Find Academic Supervisors

Morten H. ChristiansenView profile

Professor

• Cognition
• Neuroscience
• Computational Linguistics
• +4 more

Morten H. Christiansen is the William R. Kenan, Jr. Professor of Psychology at Cornell University and holds a concurrent position as Professor of Cognitive Science at Aarhus University's School of Communication and Culture and the Interacting Minds Centre. His research focuses on the interplay between biological and environmental factors in language evolution, acquisition, and processing. Key methodologies include computational modeling, neuroimaging, and experimental psychology. He is an elected member of Denmark’s and Norway’s Royal Academies of Sciences and a Fellow of the Association for Psychological Science and Cognitive Science Society. His work spans over 250 papers and four edited volumes, with his monograph The Language Game (2022) proposing a novel theory of language emergence through improvisation. Research interests emphasize multiword chunking, statistical learning, and individual differences in language processing. Current projects explore Danish language challenges, computational models of cultural evolution, and the impact of large language models (LLMs) on human cognition. Christiansen directs the Cognitive Science of Language Lab, teaches undergraduate and graduate courses (e.g., PSYCH 2150: Psychology of Language), and collaborates internationally on projects like the Danish Gigaword Corpus. His recent work highlights language as an emergent system shaped by interactive and ecological pressures.

• Education Technology
• Science Education
• Playful Learning
• +5 more

Nathan R. Holbert is an Associate Professor of Communication, Media, and Learning Technologies Design at Teachers College, Columbia University, and the founder/director of the Snow Day Learning Lab. He holds a Ph.D. in Learning Sciences from Northwestern University, an M.A.Ed. in Instructional Process from Washington University, and a B.S. in Secondary Chemistry Education from Greenville College. His research focuses on designing playful tools and environments for children to engage with computational thinking and STEM topics. Key interests include culturally responsive education, game-based learning, and constructionist pedagogy. Holbert co-edited Designing Constructionist Futures (MIT Press, 2020) and co-authored Playful Testing (ETC Press, 2022). He also hosts the Pop & Play podcast on education and pop culture. Recent publications explore topics such as Afrofuturism in design, educational video games as cognitive tools, and leveraging cultural values in maker activities. His work emphasizes equity and accessibility in educational technology. The Snow Day Learning Lab serves as a hub for his experimental projects in playful and culturally grounded learning.

Marco LazzariView profile

Professor

• Educational Technology
• Digital Storytelling
• Inclusive Education
• +3 more

Marco Lazzari is a Full Professor at the University of Bergamo in the Department of Human and Social Sciences. His research focuses on educational technology integration, digital storytelling applications in teaching, and inclusive education methodologies. Previously he served as Department Head (2018-2024) and coordinates doctoral studies in Human Sciences and Welfare Innovation. Education Background: Ph.D. in Computer Science M.S. in Information Sciences B.S. in Information Sciences Research Interests: Digital storytelling for inclusive pedagogy Educational robotics applications Technology-mediated distance learning Teacher training in digital literacy Social media's educational impact His recent publications demonstrate strong focus on educational technology in post-pandemic contexts, inclusive robotics programs, and digital storytelling frameworks. Research trends include addressing educational disparities through technology, evaluating pandemic learning impacts, and developing sustainable digital pedagogies across educational levels. Scientific Awards: No major awards listed in provided materials As Director of the Educational Technologies Center (2005-2018), he established technology-enhanced learning initiatives. Currently supervises doctoral students investigating educational technology efficacy, digital inclusion, and robot-assisted learning. Research collaborations span computer science, pedagogy, and special education disciplines. Laboratory Focus: Educational robotics implementation Digital storytelling frameworks Teacher technology training programs Accessibility in educational technology

Jody Clarke-MiduraView profile

Professor

• STEM Education
• Computational Thinking
• Game-Based Learning
• +3 more

Dr. Jody Clarke-Midura is a Professor and Associate Dean of Graduate Studies in the Emma Eccles Jones College of Education and Human Services at Utah State University. She holds an Ed.D. from Harvard Graduate School of Education, an M.Ed. in Technology in Education, and a B.A. in English and Women’s Studies from the University of Massachusetts. Her research focuses on designing playful, technology-enhanced learning environments to foster STEM engagement and computational thinking in K-12 and early childhood education. Key areas include game-based learning, computational thinking integration, and technology-enhanced assessments. She collaborates on interdisciplinary teams and leads externally funded projects. Clarke-Midura co-directs the Playful Explorations Lab (PEL), which designs STEM learning experiences for elementary classrooms. Her work emphasizes hands-on, student-driven exploration and connects theory to classroom practice. She mentors graduate and undergraduate students, supporting their research development. Her publications address topics like formative assessments, computational thinking assessment frameworks, and the role of coding toys in early math learning. She has contributed to curriculum models integrating computer science and mathematics, and explored cultural relevance in co-design processes. Professional activities include leading graduate studies administration, advising on educational technology initiatives, and advancing inclusive pedagogical practices.

Kevin W. HamlenView profile

Professor

• Cybersecurity
• Software Security
• Formal Methods
• +6 more

Kevin W. Hamlen is the Louis A. Beecherl, Jr. Distinguished Professor in the Department of Computer Science at the University of Texas at Dallas. He serves as Executive Director of UT Dallas' Cyber Security Research and Education Institute. His research focuses on language-based security , binary software hardening , cyberdeception , and formal program verification . He has received multiple grants from agencies like AFOSR, NSF, DARPA, and industry partners including Lockheed Martin and Intel. PhD and MS from Cornell University BS from Carnegie Mellon University His research explores automated approaches to software security through techniques like binary disassembly , control-flow integrity , and honey-patching . He has pioneered methods for malware defense and cloud/web/mobile security . Recent work examines adaptive cyberdeception and GPU-based security frameworks . His publications span binary code manipulation , malware mitigation , and blockchain security . Key awards include the NSF IUCRC Technology Breakthrough Award and two CSAW Best Paper 2nd Prizes . He advises numerous PhD students, many of whom now work at Google, IBM, and Microsoft. His book Autonomous Cyber Deception (Springer, 2019) with Ehab Al-Shaer and Cliff Wang provides comprehensive coverage of adaptive cyberdeception strategies.

• Optimization and optimal control
• Inverse problems
• Mathematical imaging
• +5 more

Karl Kunisch is a Professor at the Department of Mathematics and Scientific Computing at the University of Graz and serves as Scientific Director of the Radon Institute of the Austrian Academy of Sciences in Linz. With a distinguished career spanning several decades, he has established himself as a leading researcher in optimization and control theory. Prof. Kunisch completed his PhD and Habilitation at the Technical University of Graz in 1978 and 1980, respectively. His academic journey includes significant positions at Brown University's Lefschetz Center for Dynamical Systems, INRIA Rocquencourt, Universite Paris Dauphine, and he previously served as a professor of numerical mathematics at the Technical University of Berlin. Research Interests: Prof. Kunisch's research focuses on optimization and optimal control, inverse problems and mathematical imaging, numerical analysis and applications, with current emphasis on life sciences applications. His specific areas include Optimal Control of Partial Differential Equations, Nonsmooth Optimization in Function Spaces, and Applications of Optimization and Control in the Life Sciences. His work bridges theoretical mathematics with practical applications across various scientific domains. His recent publications demonstrate a continued focus on advancing the theoretical foundations of optimal control while developing practical numerical methods. Key trends include work on infinite horizon control problems, feedback stabilization techniques, applications to PDE-constrained optimization, and the integration of machine learning approaches with traditional control theory. His research group actively explores connections between theoretical developments and applications in the life sciences. Scientific Recognition: W.T. and Idalia Reid Prize 2021 SIAM Fellow (2017) European Research Council Advanced Grant (2015) Alwin Walther Medaille (2008) SIAM Outstanding Paper Prize (2006) Prof. Kunisch has made substantial contributions to the mathematical community through his editorial work, serving as editor for prestigious journals including SIAM Journal on Control and Optimization, SIAM Journal on Numerical Analysis, and the Journal of the European Mathematical Society. He leads the Research Group on Optimization and Optimal Control at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) and is involved in the ERC-Project OCLOC "From Open to Closed Loop Control".

Christopher Thomas RyanView profile

Associate Professor

• Optimization
• Game Theory
• Theoretical Economics
• +7 more

Christopher Thomas Ryan is an Associate Professor in the Operations and Logistics Division at the UBC Sauder School of Business. He holds a PhD and BA from UBC in Management Science and Mathematics, respectively. His research spans optimization theory, game theory, theoretical economics, video game design, and pedagogy for business education. He has held positions at the University of Chicago Booth School of Business and is affiliated with the Colibri Learning Foundation. Education: BA (Mathematics) and PhD (Management Science) from UBC; BA (Sociology and Economics) from University of Guelph. He has authored numerous textbooks and case studies, including works on research careers, operations management, and teaching methodologies. His work integrates human-centric approaches to operations and education systems. Research Interests include small business operations, education operations, theoretical economics, and the design of video games. He has published in top journals like Management Science, Econometrica, and Mathematical Programming. His grants include SSHRC and NSERC awards. Ryan advises students across multiple institutions and has mentored prominent faculty in marketing, operations, and MIS fields. Teaching focuses on core operations management for MBA programs and pedagogical innovation. He has developed cases on AI in education, early childhood development, and small business sustainability. Ryan’s work bridges academic research with practical applications in business and education sectors.

Archie ChapmanView profile

Associate Professor

• Artificial Intelligence
• Game Theory
• Optimization
• +8 more

Associate Professor Archie Chapman is an Associate Professor in Computer Science and Deputy Director (Teaching and Learning) at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on applying artificial intelligence, game theory, optimization, and machine learning to address challenges in future power systems, including renewable energy integration and battery storage optimization. Prior to UQ, he held roles as a Research Fellow in Smart Grids at the University of Sydney (2011-2019) and a postdoc at the University of Southampton (2009-2010), where he completed his PhD in 2004. Research interests include: - Large-scale optimization for energy systems - Demand response and peer-to-peer energy trading - Renewable energy integration and grid stability - Battery storage strategies for smart grids - Algorithmic game theory for energy market design Notable projects: - Bruny Island battery trial for network congestion management - Analysis of tariff impacts on solar households - Development of decentralized energy management frameworks Publications span over 100 articles, with recent work focusing on: - Prosumer battery systems and capacity firming (2025) - Unbalanced optimal power flow benchmarks (2024) - P2P energy trading mechanisms (2021-2023) Expertise includes: - Techno-economic analysis of energy systems - Distributed optimization algorithms - Policy and market design for renewable integration

Joshua WeidlichView profile

Assistant Professor

• Educational Technology
• Learning Analytics
• Online Learning
• +3 more

Prof. Dr. Joshua Weidlich is an Assistant Professor for Digital Higher Education at the University of Zurich (UZH), collaborating with Zurich University of Education as a DIZH Bridge Professor. His academic background includes a PhD in Educational Technology from FernUniversität in Hagen, where he earned the Early Career Award for Outstanding Dissertation. He holds a BA in Educational Science from TU Chemnitz and an MA in E-Learning and Media Education from the University of Education Heidelberg. His research focuses on leveraging digital technologies to enhance higher education, emphasizing personalized feedback, feedback literacy, learning analytics, AI-driven environments, and social presence in online learning. Prior to UZH, he worked at the DIPF | Leibniz Institute for Research and Information in Education, contributing to both research and teaching. Key achievements include pioneering work on social presence in distance learning, validated through frameworks like the Social Presence Measure, and exploring gender dynamics in STEM education. His recent studies address the impact of AI tools like ChatGPT in education and the role of feedback literacy in student success. Prof. Weidlich’s publications span educational technology, gender studies, and learning analytics, reflecting his commitment to advancing digital pedagogy and equitable educational practices. He actively contributes to the Digital Society Initiative (DSI) at UZH, fostering interdisciplinary dialogue on societal impacts of technology.

Dilian GurovView profile

Professor

• Software Specification and Verification
• Multi-Agent Strategic Planning
• Formal Methods
• +3 more

Dilian Gurov is a Professor in Computer Science at KTH Royal Institute of Technology, associated with the Digital Futures Faculty and the Division of Theoretical Computer Science. He also coordinates the Doctoral Programme in Computer Science at the CSC school. Before joining KTH in 2002, he earned a Ph.D. from the University of Victoria, Canada (1998), and worked at the Swedish Institute of Computer Science (1997-2002). His research focuses on software specification and verification, including contracts, program models, logics, and tools, as well as multi-agent strategic planning involving knowledge-based strategies in imperfect information settings. Key contributions include the CAV Distinguished Paper Award 2023 for 'Automatic Program Instrumentation for Automatic Verification' and an EASST award for 'Checking Absence of Illicit Applet Interactions: A Case Study' (2004). He leads projects funded by VR (SEFROS, ContraST) and Vinnova (AVerT2) and collaborates with industries like Scania on formal verification of C programs. His service roles span over 30 conference committees and organization roles, including PC memberships for iFM, TAP, and ISoLA. Teaching responsibilities include courses such as 'Formal Methods,' 'Program Semantics and Analysis,' and 'Knowledge in Games with Imperfect Information.' His work emphasizes practical applications of formal methods, bridging academic research with industry needs through collaborations and tool development (e.g., CVPP, ProMoVer, TriCo).

Aritra MitraView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Control Theory
• +6 more

Aritra Mitra is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from Purdue University (2020), an M.Tech. from IIT Kanpur (2015), and a B.E. from Jadavpur University (2013). Before joining NC State, he was a postdoctoral researcher at the University of Pennsylvania. His research focuses on enabling reliable, efficient learning and decision-making in large-scale distributed systems, addressing challenges like computation, communication constraints, and adversarial robustness. Key areas include control theory, machine learning, signal processing, and network science. Education: Ph.D., Electrical and Computer Engineering, Purdue University (2020) M.Tech., Electrical Engineering, Indian Institute of Technology Kanpur (2015) B.E., Electrical Engineering, Jadavpur University (2013) Research Interests: Dr. Mitra’s work bridges theoretical foundations with practical applications in distributed systems. He designs algorithms for federated learning, reinforcement learning, and adversarial robustness, with applications in control systems and networked environments. Recent efforts emphasize finite-time analysis of TD learning, heterogeneous federated systems, and resilient control under communication constraints. His contributions often integrate tools from stochastic approximation, optimization, and signal processing. Publications: His articles explore cutting-edge topics like federated TD learning, robust system identification under heavy-tailed noise, and distributed multi-agent optimization. Recent trends highlight advancements in asynchronous algorithms, delay-adaptive systems, and model-free control under communication bottlenecks. Grants & Labs: While specific grants are not detailed, his research aligns with themes in distributed computing and control, suggesting potential involvement in NSF or industry-funded projects. No lab-specific details are provided in the text.

Chao TianView profile

Associate Professor

• Information Theory
• Distributed Data Storage
• Coding Theory
• +6 more

Dr. Chao Tian is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Cornell University (2005) and a B.E. from Tsinghua University (2000). His research focuses on information theory, distributed storage systems, coding theory, and machine learning applications, including large language models (LLMs) and privacy-preserving techniques like steganography and private information retrieval (PIR). He has received notable awards, including the 2017 IEEE Jack Wolf ISIT Best Student Paper Award and the 2014 IEEE ComSoc DSTC Best Paper Award. Dr. Tian’s work bridges theoretical foundations and practical applications, such as optimizing storage codes for distributed systems, developing secure PIR protocols, and exploring LLMs in energy and steganographic contexts. His group has pioneered methods for variable-order Markov chain modeling with transformers and designed quantization techniques for perceptual quality. Recent activities include a faculty development leave at MIT/Harvard and delivering distinguished lectures globally on AI and information theory. He advises students on cutting-edge topics like LLM-based steganography and diffusion models. His publications span journals like IEEE Transactions on Information Theory and conferences such as NeurIPS and ISIT, with a focus on coding theory, privacy, and machine learning. He has also contributed to open-source tools like the CAI toolbox for investigating information-theoretic limits. Current projects include optimizing cryptocurrency mining in energy markets and advancing federated learning with adversarial robustness.

Marcus BotacinView profile

Assistant Professor

• Malware Analysis
• Hardware-Assisted Security
• Antivirus Technology
• +4 more

Marcus Botacin is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU), USA. Previously, he held positions as Visiting Assistant Professor (2022-2024) and Lecturer at the Federal University of Paraná (Brazil). His research focuses on malware analysis, hardware-assisted security, and antivirus technology. He earned his Ph.D. in Computer Science from UFPR (2021), M.Sc. from UNICAMP (2017), and B.Sc. in Computer Engineering from UNICAMP (2015). Education: Ph.D., Computer Science, Federal University of Paraná, Brazil (2021) M.Sc., Computer Science, University of Campinas, Brazil (2017) B.Sc., Computer Engineering, University of Campinas, Brazil (2015) Research Interests: Botacin's work addresses challenges in malware detection (static/dynamic methods, sandboxing), hardware-enhanced security mechanisms (e.g., branch monitoring), and antivirus internals. He emphasizes practical solutions and ethical considerations in cybersecurity, advocating for culturally aware threat models (e.g., Brazilian financial malware studies). Publications & Trends: His recent work explores adversarial ML attacks on malware detectors, HPC-based detection frameworks, and automated malware generation risks. He frequently publishes in top venues like ACM CCS, USENIX Security, and IEEE TDSC. Awards: Top-3 Best Ph.D. Thesis in Computer Security (Brazilian Computer Society, 2022) Best PhD Thesis Award from UFPR (2022) MLSec Evasion Challenge 1st place (2021/2020) Advising & Grants: Supervises 15+ students at TAMU and leads NSF-funded projects (e.g., $523K grant for HPC malware detection frameworks). Serves on 21+ conference program committees and reviews for over 50 journals. Lab & Projects: Develops Corvus (public malware analysis sandbox) and explores hardware-accelerated AV solutions like Terminator and HEAVEN.

Peter ChinView profile

Professor

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +8 more

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

• Visual Analytics
• Mobility Data Science
• Machine Learning
• +4 more

Natalia Andrienko is a Professor of Computer Science at City University London and Lead Scientist in the Knowledge Discovery department at Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme. Her work bridges visual analytics with mobility data science and machine learning, focusing on human-in-the-loop systems for pattern discovery and spatiotemporal data exploration. Professor, Computer Science, City University London (2013-present) Lead Scientist, Knowledge Discovery, Fraunhofer Institute (1997-present) Research interests center on visual analytics methodology for spatiotemporal data, human-centered machine learning, and mobility pattern analysis. She develops frameworks for interactive dashboards, trust visualization in ML, and semantic exploration of location-based data, with a focus on scalable and privacy-respecting techniques. Her recent publications investigate hybrid human-machine discovery of movement patterns, contextual visual analytics for multivariate events, and the integration of temporal periodization with spatial analysis. Articles emphasize applications in sports analytics, transportation systems, and collaborative visual analysis workflows. Key collaborations include work with Gennady Andrienko and Salvatore Rinzivillo. She has contributed to journals like Visual Informatics , IEEE Transactions on Visualization and Computer Graphics , and International Journal of Cartography , maintaining active research output across visual analytics, mobility science, and geospatial data modeling.