیگر

Robert Azencott is a Professor of Mathematics at the University of Houston and holds the title of Emeritus Professor at École Normale Supérieure in France. He specializes in interdisciplinary research at the intersection of mathematics, biosciences, and imaging. His work spans stochastic processes, data mining, and medical imaging applications such as 3D-echocardiography analysis and deformable shape matching. Azencott has contributed to probabilistic methods for bacterial genetic evolution models and financial market microstructure analysis. His research also addresses texture classification, kernel-based learning, and stochastic differential equations (SDEs) in finance and biology. Research Interests Genomics & Proteomics : Sparse modeling of gene interactions, microRNA impact on cancer survival, proteomic mass spectra analysis. Bacterial Evolution : Stochastic models for genetic evolution with periodic selection and large deviations theory. Medical Imaging : 3D-echocardiography strain analysis, deformable shape dynamics via diffeomorphic matching, and ROI reconstruction in X-ray tomography. Probability & Statistics : SDE parameter estimation, kernel-based clustering, and applications to algorithmic trading and option pricing. Labs & Collaborations Azencott collaborates on projects such as digital stains for live-cell microscopy and intra-cardiac echography analysis to assess myocardium deformations. His work intersects with teams in cardiology, computational biology, and financial engineering.

Xuemiao Haoمشاهده پروفایل

دانشیار

• Risk Theory
• Financial Risk Management
• Stochastic Processes

Xuemiao (Samuel) Hao is an Associate Professor at the Warren Centre for Actuarial Studies and Research within the Asper School of Business at the University of Manitoba. He holds dual bachelor's degrees in mathematics and economics from Peking University, and PhD in statistics from University of Iowa. Research focuses on: Mathematical modeling of insurance/financial risks Applications of stochastic processes Credit derivative pricing Ruin theory with heavy-tailed distributions His methodological work employs advanced techniques including Lévy processes, Fourier transforms, and asymptotic analysis for risk quantification.

• Optimal Control
• Partial Differential Equations
• Mean Field Games
• +۶ مورد دیگر

Associate Professor at the University of Pavia's Department of Economics and Management, specializing in optimal control, partial differential equations, and mean field games with applications to economics and finance. Holds an Italian qualification (ASN) for associate professorship and a French qualification (maître de conférences). Research focuses on dynamic optimization, stochastic systems, and economic modeling of human capital, environmental quality, and pandemic impacts. Active in teaching mathematics for economics and optimization at undergraduate and graduate levels. Education: PhD in Mathematics from University of Padua (2015), postdocs at University of Graz (Austria) and University of Padua. Extensive visiting collaborations in France, Chile, and Brazil. Research Highlights: Pioneering work on mean field games modeling spatial interactions in human capital dynamics and environmental quality regulation. Contributions to sparse optimization, inverse problems in fracture mechanics, and large deviations in stochastic volatility models. Recent Publications Trends: Focus on infinite-dimensional mean field games (e.g., Hilbert spaces), pandemic economics, and environmental policy modeling. Over 15 peer-reviewed articles spanning optimal control, PDEs, and economic applications. Grants & Leadership: Coordinator of Italian GNAMPA projects on MFGs and environmental models. Organized conferences including AMASES 2022 and special sessions at international optimal control conferences. Referee for top journals like SIAM J. Control Optim. and J. Mathematical Economics. Teaching: Courses include 'Mathematics for Management,' 'General Mathematics,' and PhD-level 'Static and Dynamic Optimization.' Oversees tutoring programs and international collaborations via ERASMUS+.

• Finance
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

João Amaro de Matos is a Professor and Vice Rector at Nova University of Lisbon, holding dual appointments at the Nova School of Business and Economics. He has served as Associate Dean for International Development (2005–2017) and Director of PhD Programs. His administrative roles include membership on the Executive Board of the Community of European Management Schools (CEMS) and chairing its Research and Doctoral Education Committee. With a dual PhD in Management (INSEAD, 1995) and Physics (São Paulo University, 1988), he has held visiting positions at Heidelberg University, London School of Economics, and MIT. His teaching expertise spans Corporate Finance, Derivatives, and Continuous-Time Finance across multiple institutions. Dr. de Matos' education includes a Bachelor's in Physics (USP, 1981), a Master's in Physics (USP, 1984), a Bachelor's in Business Administration (FGV, 1983), and two PhDs (Physics and Management). He has authored foundational texts like Theoretical Foundations of Corporate Finance (Princeton, 2001) and Sustainability and Organizational Change Management (Routledge, 2015). His research bridges finance, stochastic processes, and social networks, with over 50 publications in journals such as Journal of Econometrics and Mathematical Finance . His publications demonstrate interdisciplinary reach, addressing topics from financial derivatives pricing to the dynamics of international academic cooperation. He initiated the Luso-Brazilian Finance Meetings (2007) and has advised numerous graduate students in Finance and Economics. His work frequently intersects with policy, exploring market liquidity constraints, network effects in corporate governance, and the impact of social networks on organizational change. De Matos' contributions extend beyond academia through leadership in international educational initiatives, including advising Cuban universities' internationalization and shaping European transnational education policies. His research methodologies blend mathematical rigor with applied policy analysis, reflecting his dual background in physics and management sciences.

Gustavo Schwenklerمشاهده پروفایل

دانشیار

• Asset Pricing
• Econometrics
• Risk Management
• +۳ مورد دیگر

Gustavo Schwenkler is an Associate Professor of Finance at Santa Clara University's Leavey School of Business. His research focuses on asset pricing, econometrics, risk management, and derivatives, with notable contributions to understanding systemic risk and financial networks. He holds a PhD in Management Science and Engineering from Stanford University (2013) and a diploma in Applied Mathematics and Economics from the University of Cologne. Educations: PhD in Management Science and Engineering, Stanford University, 2013 Master's Diploma in Applied Mathematics and Economics, University of Cologne Research Interests: Dr. Schwenkler explores how news and information shape financial markets, particularly in crypto markets and firm networks. He develops econometric models to analyze risk dynamics, default clustering, and systemic stability. His work bridges theoretical finance with practical applications in policy and risk management. Professional Roles: Associate Editor at Management Science , Journal of Business & Economic Statistics , and Digital Finance Board Member at Cointree and Indicia Labs Articles Trends: Recent studies emphasize crypto market dynamics, news-driven volatility, and systemic risk in financial networks. His 2020s work explores pandemic policy impacts and venture capital mechanisms, reflecting growing interest in real-world economic interventions and emerging financial instruments.

Michael Grabchakمشاهده پروفایل

استاد

• Tempered Stable Distributions
• Infinitely Divisible Processes
• Heavy Tails
• +۳ مورد دیگر

Michael Grabchak is a Professor in the Department of Mathematics and Statistics at the University of North Carolina at Charlotte (UNC Charlotte). His research focuses on tempered stable distributions, infinitely divisible processes, heavy-tailed phenomena, quantitative finance, and entropy estimation. He has developed software packages such as SubTS , SymTS , and EntropyEstimation for statistical analysis and simulation. His work bridges theoretical probability and applications in finance, statistics, and computational methods. Key contributions include studies on Lévy processes, risk estimation, and biodiversity metrics using entropy-based techniques. He has authored a monograph on tempered stable distributions and edited volumes on extreme value theory. His teaching includes advanced courses on applied probability and statistical methods, and he has presented his research at international conferences across the globe. Grabchak’s research has been published in top journals like Statistics and Computing , Journal of Applied Probability , and Quantitative Finance . He actively contributes to academic service through invited lectures, editorial roles, and software development for statistical education and applications.

Anna Frebelمشاهده پروفایل

استاد

• Observational Stellar Astrophysics
• Stellar Archaeology
• Dwarf Galaxy Archaeology
• +۳ مورد دیگر

Anna Frebel is a Professor of Physics and Division Head for Astrophysics at MIT. Her research focuses on observational stellar astrophysics, particularly studying the oldest, metal-poor stars and dwarf galaxies to understand the early universe's chemical and physical conditions. She leads the Frebel Research Group and collaborates with the MIT Kavli Institute for Astrophysics and Space Research and the Magellan Telescopes Consortium. Education: PhD in Astrophysics from Australian National University (2007) Postdoctoral positions at University of Texas (2006-2008) and Harvard-Smithsonian Center for Astrophysics (2009-2012) Her work bridges observations with cosmological simulations, using Magellan telescopes for high-resolution spectroscopy. Key interests include r-process nucleosynthesis, first stars/galaxies, and galactic archaeology. Awards Overview: APS Fellow (2022) NSF CAREER Award (2013) AAS Annie Jump Cannon Award (2010) Her advising and grants include MIT's Earll Murman Award (2019) and multiple international fellowships. She leads the Frebel Research Group and contributes to public science communication through lectures, articles, and her book Searching for the Oldest Stars .