Find Academic Supervisors

Shengchao QinView profile

Professor

• Formal Methods
• Programming Languages
• Software Engineering
• +3 more

Dr. Shengchao Qin is a Professor of Computer Science at Teesside University's School of Computing & Digital Technologies, specializing in formal methods and software verification. He holds a BSc and PhD from Peking University, with postdoctoral experience at the Singapore-MIT Alliance. As Associate Dean for Research & Innovation (2016-2019), he led strategic research initiatives. His research focuses on automated verification of software safety/security, program analysis, and cyber-physical systems. Major funded projects include EPSRC grants for resource analysis of embedded software (£403k) and inference mechanisms for separation logic domains (£213k). He actively contributes to academic service as Program Committee Chair for ICFEM/TASE conferences, steering committee member for formal methods symposiums, and editorial roles for IEEE Access and Science of Computer Programming. Collaborations span top institutions including National University of Singapore and Peking University. Research Supervision: Current PhD advisees: Chris Curry, Yang Liu, Colin Joy, Ndidi Ogbo Former supervisees: Zhang Zhang (PhD '20), Florin Craciun (Postdoc), Ryuta Arisaka (PhD '13)

Paolo ModestiView profile

Senior Lecturer

• Cybersecurity
• Formal Methods for Security
• Security Protocols
• +5 more

Paolo Modesti is a Senior Lecturer in Cybersecurity at Teesside University's School of Computing, Engineering and Digital Technologies. He holds a PhD in Computer Science from Ca' Foscari University Venice, Italy, and an MSc from the University of Udine. His research focuses on security protocols, formal methods, and tool development for secure systems. He is a Fellow of the Higher Education Academy and a licensed professional engineer. Dr. Modesti's work includes designing formal verification frameworks (e.g., AnBx compiler/IDE) and analyzing protocols like Open Banking APIs and Bitcoin's Payment Protocol. He actively contributes to academic conferences (e.g., IEEE, ACM) and serves on program committees for security-related events. His teaching roles include leading the BSc Cyber Security program and modules on Ethical Hacking and Security Analysis. Research interests span security protocol modeling, automatic code generation, intrusion detection systems, and blockchain security. He advocates bridging the gap between academic tools and industry practices through frameworks like PTES and MITRE ATT&CK. His tools, such as the AnBx ecosystem, aim to simplify formal methods adoption for practitioners.

Jim SamuelView profile

Associate Professor

• Artificial Intelligence
• Natural Language Processing
• Data Science
• +5 more

Jim Samuel is an Associate Professor of Practice and Executive Director of the Master of Public Informatics program at the Bloustein School of Planning and Public Policy, Rutgers University. He holds a Ph.D. in Business from Baruch College (CUNY), an MBA in International Finance from Thunderbird School of Global Management, and an M.Arch from Sir J.J. College of Architecture. His research bridges artificial intelligence, natural language processing, and public policy, focusing on AI ethics, human-AI interaction, and culturally adaptive frameworks. Education: Ph.D. (Baruch College), MBA (Thunderbird School), M.Arch (Sir J.J. CoA) Dr. Samuel’s work addresses AI’s socioeconomic implications, including bias mitigation, transparency, and governance. He explores AI applications in healthcare, education, and urban informatics. His frameworks like AICMA (ambiguity mitigation) and ESHRO (mental health chatbots) exemplify his commitment to ethical AI innovation. He advises organizations on AI-driven strategies and teaches courses such as Artificial Intelligence: Practice, Principles & Strategy and Studio in Public Informatics with NLP . His interdisciplinary approach integrates data science, policy, and design to solve public-sector challenges. Notable collaborations include work with multinational corporations and initiatives on open data ecosystems in New Jersey. Dr. Samuel advocates for proactive AI policies and culturally sensitive AI education to empower human-AI collaboration.

Monica S. LamView profile

Professor

• Virtual Assistants
• Natural Language Processing
• Machine Learning
• +6 more

Monica S. Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in the Computer Science Department at Stanford University's School of Engineering, where she has been a faculty member since 1988. She serves as Faculty Director of the Open Virtual Assistant Lab (OVAL) and holds a courtesy appointment in the Electrical Engineering Department. Lam received her B.Sc. from the University of British Columbia in 1980 and her Ph.D. in Computer Science from Carnegie Mellon University in 1987. Her research spans multiple domains with a focus on creating trustworthy virtual assistants based on Large Language Models. She is particularly renowned for her work on the Almond virtual assistant, which emphasizes privacy preservation and open-source development. Her research interests include virtual assistants, natural language programming, machine learning, distributed mobile computing, and compiler technologies. Lam's recent publications reveal a strong focus on addressing key challenges in virtual assistant technology, particularly around hallucination reduction in LLMs, multilingual dialogue systems, semantic parsing, and multimodal interactions. Her work consistently emphasizes privacy preservation, open-source development, and user control over data. Member of the National Academy of Engineering ACM Fellow NSF Young Investigator Award (1992) ACM Most Influential Programming Language Design and Implementation Paper Award (2001) Wikimedia Foundation's Research of the Year Award (2024) for eliminating LLM hallucinations Popular Science's Best of What's New Award in Security (2019) for Almond virtual assistant Lam has advised numerous PhD students who have gone on to prominent positions at institutions like MIT, Harvard, Google, and Amazon. Her research has been supported by significant funding including NSF grants and industry partnerships. She leads the Open Virtual Assistant Lab (OVAL), which focuses on developing open, privacy-preserving virtual assistant technologies that give users control over their data while maintaining functionality.

Hongyang GaoView profile

Assistant Professor

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

Hongyang Gao is an Assistant Professor at Iowa State University, focusing on AI, Machine Learning, and Data Science. His work bridges theoretical foundations and practical applications in neural networks, graph representation learning, and cybersecurity. Education: PhD in Computer Science, Texas A&M University MS in Computer Science, Tsinghua University B.S. in Biomedical English, Peking University Research Interests: Explores neural ODEs, graph neural networks (GNNs), and their applications in molecular modeling, vulnerability detection, and trustworthy AI systems. His recent work emphasizes model interpretability (e.g., MotifExplainer), optimization theory for implicit networks, and graph-based explainability techniques. Publications Trends: Recent papers (2022-2025) highlight advancements in graph learning frameworks, such as G2T-LLM for molecule generation and MAGE for GNN explainability. His work on Neural ODEs explores activation function impacts on convergence, while cybersecurity papers apply dataflow analysis for vulnerability detection. Advising: Collaborates with students on projects involving motif-based methods (e.g., Motifpiece), meta-learning (Meta-AdaM), and trustworthy AI (data preconditions research). No grants explicitly listed in provided texts.

Joe ChildsView profile

Professor

• Entrepreneurship
• Economics
• Business Management
• +3 more

Joe Childs is a Professor and Executive Director of Innovation Projects at Indiana Wesleyan University’s DeVoe School of Business, Leadership, and Technology. He joined IWU in 2020 and leads initiatives like the DeVoe Entrepreneurial Experience, supporting over 3,000 international students. Previously, he served as Dean at Southeastern University and Associate Dean/Chair at Bethany University. His consulting practice focuses on Silicon Valley corporations and entrepreneurs. DBA in Management, Golden Gate University (2008) MA in General Ministry, Western Seminary (1997) MS in Public Relations, University of Southern Mississippi (1984) BA in Communication, University of Southern Mississippi (1981) Research interests span entrepreneurship, economics, organizational leadership, and higher education innovation. His work integrates academic rigor with practical business challenges, including AI applications in education, aviation operations, and economic policy. Publications reflect interdisciplinary expertise, with recent focus on LLMs in business research and historical contributions to sleep economics and market ethics. His articles address topics from CPA exam outcomes to biblical integration in business curricula. Led major institutional projects including CCCU student outcome studies and international entrepreneurship training programs. Active in executive coaching for leaders across industries.

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +3 more

Reza Haffari is a Professor in the Department of Data Science & AI at Monash University, within the Faculty of Information Technology. His research focuses on Artificial Intelligence, particularly in low-level perception and high-level reasoning under uncertainty from text and other modalities like vision and speech. Key areas include natural language understanding, semi-supervised learning, and model explainability for critical applications such as healthcare. Education: Doctor of Philosophy in Computer Science, Simon Fraser University (2010) Master of Science in Artificial Intelligence, Sharif University of Technology (2002) Bachelor of Science in Software Engineering, Sharif University of Technology (2000) Teaching Commitments: Chief Examiner for courses: FIT1045, FIT1053, FIT2004, FIT5201 Lecturer for units: FIT1045, FIT2004, FIT3080, FIT4005/FIT5185, FIT4009, FIT5201 Research Projects (2023–2027): Principal Investigator for Aligning Large Language Models with Human Intention Chief Investigator in Trustworthy Generative AI , Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems , and CSIRO Mental Health AI Research Trends in Articles: Recent work emphasizes continual learning , multimodal models , and ethical AI alignment . Key themes include adapting models to dynamic environments, ensuring safety in large language systems, and integrating causal reasoning with machine learning. Labs & Teams: Active in the Data Science & AI Group at Monash, collaborating on projects involving generative AI, neuro-symbolic systems, and healthcare applications.

Nata StulovaView profile

Researcher

• Software Engineering
• Formal Methods
• Natural Language Processing
• +3 more

Nata Stulova is a Researcher currently working as a staff research scientist at MacPaw's Technological R&D Center, focusing on software engineering research involving formal and informal program specifications. Formerly, she held academic roles including senior postdoctoral researcher at the University of Bern (SCG), researcher at EPFL's LARA Lab, and PhD work at the Technical University of Madrid (UPM). Her research spans software documentation consistency, NLP applications, runtime verification, and low-code tools. She has led multiple projects addressing code comment quality, empirical studies on documentation practices, and tool development for requirements engineering. Despite transitioning to industry in 2022, she maintains active collaboration with academia, publishing in top conferences like ICSE and IEEE Transactions. She holds a PhD in Computer Science from UPM and has authored numerous peer-reviewed articles and conference papers. Her work emphasizes bridging academic research with industry applications, particularly in software analysis and developer productivity. Education: PhD in Computer Science, Technical University of Madrid (2018) MSc in Artificial Intelligence, Technical University of Madrid (2013) BSc in Applied System Analysis, NTUU "KPI" (2012) Research Interests: Her work focuses on improving software quality through automated documentation tools, runtime verification techniques, and low-code solutions for requirements engineering. She explores NLP methods for detecting inconsistent or outdated comments and optimizing static analysis efficiency. Recent projects include SwiftEval (LLM-generated code evaluation) and RepliComment (cloned comment detection). She also emphasizes practical applications of formal methods in dynamic languages like Prolog and Python. Grants & Funding: ASA: Agile Software Assistance TRACES: Resource-Aware Software Tools N-GREENS: Energy-Efficient Software Labs/Teams: Core member of the Ciao system development team at IMDEA Software Institute, contributor to the Software Composition Group (SCG) at UniBe, and collaborator with MacPaw's TR&D team on industry-focused R&D.

• Theorem Proving
• Runtime Verification
• Automated Reasoning
• +3 more

Giles Reger is a Lecturer in the Formal Methods Group within the School of Computer Science at the University of Manchester. His primary roles include research and teaching in theorem proving, runtime verification, and formal methods. He has been actively involved in projects such as the Centre for Digital Trust and Society and the SCorCH project focusing on secure code verification. Educational background: Giles holds a BA in Computer Science from the University of Cambridge (2009), an MSc in Advanced Computer Science from the University of Manchester (2010) with the Highest Achiever of the Year Award, and a PhD from the University of Manchester (2014) titled Automata based monitoring and mining of execution traces . Research interests focus on two core areas: theorem proving (quantifier reasoning, first-order theories, collaborative proof search, and integration of SAT/SMT solvers) and runtime verification (temporal specifications, monitoring algorithms, violation explanation, and benchmarking). His work emphasizes practical applications in tool development and real-software analysis. Notable awards include the ACM SIGSOFT Distinguished Paper Award (2024) for his paper on LLM-generated invariants. His research contributions span over 50 publications, with active involvement in funded projects like SCorCH (Secure Code for Capability Hardware) and the Centre for Digital Trust and Society. Advising and grants: Reger welcomes PhD/MSc students in theorem proving, program verification, and related fields. He has supervised multiple research students and leads collaborative projects involving international researchers. His lab, the Formal Methods Group, develops tools like the Vampire theorem prover and contributes to runtime verification frameworks.

Michele MagnoView profile

Senior Lecturer

• Smart Sensing
• Low-Power Machine Learning
• Wireless Sensor Networks
• +4 more

Michele Magno is a Senior Lecturer and Privatdozent at ETH Zürich's Department of Information Technology and Electrical Engineering (D-ITET), leading the D-ITET Center for Project-based Learning (pbl.ee.ethz.ch). He holds a PhD in Electronic Engineering from the University of Bologna (2010) and has held visiting roles at institutions like the University of Nice and Mid Sweden University. His research focuses on low-power systems, wearable devices, energy harvesting, and IoT applications. Magno has authored over 350 peer-reviewed papers, with a Google H-index of 49. Notable awards include the 2024 Best Paper Award at ECCV and multiple best poster/demo recognitions at IEEE conferences. His industrial collaborations include projects with STMicroelectronics, Texas Instruments, and Logitech. Teaching contributions include courses on embedded systems, FPGA programming, and machine learning on microcontrollers. Magno's innovations span smart sensors for wind turbines, bio-medical monitoring, and autonomous racing systems, with patents in touch communication and energy-neutral devices. Recent work emphasizes ultra-low-power solutions for AI-integrated wearables, energy-efficient IoT nodes, and real-time embedded vision systems. His labs and teams pioneer technologies like TinyssimoRadar for in-ear gesture recognition and WakeMod for ultra-low-power IoT connectivity.

• Healthcare Informatics
• Implementation Science
• Population Health Management
• +3 more

Dr. Adrian H Zai is an Associate Professor at UMass Chan Medical School , affiliated with the Division of Health Informatics and Implementation Science and the Population and Quantitative Health Sciences department. He holds a PhD in Pathology , an MD , and an MPH in Healthcare Management , with training from Boston University, Harvard University, and Boston University School of Medicine. Chief Research Informatics Officer at UMass Chan Co-director of Health Informatics and Implementation Science graduate program Faculty in Masters in Clinical Investigation program His research focuses on population-based informatics initiatives that integrate decision support systems , artificial intelligence , and implementation science to optimize patient population management. Key areas include: Virtual desktop implementation for clinical research Large language models for informed consent AI-driven public health interventions COPD remote monitoring systems SARS-CoV-2 diagnostic performance analysis Health equity in test distribution Recent publications (2022-2025) emphasize generative AI infrastructure , decentralized clinical trials , and digital health equity . His work spans medical informatics , primary care innovation , and public health technology , with cross-disciplinary applications in cardiology, oncology, and infectious disease management. Dr. Zai leads the Research Informatics Core , directing strategic data science infrastructure at UMass Chan. He has collaborated extensively with multidisciplinary teams across institutions, focusing on: AI chatbot development for vaccination outreach Telemonitoring systems for chronic conditions Health equity analytics Clinical workflow optimization Epidemiological modeling Health IT cost-benefit analysis

Cécile DE TERWANGNEView profile

Professor

• Human rights and new technologies
• Data protection and privacy
• Transparency of administration
• +5 more

Cécile DE TERWANGNE serves as Vice-Rector for Academic Affairs and Professor at the Faculty of Law, University of Namur. She is actively affiliated with the Namur Digital Institute (NADI) and the Center for Research on Information, Law and Society (CRIDS), where she bridges legal scholarship with practical digital governance challenges. Her leadership extends to multiple institutional committees including the General Assembly, Board of Directors, and Rectoral Council. Education: Doctor of Law, University of Namur (FUNDP), 2001 LLM in International and European Law, European University Institute, Florence Bachelor of Laws, UCL Professor DE TERWANGNE's research program centers on human rights in digital environments , with particular focus on data protection frameworks, transparency mechanisms, and electronic governance systems. Her scholarship demonstrates a consistent thread connecting fundamental rights with technological innovation in public administration. Her interdisciplinary approach examines how traditional legal frameworks adapt to emerging technologies while maintaining essential safeguards for citizens. This work positions her at the forefront of legal scholarship addressing the complex challenges of digital governance in European contexts. Analysis of her recent publications reveals a strategic progression toward examining practical implementation challenges of digital governance frameworks. Her work increasingly addresses the tension between technological innovation in public administration and legal compliance requirements, particularly regarding AI applications, data protection, and privacy rights within European legal frameworks. Research Leadership: Principal Investigator for 'Legal handling of AI-enabled emotion recognition and exploitation in online disinformation' (2024-2027) Key contributor to modernization of Convention 108 for data protection Project leader for BESOCIAL: sustainable social media archiving strategy for Belgium (2019-2023) Significant role in DIGI4FED project on digital transformation in Belgian Federal Government (2019-2022) As Vice-Rector for Academic Affairs, Professor DE TERWANGNE oversees academic policy while maintaining an active research agenda. She has supervised 15 documented research works and teaches courses including 'Human Rights and Information Society,' 'Digital, fundamental rights and cybercrime,' and 'Privacy and Data Protection.' Her grant portfolio demonstrates strong collaboration with European institutions on interdisciplinary research addressing complex governance challenges in the digital age. Her leadership in the Namur Digital Institute and CRIDS research centers facilitates critical examination of societal impacts of digital transformation. Through projects like FLEXPUB, she contributes to developing flexible geospatial public e-services that balance technical innovation with legal frameworks and citizen rights.

Niklas ElmqvistView profile

Professor

• Data Visualization
• Immersive Analytics
• Human-Computer Interaction
• +3 more

Niklas Elmqvist is a Professor in the Department of Computer Science at Aarhus University. His research focuses on immersive analytics, data visualization, and human-computer interaction, emphasizing user-centered design and interdisciplinary approaches. He leads projects on interactive visualization systems, collaborative platforms, and accessibility in data analysis. Key research areas include agentic visualization systems, attention-aware interfaces, and ubiquitous analytics environments. He explores novel interaction techniques, such as bimanual gestures and multimodal feedback, to enhance data exploration experiences. His work bridges computer science with domains like epidemiology, cybersecurity, and creative writing. Elmqvist has contributed to influential platforms like DashSpace (collaborative immersive analytics) and Datamancer (gesture-driven analytics). He investigates challenges in accessible visualization for visually impaired users and the use of large language models for automated design feedback. His recent work addresses human-centered AI integration in visualization tools and the ethics of automated decision-making systems. He collaborates internationally on projects such as Riverside (cybersecurity visualization) and Lodestar (data science workflow recommendations). His research has been published in top journals like IEEE Transactions on Visualization and Computer Graphics, emphasizing both theoretical advancements and practical system implementations.

Sonia ChernovaView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +4 more

Sonia Chernova is an Associate Professor in the School of Interactive Computing at Georgia Tech's College of Computing. She leads the Robot Autonomy and Interactive Learning (RAIL) lab, focusing on developing intelligent and interactive autonomous systems. Her research spans robotics and AI, emphasizing semantic reasoning, human-robot interaction, and explainable AI. She directs the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), which develops AI systems to support aging adults in home environments. Research Interests: Chernova’s work integrates human-centric approaches to robotics, including adaptive autonomy, explainable AI, and AI for health. She explores how robots can learn from human feedback, navigate ambiguous tasks, and provide proactive assistance in daily environments. Her lab’s projects emphasize real-world applications in healthcare, smart homes, and collaborative human-robot teams. Key Contributions: Developed frameworks for preference adaptation in object rearrangement and human-robot collaboration Pioneered explainable AI techniques for robot failures and decision-making transparency Advanced semantic reasoning methods for robots operating in everyday environments Led initiatives in proactive assistive robotics and aging-in-place technologies Awards & Recognition: 2021 IEEE/ACM HRI Best Paper Award 2018 NAE Frontiers of Engineering Top 100 Researcher 2014 ONR Young Investigator Award 2012 NSF CAREER Award Teaching & Outreach: Teaches courses on robotics, human-robot interaction, and robotics research. Co-chaired the Conference on Robot Learning (CoRL 2023) and developed outreach programs to bridge pre-college computer science education gaps. Affiliations: GVU Center, Institute for Robotics and Intelligent Machines (IRIM), and the NSF AI-CARING Institute.

• Computer Science Education
• Artificial Intelligence Ethics
• Educational Technology
• +3 more

Konstantinos Liaskos is an Associate Professor in the School of Mathematical & Computer Sciences at Heriot-Watt University, specializing in Computer Science education and technology. His work focuses on bridging academia-industry gaps, AI ethics in education, and innovative pedagogical practices. He actively contributes to international discussions on computing curriculum standards and professional competency development. Research Interests: His research spans artificial intelligence ethics, educational technology adoption, automated assessment systems, and global computing education policies. Notable areas include the impact of ChatGPT on pedagogy, genetic algorithms in learning systems, and collaboration frameworks in higher education. Recent Contributions: Recent work examines practitioners' views on LLMs in engineering education, automated marking in programming classes, and ethical implications of AI tools. His collaborative efforts address non-technical skill development and professional dispositions in computing students. Grants & Advising: No specific grants or advisees listed in available records. Collaborates widely with international academics and industry partners through conference publications and working groups.