Find Academic Supervisors

Henry KautzView profile

Professor

• Artificial Intelligence
• Pervasive Computing
• Data Science
• +3 more

Henry Kautz is a Professor in the Department of Computer Science at the University of Rochester, where he has held leadership roles including Director of the Institute for Data Science (2014–Present) and Chair of the Department of Computer Science (2007–2014). Previously, he served as Director of Intelligent Systems at Kodak Research (2006–2007), and held academic positions at the University of Washington and AT&T Laboratories. He currently serves as a Fellow of the AAAS, ACM, and AAAI. His research focuses on artificial intelligence, pervasive computing, and data science, with applications in public health monitoring, natural language processing, and social media analysis. He has over 20,000 citations with an h-index of 65 and i10-index of 124. His research interests include data mining social media for disease tracking, grounded language learning through text-video alignment, and knowledge representation systems combining logic and probability. Notable projects include deploying the 'nEmesis' system to prevent foodborne illness via social media analysis and developing tools like OPUS for social media user studies. His work bridges theoretical AI advancements with real-world societal challenges, emphasizing ethical and practical applications of technology. Dr. Kautz has received prestigious awards including the AAAS and ACM Fellowships, reflecting his contributions to advancing AI and computational science. His research trends show a strong focus on leveraging big data and machine learning for societal good, particularly in health surveillance, mental health monitoring, and urban mobility analysis. Collaborations with interdisciplinary teams highlight his commitment to applying AI to solve complex global problems. In terms of leadership, he has directed major initiatives in data science education and research, fostering innovation at the University of Rochester. His career spans academia and industry, with impactful contributions to both theoretical and applied domains of computer science.

• Artificial Intelligence
• Machine Learning
• Generative AI
• +5 more

Jesse Spencer-Smith is a Professor of the Practice of Computer Science at Vanderbilt University's School of Engineering. His research focuses on applying artificial intelligence and machine learning to interdisciplinary challenges, including bioinformatics, astrophysics, software engineering, and archaeology. He is particularly known for pioneering work in generative AI frameworks and prompt engineering methodologies. Recent research trends include developing AI tools for protein structure analysis (e.g., SuperMetal and SuperWater), gravitational wave signal processing, and optimizing large language models for coding and archaeological artifact analysis. His work bridges computational methods with domain-specific applications, emphasizing practical problem-solving across multiple scientific disciplines. Dr. Spencer-Smith has contributed to foundational studies in prompt engineering, publishing multiple catalogs of prompt patterns to systematize interactions with AI systems like ChatGPT. His interdisciplinary approach reflects a commitment to advancing AI's role in both theoretical and applied contexts. No scientific awards or grants were explicitly mentioned in the provided texts. He currently advises no formally listed students, though his research collaborations likely involve graduate and undergraduate participants. No specific lab affiliations or teams were detailed in the available information.

Stormy AttawayView profile

Assistant Professor

• Engineering Education
• MATLAB Programming
• Curriculum Development
• +3 more

Stormy Attaway, PhD, is an Assistant Professor of Mechanical Engineering in the College of Engineering at Boston University. She serves as Director of Curricular Assessment and Improvement, overseeing programs to enhance undergraduate academic experiences. Her work focuses on engineering education, particularly MATLAB-based instruction for freshmen. Education: PhD in Mechanical Engineering from Boston University. Research emphasizes integrating MATLAB programming into foundational engineering education, highlighted by her 2009 book detailing this approach. Her 2014 Metcalf Cup and Prize recognizes her contributions to engineering education. Advising/Grants: No formal advisees listed, but her role in curriculum improvement involves collaborative efforts across departments. No specific grants mentioned. Labs/Teams: Engaged in educational program development within the College of Engineering's curricular initiatives.

Mehran OraeeView profile

Lecturer

• Building Information Modeling (BIM)
• Digital Engineering
• Construction Technologies
• +7 more

Dr. Mehran Oraee is a Lecturer in Building Information Modelling (BIM) at the University of Melbourne's Melbourne School of Design, within the Faculty of Architecture, Building and Planning. With over 10 years of industry experience in construction roles such as project supervisor and builder, his expertise bridges academic research and practical application. His PhD focused on BIM collaboration within Architecture, Engineering, Construction, and Operations (AECO) industries, leading to the development of an integrated collaboration model for BIM-enabled organizations. Research interests include BIM implementation challenges, sustainable construction practices, digital engineering innovations, and education frameworks for T-shaped BIM professionals. He teaches BIM specialization subjects (CONS30001/CONS30002) and construction research modules (ABPL90358/ABPL90359), emphasizing collaborative problem-solving and CPS principles in education. Recent publications highlight trends in BIM legal/contractual risks, sustainable waste management, and AR adoption. His work addresses barriers in BIM integration for SMEs, curriculum design for competency frameworks, and policy incentives for green construction practices. No scientific awards are listed, but his contributions to AECO industry collaboration and education frameworks are notable. He advises on construction management and architectural disciplines, though no named students are recorded. His research aligns with the University's Future Cities and Construction Innovation priorities.

Lisette de Jonge-HoekstraView profile

Assistant Professor

• Developmental Psychology
• Cognitive Development
• Multimodal Communication
• +6 more

Dr. Lisette de Jonge-Hoekstra is an Assistant Professor at the Department of Developmental Psychology , part of the Faculty of Behavioural and Social Sciences at the University of Groningen. Her research focuses on multimodal communication, cognitive development, and the interplay between gestures, speech, and complex dynamical systems. She explores how children's hand movements and speech patterns coordinate during learning and problem-solving tasks, emphasizing embodied cognition and ecological psychology frameworks. Her expertise spans developmental psychology, educational research, and the application of complexity science to understanding cognitive variability. She has contributed to methodologies using motion capture and computational tools like OpenPose to analyze movement data. Dr. de Jonge-Hoekstra has organized academic events such as the Heymans Colloquium on embodied music pedagogy and participates in editorial roles for Frontiers in Psychology . Research Awards: Recipient of the Aanjaagfonds Universiteit van het Noorden (2023). Teaching Roles: Leads courses on academic skills, research methods, and complexity in developmental psychology for bachelor and master programs. Key Projects: Investigates 'Differentiatie Binnenstebuiten' and hosts the 'Podcast grow with the flow'. Her work bridges Piagetian theory with modern dynamical systems approaches.

Felix HerrmannView profile

Professor

• Computational Imaging
• Inverse Problems
• Machine Learning
• +5 more

Felix Herrmann is a Professor at the Georgia Institute of Technology, holding a joint appointment between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering within the College of Computing. He leads the Seismic Laboratory for Imaging and Modeling (SLIM) and co-directs the Center for Machine Learning for Seismic (ML4Seismic). His research focuses on computational imaging, inverse problems, and machine learning applications in geophysics, particularly in seismic and medical imaging. Herrmann has pioneered innovations in compressive sensing for time-lapse seismic data acquisition, earning the SEG Reginald Fessenden Award in 2020. His work emphasizes Bayesian inference, uncertainty quantification, and scalable computational methods for subsurface monitoring, including carbon sequestration and reservoir characterization. Education: Ph.D. in Engineering Physics (Delft University of Technology, 1997), followed by postdoctoral roles at Stanford University and MIT before joining the University of British Columbia (2002). He transitioned to Georgia Tech in 2017 as a Georgia Research Alliance Eminent Scholar in Energy. Research Interests: Herrmann’s cross-disciplinary program integrates randomized linear algebra, PDE-constrained optimization, and high-performance computing. Key areas include digital twin technologies for subsurface monitoring, generative AI for geostatistical modeling, and scalable inversion algorithms (e.g., wavefield reconstruction inversion). His methodologies aim to reduce costs and improve reliability in seismic imaging and carbon storage monitoring. Scientific Contributions: Over 150+ peer-reviewed articles, including seminal work on compressive sensing in seismic acquisition and Bayesian experimental design. His software contributions include InvertibleNetworks.jl and Devito , advancing computational geophysics. Labs & Collaborations: Directs SLIM, fostering industry partnerships through ML4Seismic to develop AI-driven seismic imaging tools. Active in cloud-based scalable workflows and event-driven seismic imaging systems.

• Software Engineering
• Cloud Computing
• Service Oriented Architecture
• +3 more

Dr. Mustafa Bozkurt is a Senior Lecturer in Software Engineering at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on applying multi-objective optimization and machine learning to address challenges in software engineering, including automated testing, software design, requirements analysis, and maintenance. He actively contributes to areas such as service-oriented architecture, cloud computing, and search-based software engineering. Teaching responsibilities include undergraduate and postgraduate courses such as Database Systems, Introduction to Object-Oriented Programming, and Software Engineering. He also oversees the Software Engineering Project module, emphasizing collaborative problem-solving and practical application of engineering principles. Research interests span service-oriented architecture, software testing methodologies, cloud computing, and requirements engineering. Tools like Visual Paradigm are utilized for UML and BPMN modeling in academic projects. His publications emphasize optimization techniques in testing, cost-aware strategies for service-centric systems, and realistic test input generation. Dr. Bozkurt’s work bridges theoretical advancements with practical software development challenges, aiming to enhance software quality and efficiency through innovative methodologies.

Richard ZhaoView profile

Assistant Professor

• Serious Games
• Artificial Intelligence
• Immersive Technologies
• +3 more

Richard Zhao is an Assistant Professor in the Department of Computer Science at the University of Calgary. He leads the Serious Games Research Group , focusing on developing immersive technologies (e.g., VR, eye-tracking) and AI algorithms to enhance training, learning, and education through serious games. His work emphasizes adaptive experiences and automated content generation for educational applications. Research Interests: Serious Games for Education Artificial Intelligence in Virtual Environments Immersive Technologies (VR/AR) Collaborative Systems Procedural Content Generation Recent Work Trends: Recent publications highlight advancements in AI-driven poetry generation, VR interaction techniques, and collaborative problem-solving models. His work often bridges computational methods with human-centric design, particularly in manufacturing simulations and educational VR systems. Labs/Teams: He oversees the Serious Games Research Group, hosting projects like the Immersive Virtual School (IVS) and GEARS: Gearing up for Education . These initiatives integrate AI and VR to address real-world training challenges in engineering and education. Teaching: Teaches graduate and undergraduate courses on Game AI, Game Development, and introductory computer science. Courses include CPSC 686 (Research in Game AI) and CPSC 586 (Game AI) .

Giuliano GrossiView profile

Associate Professor

• Sparse representation models
• Signal processing
• Face recognition
• +5 more

Giuliano Grossi is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on sparse representation models, signal processing, and GPU algorithms with applications in biomedical engineering and affective computing. He leads projects such as 'Stairway to Elders' for elderly wellbeing and contributes to tools like pyVHR for remote health monitoring. Grossi has supervised PhD students in areas including high-performance computing and stochastic models. Notable awards include Best Paper recognitions in 2017 and 2020. His work spans interdisciplinary fields from bioinformatics networks to facial recognition systems. Education: PhD in Computer Science (1999), University of Milan Positions: Assistant Professor (2001-2022), Associate Professor (2022–present) Research Interests: Sparse recovery methods, parallel algorithms, emotion recognition via multimodal analysis, and graph-based models for learning. Projects include EU T-NOVA for network virtualization and emotion interpretation frameworks funded by MIUR. Scientific Contributions: Over 100 publications in journals like IEEE Transactions and conference proceedings. Key contributions include GPU-accelerated algorithms, network biology analysis tools (UNIPred-Web), and frameworks for photoplethysmography (pyVHR).

Bryan HeidornView profile

Professor

• Text Mining
• Data Curation
• Biodiversity Informatics
• +3 more

Bryan Heidorn is a Professor and Associate Dean for Research and Graduate Academic Affairs at the University of Arizona’s College of Information Science. He also directs the Center for Digital Society and Data Studies. Previously, he served as Director of the School of Information (merged from SIRLS and SISTA) from 2009 to 2019 and held roles at the University of Illinois at Urbana-Champaign and the National Science Foundation. His research focuses on text mining, data curation, and biodiversity informatics, emphasizing interdisciplinary approaches to data challenges in science and society. Heidorn’s professional journey includes leadership roles such as President and Board member of the JRS Biodiversity Foundation (2006–2013) and active participation in the Organization for Tropical Studies. His work bridges library science, information systems, and computational methods to address long-tail data problems in astronomy, biology, and environmental studies. Research Interests: Text Mining & Natural Language Processing Data Curation & Cyberinfrastructure Biodiversity Informatics Digital Society & Ethics Long-Tail Science Data Articles Trends: His recent work explores acoustic ecology in deserts, interdisciplinary data solutions for astronomy, and AI-driven phenotype prediction. Earlier contributions include frameworks for biodiversity metadata, ontology development, and NSF grant analysis. Scientific Awards: None explicitly listed in provided texts. Advising & Grants: Heidorn’s grants span NSF-funded projects on dark data in astronomy and biodiversity. His advising and leadership foster collaborations across disciplines, emphasizing data literacy and societal impact. Labs & Teams: Directs the Center for Digital Society and Data Studies, leading initiatives in ethical AI, data curation, and interdisciplinary research hubs.

• Statistical Ecology
• Wildlife Population Dynamics
• Bayesian Statistics
• +4 more

Eleni Matechou is an Honorary Academic in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. Her academic background includes a BSc in Statistics from Athens University of Economics and Business, an MSc in Statistics with Applications in Medicine from the University of Southampton, and a PhD jointly conducted at the University of Kent and the Max Planck Institute for Demographic Research. She has held positions such as Research and Teaching Fellow at Victoria University of Wellington, Departmental Lecturer at the University of Oxford, and is currently a member of the Statistical Ecology @ Kent (SE@K) group and the National Centre for Statistical Ecology (NCSE). Her research focuses on statistical ecology, particularly developing models for wildlife populations, migration patterns, and environmental DNA (eDNA) monitoring. Key areas include Bayesian and frequentist approaches to capture-recapture models, mixture models, and integrating citizen science data for ecological inference. She is actively involved in methodological advancements for biodiversity assessment and conservation, emphasizing the use of innovative technologies like robotics and eDNA. Professionally, Eleni chairs the Environmental Statistics Section of the Royal Statistical Society and serves as Book Reviews Editor for the Journal of Agricultural, Biological and Environmental Statistics . Her work bridges statistical rigor with ecological applications, addressing challenges in monitoring biodiversity and informing conservation strategies. Collaborations span institutions globally, reflecting her interdisciplinary approach to solving ecological problems.

Joshua LandonView profile

Associate Professor

• Bayesian Statistics
• Stochastic Processes
• Markov Chain Monte Carlo Methods
• +2 more

Joshua Landon is an Associate Professor of Statistics and Undergraduate Program Director at The George Washington University. His expertise includes Bayesian Statistics, Stochastic Processes, Markov Chain Monte Carlo Methods, Decision Analysis, and Reliability and Risk Analysis. He teaches courses such as Statistical Analysis for Managers, Foundations of Risk Analysis, and Introduction to Bayesian Statistics. Education: Ph.D. in Statistics from The George Washington University (2007); B.A. in Mathematics from the University of Oxford (2000). His research focuses on advancing Bayesian methodologies for complex systems, with applications in reliability engineering, software failure analysis, and decision-making under uncertainty. Notable contributions include the development of Bayesian models for Markov processes and latent-factor analysis in operational research contexts. Publications span high-impact journals like Applied Stochastic Models in Business and Industry and Technometrics . His work emphasizes interdisciplinary applications, blending statistical theory with practical problem-solving in engineering, economics, and physics. No scientific awards are explicitly listed in the provided texts. His advising and grants activities are not detailed here, though his extensive teaching portfolio indicates active mentorship roles.

Mike FaganView profile

Lecturer

• Programming Languages
• Automatic Differentiation
• High-Level Languages
• +3 more

Mike Fagan is a Lecturer in Computer Science and Research Scientist at Rice University. He holds a B.A. in Mathematics, Mathematical Sciences, and Electrical Engineering, an M.S. in Electrical Engineering, and a Ph.D. in Computer Science, all from Rice University. His research focuses on programming language design, automatic differentiation, and high-level language implementation. He has contributed to tools like HPCToolkit for performance analysis of parallel programs and explored inexact computation strategies to improve energy efficiency in supercomputing. Dr. Fagan has taught courses such as Introduction to Computer Systems, Cyber-Physical Systems, and Advanced Algorithms. His pedagogical approach emphasizes problem-solving and personalized instruction through class-wide collaborative sessions. He has collaborated on projects addressing energy-precision tradeoffs in computing systems and received a Distinguished paper award in 2009 for work on performance analysis of petascale applications. Education: B.A./M.S./Ph.D. in Mathematics/EE/CS (Rice University) Research: Programming Languages, Automatic Differentiation, Parallel Computing Tools: HPCToolkit, OpenAD/F Awards: Distinguished Paper Award (2009) His career has been entirely at Rice University, balancing research and teaching since 1991. He has explored innovative approaches to overcoming hardware limitations through inexact computing and contributed to advancing methodologies for scientific programming and libraries.

• Educational Technology
• Affective Computing
• Multimedia Learning
• +6 more

Robert Atkinson is an Associate Professor at the School of Computing and Augmented Intelligence, Arizona State University. His research focuses on educational technology, affective computing, and cognitive science, with particular emphasis on leveraging augmented reality, biometric sensors, and game-based systems to enhance learning outcomes. He explores how multimedia environments, animated pedagogical agents, and adaptive learning systems can improve STEM education, user experience, and cognitive skill acquisition. Developed AR tools like MantarayAR for probability education Conducted pioneering work on affect detection through biometric data Investigated instructional design principles for transitioning learners from examples to problem-solving Explored gender matching effects of avatars in career belief restructuring His work bridges cognitive psychology with technology, addressing challenges in educational software design, human-computer interaction, and embodied learning. Key projects include the V-Lab mobile platform for networking education and the CritViz peer critique system for large classrooms. Atkinson's contributions span over 50 peer-reviewed publications in areas like EEG-based performance prediction, adaptive game design, and embodied mLearning frameworks.

Søren HøjsgaardView profile

Associate Professor

• Statistics
• Graphical Models
• Bayesian Networks
• +7 more

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.