جستجوی استاد راهنمای دانشگاهی

Scott Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ مورد دیگر

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

Lam Jamesمشاهده پروفایل

استاد

• Networked control systems
• Vibration control
• Control theory
• +۷ مورد دیگر

Lam James is a Chair Professor of Control Engineering at the University of Hong Kong (HKU), affiliated with the Faculty of Engineering. He holds a BSc (1st Hons.) in Mechanical Engineering from the University of Manchester, and MPhil/PhD degrees from the University of Cambridge. His academic journey includes roles as a Croucher Fellow, Lecturer at the University of Melbourne, and faculty member at City University of Hong Kong before joining HKU in 1993. Professor Lam serves as Editor-in-Chief for four international journals including IET Control Theory and Applications and Journal of The Franklin Institute . His research focuses on networked control systems, vibration control, control theory, and multi-agent systems. With 600+ peer-reviewed publications, he maintains an H-index of 102 (Web of Science) and 108 (Scopus), recognized as a Highly Cited Researcher in multiple fields. Education: BSc Manchester (Mechanical Engineering), MPhil/PhD Cambridge Editorial Roles: 20+ journals, including leadership roles since 2012 His awards include Foreign Membership of Academia Europaea (2024), National Academy of Artificial Intelligence membership (2025), and two State Natural Science Awards (2015, 2019). He is a Fellow of multiple institutions including IEEE, IMechE, and HKIE.

Ying Chenمشاهده پروفایل

دانشیار

• Financial Statistics
• Machine Learning
• Quantitative Finance
• +۷ مورد دیگر

Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects

• Optimization for Machine Learning
• Collaborative Learning
• Distributed Learning
• +۷ مورد دیگر

Sebastian U. Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he has been since December 2021. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) since June 2020. His research focuses on optimization methods for machine learning, collaborative learning algorithms, privacy and security in distributed systems, and theoretical foundations of deep learning. Stich received his PhD in Theoretical Computer Science from ETH Zurich (2014), following a Master's in Mathematics at the same institution (2010-2014). Prior to CISPA, he worked as a research scientist at EPFL (2016-2021) and held positions at ETH Zurich and ICTEAM/CORE. He has been awarded the ERC Consolidator Grant 2024 , Google Research Scholar Award (2023), and Meta Privacy-Enhancing Technologies Research Award (2022). His team includes Dr. Anton Rodomanov (since 2023), Dr. Rotem Mulayoff (since 2024), Xiaowen Jiang (2023), Yuan Gao (2023), and notable alumni like Anastasia Koloskova (defended 2023). Stich actively organizes workshops (e.g., NeurIPS OPT 2024) and serves on editorial boards ( Journal of Optimization Theory and Applications , Transactions on Machine Learning Research ). He teaches advanced courses in optimization at Saarland University and has held visiting positions at MIT. Key scientific contributions include: Developing ProgFed for progressive federated learning (2021) Creating ProxSkip to accelerate communication in federated settings (2022) Formalizing SCAFFOLD with control variates for FL (2020) Introducing RelaySum mechanism for decentralized learning (2021) Proposing Lookahead-Minmax for GAN training (2021) His work addresses fundamental challenges in: Decentralized optimization theory Communication-efficient algorithms Privacy-preserving model training Handling heterogeneous data distributions Stochastic gradient dynamics Second-order optimization methods

Seddik M. Djouadiمشاهده پروفایل

استاد

• Robust Control
• Distributed Control Systems
• Wireless Communication
• +۶ مورد دیگر

Seddik M. Djouadi is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD from McGill University (1999), an M.A.Sc. from École Polytechnique (1992), and a B.Sc. from École Nationale Polytechnique (1989). His research focuses on robust control, distributed systems, wireless communication, model reduction, and power networks. He has authored over 100 publications and received awards including recognition as an outstanding reviewer by Automatica (2006-2007, 2003-2004) and the Tibbets Award (1999). Education: PhD in Electrical Engineering, McGill University, 1999 M.A.Sc., École Polytechnique, 1992 B.Sc., École Nationale Polytechnique, 1989 Research Interests: Robust and distributed control Wireless communication systems Model reduction for fluid flows Power network dynamics and renewable integration Biological systems modeling Cyber-physical systems security Awards: Outstanding Reviewer, Automatica (2006-2007, 2003-2004) Best Paper Award, Mediterranean Conference on Intelligent Systems (2008) Ralph E. Powe Junior Faculty Enhancement Award (2005) Tibbets Award (1999) Students and Advising: Djouadi has mentored over 20 graduate students and postdoctoral researchers, including current advisees Yichen Zhang and Ehsan Raoufat. Notable past students include Jin Dong (now at Oak Ridge National Laboratory) and Xiao Ma (Western Digital). Projects: Current research includes distributed control of large power networks, data-driven nonlinear model reduction, and stochastic modeling of wireless systems. Collaborations involve ORNL and TVA on energy systems and cybersecurity.

Giancarlo Ferrari Trecateمشاهده پروفایل

استاد مدعو

• Automatic control
• State estimation
• System identification
• +۷ مورد دیگر

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

Ioannis Panageasمشاهده پروفایل

استادیار

• Algorithmic Game Theory
• Multi-agent Reinforcement Learning
• Optimization
• +۲ مورد دیگر

Ioannis Panageas is an Assistant Professor in Computer Science at UC Irvine's Donald Bren School, directing the GOALLab. His research develops theory for learning in multi-agent systems, game dynamics, and optimization. Funded by NSF and NRF, he focuses on last-iterate convergence guarantees in games, efficient equilibrium computation, and multi-agent reinforcement learning. Recent Work: Provides first exponential lower bounds for fictitious play in potential games (NeurIPS 2023), efficient Nash equilibrium computation methods (ICLR 2023), and semi-bandit learning dynamics with no-regret guarantees (ICML 2023). Teaching: Offers courses in Algorithmic Game Theory and Optimization for Machine Learning. Currently advising 3 PhD students and 2 MS students.

Nezir KÖSEمشاهده پروفایل

استاد

• Econometrics
• Macroeconomics
• Financial Economics
• +۷ مورد دیگر

Nezir KÖSE is a Professor and currently serves as the Dean of the Faculty of Economics and Administrative Sciences at Beykent University. He has previously held academic positions at Istanbul Gelişim University and Gazi University, where he advanced from Research Assistant to full Professor. His academic career spans over three decades, with continuous contributions in teaching, research, and administrative leadership. Beykent University – Faculty of Economics and Administrative Sciences (2020–Present) Istanbul Gelişim University – Faculty of Economics, Administrative and Social Sciences (2017–2020) Gazi University – Faculty of Economics and Administrative Sciences (1990–2017) Education: Doctorate, Institute of Social Sciences, Gazi University (1992–1998) Degree, Faculty of Economics and Administrative Sciences, Gazi University (1990–1992) Licence, Faculty of Science, Gazi University (1985–1989) His primary research interests include Econometrics , Macroeconomics , Financial Economics , Time Series Analysis , and Energy and Environmental Economics . He has made significant contributions to the analysis of inflation, exchange rate volatility, foreign direct investment, oil price impacts, and financial stability, with a regional focus on Turkey and emerging markets. His recent publications (2023–2025) reflect a dynamic research agenda involving cryptocurrency markets , climate change economics , machine learning applications , and nonlinear macroeconomic modeling . His work frequently employs advanced econometric techniques such as panel data analysis, VAR/SVAR models, GARCH models, and time-varying parameter estimation. Scientific Awards: No awards mentioned in the provided text. Nezir KÖSE has supervised numerous graduate students, including PhD candidates who have completed theses on topics such as foreign direct investment, financial stability, oil price effects, and inflation uncertainty. He has also contributed to academic grants and collaborative research projects, particularly in energy and financial economics. He teaches core courses including Econometrics I & II , Time Series Analysis , and Nonparametric Statistics , demonstrating a strong commitment to pedagogy. He has authored several textbooks in econometrics and statistics, enhancing educational resources in Turkish academia. There is no indication of lab or team leadership, but his collaborative publications suggest active participation in research groups.

Abhijit Sarkarمشاهده پروفایل

استاد

• Dynamics
• Probabilistic Mechanics
• Uncertainty Quantification
• +۵ مورد دیگر

Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).

Hak-Keung Lamمشاهده پروفایل

استاد

• Fuzzy Control Systems
• Neural Networks
• Control Theory
• +۴ مورد دیگر

Dr. Hak-Keung Lam is a Reader in the Department of Engineering at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds an IEEE Fellowship and has been a Clarivate Web of Science Highly Cited Researcher since 2018. His research focuses on fuzzy control systems, neural networks, stability analysis, and their applications in biomedical and engineering domains. Education: Dr. Eng. (2000), B. Eng. (1995), both from Hong Kong Polytechnic University. Research Interests: Fuzzy modeling, neural network-based control, computational intelligence, machine learning, and biomedical applications such as ECG/EEG signal classification. His work bridges theoretical advancements with practical implementations in robotics, autonomous systems, and healthcare technology. Publications: Over 480 publications (as of 2023) in top-tier journals and conferences, with a focus on control systems, fuzzy logic, and intelligent systems. Recent work includes fault-tolerant control, cyber-physical systems, and explainable AI. Awards: IEEE Fellow (2019), 1st Place in PhysioNet Computing in Cardiology Challenge (2022). Grants/Projects: Active projects include fuzzy control system stabilization, autonomous robots in healthcare environments, and networked control of robotic systems. Labs/Teams: Center for Robotics Research, contributing to solutions for societal challenges through robot-centric approaches.

• Optimization for Machine Learning
• Collaborative Learning
• Federated Learning
• +۶ مورد دیگر

Sebastian U. Stich is a tenured Professor at CISPA Helmholtz Center for Information Security and a member of the European Lab for Learning and Intelligent Systems (ELLIS). His research focuses on optimization for machine learning, collaborative learning (distributed, federated, and decentralized methods), efficient optimization techniques, adaptive stochastic methods, and privacy/security in machine learning. Recent appointments include ERC Consolidator Grant 2024 for the CollectiveMinds project. Key contributions in federated/decentralized learning, uncertainty estimation, and communication-efficient optimization. Active in workshop organization, including the Optimization for Machine Learning workshop at NeurIPS 2024. Teaching modern optimization methods at Saarland University (2023-2025). His work has been recognized with awards such as the Google Research Scholar Award (2023) and Meta Privacy-Enhancing Technologies Award (2022) . His research group includes postdocs Dr. Anton Rodomanov and Dr. Rotem Mulayoff, and PhD students Xiaowen Jiang and Yuan Gao.

Efthymios Pavlidisمشاهده پروفایل

استاد

• Macroeconomics
• International Finance
• Time Series Econometrics
• +۴ مورد دیگر

Professor Efthymios Pavlidis is a faculty member in the Department of Economics at Lancaster University Management School (LUMS). He holds the rank of Professor and specializes in macroeconomics, international finance, and time series econometrics. His research focuses on housing market dynamics through collaborations like the International Housing Observatory (with the Federal Reserve Bank of Dallas) and the UK Housing Observatory. He is a Fellow of the Higher Education Academy, reflecting his commitment to academic excellence in teaching and research. His research interests include speculative bubble detection, real estate price forecasting, and testing parity conditions in financial markets. Pavlidis actively supervises PhD students in applied time series econometrics, emphasizing practical applications in financial markets and housing economics. He is involved in numerous academic activities, including organizing conferences and workshops such as the Dynare Conference and the Lancaster Economics Seminar. Key contributions include developing econometric methods for detecting market exuberance and analyzing real exchange rates. His work bridges theoretical econometrics with practical policy implications, particularly in housing and energy markets. Pavlidis collaborates internationally, evidenced by his participation in global academic networks and institutions like the European Economic Association and the Royal Economic Society. His teaching includes the course ECON222 Intermediate Macroeconomics I, and he maintains an office in the Management School (B015), with weekly office hours on Tuesdays. A comprehensive overview of his research and projects is available at his personal webpage: https://sites.google.com/view/etpavlidis/ .

Vito Latoraمشاهده پروفایل

استاد

• Complex Systems
• Network Science
• Applied Mathematics
• +۱۳ مورد دیگر

Vito Latora is a Professor of Applied Mathematics and Chair of Complex Systems at the School of Mathematical Sciences, Queen Mary University of London, and also holds the position of Professor of Theoretical Physics at the University of Catania. He leads the Complex Systems and Networks Group, driving cutting-edge research at the intersection of physics, mathematics, and interdisciplinary sciences. His research focuses on complex systems, particularly the structure and dynamics of networks, including multiplex, temporal, and higher-order networks such as simplicial complexes and hypergraphs. He explores applications in social, biological, financial, and cognitive systems, with recent work on creativity, innovation, and success through network analysis. The 15 most recent publications reveal a strong trend in advancing network theory beyond pairwise interactions, with a focus on higher-order structures, memory effects, synchronization, and epidemic spreading. His work combines rigorous mathematical modeling with real-world applications, often published in high-impact journals like Nature Communications , Physical Review Letters , and Science Advances . Dual communities in spatial and biological networks Modeling epidemics with limited detection resources Synchronization via higher-order and directed interactions AI-driven financial risk management Evolutionary games on hypergraphs Interdisciplinary success and funding dynamics Vito Latora has mentored several researchers who appear as co-authors, including Iacopini, Williams, Di Bona, and Lacasa. While specific grants are not listed, his collaborative projects with neuroscientists and anthropologists, along with frequent publications, suggest active funding. He is involved in major scientific events such as NetSci 2023, indicating leadership in the network science community. He leads the Complex Systems and Networks Group at Queen Mary, fostering a collaborative environment for studying complex systems through theoretical, computational, and data-driven approaches.

Nikolaus Hautschمشاهده پروفایل

استاد

• Econometrics
• Finance
• Market Microstructure
• +۳ مورد دیگر

Nikolaus Hautsch is a full Professor at the Faculty of Economics, Institute of Statistics and Operations Research. His work focuses on econometrics, finance, and high-frequency data analysis. Research Interests : Market microstructure, volatility modeling, transaction costs, systemic risk, and machine learning applications in finance. Publication Trends (2025–2018): 2025: High-dimensional portfolio optimization, dynamic systemic risk 2024: Blockchain asset arbitrage, DeFi, polarization metrics, jump detection 2023–2022: Microstructural noise, volatility forecasting, neural networks Scientific Awards : Fellow of the Society for Financial Econometrics (2014) Projects : Artificial Intelligence in Rowing (2022–2025) Vienna Graduate School of Finance (2018–2022) Risk management of CCPs

Mohsen Pourahmadiمشاهده پروفایل

استاد

• Time Series Analysis
• Multivariate Statistics
• Covariance Estimation
• +۷ مورد دیگر

Mohsen Pourahmadi is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing methodologies for modeling covariance matrices in multivariate and time series data, with applications to financial analysis, longitudinal studies, neuroeconomics, and high-dimensional data. Key tools include graphical lasso algorithms, Cholesky decomposition, and Bayesian approaches. He emphasizes extending generalized linear models (GLM) to covariance matrix estimation, leveraging prediction theory and stochastic processes. Education details are not explicitly provided in the text. His work spans theoretical advancements in covariance estimation, such as sparse VAR models, nonstationary process analysis, and regularized multivariate regression. He has contributed to applications like detecting cyber attacks on infrastructure systems and analyzing breast cancer data through Bayesian networks. Research interests include time series graphical models, antedependence models for longitudinal data, and regularization techniques for high-dimensional covariance matrices. His recent work explores fused-lasso penalties, Bayesian correlation matrix estimation, and stationary subspace analysis. Pourahmadi has authored numerous articles on topics ranging from multivariate volatility modeling to nonparametric covariance estimation, emphasizing both computational efficiency and theoretical rigor.