جست‌وجوی استاد و پژوهشگر

• Synthetic Biology
• Biomedical Engineering
• Systems Biology
• +۷ مورد دیگر

Professor Richard Ian Kitney is Professor of Biomedical Systems Engineering in the Department of Bioengineering within Imperial College London's Faculty of Engineering. He serves as Chairman of the Institute of Systems and Synthetic Biology and Co-Director of the EPSRC National Centre for Synthetic Biology and Innovation. His institutional affiliations include the Centre for Synthetic Biology, Centre for Translational Nutrition and Food Research, and the Industrial Biotechnology Hub CsynBI. His academic qualifications include a DSc (Eng) from the University of London and a PhD in Electrical Engineering from Imperial College London. He holds professional designations as Chartered Engineer (CEng), Fellow of the Institution of Electrical Engineers (FIEE), and Chartered Scientist (CSci). Kitney's research focuses on synthetic biology, systems biology, and biomedical engineering, with emphasis on mathematical modeling, medical imaging, and engineering biology principles. He pioneered the integration of automation and standardization in biological design, establishing Imperial College as a global leader in synthetic biology through the development of the Imperial College Hub for Synthetic Biology and SynbiCITE, the UK's national industrial translation center. His work bridges fundamental research with industrial applications in biomanufacturing and sustainable solutions. Analysis of his recent publications reveals a decisive shift toward engineering biology as a systematic discipline, characterized by automation-driven workflows, biofoundry infrastructure development, and applications addressing global challenges including pandemic response and sustainable vaccine production. Key trends include standardization of biological parts, computational design tools, and policy frameworks enabling the advanced bioeconomy. His distinguished honors include: Order of the British Empire (OBE) for services to Information Technology in Biomedicine (2001) Fellowship in the World Technology Network (1999) Academician of the International Academy of BioMedical Engineering (2003) Honorary Fellowships of The Royal College of Physicians and Surgeons (2006) Fellowships of The Royal Academy of Engineering and Royal Society of Edinburgh Kitney has secured transformative funding through leadership of national initiatives including the EPSRC National Centre for Synthetic Biology and Innovation and SynbiCITE. He chaired The Royal Academy of Engineering Inquiry into Synthetic Biology and co-authored the UK Roadmap for Synthetic Biology, establishing critical policy frameworks. His grants portfolio emphasizes industrial translation, biofoundry development, and building the UK's engineering biology capability. He directs the Imperial College Synthetic Biology Hub (CsynBI) and The Foundry DNA synthesis facility, which form the core of Imperial's engineering biology infrastructure. These centers operate as integrated platforms connecting academic research with industrial applications through standardized workflows, automated design-build-test cycles, and collaborative projects with pharmaceutical and biotechnology companies.

George Papanicolaouمشاهده پروفایل

استاد

• Applied Mathematics
• Imaging Science
• Signal Processing
• +۳ مورد دیگر

George Papanicolaou is the Robert Grimmett Professor of Mathematics at Stanford University. His research focuses on applied mathematics, imaging science, signal processing, stochastic analysis, and mathematical finance. He holds a B.E.E. from Union College (1965), an M.S. and Ph.D. in Mathematics from New York University's Courant Institute (1967, 1969). Education: B.E.E., Union College, 1965 M.S., New York University (Courant Institute), Mathematics, 1967 Ph.D., New York University (Courant Institute), Mathematics, 1969 Research Interests: Papanicolaou's work bridges mathematical theory and practical applications. He explores imaging techniques in complex media, stochastic processes in financial systems, wave propagation, and sparse signal recovery. His methodologies often integrate advanced algorithms with real-world data challenges, such as noise mitigation and high-dimensional analysis. Articles Trends: Recent publications emphasize innovations in imaging (e.g., super-resolution in disordered media, 3D holographic reconstruction) and financial modeling (e.g., eigenportfolios, statistical arbitrage). His work also addresses robustness in signal processing and algorithmic design for incomplete or corrupted datasets. Advising & Grants: No specific advisees or grant details are listed in the provided texts. Labs/Teams: Affiliated with Stanford's Mathematics Department, though specific lab affiliations are not explicitly mentioned.

• Wireless Networks
• Mobile Computing
• Smart Grid Systems
• +۴ مورد دیگر

Vincent Gauthier is a Professor at Telecom Paris, part of Institut Polytechnique de Paris, with a distinguished research career spanning over two decades in mobile network analysis, urban mobility modeling, and smart infrastructure systems. His research portfolio demonstrates deep expertise in leveraging mobile network metadata for urban population estimation, transportation optimization, and smart grid applications. Dr. Gauthier's research interests center on wireless networks, mobile computing, smart grid systems, urban mobility analysis, and electric vehicle infrastructure. His work bridges theoretical network modeling with practical urban applications, particularly evident in his studies of mobility patterns in Paris and Peru. His research methodology combines mobile network metadata analysis with advanced computational techniques to address urban infrastructure challenges. His recent publication trends show a strategic pivot toward sustainable transportation solutions, with multiple 2024-2025 publications focusing on electric vehicle charging infrastructure optimization. Earlier work established foundational contributions to understanding urban dynamics through mobile network data, with significant publications on population estimation and transport mode detection. His research consistently demonstrates interdisciplinary approaches, connecting telecommunications engineering with urban planning and environmental sustainability. Dr. Gauthier has maintained long-term collaborations with researchers including Monique Becker, Michel Marot, and Hassine Moungla, with whom he has co-authored numerous publications over multiple years. His work shows particular strength in applications to Peru and other LMIC countries, reflecting a commitment to globally relevant research. His research methodology combines theoretical network modeling with practical urban applications, particularly evident in his studies of mobility patterns in Paris and Peru. His work bridges telecommunications engineering with urban planning and environmental sustainability, creating impactful solutions for modern cities.

Aygul Zagidullinaمشاهده پروفایل

مدرس ارشد

• Data Science
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Aygul Zagidullina is a Senior Lecturer and Researcher at the Lucerne University of Applied Sciences and Arts, specifically affiliated with the Lucerne School of Computer Science and Information Technology. She serves as Head of Applied Data Intelligence in Continuing and Executive Education, contributes to teaching and research in Data Science, Machine Learning, and Artificial Intelligence, and co-organizes academic initiatives like Women in AI and the Women in Data Science (WiDS) conference in Zürich. Ph.D. in Quantitative Methods (specialization: High-Dimensional Covariance Matrix Estimation) Diploma in Applied Mathematics (B.Sc. + M.Sc.) Postgraduate Machine Intelligence Program in Computer Science Certificate in Didactics for Higher Education Her research focuses on data-driven solutions at the intersection of Statistics, Computer Science, and Finance, with applications in portfolio optimization, NLP bias mitigation, and image quality assessment. She emphasizes the societal impact of AI and its integration into industry workflows. Recent projects include hydropower digital twins, AI for procurement documentation, and LLM training via user feedback. She authored a Springer textbook on High-Dimensional Covariance Matrix Estimation in 2021 and has co-authored peer-reviewed articles in the Journal of Financial Econometrics (2021, 2024). Her work spans theoretical rigor and practical implementation across finance, energy, and healthcare domains. Aygul contributes to academic leadership through roles such as Module Responsible in AI Competition (Kaggle Challenge), Co-Head of the CAS Machine Learning and Data Science for Medicine and Health programs, and Thesis Supervisor in AI & ML. She collaborates with industry partners (e.g., Paretolabs, Konplan) and leads research at the AI Robotics Research Lab.

Carlos M. Carvalhoمشاهده پروفایل

استاد

• Bayesian Statistics
• Causal Inference
• Time Series Analysis
• +۴ مورد دیگر

Carlos M. Carvalho is a La Quinta Centennial Professor in Business and Professor of Statistics at The University of Texas at Austin McCombs School of Business. He joined UT Austin in 2010 after serving on the faculty at the University of Chicago Booth School of Business. Carvalho also serves as the Executive Director of the Salem Center for Policy, which supports research, education, and dialogue around the impact of economic policies on markets and the free enterprise system. Dr. Carvalho's educational background includes: Ph.D. in Statistics, Duke University, 2006 His research focuses on Bayesian statistics in complex, high-dimensional problems with applications ranging from finance to genetics. Carvalho's work encompasses several key areas including causal inference , machine learning , policy evaluation , and empirical asset pricing . His research interests specifically include modern statistical tools for causal inference, dimensionality reduction in high-dimensional models, advanced statistics in asset pricing, and time series analysis. His work often bridges theoretical statistical methodology with practical applications in various domains. Analysis of Carvalho's recent publications reveals a strong focus on Bayesian methods, causal inference, and high-dimensional statistics. His work spans multiple disciplines including statistics, finance, medicine, and social science. A notable trend is the application of Bayesian techniques to complex real-world problems, particularly in causal inference and treatment effect estimation. His research shows increasing interdisciplinary collaboration, with applications in healthcare, finance, and social policy. Dr. Carvalho holds the prestigious La Quinta Centennial Professorship at UT Austin, recognizing his significant contributions to the field of statistics and business. While specific awards aren't detailed in the provided text, his extensive publication record demonstrates recognition by his peers. As an academic leader, Carvalho directs the Salem Center for Policy, which facilitates research on economic policies and market impacts. His work appears to involve collaboration with researchers across disciplines, particularly in finance, healthcare, and social sciences, though specific lab structures aren't detailed in the provided text.

Xinyi Xuمشاهده پروفایل

استاد

• Bayesian Statistics
• Model Uncertainty and Selection
• Decision Theory
• +۴ مورد دیگر

Xinyi Xu is a Professor of Statistics and Vice Chair for Graduate Studies at The Ohio State University's Department of Statistics. She is affiliated with the Institute for Population Research and serves as an Associate Editor for Bayesian Analysis . Her research focuses on Bayesian hierarchical modeling, decision theory, and high-dimensional data analysis with applications in biostatistics, sociology, and finance. Education Ph.D. in Statistics, University of Pennsylvania (2005) M.A. in Statistics, University of Pennsylvania (2003) B.S. in Mathematics, University of Science & Technology of China (2001) Research Interests Bayesian hierarchical modeling Statistical decision theory Model selection and averaging Applications in biostatistics, sociology, and finance Grants National Science Foundation grants (DMS-2015552, DMS-1613110, DMS-1209194) National Institute of Child Health grants National Center for Research Resources grants Labs/Teams Collaborates with the Translational Data Analytics center at Ohio State and interdisciplinary experts.

Juntao Duanمشاهده پروفایل

مدرس

• Machine Learning
• Statistics
• Data Science
• +۳ مورد دیگر

Juntao Duan is a Lecturer at the University of California, Santa Barbara (UCSB). His research focuses on machine learning, statistical methodologies, and their applications in healthcare and finance. Key areas include developing advanced algorithms for text classification, optimizing portfolio strategies through covariance voting, and analyzing medical data related to hemodialysis patients and SARS-CoV-2 infections. Notably, he employs deep neural networks and Gaussian processes in his work, alongside exploring theoretical foundations like random projection invariance principles. Duan has contributed to both applied and theoretical domains, with publications ranging from biomedical informatics to computational finance. His educational background and specific institutional affiliations beyond UCSB are not detailed in the provided text. No scientific awards or grants are explicitly mentioned, and he currently has no listed advisees or students. His office is located in Old Gym 1203 at UCSB, reflecting his active role within the university's academic community.

• Power Systems
• Smart Grid
• Power System Protection
• +۴ مورد دیگر

Dilan Senaratne is a Lecturer in the School of Electrical Engineering and Computer Science at Oregon State University, part of the College of Engineering. He joined the faculty in 2023 after completing his Ph.D. in Electrical and Computer Engineering with a minor in Artificial Intelligence at the same institution (2023), following an M.S. (2020) and B.Sc. (Honors, University of Moratuwa, Sri Lanka, 2015). His teaching portfolio includes courses on electrical fundamentals, power system analysis, protection, and smart grid technologies (ENGR 201, ECE 433/533, ECE 536, ECE 437/537). His research focuses on power system resilience, PMU data analysis, hardware-in-the-loop testing, and AI-driven solutions for energy infrastructure. He has authored publications addressing parameter correction algorithms, fault detection systems, and PMU-based event classification. Notable technical contributions include spatio-temporal analysis of PMU data for unsupervised event detection (2021) and sparse regression techniques for power network parameterization (2019). His early work included foundational contributions to 40Gbps Ethernet PCS implementation (2015). No awards or grants are explicitly listed in the provided materials. His research interests bridge electrical engineering fundamentals with modern analytical techniques, emphasizing grid stability, protection systems, and smart grid innovation. Current academic activities include advancing hardware-software co-design methods for power system testing environments.

Jonas Striaukasمشاهده پروفایل

استادیار

• Econometrics
• Statistical/Machine Learning Methods
• Mixed Frequency Data Econometrics
• +۳ مورد دیگر

Jonas Striaukas is an Assistant Professor of Statistics and Finance at the Copenhagen Business School (CBS), Department of Finance, and a Marie Skłodowska-Curie Action fellow. His research focuses on econometrics, statistical/machine learning methods, and mixed frequency data modeling. He holds a PhD from Université Catholique de Louvain (2022), supervised by Professors Andrii Babii and Eric Ghysels. Prior to CBS, he was a research fellow at Fonds de la Recherche Scientifique—FNRS in Belgium. Research Interests: High-dimensional econometric modeling, nowcasting techniques, factor-augmented regressions, and applications of machine learning to financial and macroeconomic data. His work emphasizes robust statistical methods for time series analysis and sparse/dense model comparisons. Key Projects: Developed the 'midasml' and 'FAS' R packages for econometric analysis. Conducted a 2022 project at the National Bank of Belgium on machine learning-based nowcasting models. Affiliations: Department of Finance, Center for Statistics, CBS. Member of the Marie Skłodowska-Curie fellowship program.

Laurent El Ghaouiمشاهده پروفایل

استاد

• Robust Optimization
• Decision-making under Uncertainty
• Statistical Estimation
• +۵ مورد دیگر

Laurent El Ghaoui is a Professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. He joined the faculty in 1999 after serving as Acting Associate Professor. Prior to Berkeley, he held academic positions at the École Nationale Supérieure de Techniques Avancées (Paris) and part-time roles at École Polytechnique and Université de Paris-I. His research focuses on robust optimization, decision-making under uncertainty, statistical estimation, and applications in air traffic management, bioinformatics, and finance. He is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media (BCNM), and CLIMB. El Ghaoui holds a Ph.D. in Aeronautics and Astronautics from Stanford University (1990) and a B.S. in Mathematics from École Polytechnique (1985). He has been recognized with prestigious awards, including the SIAM Optimization Prize (2008) and the CNRS Bronze Medal (1998). His work integrates optimization theory with real-world challenges, such as robust filtering, dynamic routing, and financial risk modeling. His research spans machine learning, control systems, and stochastic processes. Notable contributions include algorithms for sparse graphical models, distributionally robust optimization, and air traffic flow scheduling. He has collaborated on projects involving microarray data analysis and MEMS design. His academic career combines teaching and industry experience, including a leave at SAC Capital Management (2003–2006).

Lea Petrellaمشاهده پروفایل

استاد

• Quantile regression
• Graphical models
• Random Forest
• +۳ مورد دیگر

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

Michael Mumaمشاهده پروفایل

استاد

• Robust Data Science
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Sylvain Sardyمشاهده پروفایل

دانشیار

• statistical machine learning
• sparsity
• optimization
• +۶ مورد دیگر

Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.

Nurdal Watsujiمشاهده پروفایل

استادیار

• Electrical-Electronics Engineering
• Image Processing
• Signal Processing
• +۱ مورد دیگر

Nurdal Watsuji serves as Assistant Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, specializing in Electrical-Electronics Engineering with research emphases on Image Processing, Signal Processing, and Digital Design. His academic foundation includes: Doctorate in Electrical and Electronic Engineering from Imperial College London (1987-1991) Degree in Electrical-Electronic Engineering from Middle East Technical University, Gaziantep (1982-1985) Licence in Electrical Engineering from Middle East Technical University, Gaziantep (1975-1981) Watsuji's research integrates theoretical signal processing with practical engineering applications, particularly in LED/OLED lighting systems and image analysis. His work demonstrates consistent innovation in optical efficiency optimization, compressed sensing algorithms, and wavelet-based texture classification, bridging electrical engineering principles with real-world implementation challenges in energy systems and computer vision. Publication analysis from 2003-2016 reveals evolving expertise from foundational wavelet techniques to advanced optical efficiency modeling in solid-state lighting. The recurring theme involves mathematical optimization of signal/image processing methods for specific engineering constraints, with significant contributions to nonconvex compressed sensing and LED reflector design. His graduate mentoring spans eight students: PhD candidates: İpek Atık (2016) on LED reflector optimization and Taner Ince (2012) on compressed sensing Master's students: Ugur Polat (2022) on energy analyzers, Gulden Akay Ozbay (2008) on hand recognition, Taner Ince (2006) on medical image processing, Recep Oz (2004) on 3D rendering, Omer Cengiz Celebi (2003) on volume rendering, and Bayram Kara (2003) on wavelet texture analysis This diverse portfolio reflects sustained engagement with computational methods across lighting systems, biomedical imaging, and computer graphics applications.

• Applied Probability
• Financial Mathematics
• Numerical Analysis
• +۴ مورد دیگر

Jean-François CHASSAGNEUX is a Full Professor in Finance at Université Paris, holding a permanent position at CREST (Center for Research in Economics and Statistics). His academic career focuses on the intersection of probability theory, numerical analysis, and financial mathematics, with particular expertise in backward stochastic differential equations and their applications to financial modeling. His research interests span Applied Probability, Financial Mathematics, Numerical Analysis, Stochastic Analysis, Backward Stochastic Differential Equations (BSDE), Large Population Stochastic Control, Non-linear pricing methods, and Markets with imperfections . His work bridges theoretical mathematics with practical financial applications, particularly in derivative pricing, risk management, and sustainable finance. CHASSAGNEUX has developed novel numerical methods for solving complex financial models, including probabilistic approaches to non-linear partial differential equations and advanced techniques for hedging problems. His publication record demonstrates consistent contributions to top journals in mathematics and finance, with a recent focus on sustainable finance applications including carbon markets and impact investing. His collaborative work with researchers like D. Crisan, G. Pagès, and A. Richou has significantly advanced the field of probabilistic numerical methods for financial engineering. As an educator, CHASSAGNEUX teaches advanced courses in Financial Mathematics (APM_4FI02_AE) and Numerical Methods in Financial Engineering (APM_5FI10_AE), training the next generation of quantitative finance professionals. His teaching combines theoretical foundations with practical computational techniques essential for modern financial engineering.