Find Academic Supervisors

Leonie BaumannView profile

Assistant Professor

• Economic Theory
• Networks
• Mechanism Design
• +1 more

Dr. Leonie Baumann is an Assistant Professor in the Department of Economics at McGill University, specializing in Economic Theory, Networks, Mechanism Design, and Game Theory. She holds a Ph.D. (summa cum laude) and M.Sc. in Economics from the University of Hamburg, and dual B.A. degrees from the University of Siegen. Currently on maternity leave, Dr. Baumann previously served as a Postdoctoral Research Associate at the University of Cambridge. Her research explores social interactions in microeconomic theory, with a focus on network formation dynamics, strategic evidence disclosure, and robust implementation mechanisms. Recent work examines how network structures influence economic decision-making and resource allocation. Dr. Baumann's publications demonstrate consistent focus on network economics and game-theoretic modeling, with emerging applications in discrimination mechanisms and evidence disclosure. Her methodological approaches combine theoretical rigor with computational innovations. Awards & Recognition: Vice-Chancellor's Innovation Award (2020) Econometric Society Travel Grant (2015) Multiple research grants including SSHRC and FQRSC funding Supervision & Service: Currently advising 4 doctoral students on topics ranging from biosolvent characterization to indoor air quality Active editorial board member for Journal of Mathematical Economics Organizes international conferences on economic theory

Timothy A. McKayView profile

Professor

• Data Science
• Learning Analytics
• Physics and Astronomy Education
• +2 more

Timothy A. McKay serves as the Arthur F. Thurnau Professor of Physics, Astronomy, and Education at the University of Michigan's College of Literature, Science, and the Arts (LSA), where he also holds the administrative role of Associate Dean for Undergraduate Education. His dual expertise bridges astrophysics research and educational innovation, with significant contributions to both observational cosmology and learning analytics. His educational background includes: B.S. in Physics from Temple University (1986) Ph.D. in Physics from the University of Chicago (1992) McKay's research spans two interconnected domains. In observational cosmology, he pioneered work with major astronomical surveys including the Sloan Digital Sky Survey (SDSS), Robotic Optical Transient Search Experiment (ROTSE), and Dark Energy Survey (DES), focusing on galaxy clusters, cosmic rays, and large-scale structure. Since 2015, he has strategically shifted toward learning analytics, applying data science to transform STEM education. His innovative projects include E 2 Coach (a personalized student support system) and the NSF-funded REBUILD initiative, which creates intergenerational research teams to develop evidence-based teaching practices across physics, chemistry, astronomy, biology, and mathematics. Analysis of his publication trajectory reveals a deliberate pivot from astrophysics to educational research around 2015. While his early work centered on galaxy clusters and cosmological phenomena, recent publications (2020-2024) overwhelmingly focus on systemic equity gaps in STEM education, data-driven interventions, and multi-institutional collaborations. This evolution demonstrates how his data science methodology transitions seamlessly between cosmic structures and educational ecosystems. His scientific recognition includes: Prestigious Arthur F. Thurnau Professorship (awarded for exceptional undergraduate teaching) McKay directs the NSF-funded REBUILD project and the Digital Innovation Greenhouse, securing substantial research funding while mentoring undergraduate and graduate students in interdisciplinary teams. His work with the Big Ten Academic Alliance (CIC) has generated cross-institutional studies on grading patterns, performance disparities, and student support systems, with practical applications implemented across multiple universities. He actively collaborates with faculty across STEM disciplines to develop scalable educational technologies. His research infrastructure includes the Digital Innovation Greenhouse (an educational technology incubator) and REBUILD project teams, which integrate undergraduates, graduate students, postdocs, and faculty in evidence-based educational research. These teams operate at the intersection of data science and pedagogy, developing tools that analyze institutional datasets to personalize student support while maintaining rigorous scientific methodology.

Caglar OskayView profile

Professor

• Multiscale Computational Modeling
• Composite Materials
• Structural Failure Analysis
• +7 more

Caglar Oskay is the Cornelius Vanderbilt Professor of Engineering and Chair of the Department of Civil and Environmental Engineering at Vanderbilt University. He is also a Professor of Mechanical Engineering. His research focuses on multiscale computational modeling of material and structural systems under extreme conditions, with expertise in composite materials, failure mechanisms, and computational mechanics. Dr. Oskay earned his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute (2003) and has held academic roles there before joining Vanderbilt in 2006. He was honored as an ASME Fellow in 2017 and as a Chancellor Faculty Fellow in 2016. Key research areas include multiscale failure modeling, life prediction of heterogeneous materials, and computational methods for composites and multiphysics systems. His work integrates advanced simulation techniques with experimental validation, addressing challenges in infrastructure resilience, additive manufacturing defects, and quantum computing applications in engineering. Education: Ph.D., Civil Engineering, Rensselaer Polytechnic Institute (2003) M.S., Civil Engineering, Rensselaer Polytechnic Institute M.S., Applied Mathematics, Rensselaer Polytechnic Institute B.S., Civil Engineering, Middle East Technical University Recent research highlights include stochastic modeling of geotechnical infrastructure failures, quantum-enhanced finite element methods, and predictive analytics for additive manufacturing defects. He leads interdisciplinary efforts on backward erosion piping in flood protection systems and has secured grants for multiscale modeling of titanium alloys and composites. Awards: ASME Fellow (2017) Chancellor Faculty Fellow (2016) Advising and grants: Dr. Oskay’s grants include NSF-funded studies on backward erosion piping and quantum computing applications. His research group collaborates with industry partners on materials for aerospace and energy sectors, emphasizing computational tools for failure prediction and material design. His work bridges computational mechanics with practical engineering challenges, advancing methods for infrastructure resilience, advanced materials, and sustainable design through multiscale modeling innovations.

Cristiano PorcianiView profile

Professor

• Cosmology
• Large-scale Structure
• Galaxy Formation
• +2 more

Cristiano Porciani is Professor of Astrophysics at the University of Bonn's Argelander Institute for Astronomy, specializing in cosmological structure formation and galaxy evolution. He leads a research group working on numerical simulations of large-scale structure and theoretical cosmology. His research focuses on dark matter distribution, galaxy bias, and cosmological parameter estimation using perturbation theory and high-performance computing. Recent work examines relativistic effects in large-scale structure and intensity mapping techniques. Publications show strong emphasis on Euclid mission science, including instrument characterization, survey simulations, and cosmological tests. Article trends reveal consistent development of statistical methods for analyzing next-generation sky surveys. Supervises 9 graduate students working on cosmological simulations, galaxy clustering statistics, and radiative transfer modeling. Leads research projects within the Euclid Consortium and Transregional Collaborative Research Centre.

Anirban MondalView profile

Associate Professor

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +5 more

Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu

Tony LindebergView profile

Professor

• Computer Vision
• Computational Neuroscience
• Medical Image Analysis
• +4 more

Tony Lindeberg is a Professor of Computer Science—Computational Vision at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. He teaches the course Image Analysis and Computer Vision (DD2423). His research focuses on scale-space theory, early vision, and computational modeling of biological and auditory vision systems. Key contributions include theories on receptive fields, time-causal spatio-temporal models, and feature detection algorithms. Research interests span computational neuroscience, medical image analysis, and spatio-temporal recognition. Lindeberg has pioneered work on scale-invariant image features, affine transformations, and Galilean diagonalization for motion analysis. He is the author of the foundational book Scale-Space Theory in Computer Vision (1993). His work bridges computer vision and biological vision systems, with applications in gesture recognition, dynamic texture analysis, and neural networks. He leads the Vision Lab and Computational Brain Science Lab at KTH, emphasizing theoretical rigor and practical algorithms for visual perception tasks.

Steve MaddockView profile

Senior Lecturer

• Facial Modelling and Animation
• Augmented Reality
• Virtual Reality
• +3 more

Dr Steve Maddock is a Senior Lecturer in Computer Graphics and Acting Head of the Visual Computing research group at the University of Sheffield's School of Computer Science. He holds a Class I Degree in Computer Science (University of Sheffield), a PGCE in Mathematics (11-18), and a PhD in computer graphics modeling and animation, all from the University of Sheffield. With over 30 years of experience in computer graphics software development, he has contributed to the computer games industry through a six-month secondment at Gremlin/Infogrames. His research focuses on facial modeling and animation, augmented/virtual/mixed reality applications, and sketch-based interfaces. Key areas include 3D computer graphics, real-time rendering, and human-robot collaboration systems. Maddock has led and co-led several grants, including projects on game software engineering, rail network surveillance, and heritage visualization using immersive technologies. He is a member of INSIGNEO, Sheffield Robotics, and the Cultural Industries Research Network. Publications highlight contributions to facial analysis for medical diagnostics, style transfer techniques for games, and safety zone visualization in robotics. His work integrates interdisciplinary approaches, combining computer science with fields like biology and robotics. Maddock's Visual Computing research group explores cutting-edge solutions in graphics, virtual environments, and computational tools for real-world applications.

Ferrante NeriView profile

Professor

• Machine Learning
• Artificial Intelligence
• Optimization
• +5 more

Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.

Sergej StoppelView profile

Researcher

• Visual Data Science
• Data Visualization
• Human-Computer Interaction
• +3 more

Sergej Stoppel is currently a researcher (PostDoc) in the visualization group at the University of Bergen. His academic background includes a Mathematics master's degree (2014) and a PhD in 2018, both from the University of Bergen. His research focuses on visual data science, with an emphasis on interactive visualization techniques, human-computer interaction, and physical visualization systems. He has authored/co-authored numerous papers on topics such as hexagonal map enhancements, spatio-temporal data interaction, and low-cost physical art generation. Education: M.Sc. Mathematics (2014), University of Bergen PhD in Visualization (2018), University of Bergen Research Interests: His work spans data visualization, scientific visualization, and interactive techniques. Notable contributions include Vol²velle (printable interactive volume visualization widgets), Firefly (virtual illumination drones), and LinesLab (low-cost art generation systems). He explores methods to bridge the gap between virtual and physical visual representations, emphasizing user-centric design. Publications: His recent work includes advancements in hexagonal map visualization (2022), spatio-temporal selection techniques (2020), and illumination automation (2019). These contributions highlight his focus on enhancing data interpretation through novel interaction paradigms. Labs/Teams: Active member of the VisGroup at the University of Bergen, collaborating on projects like MetaVis and VIDI. His work is supported by grants involving visualization and medical imaging applications.

Sharon LubkinView profile

Professor

• Biological Modeling
• Continuum Mechanics of Tissues
• Morphogenesis
• +4 more

Sharon Lubkin is a Professor in the Department of Mathematics at North Carolina State University (NCSU). Her research focuses on modeling biological systems, continuum mechanics of tissues, and morphogenesis. She is affiliated with the Quantitative and Computational Developmental Biology research cluster and the NCSU/UNC Department of Biomedical Engineering. Dr. Lubkin holds roles such as SIAM representative to the Joint Committee on Women in Mathematics (2017-23) and former Publications Chair of the Society for Mathematical Biology (2004-2016). Education: Ph.D. in Applied Mathematics from Cornell University (1992). Research Interests: Modeling biological systems Continuum mechanics of soft tissues Mechanobiology and drug delivery Biomechanics of morphogenesis Collaborations with experimental biologists and engineers Funding: Supported by the Simons Foundation, NSF, and NIH. Advises students in Biomathematics, Applied Mathematics, Biomedical Engineering, and Mechanical Engineering. Professional Activities: National SIAM committee roles Leadership in mathematical biology organizations Active in promoting interdisciplinary collaborations

Christos GagatsosView profile

Assistant Professor

• Quantum Technologies
• Quantum Sensing
• Quantum Communications
• +10 more

Christos Gagatsos is an Assistant Professor in the Department of Electrical and Computer Engineering and a member of the Wyant College of Optical Sciences at the University of Arizona. He joined the university in 2018 as a postdoctoral research associate, was promoted to Assistant Research Professor in 2020, and became an Assistant Professor in ECE in 2023. Prior to this, he was a postdoctoral research fellow at the University of Warwick, UK. His educational background includes: PhD in Engineering Sciences and Technology, Université Libre de Bruxelles and École Polytechnique, Belgium (2014) MSc in Physics of Elementary Particles, University of Athens, Greece (2010) BSc in Physics, University of Athens, Greece (2007) Christos Gagatsos's research lies at the intersection of quantum information, quantum sensing, and quantum communications, with a strong theoretical focus on bosonic systems. His work explores fundamental concepts such as entanglement, non-Gaussianity, and Bayesian estimation in quantum systems. He is particularly interested in pushing the limits of quantum-enhanced sensing, including optical phase and transmissivity estimation, and in developing theoretical frameworks for quantum detection and discrimination. His teaching interests include quantum information, quantum optics, probability theory, and applied mathematics. The recent trend in his publications reflects a deep engagement with Bayesian methods in quantum parameter estimation, quantum change point detection, and the characterization of quantum states through measures like Wigner entropy. His work spans both fundamental quantum theory and practical applications in sensing and communication, often bridging classical and quantum approaches. He advises several graduate students across departments, including Boyu Zhou (Physics), Ali Cox (Physics), Qipeng Qian (Mathematics), and Leo Bia (Optical Sciences). While no formal scientific awards are listed in the provided text, editorial recognition such as an Editor’s Pick in APL Quantum highlights the impact of his research. Christos Gagatsos leads a research group focused on theoretical quantum information, actively collaborating with quantum research groups across the University of Arizona, Arizona State University, and international institutions in the USA and Europe. His lab investigates quantum sensing, communications, and foundational aspects of quantum mechanics using bosonic platforms, fostering a collaborative and interdisciplinary research environment.

Jeppe Revall FrisvadView profile

Associate Professor

• Computer Graphics
• Digital Image Synthesis
• Realistic Rendering
• +7 more

Jeppe Revall Frisvad is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), within the Visual Computing group. His work bridges computer graphics, material science, and applied optics, focusing on realistic rendering and material appearance modeling. Ph.D. in Computer Graphics, DTU Informatics (2008) M.Sc. in Engineering (Applied Mathematics), DTU (2004) Education in University Teaching, LearningLab DTU (2008–2009) His research centers on computing material appearance from physical and chemical properties, developing faster and more accurate physically based rendering methods. Key areas include light scattering, bidirectional reflectance distribution functions (BRDF), anisotropy, and translucency modeling. Applications span computer games, movies, digital prototyping, architectural visualization, and training simulators. His recent publications (2024–2025) reflect a strong trend in digitizing material appearance, especially for 3D printing and food science, using spectrophotometry and optical validation. The work combines computer graphics with interdisciplinary applications in food structure and biological imaging, emphasizing measurement, modeling, and simulation. Scientific Awards: Research travel prize from AEG Elektronfonden (2006) IGDA scholarship for GDCE 2005 DTU Ph.D. scholarship (2004) He actively supervises Ph.D. students and leads multiple research projects, including those on organ-on-chip imaging, cheese quality analysis, and optical modeling of teeth. He collaborates across disciplines and institutions, with external research stays at UC San Diego and the University of Otago. He is involved in projects integrating AI with 3D imaging and material digitization. Jeppe is a key member of the Visual Computing research environment at DTU, contributing to both fundamental rendering algorithms and practical applications in industry and science.

Stan SclaroffView profile

Professor

• Computer Vision
• Pattern Recognition
• Machine Learning
• +3 more

Stan Sclaroff is a Professor of Computer Science and Dean of the College of Arts & Sciences at Boston University. He holds affiliated faculty status in the Department of Electrical and Computer Engineering. His research focuses on computer vision, pattern recognition, and machine learning, with expertise in tracking, human motion analysis, and multimedia retrieval systems. He co-leads the Image and Video Computing research group and has contributed pioneering work like the ImageRover content-based image retrieval system. Education: PhD in Media Arts & Sciences from MIT (1995), SM from MIT (1991), and BS in Computer Science and English from Tufts University (1984). Research Interests: Human motion tracking, sign language analysis, deformable shape matching, and multimedia indexing. Notable contributions include early work on content-based image retrieval and foundational techniques in video analysis. Honors: IEEE & IAPR Fellowships, NSF CAREER Award (1996), ONR Young Investigator Award (1996), and BU's Mentor of the Year (2018). Over 40+ students advised, many now in academia and industry leadership roles. Key roles: Chair of BU Computer Science (2007–2013), Associate Dean for Mathematical & Computational Sciences (2015–2018), Interim Dean (2018–2019), and current Dean since 2019.

Anirban ChakrabortiView profile

Professor

• Econophysics
• Sociophysics
• Complex Systems
• +14 more

Anirban Chakraborti is a Professor at the School of Computational and Integrative Sciences, Jawaharlal Nehru University (JNU), New Delhi, India, where he has been a faculty member since 2014. He previously held academic positions at École Centrale Paris (France) as Chercheur Senior (Associate Professor) and Chargé de Recherche (Assistant Professor), and earlier roles at Banaras Hindu University, Brookhaven National Laboratory (USA), and Helsinki University of Technology (Finland). He is a leading figure in the interdisciplinary field of econophysics and complex systems. Education: Diplôme d’Habilitation à Diriger des Recherches (2013), Université Pierre et Marie Curie – Paris VI, France (Physics) Ph.D. in Physics (2003), Saha Institute of Nuclear Physics, Jadavpur University, India Post-M.Sc. in Physics (1999), Saha Institute of Nuclear Physics, Jadavpur University, India (Ranked First) M.Sc. in Physics (1998), University of Calcutta, India (Ranked First) B.Sc. in Physics (1996), Scottish Church College, University of Calcutta, India His research interests lie at the intersection of physics, economics, and data science. He is particularly known for pioneering work in econophysics , including the statistical mechanics of money, wealth distribution, agent-based market models, and network-based analysis of financial and social systems. He also works on complex systems , computational finance , statistical physics , and nanosciences , with applications in sensing and imaging. His work often involves modeling socio-economic phenomena using tools from statistical physics. His recent publications span topics such as financial fluctuations, wealth inequality, network analysis of conflicts, order book dynamics, and nanomaterial characterization. These works reflect a strong trend toward interdisciplinary research combining physics, economics, and data analytics, with a focus on real-world applications in finance, inequality, and social systems. Scientific Awards: Indian National Science Academy Young Scientist Medal (2009) He has advised Ph.D. students such as Kiran Sharma and leads the ETC (Experimental-Theoretical-Computational) Lab at JNU, which brings together physicists, computer scientists, and mathematicians. The lab has been involved in international collaborations, including projects funded by the Estonian Ministry of Education and Research and consultancies with TCS Innovation Labs and DONO Consulting. His research has been supported through grants and collaborative projects, reflecting strong industry and global academic engagement.

Vasil I. ShteriyanovView profile

Researcher

• Artificial Intelligence
• Deep Learning
• Software Development
• +4 more

Vasil I. Shteriyanov is a doctoral candidate at the School of Mathematics and Computer Science, Eindhoven University of Technology, with a focus on the Department of Software Engineering and Technology. His research bridges artificial intelligence and engineering applications, particularly in software development for industrial contexts. His work centers on Artificial Intelligence and Deep Learning applied to Engineering Drawings , Construction Sector , and Cost Estimation . He has contributed to automating tasks in Piping and Instrumentation Diagrams (P&IDs) and improving symbol recognition in technical drawings. Research Outputs 2025: Automation of instrument typical expansion in P&IDs (AAAI-25) 2025: Line number detection in P&IDs (ICMLA 2024) 2024: AI-driven cost estimation in EPC projects (SANER 2024) 2024: Density and noise impact on symbol recognition (IS 2024) 2023: Thesis on P&ID symbol detection for material take-off generation