Find Academic Supervisors

Michael EagleView profile

Assistant Professor

• Educational Data Mining
• AI in Education
• Intelligent Tutoring Systems
• +4 more

Michael Eagle is an Assistant Professor in the Information Sciences and Technology Department at George Mason University, and a founding member of the Center for Advancing Human-Machine Partnership (CAHMP). His work focuses on deriving insights from complex interaction data in intelligent tutors and video games, educational game design, and individualized student modeling in genetics education. He holds a PhD from North Carolina State University (2015) and completed a postdoctoral fellowship at Carnegie Mellon University's HCI Institute until 2018. Prior academic roles include data science positions at Blizzard Entertainment and Warner Bros. Interactive Entertainment. Research interests include educational data mining, AI-driven pedagogy, and measuring computational thinking through block-based programming environments. He has pioneered frameworks for hypothesis-driven learning analytics and game-based science education. His work bridges traditional computer science with innovative educational technologies, emphasizing real-time analytics and adaptive learning systems. Awards: NSF GRFP Honorable Mention, GAANN Fellowship, Freeman-ASIA Grant International Collaboration: Conducted NSF-funded research in Japan with University of Electro-Communications Labs/Teams: Co-leads CAHMP initiatives at George Mason Eagle's recent publications emphasize predictive modeling for learner interventions, real-time instructor dashboards, and the impact of AI-powered educational tools. His research demonstrates significant contributions to both theory and practical applications in edtech innovation.

Marty AllenView profile

Academic

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Marty Allen is an Associate Teaching Professor and Director of Online Programs in the Department of Computer Science at Tufts University's School of Engineering. He holds a PhD in Computer Science from the University of Massachusetts Amherst (2009), with earlier degrees in Philosophy from Simon Fraser University (1996 B.A.) and the University of Pittsburgh (2001 M.A.). His research focuses on multiagent reinforcement learning and applications of AI to biological systems, including influenza phenotype prediction and complex ecological modeling. He has authored or co-authored over 40 publications across journals like Emerging Microbes and Infections and conferences such as AAAI and NIPS. Allen has led multiple grants including a $1.2M REU program and a Disc Seed Grant for AI-driven viral research. He teaches courses in discrete mathematics, algorithms, and capstone projects, and has held leadership roles in curriculum development including the Strategic Planning Transformational Education Action Group at University of Wisconsin–La Crosse (2016–2019). Professional activities include service on Tufts' Computer Science External Advisory Board and equity initiatives as Department Equity Liaison. His work bridges theoretical computing with interdisciplinary applications in both natural and social sciences.

Theodore M. BesmannView profile

Professor

• Nuclear Fuel Development
• Nuclear Waste Forms
• Thermochemical Modeling
• +2 more

Theodore M. Besmann is a Professor and Smart State Chair in Energy and Nuclear Security at the University of South Carolina's Molinaroli College of Engineering and Computing, affiliated with the Department of Mechanical Engineering. His research focuses on thermochemical modeling for nuclear fuel development, in-reactor behavior, and advanced waste forms. Education: Ph.D., Nuclear Engineering, Pennsylvania State University (1976) M.S., Nuclear Engineering, Iowa State University (1971) B.E., Chemical Engineering, New York University (1970) Research Interests: Dr. Besmann specializes in thermochemical experimentation and computational modeling of nuclear materials. Key areas include: Nuclear fuel performance under reactor conditions Development of durable actinide waste forms Thermal property measurements for fuel performance codes Molten salt chemistry for reactor systems Phase equilibria in advanced fuel compositions Scientific Awards: Smart State Chair in Energy and Nuclear Security

Wenbin ZhangView profile

Assistant Professor

• Societal Aspects of AI
• Generative AI
• Health Informatics
• +5 more

Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing & Information Sciences at Florida International University and an Associate Member at the Te Ipu o te Mahara Artificial Intelligence Institute. His research focuses on the theoretical foundations of machine learning with societal impact, including fairness, generative AI, health informatics, and interdisciplinary applications in healthcare, digital forensics, and energy. He has received awards such as the NSF CRII Award and recognition in the AAAI’24 New Faculty Highlights. Zhang serves in organizing committees for major conferences like AAAI, WSDM, and AIES, contributing to academic leadership. Ph.D., University of Maryland, Baltimore County (2020) Research interests span societal aspects of AI, generative models, and fairness-aware machine learning. His work bridges theory and practice, addressing ethical challenges in AI deployment across domains like healthcare and cybersecurity. Key contributions include frameworks for fair graph learning, bias mitigation in LLMs, and adaptive pruning techniques for large models. Publications emphasize fairness in ML systems, digital forensics, and interdisciplinary applications. Over 50 papers span venues like FAccT, ICDM, and AAAI, with multiple best-paper recognitions. His NSF-funded research highlights innovation in fair AI and robust model adaptation. Scientific Awards: NSF CRII Award, FAccT’23 Best Paper Candidate, ICDM’23 Best Paper Academic service includes roles as Travel Award Chair (AAAI’24), Volunteer Chair (WSDM’24), and Student Program Chair (AIES’23). Teaching and mentorship are integral to his mission, fostering next-generation AI researchers through rigorous training in ethics-driven innovation. Labs/Teams: Active collaborations with the Te Ipu o te Mahara Institute, focusing on AI ethics and societal impact.

Sophya GarashchukView profile

Professor

• Theoretical Chemistry
• Computational Chemistry
• Quantum Dynamics
• +3 more

Sophya Garashchuk is a Professor in the Department of Chemistry and Biochemistry at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. Her research focuses on theoretical and computational chemistry, with an emphasis on quantum dynamics, nuclear quantum effects, and their applications to materials and biomolecular systems. She holds a M.S. from the Moscow Institute of Physics and Technology and a Ph.D. from the University of Notre Dame. Research Interests: Garashchuk's work explores quantum effects in molecular dynamics, including zero-point energy, tunneling, and proton transfer processes. Her group develops approximate quantum trajectory methods scalable to large systems, with applications in energy materials, enzyme catalysis, and nanomaterials. Key projects include studying nuclear quantum effects in polymers, graphene interactions, and inorganic frameworks like MOFs. Selected Achievements: Fellowship at Max Planck Institute for Physics of Complex Systems (2023) USC Rising Star Award (2012) NSF Career Award (2011) IBM-Lowdin Fellowship (2004) Advising & Collaborations: Garashchuk mentors graduate students and postdoctoral researchers, with recent advisees including James Mazzuca and Bing Gu. Her collaborations span institutions like Oak Ridge National Laboratory and the University of Chicago. She contributes to software development (e.g., Libra for quantum dynamics) and teaches courses in quantum chemistry and computational methods. Labs/Teams: Her research group develops novel theoretical tools and applies them to problems in energy, catalysis, and materials science. The group's work often bridges computational chemistry with experimental validation, yielding insights into quantum phenomena at the molecular scale.

Michael K. BaneView profile

Senior Lecturer

• Green Software Engineering
• High Performance Computing (HPC)
• Energy Efficiency in Computing
• +3 more

Dr. Michael K. Bane is a Senior Lecturer in Green Software Engineering & Performance Computing at Manchester Metropolitan University , within the Department of Computing & Mathematics, Faculty of Science and Engineering. A Senior Fellow of the Higher Education Academy (HEA) and founder of GreenCompute.UK , he leads the university's Greener Compute Club and the £120K UKRI Net Zero project "ENERGETIC" on heterogeneous computing for carbon reduction. Current teaching: Unit Lead for High Performance Computing & Big Data (UG/MSc) and Green Software Engineering modules Previous roles: University of Liverpool lecturer, UKRI Hartree Centre research scientist, and University of Manchester HPC team leader Research focuses on Energy Efficient Performant Computing (EEPC), exploring: Heterogeneous architectures (FPGA/GPU/accelerator integration) Carbon-aware scheduling systems Quantifying ICT's environmental footprint Green AI development Emerging hardware-software synergies Key contributions: Co-edited Introduction to Aerosol Modelling: From Theory to Code (2022) Authored 20+ publications on HPC, parallel algorithms, and sustainable computing Recipient of EMS 'best paper' award for climate modeling work

Dan BurnstonView profile

Associate Professor

• Philosophy of Cognitive Science
• Philosophy of Mind
• Philosophy of Science
• +8 more

Dan Burnston is an Associate Professor in the Department of Philosophy at Tulane University, serving as Director of the Tulane Cognitive Studies Program and faculty member in the Tulane Brain Institute. He also co-manages the Brains Blog. His research focuses on functional decomposition of cognitive and biological systems, representation in science, and decision-making mechanisms. He holds a PhD from the University of California, San Diego (2015), an MA from Georgia State University (2009), and a BA from Rutgers University (2007). Education: Ph.D., Philosophy and Cognitive Science, UC San Diego (2015) M.A., Georgia State University (2009) B.A., Rutgers University (2007) Research interests include philosophy of neuroscience, perceptual architecture, representation in biology, and agency. His work addresses topics like contextualist functional localization, embodied cognition, and the integration of pluralistic explanatory frameworks. Recent projects explore cognitive ontologies and the role of diagrams in scientific representation. He has presented at leading venues such as the Philosophy of Science Association and the Southern Society for Philosophy and Psychology. His work appears in journals like Philosophy of Science , Synthese , and Consciousness and Cognition . He co-leads the WORking Group on Diagrams in Science (WORGODS), emphasizing inferentialist accounts of scientific representation. Awards and grants: None explicitly listed in provided materials. Labs/Teams: Active contributor to the Tulane Cognitive Studies Program and collaborator in interdisciplinary neuroscience initiatives.

Shang-Ming ZhouView profile

Professor

• Health Data Science
• AI in Healthcare
• Electronic Health Records
• +4 more

Shang-Ming Zhou is Professor of e-Health at the University of Plymouth, directing the Centre for Health Technology and NHS Kernow Datalab. He holds affiliations with Health Data Research UK. His research develops AI methods for health informatics, focusing on explainable machine learning for electronic health records analysis. Funded by HDRUK, MRC, and NSF, his projects address disease phenotyping, polypharmacy patterns, and clinical decision support systems. Recent publications demonstrate innovations in Parkinson's diagnosis, tuberculosis detection, and XAI frameworks. His teaching includes machine learning for healthcare and health data analytics courses for nursing and computing students. Outstanding Reviewer Awards (Journal of Biomedical Informatics, IEEE Transactions) Springer Nature Best Paper Award RCP Best Poster Prize

Anita RajaView profile

Professor

• Artificial Intelligence
• Multiagent Systems
• Metacognition
• +7 more

Anita Raja is a Professor of Computer Science at Hunter College and a member of the doctoral faculty at the Graduate Center, CUNY. She previously served as Acting Chair of the Department (2024-2025), Associate Dean of Research and Graduate Programs at The Cooper Union (2014-2019), and Associate Professor at the University of North Carolina at Charlotte (2003-2014). Educational Background: B.S. Honors in Computer Science with Mathematics minor (summa cum laude, Phi Beta Kappa) from Temple University (1996) M.S. (1998) and Ph.D. (2003) in Computer Science from University of Massachusetts Amherst Her research focuses on bounded rationality, distributed problem-solving, and artificial intelligence. She has pioneered real-time multiagent systems under uncertainty and limited resources, with significant contributions in automated refactoring of deep learning programs and healthcare risk prediction models. Scientific Awards: FASE 2025 Distinguished Paper Award 2024 TEDxCUNY Speaker 2021 NIH Decoding Maternal Morbidity Challenge Prize 2019 Crain's Notable Women in Tech 2010 IEEE IAT Best Paper Award 2006 UNCC Essam El-Kwae Research Award As director of the Distributed Artificial Intelligence Research (DAIR) Lab, Raja's work is supported by NSF, NIH, ONR, DARPA, DHS, and Pacific Northwest National Laboratory (PNNL). She co-chairs the Civic-Led Urban Adaptation Research Center (CIVIC-UARC) and serves on the AAAI Executive Council (2022-present).

Effat FarhanaView profile

Assistant Professor

• Machine Learning
• Data-Centric Artificial Intelligence
• Machine Learning Applications for Education and Healthcare
• +2 more

Effat Farhana is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. She holds a Ph.D. in Computer Science from North Carolina State University and a B.S. in Computer Science and Engineering from Bangladesh University of Engineering and Technology. Her research focuses on Machine Learning, Data-Centric AI, and their applications in education and healthcare, alongside empirical software engineering and natural language processing. She explores topics like student learning analytics, AI-driven educational tools, and defect analysis in software projects. Her work bridges cognitive science and AI through projects like theory of mind modeling for humans and AI systems, and developing frameworks for K-12 physics instruction. She also investigates software engineering challenges in infrastructure as code and pandemic-related software systems. Her contributions span both foundational AI research and applied educational technology solutions. Farhana's publications emphasize empirical studies of software defects, cognitive aspects of learning, and AI's role in improving education and healthcare systems. She collaborates on projects involving online learning platforms, science literacy tools, and cognitive-inspired neural architectures.

• Vision Science
• Visual Perception
• Color Constancy
• +3 more

Marina Bloj is a Professor of Health Research at the University of Bradford's Faculty of Health Studies. Her research focuses on understanding human visual perception, particularly how eyes and the brain collaborate to process visual information. Her work integrates physics principles with experimental techniques like eye-tracking and reaction-time measurements to explore topics such as color constancy, binocular motion-in-depth perception, and visual memory. Current projects include collaborations with Professors Julie Harris (University of St Andrews) and Alex Wade (University of York) on neural processing of binocular motion, and work with Prof Karl Gegenfurtner in Germany on color memory of real objects. She has secured sustained EPSRC funding through interdisciplinary collaborations and leads projects addressing applied vision challenges. Her lab develops computational models of vision that predict human performance in tasks like camouflage detection and object recognition. Research interests span fundamental visual processes (e.g., light interaction with surfaces, depth perception) and applied areas such as clinical vision screening and public health interventions. She investigates how visual abilities develop over time, including aging effects and literacy impacts of vision correction. Her interdisciplinary approach bridges physics, neuroscience, and computer science, with outputs ranging from fMRI studies to psychophysical experiments. Notable collaborations include EPSRC-funded projects on lightness constancy and BBSRC-supported work on binocular motion pathways. Her work emphasizes real-world relevance, linking laboratory findings to everyday visual tasks such as driving, reading, and object recognition in dynamic environments. She actively publishes in high-impact vision science journals and mentors early-career researchers in her field.

• Cognitive Neuroscience
• Decision Making
• Reward Processing
• +2 more

James Howard is Assistant Professor of Psychology at Brandeis University, affiliated with the Volen National Center for Complex Systems. His research examines how the human brain constructs outcome expectations to guide decision-making. Research focuses on chemosensory rewards, using neuroimaging and non-invasive brain stimulation to study associative learning mechanisms. Investigations analyze functional network coordination supporting learning processes and how network perturbations affect decision guidance. Recent publications investigate orbitofrontal cortex functions in reward processing, identity-specific reward coding, and sensory prediction errors. Studies combine behavioral paradigms with neuroimaging and stimulation techniques to establish causal relationships.

• Comparative Democratization
• Cybersecurity
• Social Media Politics
• +3 more

Steven Lloyd Wilson is an Associate Professor of Politics at Brandeis University's Department of Politics. He holds multiple key roles including Project Manager for the Varieties of Democracy (V-Dem) Institute, Chief Technical Officer for the Demscore Project, and co-Principal Investigator of the Digital Society Project. Wilson specializes in comparative democratization, cybersecurity, and the impact of social media on authoritarian regimes, with a focus on post-Soviet states. His work bridges computational social science and policy analysis, leveraging large-scale datasets to study global political dynamics. Education: PhD from the University of Wisconsin-Madison, two M.A. degrees from the University of Wisconsin-Madison and University of San Diego. Research Interests: Wilson examines how digital technologies shape political processes, particularly in non-democratic contexts. His work analyzes Twitter networks in Israel, vaccine hesitancy misinformation, and the role of social media in protest movements like Venezuela’s anti-Maduro and Hong Kong’s anti-extradition protests. He also develops innovative datasets such as the Digital Society Project (DSP) and V-Dem’s comprehensive democracy metrics. Awards: Postdoctoral Research Fellow (V-Dem 2016-2017), Nominee for Carnegie Fellowship (2017), Honorary Fellow at University of Wisconsin (2018-2019), Norman Fund Recipient (Brandeis 2020). Served as Visiting Professor at NYU (2020) and University of Gothenburg (2023). Grants & Advising: Primary investigator on multiple projects including Demscore and Digital Society Project. Advises on computational infrastructure for V-Dem and collaborates internationally through affiliations like NYU’s Social Media and Politics Center. Labs/Teams: Leads computational research teams at V-Dem and DSP, focusing on data-driven analysis of global political trends. Active in developing open-source tools for measuring democracy and digital politics.

Josh WeeseView profile

Assistant Professor

• Computer Science Education
• Computational Thinking
• K-12 Outreach
• +2 more

Josh Weese is an Assistant Professor in the Department of Computer Science at Kansas State University's College of Engineering and serves as Undergraduate Program Director. He holds a Ph.D. (2017) and M.S. (2013) from K-State and a B.S. from K-State Polytechnic (2011), with research expertise in computer science education, computational thinking, and K-12 outreach. As co-director of the Advancing Learning and Teaching Computer Science (ALT+CS) Lab, he develops curriculum for K-12 computer science education and leads statewide teacher training initiatives. His teaching spans programming fundamentals, data structures, databases, and CS education courses. He has supervised 6 graduate students to completion and leads multiple NSF and Kansas Department of Education grants totaling over $5M. Awards: Kevin and Jacquie Elmore - Carl and Mary Ice Teaching Scholar (2020-2025) Teaching Excellence Award (2020-2023) College of Engineering Team Award of Excellence (2021) His outreach impacts 4,000+ K-12 students through programs like Kansas STARBASE, Girl Scouts AI workshops, and rural STEM camps. He contributed to Kansas PreK-12 Computer Science Standards development and advises student organizations including ACM and Hack K-State.

Rezaul ChowdhuryView profile

Associate Professor

• Algorithms and Data Structures
• Cache-efficient Computing
• Computational Biology
• +8 more

Rezaul Chowdhury is an Associate Professor in the Department of Computer Science at Stony Brook University (SBU), with a joint appointment at the Institute for Advanced Computational Sciences (IACS). His research focuses on algorithms and data structures for efficient serial and parallel computing, computational biology, and experimental algorithmics. He leads the Theoretical and Experimental Algorithmics (TEA) Group, emphasizing both algorithm design and engineering. Notable contributions include the Pochoir stencil compiler and the AutoGen system for dynamic programming algorithms. Chowdhury earned his Ph.D. from UT Austin, working on cache-efficient algorithms, and held postdoctoral positions at MIT and UT Austin. He has received prestigious awards, including the NSF CAREER Award and a Best Paper Award at IPDPS 2010. His work spans parallel programming, cache-oblivious algorithms, and bioinformatics applications like protein-protein docking (F2Dock). Teaching includes advanced courses on algorithms, parallel computing, and supercomputing. He advises multiple Ph.D. and master’s students and actively contributes to competitive programming through the SBU teams. His research projects are NSF-funded, focusing on stencil computations and resource-oblivious algorithms. Key software contributions include Pochoir (for stencil computations), F2Dock (protein docking), and AutoGen (automated algorithm discovery). His work bridges theory and practice, addressing challenges in multicore and distributed systems.