Find Academic Supervisors

Alicia Gonzalez-BuelgaView profile

Senior Lecturer

• Dynamics and Control
• Energy Harvesting
• Non-Linear Dynamics
• +4 more

Dr. Alicia Gonzalez-Buelga is a Senior Lecturer at the University of Bristol, affiliated with the School of Electrical, Electronic and Mechanical Engineering. She is an active member of the Dynamics and Control research group and contributes to multiple initiatives within the Cabot Institute for the Environment, including City Futures Research, Natural Hazards and Disasters Research, and Environmental Change Research. Her work bridges engineering dynamics with environmental challenges, focusing on practical solutions for seismic protection and sustainable infrastructure. Education: Bachelor of Engineering (BEng) from University of Oviedo, Spain Doctor of Philosophy (PhD) from University of Oviedo, Spain Dr. Gonzalez-Buelga's research spans two primary domains: structural dynamics and engineering education. In structural dynamics, she specializes in vibration control systems, energy harvesting devices, and seismic isolation strategies, with particular focus on developing low-cost solutions applicable in developing countries. Her work on rocking columns, tuned mass dampers, and inerter-based systems demonstrates innovative approaches to structural resilience. In engineering education, she investigates student experiences, particularly in online and blended learning environments, and develops outreach programs to enhance engagement with mechanical engineering. Her recent publications reveal a trend toward integrating practical engineering solutions with educational innovation. The research shows increasing attention to post-pandemic educational approaches while maintaining strong focus on vibration control and seismic protection technologies. Her work bridges theoretical dynamics with real-world applications, particularly in developing affordable seismic isolation systems and advancing engineering pedagogy through partnership and co-creation methodologies. Dr. Gonzalez-Buelga actively contributes to professional service, including peer review activities for journals such as Vibration. Her research collaborations span multiple disciplines, connecting engineering dynamics with environmental challenges through the Cabot Institute. She is involved with the Dynamics and Control research group at Bristol and contributes to several Cabot Institute initiatives focused on urban resilience and environmental change. Her work connects structural engineering solutions with broader environmental sustainability goals, particularly in the context of natural disaster mitigation and sustainable infrastructure development.

Veronica PiccialliView profile

Professor

• Nonlinear Optimization
• Semidefinite Programming
• Game Theory
• +6 more

Veronica Piccialli is a Full Professor at the Department of Computer, Control and Management Engineering "Antonio Ruberti" (DIAG) at Sapienza University of Rome. She teaches Geometry I for Management Engineering and Optimization Methods for Machine Learning for Data Science. Previously, she was Associate Professor at the University of Rome Tor Vergata (2020-2021) and Researcher there from 2008 to 2020. She serves as Associate Editor for INFORMS Journal on Computing (since 2019) and EURO Journal on Computational Optimization (since 2021). Dr. Piccialli earned her degree in Computer Engineering (summa cum laude) and PhD in Operations Research from Sapienza University of Rome in 2000 and 2004 respectively. In 2006, she completed a postdoc at the Combinatorics & Optimization department of the University of Waterloo, Canada. She obtained Italian national scientific qualifications as Associate Professor in 2013 and as Full Professor in 2017. Her research focuses on the intersection of optimization and machine learning, with particular expertise in nonlinear optimization, semidefinite programming, and mixed integer nonlinear programming. She applies these methods to diverse areas including Brain Computer Interfaces, electric consumption disaggregation, and process engineering for membrane systems. Her work demonstrates how advanced optimization techniques can enhance machine learning algorithms and solve complex engineering problems. Her recent publications show a strong trend toward integrating optimization with machine learning, particularly in clustering algorithms, support vector machines, and neural networks. She has developed exact algorithms for semi-supervised learning problems and applied optimization techniques to real-world challenges in logistics, energy systems, and process engineering. Her interdisciplinary approach bridges theoretical advances in optimization with practical applications across multiple domains. Dr. Piccialli has authored or co-authored over 40 articles in prestigious journals including Mathematical Programming, SIAM Journal on Optimization, IEEE Transactions on Neural Networks and Learning Systems, and Computational Optimization and Applications. She has also contributed 3 refereed book chapters to international publications. As an educator, she supervises student research and teaches advanced courses in optimization methods and geometry. Her teaching materials demonstrate a commitment to connecting theoretical concepts with practical applications, particularly in data science and machine learning contexts. Her current research involves collaborations with Université de Lorraine on membrane systems for gas filtration and with RFI (Rete Ferroviaria Italiana) on optimizing last-mile facilities for freight trains, demonstrating the real-world impact of her optimization expertise.

Martin SahlénView profile

Associate Professor

• astrophysics
• astronomy
• cosmology
• +9 more

Martin Sahlén is an Associate Professor in the Department of Physics and Astronomy at Uppsala University, specializing in astronomy with a focus on astrophysics. His research spans theoretical and observational studies of the large-scale structure of the universe through galaxy clusters, voids, and the universe's first galaxies to understand galaxy physics, star formation, dark energy, dark matter, cosmic inflation, and the reionization process in the early universe. Dr. Sahlén's research interests center on cosmology, particularly the intersection of theoretical models and observational data. He employs Bayesian inference techniques to analyze cosmological datasets, with special attention to the philosophy of cosmology, including model inference, fine-tuning of natural constants, and multiverse explanations. His work bridges observational astronomy with theoretical physics to address fundamental questions about the universe's structure and evolution. His recent publications (2024-2025) predominantly focus on the Euclid space mission, representing a major portion of his scholarly output. These works span instrumentation, data analysis techniques, cosmological simulations, and theoretical interpretations. Key themes include constraints on modified gravity models, galaxy cluster analyses, weak lensing studies, and machine learning applications for extracting physical parameters from astronomical data. His research demonstrates a consistent focus on leveraging next-generation observational data to test fundamental cosmological theories. Dr. Sahlén serves as Principal Investigator for multiple significant research grants, including: (1) 'The Galaxy – Dark Matter Connection with HST, JWST and EUCLID' (2021-2026, 8 MSEK); (2) 'Searching for New Physics in the Early Universe' (2022-2024, 1 MSEK); and (3) 'Astronomical object classification and anomaly detection with complex spectroscopic 4MOST data sets' (2023-2027, 4 MSEK). He actively participates in major scientific collaborations including 4MOST, BUFFALO, Euclid and SKA Observatory, and contributes externally to the Dark Energy Survey. As an educator, Dr. Sahlén is responsible for and teaches the interdisciplinary course 'Three Approaches to Cosmology' (10 credits), which likely reflects his broad expertise spanning observational, theoretical, and philosophical aspects of cosmology.

Remi DraiView profile

Professor

• Guidance, Navigation, and Control (GNC)
• Aerospace Engineering
• Spacecraft Dynamics
• +4 more

Remi Drai is Professor of Mathematics at Grove City College, Pennsylvania. His primary appointment resides within the Department of Mathematics, where he teaches and conducts research in applied mathematics with a strong emphasis on guidance, navigation, and control (GNC) systems for aerospace applications. Prof. Drai's research portfolio, as evidenced by more than two-dozen peer-reviewed publications between 1997 and 2014, centers on the mathematical foundations of spacecraft navigation and control. His interests span vision-based landing algorithms for lunar and planetary missions, robust multi-objective control synthesis via convex optimization, and autonomous guidance schemes for near-Earth asteroid rendezvous. A recurring theme is the fusion of inertial sensors with vision/GNSS data to achieve pinpoint landing accuracy under stringent mission constraints. Across his publications, Drai addresses both theoretical contributions—such as dissipative control theory and linear matrix inequality (LMI) methods—and practical validation through laboratory demonstrations and flight testing. This dual focus highlights a commitment to bridging rigorous mathematical analysis with real-world aerospace engineering challenges. Laboratory & Collaborations: While explicit laboratory names are not provided, the breadth of flight-test campaigns (e.g., precision landing GNC facility tests, ExoMars EDL demonstrator) indicates extensive collaboration with European space agencies and industry partners. Contact: Email: DraiR@gcc.edu Phone: 724-458-2105

• Radiation Dosimetry
• Medical Physics
• FLASH Radiotherapy
• +7 more

Maurizio Marrale serves as Associate Professor in the Department of Physics and Chemistry at the University of Palermo (Unipa), holding position PHYS-06/A. His office is located at the Department of Physics and Chemistry "Emilio Segre" (DIFC), Viale delle Scienze, Building 18, with regular office hours on Thursdays from 3:00 PM to 5:00 PM. His research spans radiation dosimetry, medical physics, and advanced imaging techniques. Key focus areas include FLASH radiotherapy verification using alanine/EPR dosimetry and silicon carbide detectors, transcranial magnetic resonance-guided focused ultrasound for neurological disorders (particularly essential tremor), and MRI-based dosimetry with Fricke gels. His work bridges physics principles with clinical applications in radiation oncology and diagnostic imaging. Analysis of his 15 most recent publications reveals strong emphasis on Ultra-high dose rate (UHDR) beam characterization for FLASH radiotherapy Development of novel dosimetry systems (SiC detectors, ionoacoustic sensors) Advanced neuroimaging techniques for thalamic targeting Diffusion correction methodologies in 3D dosimeters Clinical MRI applications for neurological disorders His research demonstrates consistent integration of computational modeling (Geant4, deep learning) with experimental validation. As principal supervisor, he has guided over 35 master's theses at Unipa between 2009-2024, primarily in Physics and Medical Radiology Techniques programs. His supervisory work focuses on radiation dosimetry applications, MRI physics, and radiotherapy technology validation.

• Missing Data Imputation
• Machine Learning
• Healthcare Informatics
• +3 more

Thu Nguyen is a Research Fellow in the Department of Holistic Systems at Simula Metropolitan, a leading Norwegian research institute specializing in data science and computing. Her work focuses on developing robust methodologies for handling missing data across critical domains including healthcare diagnostics and environmental monitoring systems. Her primary research interests include: Advanced missing data imputation techniques Machine learning explainability under incomplete data Healthcare informatics and medical data analysis Environmental data processing for air quality prediction Multimodal data integration challenges Analysis of her publication record reveals a strong trajectory in developing computationally efficient imputation algorithms with real-world applications. Key trends include the application of principal component analysis and generative models to healthcare datasets (sperm tracking, medical imaging), environmental monitoring (air quality prediction), and multimodal systems. Her work consistently bridges theoretical statistical foundations with practical implementation needs, emphasizing model transparency and performance validation. Dr. Nguyen actively collaborates within Simula Metropolitan's data science ecosystem, contributing to interdisciplinary projects that address fundamental challenges in data completeness and usability across scientific domains.

Raj Rao NadakuditiView profile

Associate Professor

• Statistical Signal Processing
• Random Matrix Theory
• Machine Learning
• +7 more

Raj Rao Nadakuditi is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, College of Engineering. His research bridges statistical signal processing, random matrix theory, and machine learning with applications in sensing, imaging, and communications. Research Interests: Statistical Signal Processing and Learning Random Matrix Theory and Applications Signal Processing for Sensor Networks Light Transport Through Opaque Media Eigen-wavefront Imaging for Brain Circuit Mapping High-Dimensional Data Analysis His recent work focuses on overcoming fundamental limits in imaging through highly scattering media, such as brain tissue, by leveraging eigen-wavefronts and algorithmic innovations. This has direct implications for the BRAIN Initiative and optical neuroimaging. Publications Trends: His most recent articles reflect a strong emphasis on the application of random matrix theory to signal detection, covariance estimation, and imaging through random media. Key themes include eigenvalue analysis, high-dimensional statistics, spectral methods, and wavefront shaping for deep-tissue imaging. Scientific Awards: DARPA Young Faculty Award (2014) ONR Young Investigator Program Award AFOSR Young Investigator Program Award Advising and Grants: Prof. Nadakuditi has advised students in advanced signal processing and machine learning. His research is generously supported by the National Science Foundation, DARPA, Office of Naval Research, Air Force Office of Scientific Research, and Army Research Office’s MURI program. He has led projects on fundamental limits of imaging and robust signal processing in low-SNR environments. Labs and Teams: He collaborates with Prof. Eric Michielssen on computational electromagnetics and is part of the broader EECS research community at the University of Michigan focused on autonomy, AI, and precision health. His group works on theoretical and applied aspects of sensing and imaging systems.

Gunnar SchmidtmannView profile

Associate Professor

• Visual Neuroscience
• Shape Perception
• Object Recognition
• +19 more

Gunnar Schmidtmann is an Associate Professor of Optometry and Vision Science at the School of Health Professions, University of Plymouth, where he has been employed since 2017, first as a Lecturer and promoted to Associate Professor in 2021. He holds a PhD in Vision Science from Glasgow Caledonian University and completed postdoctoral research at McGill University in Montreal, Canada. He is actively involved in teaching, research, and supervision, with a focus on visual perception and clinical vision science. Education: PhD in Vision Science, Glasgow Caledonian University, UK (2008–2013) BSc in Optometry, University of Applied Sciences, Lübeck, Germany (2005–2008) Medical School, University of Lübeck, Germany (2001–2005) Research Interests: His research spans visual neuroscience, shape perception, object recognition, visual psychophysics, and face perception. He investigates contour and curvature encoding, spatial and probability summation, peripheral vision, visual illusions, and the visual functions of patients with traumatic brain injury, stroke, and glaucoma. His work combines computational modeling with experimental psychophysics. Publication Trends: His recent articles (2023–2025) focus on sensorimotor aging, tablet-based vision testing, letter identification biases, and visual illusions such as the 'Pint Glass Illusion'. These reflect a strong trend toward developing accessible, quantitative tools for clinical and research use, while maintaining a deep theoretical interest in visual perception mechanisms. Scientific Engagement: Active member and elected committee member of the Applied Vision Association (AVA) Principal contributor to the McGill Face Database for complex mental state recognition Regular presenter at AVA and ECVP conferences Supervision and Grants: He supervises PhD students and is a Co-Investigator on an active NHS-funded project Portable Low-Cost Haptic Interfaces for Motor Injury Assessment (2025–2026). His teaching includes module leadership in Visual Perception, Human Anatomy, and Applied Quantitative Research Methods at both undergraduate and postgraduate levels. Labs and Teams: He leads research within the Eye and Vision Research Group at the University of Plymouth and collaborates with the Centre for Cognitive Neuroscience at Brunel University London. His lab focuses on psychophysical experiments and computational modeling of visual perception.

• Control Systems Engineering
• Nonlinear Systems
• Observer Design
• +7 more

Rajesh Rajamani is a Professor in the Department of Mechanical Engineering at the University of Minnesota, College of Science and Engineering. His research spans control systems, nonlinear dynamics, and applications in transportation and medical cyber-physical systems. He actively leads multiple funded research projects and contributes to interdisciplinary innovation. His research interests lie at the intersection of control theory and real-world engineering applications. Key areas include nonlinear observer design , actuator and sensor systems , vehicle dynamics and safety , and medical cyber-physical systems . He applies these to diverse domains such as autonomous vehicles, smart infrastructure, combustion engines, and closed-loop drug delivery. The recent publication trends reflect a strong focus on intelligent transportation (e.g., e-scooter safety, smart traffic signs), health monitoring using non-intrusive sensors, and control of pharmacological systems. His work combines deep theoretical control methods with practical implementation using real-time estimation, sensor fusion, and machine learning. Principal Investigator on multiple NSF and DOT-funded projects Expert commentator in media on transportation safety and automation Active collaborations with researchers in mechanical, biomedical, and electrical engineering Extensive publication record in top journals including IEEE Transactions and Mechanical Systems and Signal Processing Rajamani has secured significant grant funding, including recent awards from the National Science Foundation and the Minnesota Department of Transportation, supporting projects on smart traffic systems, non-contact actuator sensing, and medical cyber-physical systems. These grants highlight sustained research impact and leadership. He is involved in developing innovative technologies through university-industry partnerships and technology transfer initiatives, particularly in sensing and control for industrial and medical applications. His lab focuses on real-time estimation, feedback control, and system integration for safety-critical systems.

• Stochastic Control
• Computational Finance
• Machine Learning
• +7 more

Christoph Reisinger is a Full Professor of Applied Mathematics at the Mathematical Institute, University of Oxford, a position he has held since 2017 (previously Associate Professor from 2006-2017). He is a Fellow at St Catherine's College (since 2006) and an Associate Member of the Oxford-Man Institute of Quantitative Finance (since 2008). He serves as Director of Graduate Studies (Teaching) at the Mathematical Institute (2023-2026) and was Co-Director of the Centre for Doctoral Training on The Mathematics of Random Systems (2022/23). He is also a member of the Mathematical and Computational Finance Group (since 2006) and the Data Science Group (since 2018). Professor Reisinger's research lies at the intersection of stochastic modeling, computational mathematics, and machine learning. His work spans stochastic control, numerical analysis of nonlinear PDEs, mathematical foundations of reinforcement learning and deep learning, and computational finance. Current research focuses on approximation and reinforcement learning of stochastic control problems, mathematical analysis of neural networks, mean-field limits of particle systems, numerical approximation of high-dimensional SDEs and PDEs, and mathematical modeling of financial markets. His publication record shows a strong trend toward integrating machine learning with traditional computational finance methods. Recent work explores policy gradient methods for stochastic control, mean-field game theory applications to financial markets, and novel numerical schemes for McKean-Vlasov equations. His research bridges theoretical mathematics with practical applications in quantitative finance, particularly in option pricing, risk management, and market microstructure modeling. Professor Reisinger has supervised numerous doctoral students, many of whom have gone on to prestigious positions in academia and industry. His current PhD students include Boris Baros, William Gibson, Matthieu Meunier, Zihan Guo, Maria Olympia Tsianni, Filippo De Angelis, and Michael Giegrich. His past students include several who have received awards such as the Graduate Paper Competition First Prize and G-Research DPhil Prizes. He serves as Editor-in-Chief of the Journal of Computational Finance (since 2018) and holds associate editor positions at Applied Mathematics and Optimization (2022-2024), Applied Mathematical Finance (since 2018), and International Journal of Computer Mathematics (since 2012).

Daniel KuhnView profile

Professor

• Data-driven Optimization
• Stochastic Programming
• Robust Optimization
• +3 more

Daniel Kuhn is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he holds the Chair of Risk Analytics and Optimization in the College of Management of Technology. His research focuses on developing computational methods for data-driven decision-making under uncertainty, with applications in engineered systems, machine learning, and finance. Previously, he held positions at Imperial College London and Stanford University. Education includes: PhD in Economics, University of St. Gallen MSc in Theoretical Physics, ETH Zurich Research interests span data-driven optimization , stochastic programming , and robust decision-making frameworks . His work develops computationally tractable methods for uncertainty quantification in complex systems, bridging operations research with statistical learning. Current investigations focus on distributionally robust optimization using Wasserstein metrics and applications in energy markets and fair machine learning. Publication analysis reveals three primary trends: 1) Fundamental advances in distributionally robust optimization theory, 2) Machine learning applications with uncertainty guarantees, and 3) Energy system optimization under regulatory constraints. His methodological work consistently emphasizes computational tractability and practical applicability. As lab director of the Risk Analytics and Optimization group, he leads research on: Stochastic control systems Data-driven decision frameworks Robust machine learning Current PhD students include researchers working on federated learning fairness, optimal power flow, and reinforcement learning theory. Past graduates have made significant contributions to Wasserstein distributionally robust optimization and vehicle-to-grid frequency regulation.

Bryan RoutledgeView profile

Associate Professor

• Finance
• Artificial Intelligence
• Blockchain Technology
• +7 more

Bryan Routledge is an Associate Professor of Finance at Carnegie Mellon University's Tepper School of Business. He holds a Ph.D. from the University of British Columbia (1996) and a Bachelor of Commerce from Queen's University (1987). His academic affiliations include CyLab Security and Privacy Institute where he researches blockchain and cryptocurrency economics. Research interests span quantitative finance with AI/NLP applications, including: Text analysis of financial disclosures and social media Cryptocurrency markets and blockchain systems Asset pricing dynamics and macroeconomic forecasting Behavioral finance and investor decision-making Energy economics and climate finance applications Recent publications demonstrate strong interdisciplinary focus, with 62% applying NLP/AI to finance (2021-2023), 25% analyzing blockchain/crypto systems, and 13% examining behavioral asset pricing. Research consistently bridges computational methods with market analysis. Teaching includes MBA core finance, Financial Economics (MSCF), and specialized courses like 'Alpha: Implementing Quantitative Strategies' and 'Fintech'. Actively involved in Tepper's Executive Education programs. Holds leadership roles as Secretary/Treasurer of the Western Finance Association and Co-Chair of Tepper Quad Working Group. Research featured in national media regarding cryptocurrency regulation challenges.

Feng XuView profile

Associate Professor

• 3D Vision
• Computer Graphics
• Digital Health
• +4 more

Feng Xu is an Associate Professor at the School of Software, Tsinghua University, Beijing, China. His research bridges computer science , medicine , and neuroscience , focusing on interdisciplinary applications in 3D vision, digital health, and human motion capture. Education : Ph.D. in Automation (2007-2012) and B.S. in Physics (2003-2007), both from Tsinghua University. Research Interests : 3D Vision, Computer Graphics, Digital Health, Physics-based Modeling, and Real-time Human Motion Tracking. His work explores 3D vision and graphics through applications like IMU-based motion capture , NeRF editing , and facial geometry reconstruction . Recent publications emphasize integrating physics-aware models with deep learning for real-time performance, particularly in healthcare contexts such as mediastinal neoplasm diagnosis and radiology report generation . Notable contributions include the EditableNeRF framework for topology editing and Transformer IMU Calibrator for dynamic sensor calibration. His work has received the Best Student Paper Award at IEEE ICME 2019 . Contact : Email: xufeng2003@gmail.com Email: feng-xu@tsinghua.edu.cn

Stephen P. BoydView profile

Professor

• Convex Optimization
• Control Theory
• Mathematical Optimization
• +5 more

Stephen P. Boyd is the Samsung Professor in the School of Engineering and Professor in the Department of Electrical Engineering at Stanford University. He is also a Member of the Institute for Computational and Mathematical Engineering. His office is located in Packard 254 at 350 Jane Stanford Way, Stanford, CA 94305. Professor Boyd's research spans multiple domains where mathematical optimization serves as the unifying framework. His work focuses on convex optimization theory, algorithms, and applications across diverse fields including control systems, signal processing, machine learning, finance, and circuit design. He has pioneered methods for real-time embedded convex optimization and developed software frameworks that have made optimization accessible to researchers and practitioners across disciplines. His recent publications demonstrate a consistent trend toward practical applications of convex optimization, with significant emphasis on financial engineering (portfolio optimization, tax-efficient strategies), machine learning integration (differentiable optimization, parametric models), and computational efficiency (GPU acceleration, code generation). The breadth of co-authors across different papers highlights his collaborative approach spanning electrical engineering, computer science, finance, and applied mathematics. Professor Boyd teaches several optimization-related courses including ENGR108 (Introduction to Applied Linear Algebra), EE364a (Convex Optimization I), and EE364b (Convex Optimization II), maintaining an active teaching schedule with courses planned through the 2025-26 academic year. His office hours are held on Tuesdays from 1:15pm-2:30pm in Packard 254.

Martin WainwrightView profile

Professor

• Statistics
• Machine Learning
• Information Theory
• +3 more

Martin Wainwright is the Cecil H. Green Professor in Electrical Engineering and Computer Science and Mathematics at MIT, affiliated with the Laboratory for Information and Decision Systems and the Statistics and Data Science Center. He joined MIT in 2022 from UC Berkeley, where he held the Howard Friesen Chair. He earned his B.Math from the University of Waterloo and his PhD from MIT in Electrical Engineering and Computer Science. His research focuses on statistics, machine learning, information theory, and optimization. Wainwright has received numerous awards, including the COPSS Presidents’ Award, Blackwell Award, and Sloan Fellowship. He co-authored influential books on graphical models, sparse statistical modeling, and high-dimensional statistics. His work spans theoretical foundations and applications in reinforcement learning, statistical inference, and algorithmic design. He advises students in interdisciplinary areas and leads research initiatives at the MIT Institute for Data, Systems, and Society (IDSS), fostering collaborations across engineering, statistics, and data science.