جست‌وجوی استاد و پژوهشگر

Gabriella Boscoمشاهده پروفایل

استاد

• Optical Communications
• Fiber Optics Transmission Systems
• Digital Signal Processing (DSP)
• +۳ مورد دیگر

Gabriella Bosco is a Full Professor at Politecnico di Torino in the Department of Electronics and Telecommunications. She specializes in optical communication systems, focusing on fiber optics transmission, digital signal processing (DSP), and nonlinear effects mitigation. Her roles include Editor-in-Chief of the IEEE/OSA Journal of Lightwave Technology and Board member of the IEEE Photonics Society. She earned her Telecommunication Engineering degree (1998) and PhD (2002) from Politecnico di Torino. Research Interests: Performance analysis and design of optical transmission systems Application of DSP in optical links Fiber-optic sensor networks and anomaly detection Nonlinear interference mitigation techniques Awards: OSA Fellow (2017) IEEE Fellow (2019) 2014 & 2015 Best Paper Award in Journal of Lightwave Technology Professional Service: Program Chair/General Chair of OFC 2017-2019 Editorial roles in major photonics journals EU-funded projects: E-Photon/One, Nobel, BONE Labs & Groups: Leading the OptCom Group, a pioneer in optical communications research since 2000. Collaborates with industry leaders including Huawei and Cisco.

Ruggero Carliمشاهده پروفایل

دانشیار

• Control Systems
• Robotics
• Machine Learning
• +۶ مورد دیگر

Ruggero Carli is an Associate Professor at the Department of Information Engineering, University of Padova. His research focuses on control systems, robotics, and optimization, with emphasis on model-based reinforcement learning, distributed optimization algorithms, and energy systems. His work bridges theoretical advancements with real-world applications, including autonomous robotics, smart grids, and nonlinear control. Key contributions include physics-informed machine learning frameworks, ADMM-based distributed optimization methods, and MPC-driven control solutions for underactuated systems. Research interests include: Model-Based Reinforcement Learning for Robotics Nonlinear Model Predictive Control (NMPC) Distributed Optimization and ADMM Variants Energy Networks and Smart Grids Robot Dynamics and System Identification Recent publications emphasize: Continual learning for driver behavior analysis Physics-informed control for underactuated systems Robust optimization in unreliable networks Autonomous robotic manipulation with large language models His research integrates control theory with modern machine learning techniques, addressing challenges in edge computing, distributed systems, and real-time implementation.

Matt Sheldonمشاهده پروفایل

دانشیار

• Nanophotonics
• Nanomaterials
• Plasmonic Materials
• +۴ مورد دیگر

Dr. Matt Sheldon is an Associate Professor in the Department of Chemistry at the University of California, Irvine. His research focuses on optical energy conversion in nanoscale materials, particularly plasmonic and inorganic systems. Key areas include nanophotonics, nanomaterials, and applications in solar energy and photonic technologies. He leads the Sheldon Group, which explores topics like plasmon dynamics, perovskite nanocrystals, and vibrational strong coupling. Sheldon has secured significant funding from organizations like the National Science Foundation (NSF) and the Air Force Office of Scientific Research (YIP Award). His lab has trained numerous graduate and undergraduate students, emphasizing interdisciplinary approaches to energy and materials science. Notable achievements include pioneering work on light-induced magnetism in plasmonic nanoparticles and innovative studies on optical cooling using perovskite nanocrystals. Education & Background While specific educational details are not listed, his academic trajectory aligns with a career in physical chemistry and nanotechnology, given his research focus and faculty position. His group collaborates with institutions like Texas A&M University and Foothold Labs, reflecting a network of academic and industrial partnerships. Research Interests Optical energy conversion mechanisms in plasmonic systems Thermally activated single-photon up-conversion in perovskites Plasmoelectric effect for power conversion Vibrational strong coupling's impact on chemical reactions Surface polaron states in nanomaterials Optical refrigeration with perovskite nanocrystals Funding & Awards Sheldon has been awarded grants from NSF (CHE-1359175, DMR-2004810, etc.), the Welch Foundation (A-1886), and the Gordon and Betty Moore Foundation (GBMF6882). His 2016 Young Investigator Program (YIP) Award highlights recognition of his early-career contributions to plasmonics and nanophotonics. Lab & Collaborations The Sheldon Group operates at the intersection of chemistry and physics, hosting graduate students, postdoctoral researchers, and undergraduates. Current members include Ph.D. candidates like Boqin Zhao and Ju Eun Yim. Alumni have transitioned to academic and industry roles, reflecting the group's emphasis on training future leaders in nanotechnology.

Paul Carterمشاهده پروفایل

استادیار

• Dynamical Systems
• Partial Differential Equations
• Pattern Formation
• +۲ مورد دیگر

Dr. Paul Carter is an Assistant Professor in the Department of Mathematics at the University of California, Irvine, specializing in applied mathematics with focus on dynamical systems and pattern formation. His research examines nonlinear dynamics in biological and ecological contexts through PDE models. Primary research investigates vegetation pattern formation in dryland ecosystems, where he analyzes how sloped terrain influences stripe patterns through reaction-diffusion-advection models. Additional projects study neural pulse dynamics in FitzHugh-Nagumo systems and tumor invasion patterns in cancer models. He leads NSF-funded projects including a CAREER award on pattern formation in singularly perturbed PDEs and directs the Patterns and PDEs REU program. Recent publications demonstrate application of geometric singular perturbation theory to biological pattern formation. Teaching includes graduate PDE courses and undergraduate dynamical systems. He mentors undergraduate researchers through summer REU programs focusing on mathematical biology applications. Professional service includes editorial work for Physica D and organization of SIAM conferences on nonlinear waves. Recent invited talks address pattern-forming instabilities at international mathematics conferences.

Ivan Jeliazkovمشاهده پروفایل

دانشیار

• Bayesian Econometrics
• Markov chain Monte Carlo
• Econometric Modeling
• +۴ مورد دیگر

Ivan Jeliazkov is an Associate Professor of Economics and Statistics at the Department of Economics, University of California, Irvine. His research focuses on Bayesian econometrics and simulation-based inference, emphasizing methodologies like Markov chain Monte Carlo and econometric modeling. He holds a Ph.D. in Economics from Washington University in St. Louis and a BA in Economics and Business Administration from Coe College. His key research areas include Bayesian Econometrics, advanced simulation techniques, and causal inference applications. Notable work addresses heteroskedasticity in causal studies, quantile analysis of rental markets, and simultaneous equation models for discrete data. Recent advising includes 2024 Ph.D. graduates Robert MacDonald, Parush Arora, and Jieyu Gao. His articles span topics like dynamic factor models, regression discontinuity designs, and model comparison techniques. He has contributed to interdisciplinary research in marketing, finance, and historical economic analysis. His methodological innovations emphasize practical applications of Bayesian methods to address econometric challenges such as uncertainty quantification and model specification. Current work continues advancing computational tools for complex econometric problems.

Mehrdad Ghandhari Alavijhمشاهده پروفایل

استاد

• Power System Dynamics
• Power System Stability and Control
• FACTS and HVDC Systems
• +۳ مورد دیگر

Mehrdad Ghandhari Alavijh is a Full Professor in Electric Power Systems at KTH Royal Institute of Technology. He leads research in power system dynamics, stability, and control, with a focus on FACTS (Flexible AC Transmission Systems), HVDC systems, and advanced control strategies. His work addresses challenges in grid reliability, renewable energy integration, and environmental sustainability. Education: Received M.Sc. (1995) and Ph.D. (2000) in Electrical Engineering from KTH. His research group develops innovative methods for secure, efficient power system operation, leveraging advanced monitoring and communication technologies. Research Interests: Power System Dynamics and Stability Transient Stability Assessment HVDC and FACTS Technologies Renewable Energy Integration Publications: Recent works focus on HVDC systems, battery storage, transient stability algorithms, and frequency control. His studies emphasize real-time monitoring and control strategies to enhance grid resilience against outages. Teaching: Supervises courses in power systems engineering, including degree projects in Electric Power Systems and Energy Innovation. Serves as an examiner and course responsible for multiple graduate programs.

Qi Tangمشاهده پروفایل

استادیار

• High-Performance Computing
• Computational Plasma Physics
• Fusion Simulations
• +۳ مورد دیگر

Qi Tang is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. He joined Georgia Tech in 2024 after serving as a Staff Scientist at Los Alamos National Laboratory (LANL) from 2018 to 2024. His research focuses on computational plasma physics, high-performance computing, and scientific machine learning, with applications in fusion energy, plasma simulations, and structure-preserving neural networks. Education: Ph.D. in Applied Mathematics, Michigan State University, 2015 B.S. in Mathematics & Applied Mathematics, Zhejiang University, 2010 Research Interests: Qi’s work spans scalable numerical algorithms for exascale computing, fusion modeling, and scientific machine learning. Key areas include: High-order schemes, adaptive mesh refinement, and GPU acceleration for MHD and plasma simulations Structure-preserving neural networks for dynamical systems and multiscale physics Multi-physics modeling of tokamak disruptions and magnetic reconnection Grants & Collaborations: Principal Investigator (PI) for multiple DOE grants, including ASCR MMICC Center (CHaRMNET) Led a multi-institutional ASCR SciML team with LANL, ANL, and universities Recipient of LANL LDRD and NSF grants for fusion and plasma research Advising & Teaching: Advises Ph.D., master’s, and undergraduate students in CSE, physics, and engineering Teaches Parallel Computing Programming and Applications (CSE-6230) Co-advises students in DOE-funded programs and LANL collaborations Labs & Teams: Qi is affiliated with the DOE ASCR MMICC Center (CHaRMNET) and LANL’s Applied Mathematics and Plasma Physics Group. His team develops open-source tools like MFEM-based MHD solvers and structure-preserving ML frameworks.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +۵ مورد دیگر

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Giorgio Vinciguerraمشاهده پروفایل

پژوهشگر ارشد

• Data Compression
• Algorithm Engineering
• Learned Data Structures
• +۳ مورد دیگر

Giorgio Vinciguerra is a Research Fellow (RTD-A) at the Department of Computer Science of the University of Pisa since January 2023, and a member of the A³ Lab. His research focuses on compact data structures, data compression, and algorithm engineering, with a specialization in learned data structures that leverage machine learning to improve space-time trade-offs. He holds a PhD from the University of Pisa (2022), awarded the Best PhD thesis in Theoretical Computer Science by the Italian Chapter of EATCS. His academic journey includes postdoc research (2022), a visiting researcher role at KTH Royal Institute of Technology (2024), and Harvard University (2020). He has contributed to EU-funded projects like SoBigData.it and owns patents for innovations in data structure design. Key research interests include: Learned compression techniques for time series and string dictionaries Space-efficient indexing for massive datasets Algorithmic integration of machine learning into traditional data structures His work has been published in top venues including ICDE, Inf. Syst., and ACM Trans. Algorithms. Awards include the 2025 WSDM Outstanding Reviewer Award. He has co-supervised multiple theses on topics like adversarial query optimization and compressed indexing. Teaching roles include courses on programming, algorithms, and information retrieval at the University of Pisa. His software libraries (e.g., LeMonHash, PGM-index) are widely used in database systems and bioinformatics.

Raktim Bhattacharyaمشاهده پروفایل

استاد

• Uncertainty Quantification
• Robust Control
• Nonlinear Systems
• +۳ مورد دیگر

Raktim Bhattacharya is a Professor in the Department of Aerospace Engineering at Texas A&M University, serving as Director of the Graduate Studies Program. He holds a Ph.D. in Aerospace Engineering from the University of Minnesota (2003) and a B.Tech from IIT Kharagpur (1996). His research focuses on uncertainty quantification, robust control, and nonlinear systems, with applications in aerospace systems design and control. Dr. Bhattacharya leads the Intelligent Systems Research Laboratory (ISRL), advancing algorithms for next-generation aerospace systems operating in uncertain environments. His work integrates optimal control, stochastic modeling, and data-driven methods to enhance system reliability and performance. Notable contributions include probabilistic robustness analysis, model validation frameworks, and sparse sensing architectures. Recent research trends emphasize optimal transport theory for state estimation, privacy-aware machine learning, and sensor-actuator co-design for resource-constrained systems. His publications span topics like UAV configuration optimization, LPV control frameworks, and invariant set estimation using physics-informed neural networks. Dr. Bhattacharya’s lab collaborates on projects involving tensegrity structures, cyber-physical systems, and space situational awareness. His work addresses challenges in hypersonic flight dynamics, celestial navigation, and resilient control under actuator degradation.

Douglas Allaireمشاهده پروفایل

دانشیار

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +۴ مورد دیگر

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.

Cathy McLellanمشاهده پروفایل

دانشیار

• Interventional Cardiology
• Cardiac Program Management
• Medical Education
• +۳ مورد دیگر

Cathy McLellan is an Associate Professor and Interim Division Chair of Cardiology at Queen’s University, a role she has held since December 2024. She joined the Queen’s Cardiology Division in 2003 and served as Chair from 2017 to 2022. Dr. McLellan is also an interventional cardiologist at Kingston General Hospital, where she leads clinical roles in Percutaneous Coronary Interventions and Cardiac Program Medical Direction (2017–2020). Her work focuses on expanding regional cardiac care programs, such as the Southeastern Ontario Regional MI program, and overseeing postgraduate training in cardiology. Her expertise spans clinical cardiology, healthcare leadership, and medical education. She has held key administrative roles, including directing the Cardiology residency program and contributing to institutional governance through divisional leadership positions. While no specific awards are listed, her contributions to clinical practice and education are central to her professional profile. Dr. McLellan’s current focus includes advancing cardiac care protocols and maintaining operational excellence in hospital-based cardiology services. No formal research grants or student advisement details are provided in the text, though her administrative roles suggest involvement in training future clinicians.

Kevin Floresمشاهده پروفایل

دانشیار

• Mathematical Biology
• Precision Medicine
• Environmental Toxicology
• +۴ مورد دیگر

Kevin Flores is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), and Director of the Biomathematics Graduate Program. He leads the Flores Lab, focusing on developing mathematical and statistical methods for parameter estimation, uncertainty quantification, and forecasting in Precision Medicine, Environmental Toxicology, and Synthetic Biology. His work bridges computational approaches with biological systems analysis. Dr. Flores earned his PhD in 2009 from Arizona State University. His research groups include the Mathematical Biology cluster within the Department of Mathematics. His affiliations include Cox Hall 406D and the College of Sciences at NC State. Research interests emphasize interdisciplinary applications: (1) Mathematical Biology involving tumor heterogeneity, viral dynamics, and angiogenesis modeling; (2) Biostatistics focusing on parameter estimation in complex systems; and (3) Computational Tools for biomedical image analysis and machine learning in healthcare. His lab pioneered methods like biologically-informed neural networks and topological data analysis for biological systems. Recent work highlights include: (1) tumor spheroid modeling predicting clinical variability; (2) BK virus infection dynamics in transplant patients; (3) EEG-based brain-computer interface improvements using GANs; and (4) few-shot learning for plant phenotyping. His methodologies address challenges in sparse data scenarios and integrate mechanistic understanding with data-driven approaches. Awards and recognition : None explicitly listed in provided texts. Advising and grants: No specific advisees or grant details provided in texts. His lab's software tools support image segmentation and population modeling. Labs/teams: Directs the Flores Lab for Mathematical Biology at NC State, specializing in hybrid computational-experimental approaches. Collaborates across departments in biomathematics and engineering.

Mette S. Olufsenمشاهده پروفایل

استاد

• Mathematical Biology
• Cardiovascular Physiology
• Inverse Problems
• +۵ مورد دیگر

Mette S. Olufsen is a Professor in the Department of Mathematics at North Carolina State University (NC State), affiliated with the College of Sciences. She leads the Cardiovascular Dynamics Research Group and directs the DRUMS REU Program. Her research focuses on mathematical biology, cardiovascular physiology, and inverse problems, with expertise in differential equations and fluid mechanics applied to biomedical systems. Olufsen holds a PhD in Applied and Industrial Mathematics from Roskilde University (1998) and an MS in Mathematics and Computer Science from the same institution. Her work integrates computational models with experimental data to study cardiovascular, respiratory, and inflammatory systems. Notable projects include modeling pulmonary hypertension, autonomic cardiovascular control during orthostatic stress, and parameter optimization in single-ventricle patient models. She collaborates extensively on interdisciplinary projects, publishing in journals like Mathematical Biosciences and Biomechanics and Modeling in Mechanobiology . Her research groups span Control, Optimization, and Modeling; Mathematical Biology; and Numerical Analysis. She also contributes to educational initiatives through the REU program, fostering undergraduate research in biomathematics. Olufsen’s labs and teams focus on translating mathematical models into clinical applications, particularly in understanding cardiovascular dynamics under stress and disease. Her work bridges applied mathematics with biomedical engineering, emphasizing practical clinical relevance and parameter identifiability in complex biological systems.

Mingli Chenمشاهده پروفایل

دانشیار

• Econometrics
• Machine Learning
• Time Series Econometrics
• +۴ مورد دیگر

Mingli Chen is an Associate Professor of Economics at the University of Warwick’s Department of Economics. She holds affiliations including Turing Fellow at the Alan Turing Institute, External Fellow at the Centre for Panel Data Analysis (University of York), and Warwick-China Coordinator. Her research focuses on econometrics, machine learning, time series analysis, financial econometrics, and empirical industrial organization. She has served as an Associate Editor for the Journal of Econometrics since 2024 and organized workshops on data science and network analysis. Education: Ph.D. in Economics from Boston University (2015), B.A. in Information and Computing Science from Shanghai University (2009). She has held visiting positions at Stanford University, UC Berkeley, and the Federal Reserve Bank of Boston. Research Interests include high-dimensional econometrics, panel data models, social networks, quantile regression, and the integration of AI with econometrics. Key publications cover topics like quantile graphical models for systemic risk, latent panel quantile regression in asset pricing, and sparse β-models for network analysis. Awards include the International Partnerships Fund (2023), Turing PDRA Award (2020), and Co-Winner of the LABOUR Prize (2017). She advises Ph.D. students at Warwick and Cambridge, with placements at leading institutions like the University of Tokyo. Grants include leadership in UK-China partnerships and the Turing Institute. Teaching focuses on advanced econometrics at the Ph.D. level, including causal inference and machine learning. She co-organizes workshops and serves on conference committees, emphasizing data science and policy applications.