Find Academic Supervisors

• Databases
• Web Data Management
• Logical Methods in Computer Science
• +4 more

Michael Benedikt is a Professor of Computer Science at the University of Oxford and a Governing Body Fellow of University College. He holds the role of Director of the Advanced MSc in Computer Science program. His research focuses on databases, Web data management, logical methods in computer science, and theoretical computer science. Benedikt's work intersects with artificial intelligence, machine learning, and algorithms, with contributions to query languages, data integration, and formal methods. Education: Ph.D. in Mathematics, University of Wisconsin, 1993 Prior roles: Distinguished Member of Technical Staff at Bell Laboratories (1994–2006), visiting researcher at Yahoo! Labs Research Interests: Databases and information exchange Web and Web 2.0 data management Logical methods in computer science Formal verification and query optimization Applications in AI and machine learning Key Projects: FOX : Query-driven data acquisition from web-based sources PDQ : Proof-driven query answering over web-based data TRANCE : Transforming nested collections efficiently Awards: Best Paper Award at ICALP 2017 (Track B) EPSRC Established Career Fellowship (2015–2020) Advising & Grants: Directed the MSc in Advanced Computer Science program Supervised PhD students including Chia-Hsuan Lu and past advisees such as Luying Chen and Ben Spencer Received funding for projects like the ERC DIADEM initiative Labs & Teams: Active in the Department of Computer Science’s research groups, including the Algorithms At Large and Databases teams.

• Computational Intelligence
• Cloud Computing
• Big Data Applications
• +6 more

Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.

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.

Lillian LeeView profile

Professor

• Natural Language Processing
• Social Computing
• Computational Social Science
• +4 more

Lillian Lee is a Professor of Computer Science at Cornell University, affiliated with the College of Computing and Information Science. Her research bridges natural language processing (NLP) and social interaction, focusing on how computational methods can analyze and facilitate socially embedded processes. She co-developed the course “Natural Language Processing and Social Interaction” and leads the Cornell NLP Group. Her work spans sentiment analysis, computational social science, and multimodal interaction, with notable contributions to understanding language features in persuasion, online debate dynamics, and humor comprehension. Key research interests include analyzing digital traces of social interaction, evaluating AI systems through human-centered criteria, and exploring the interplay between language structure and societal influence. Recent projects examine pivotal moments in mental health counseling, cross-cultural historical narratives on Wikipedia, and the role of wording in message propagation. Awards: ACM Fellow, ACL Distinguished Service Award (2021), Test of Time Award, Fellow of the Association for Computational Linguistics Labs/Teams: Member of the Cornell Natural Language Processing Group Advising: Mentored numerous students whose work has driven impactful projects in NLP and computational social science

Charles GillanView profile

Senior Lecturer

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +4 more

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

Meng XuView profile

Assistant Professor

• System Security
• Software Security
• Automated Program Analysis
• +4 more

Meng Xu is an Assistant Professor in the Cheriton School of Computer Science at the University of Waterloo, Canada. He is affiliated with the Cryptography, Security, and Privacy (CrySP) group and the Cybersecurity and Privacy Institute (CPI). His research focuses on system and software security, emphasizing secure-by-design languages (e.g., Rust, Move), automated program analysis, and runtime defense techniques. Education : Ph.D., Computer Science (2020), Georgia Institute of Technology B.Eng. and B.Business (First Class Honors), Nanyang Technological University (2014) Research Interests : Secure-by-design languages Automated security analysis (fuzzing, symbolic execution) Runtime defense mechanisms (moving target defense, secure hardware) Key Awards : EAPLS Best Paper Award (2022) USENIX Security Distinguished Paper Award (2018) Grants & Funding : BlackBerry Research Grant (CAD $200,000) Amazon Research Award (USD $60,000) NSERC Discovery Grant (CAD $170,000) Labs & Collaborations : CrySP (Cryptography, Security, and Privacy Group) Cybersecurity and Privacy Institute (CPI)

Mitchell L. NeilsenView profile

Professor

• Cyber-Physical Systems
• Distributed Systems
• Scientific Computing
• +7 more

Mitchell L. Neilsen is a Professor in the Department of Computer Science at Kansas State University's College of Engineering, where he also serves as the graduate program director. He holds the Warren and Gisela Kennedy - Carl and Mary Ice Keystone Research Scholar position and maintains an active research program with multiple ongoing projects. His educational background includes a Ph.D. in Computer Science (1992), M.S. in Computer Science (1989), and M.S. in Mathematics (1987), all from Kansas State University, plus a B.S. in Mathematics Education from the University of Nebraska-Kearney (1982). After beginning his career as an assistant professor at Oklahoma State University, he returned to K-State in 1996. Research Interests: Cyber-Physical Systems: Design, Analysis, Verification of systems integrating computing, networking, and physical processes Distributed Systems: Algorithms, design, and analysis of distributed computing systems Scientific Computing: Computational Fluid Dynamics, Finite Element Analysis, High Performance Computing, and Simulation Application Areas: Agriculture technology, Dam safety analysis, Mobile applications, Natural resources management, and Real-time Embedded Systems His research program shows clear evolution toward agricultural technology applications, particularly high-throughput phenotyping, while maintaining strong foundations in cyber-physical systems and scientific computing. Recent publications indicate increasing integration of machine learning and computer vision techniques into traditional research areas. Research Funding: National Science Foundation U.S. Department of Agriculture Sandia National Laboratories Department of Homeland Security Private industry partners Dr. Neilsen has mentored numerous graduate students through their M.S. and Ph.D. programs, with recent advisees focusing on applications in agricultural technology, dam safety, and embedded systems. His advising approach emphasizes practical applications of theoretical computer science concepts. Current Teaching (Fall 2024): CIS 450 - Computer Architecture and Operations CIS 625 - Concurrent Software Systems CIS 720 - Advanced Operating Systems

Chao FanView profile

Assistant Professor

• Climate Change Adaptation and AI
• Dynamics of Integrated Socio-Environmental Systems
• Human Networks and Data Science
• +4 more

Dr. Chao Fan is an Assistant Professor in Civil Engineering and Environmental Engineering and Earth Sciences at Clemson University, affiliated with the Glenn Department of Civil Engineering. His research focuses on climate change adaptation, socio-environmental systems dynamics, and urban resilience, leveraging AI and data science. He holds a Ph.D. from Texas A&M University (2020), an M.S. from UC Davis (2017), and a B.S. from China University of Mining and Technology (2016). Dr. Fan's work integrates interdisciplinary approaches to address challenges in disaster management, smart cities, and environmental justice. Key interests include social sensing for infrastructure disruptions, equity in urban mobility networks, and leveraging digital twins for resilience planning. His recent publications explore topics like wildfire impacts, PM2.5 exposure inequity, and carbon market mechanisms for infrastructure adaptation. Professional memberships include ASCE, ACM SIGKDD, AGU, and AAAS. His lab (fanchaolab.com) develops innovative solutions for climate adaptation and equitable urban systems, emphasizing fairness in AI-driven models and network analysis.

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.

Marco DonatoView profile

Assistant Professor

• Hardware for Machine Learning
• Non-Volatile Memories
• SoC Design
• +5 more

Marco Donato is an Assistant Professor in both the Department of Electrical and Computer Engineering and the Department of Computer Science at Tufts University. He leads the TECS Lab (Testchip, Embedded Computing Systems) focused on hardware design for emerging applications. Prior to joining Tufts, he was a postdoctoral fellow at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Dr. Donato received his academic training from prestigious institutions: Ph.D. in Electrical Sciences and Computer Engineering from Brown University (2016) M.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2010) B.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2008) Dr. Donato's research primarily focuses on designing reliable and energy-efficient hardware systems leveraging emerging technologies. His work centers on co-design methodologies for building specialized architectures for machine learning applications that utilize dense, fault-prone embedded non-volatile memories. He investigates noise modeling and reliability aspects of next-generation memory technologies, with particular emphasis on how these can be effectively integrated into system-on-chip (SoC) designs for edge computing and IoT applications. His research bridges the gap between circuit-level design and system-level architecture to create holistic solutions for hardware acceleration of machine learning workloads. Analysis of Dr. Donato's publication record reveals a strong focus on hardware acceleration for machine learning, particularly through innovative memory system designs. His work spans multiple domains including non-volatile memory technologies, energy-efficient circuit design, and flexible SoC architectures. A notable trend is his exploration of how emerging memory technologies can be leveraged to create more efficient implementations of deep neural networks, with particular attention to the trade-offs between reliability, density, and energy consumption. His research often involves full-stack approaches that consider everything from device physics to system architecture. Dr. Donato is actively involved in mentoring and has indicated he is "looking for Ph.D. students." His work has been supported by significant research grants that have enabled the fabrication of multiple test chips, as evidenced by his extensive publication record in top-tier venues including IEEE Journal of Solid-State Circuits, ISSCC, and MICRO. He leads the TECS Lab at Tufts University, which focuses on testchip development, embedded computing systems, and hardware acceleration. The lab appears to maintain connections with researchers at Harvard University and other institutions, reflecting Dr. Donato's collaborative approach to research. The lab's work emphasizes practical, real-world implementations of novel hardware concepts through actual silicon fabrication, which is relatively rare in academic settings.

Latifur KhanView profile

Professor

• Data Mining
• Cybersecurity
• Machine Learning
• +2 more

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

Asta ZelenkauskaiteView profile

Professor

• Social Media Research
• User-Generated Content Analysis
• Emergent Practices Online
• +3 more

Asta Zelenkauskaite is a Professor of Communication and Graduate Faculty Member in the Department of Communication at Drexel University. She is affiliated with the Center for Science, Technology, and Society. Her research focuses on social media dynamics, misinformation, and digital communication practices, employing mixed-methods approaches to analyze emergent online behaviors. She holds a PhD in Mass Communication from Indiana University (2012). Education: PhD in Mass Communication, Indiana University, 2012 Research Interests: Social media research and user-generated content analysis Emergent online practices and ideological influence Disinformation, inauthentic behaviors, and post-truth challenges Macro- and micro-level studies of digital media landscapes Recent Work Trends: Dr. Zelenkauskaite’s recent publications emphasize AI’s role in content analysis, disinformation mitigation strategies, and cross-cultural climate change narratives. Her work bridges social science, information science, and linguistics to address societal challenges like vaccine hesitancy and digital misinformation. Awards/Grants: No specific awards listed, but her research has been supported by interdisciplinary grants focusing on AI ethics and digital media governance. Labs/Teams: Active in Drexel’s Center for Science, Technology, and Society, collaborating on projects addressing technology’s societal impacts. Her work often involves international partnerships, particularly in Eastern European contexts like Lithuania.

• Data Science
• Large Language Models
• Business Analytics
• +5 more

Denghui Zhang is an Assistant Professor in the School of Business at Stevens Institute of Technology. His research focuses on data science, large language models (LLMs), and business analytics, with particular emphasis on applications in financial systems, knowledge graphs, and spatio-temporal prediction. He is a member of the Stevens Institute for Artificial Intelligence and has held academic roles including reviewer positions for prestigious journals like Nature Communications and conferences such as AAAI and SIGKDD. Dr. Zhang holds a PhD in Information Systems from Rutgers University (2023) and an MS in Computer Science from the University of Chinese Academy of Sciences (2018). His educational background bridges computer science and business analytics, enabling his cross-disciplinary research. His research explores cutting-edge topics like federated learning optimization for LLMs, theory-of-mind reasoning mechanisms, and ethical AI governance. Notable contributions include turbulence forecasting models, traffic prediction frameworks, and venture capital investment strategies leveraging reinforcement learning. Dr. Zhang has received prestigious recognitions including the ICIS 2023 Best Student Paper Award and AAAI-23 Student Scholar distinction. His work frequently addresses practical challenges in AI ethics, financial decision-making systems, and scalable machine learning architectures. He actively contributes to academic communities through program committee roles for top conferences and has pioneered novel methodologies in multi-agent financial systems and graph neural network design.

• Artificial Intelligence
• Computer Vision
• Digital Agriculture
• +3 more

Ferdous Sohel is a Professor of Information Technology at Murdoch University and inaugural lead of the Agricultural Technologies program. His research spans AI, computer vision, and digital agriculture, with applications in medical imaging and environmental monitoring. He received the Mollie Holman Doctoral Medal and Vice Chancellor's Early Career Research Award. Research Impact: Developed innovative AI models for aquaculture oxygen prediction, 3D object tracking, quantum neural networks, and prohibited item detection. His work advances precision agriculture through hyperspectral classification frameworks and irrigation decision systems. Professional Service: Associate Editor for IEEE Transactions on Multimedia and senior IEEE member. Current projects include adversarial robustness for LiDAR systems and lightweight dormitory security networks.

Seth PolsleyView profile

Assistant Professor

• Human-Computer Interaction
• Intelligent Systems
• Educational Technology
• +4 more

Seth Polsley is an Assistant Professor in the Jeffrey S. Raikes School of Computer Science and Management at the University of Nebraska-Lincoln. His academic home resides in the School of Computing, where he bridges intelligent systems design with human-computer interaction to enhance educational and universal computing experiences. BS in Computer Engineering (2014) - University of Kansas MS (2017) & PhD (2023) in Computer Engineering - Texas A&M University His research explores: Intelligent tutoring systems Brain-computer interfaces Accessible educational technologies Machine learning for child development assessment Sketch recognition in STEM learning Recent publications demonstrate expertise in tactile learning interfaces, sketch-based developmental assessment, and equitable AI systems. Key disciplines span Human-Computer Interaction, Machine Learning, and Educational Technology. Scientific recognition includes: James Blackiston Memorial Graduate Fellowship Sigma Xi Research Award With professional experience at Lexmark International and MIT Lincoln Lab, Polsley combines practical engineering with educational innovation. His work on sketch-based tools and wearable systems addresses both technical and societal challenges in computing.