Find Academic Supervisors

Mary LacityView profile

Professor

• Blockchain Technology
• Decentralized Identity
• Metaverse
• +4 more

Dr. Mary Lacity is a Distinguished Professor of Information Systems at the Sam M. Walton College of Business, University of Arkansas, and holds the David D. Glass Endowed Chair. She is also a Visiting Professor at the London School of Economics for Spring 2025. Director of Walton College’s Blockchain Center of Excellence (2014-2019) Former Curators’ Distinguished Professor at University of Missouri Holds visiting positions at MIT, Washington University, and Oxford University Senior Editor for MIS Quarterly Executive Contributing Editor for Journal of the British Blockchain Association Her research focuses on enterprise adoption of emerging technologies, particularly blockchain, metaverse, service automation, and ethical technology use. She has conducted extensive case studies and surveys on automation and Web3 implementation across hundreds of organizations globally. Recent publications include books like Human Privacy in Virtual and Physical Worlds (2024) and Blockchain Fundamentals for the Web 3.0 (2022). She has over 30 books and 26,300 Google Scholar citations (h-index: 68). 2014: Inducted into IAOP’s Outsourcing Hall of Fame 2024: Recognized as top 2% scholar in Business and Management (Stanford) 2024: Top 1% scholar in Business and Management (ScholarGPS) Fellow of the Association of Information Systems She actively contributes to blockchain standards via the Global Standards Mapping Initiative (GSMI) of the Global Blockchain Business Council and the Linux Foundation’s Trust over IP (ToIP). Her work bridges academic research and practical applications in enterprise technology adoption.

Cleotilde GonzalezView profile

Research Professor

• Decision Science
• Cognitive Psychology
• Human-Computer Interaction
• +5 more

Cleotilde (Coty) Gonzalez is a Research Professor of Decision Sciences at Carnegie Mellon University, with primary affiliation in the Department of Social and Decision Sciences (SDS). She serves as the Founding Director of the Dynamic Decision Making Laboratory (DDMLab) and Research Co-Director of the NSF National Institute for AI for Societal Decision Making (AI-SDM). Her extensive academic affiliations include the Security and Privacy Institute (CyLab), the Societal Computing program in the Software and Societal Systems Department (S3D), the Human-Computer Interaction Institute (HCII) in the School of Computer Science, and the Center for Behavioral Decision Research (CBDR) and Center for Neural Basis of Cognition (CNBC). Dr. Gonzalez holds a Ph.D. in Management Information Systems and has developed Instance-Based Learning Theory (IBLT), a significant contribution to cognitive science that explains how people make decisions based on past experiences. Her research spans experimental studies and computational modeling of cognitive processes in dynamic decision environments, with applications in cybersecurity, human-machine teaming, and societal decision making. Her recent publications reveal a strong focus on human-AI collaboration, collective intelligence, cybersecurity, and cognitive modeling. The research trends show increasing integration of AI systems with human decision processes, particularly examining how humans and AI can complement each other in complex decision environments. Her work increasingly addresses cybersecurity challenges through behavioral science perspectives, exploring how cognitive models can improve defense mechanisms against social engineering attacks. Lifetime Fellow of the Cognitive Science Society Lifetime Fellow of the Human Factors and Ergonomics Society Member of the Governing Board of the Cognitive Science Society Member at Large of the Policy Council of the System Dynamics Society Committee member of the National Academies Division Committee for the Behavioral and Social Sciences and Education Dr. Gonzalez has mentored over 50 post-doctoral fellows and doctoral students, with many going on to successful careers in academia, government, and industry. Her research has been supported by major collaborative efforts including Collaborative Research Alliances (CRA) and Multi-University Research Initiative grants from the Army Research Laboratories (ARL) and Army Research Office (ARO), as well as projects with the Defense Advanced Research Projects Agency (DARPA). She directs the Dynamic Decision Making Laboratory, which conducts research involving laboratory experiments and cognitive computational models to derive theoretical conclusions about dynamic decision making and develop applications for societal problems.

Jan A. Van MieghemView profile

Academic

• Operations Management
• Supply Chain Operations
• Operations Strategy
• +11 more

Jan A. Van Mieghem is the A. C. Buehler Professor and Professor of Operations at the Kellogg School of Management, Northwestern University. He also serves as a Professor by Courtesy at the McCormick School of Engineering and Applied Science. His research spans product, service, and supply chain operations, combining strategic and tactical perspectives with mathematical modeling, stochastic analysis, and empirical verification. Ph.D. in Business, Graduate School of Business, Stanford University (1995) MS in Electrical Engineering, Stanford University (1990) MS in Applied Sciences, KU Leuven, Belgium (1989) His research interests focus on operations strategy, supply chain optimization, and digital operations. He explores topics such as operational hedging, capacity investment, network flexibility, and the application of AI in inventory management. His work addresses healthcare operations, dual sourcing, and the interplay between collaboration and productivity in service networks. Recent research trends center on AI-driven inventory systems, dual sourcing under uncertainty, and healthcare resource allocation. He has authored over 60 academic articles and two textbooks: Operations Strategy: Principles and Practice and Managing Business Process Flows: Principles of Operations Management . 2023: First place in POMS College of Supply Chain Management Student Paper Competition 2020: Flexibility Excellence Award 2014: Wickham Skinner Award 2007: First MSOM Best Paper Award Van Mieghem has held leadership roles at Kellogg, including Deputy Dean (2023–present), Faculty Director of the Executive MBA program (2012–2017), and Chairman of the Department of Managerial Economics and Decision Sciences (2006–2009). He advises firms on operations strategy and management and has served on editorial boards of Management Science , Manufacturing & Service Operations Management , and Operations Research . He has mentored numerous Ph.D. students, including Audrey Bazerghi, Simrita Singh, Yangzi Jiang, and Dennis J. Zhang, who have secured academic and industry positions. His teaching spans operations management and strategy across MBA, Ph.D., and executive programs. Van Mieghem's work examines the intersection of operations strategy and execution, emphasizing financial value as a guiding metric. His empirical studies leverage real-world data from healthcare, manufacturing, and e-commerce to validate theoretical models and develop practical frameworks.

• Political Geography
• Geopolitics
• Technology and Security
• +4 more

Professor Louise Amoore holds a faculty position at Durham University's Department of Geography, where she serves as Professor of Political Geography and Deputy Head of Department. Her work explores intersections of geopolitics, technology, and security, with a focus on algorithmic governance and data-driven practices. Co-editor of Progress in Human Geography Appointed to UK ethics body on biometric technologies Recipient of RCUK Global Uncertainties leadership fellowship Her research examines how data and algorithms reshape security paradigms, democracy, and societal norms. Recent projects analyze generative AI, cloud computing, and machine learning's political implications. She has secured funding from Leverhulme Trust, ESRC, EPSRC, AHRC, and NWO. Key article trends reveal expertise in algorithmic sovereignty (2024), border governance (2024), predictive security models (2023), data reuse ethics (2022), and post-9/11 surveillance practices (2021). Her work critically interrogates machine learning's epistemological foundations (2019) and data sovereignty implications (2018). RCUK Global Uncertainties leadership fellowship (2012-2015) Leverhulme Trust funding ESRC, EPSRC, AHRC, NWO grants Current PhD supervisees include Anna Okada and Charlotte Lock. She co-developed the Algorithmic Life book (2015) and serves as editor for Progress in Human Geography , maintaining active research collaborations in Europe.

Mariarosaria TaddeoView profile

Professor

• Digital Ethics
• Philosophy of Technology
• Cybersecurity
• +9 more

Mariarosaria Taddeo is Professor of Digital Ethics and Defence Technologies at the Oxford Internet Institute (University of Oxford) , where she also serves as DPhil Programme Director for Information, Communication and the Social Sciences. She is a Senior Research Fellow at the Alan Turing Institute and holds advisory roles at institutions including the Ministry of Defence (UK) Ethics Advisory Panel , the BRAID Programme , and the Leonardo Foundation . Education PhD (Doctor Europeus) in Philosophy from University of Padua Mariarosaria Taddeo’s research spans Digital Ethics, Philosophy of Technology, Cybersecurity Ethics, and AI Governance , with a focus on national defence applications. Her work addresses trust in AI , cyber conflict regulation , and ethical frameworks for autonomous weapons . She has published extensively in journals like Nature , Science , and Minds and Machines , including studies on data philanthropy , digital well-being , and quantum technology ethics . Scientific Awards 2010 Simon Award for Outstanding Research in Computing and Philosophy 2016 World Technology Award for Ethics 2018 InspiringFifty: Top 50 Italian women in technology ORBIT listings (2018, 2020) of top 100 women in AI Ethics 2020 Women’s Forum for Economy and Society: Outstanding Rising Talents ComputerWeekly Top 100 Influential Women in UK Technology (2020, 2023) Her projects include the UK MOD-funded AI Ethics Principles initiative, the NATO Cooperative Cyber Defence Centre of Excellence ethical guidance project, and contributions to the PETRAS IoT Research Hub . She advocates for ethical AI implementation in defence and digital governance, influencing EU policy through the CEPS Task Force on AI and Cybersecurity .

Krishnendu GuhaView profile

Assistant Professor

• Embedded Systems
• Cybersecurity
• Artificial Intelligence
• +4 more

Dr. Krishnendu Guha is an Assistant Professor and CONNECT Funded Investigator at the School of Computer Science and Information Technology, University College Cork. His research bridges embedded systems, cybersecurity, and quantum-safe hardware design with AI and bio-inspired strategies. PhD: University of Calcutta (Department of Science and Technology, Government of India) Postdoctoral: University of Florida Past Roles: Research Fellow at Intel India, Visiting Scientist at Indian Statistical Institute, Temporary Assistant Professor at NIT Jamshedpur His research focuses on embedded systems security , real-time security mechanisms , and quantum-safe hardware . He integrates AI (e.g., neural networks) and bio-inspired strategies (e.g., gecko crypsis behavior) into security frameworks for FPGAs and edge platforms. Recent publications highlight trends in blockchain for supply chains , quantum machine learning , secure FPGA architectures , and distributed AI systems . His work addresses energy efficiency, fault detection, and decentralized security in hardware. As a CONNECT Centre member, Dr. Guha contributes to advanced research in reconfigurable systems and cybersecurity. Grants and collaborations span quantum-safe design, cloud FPGA security, and hardware trojan mitigation.

Oskari Ville PakariView profile

Lecturer

• Nuclear Engineering
• Reactor Physics
• Radiation Detection
• +3 more

Oskari Ville Pakari is a Lecturer at the School of Basic Sciences, École polytechnique fédérale de Lausanne (EPFL), affiliated with both the Institute of Physics (IPHYS) and the Swiss Plasma Center (SPH-ENS). He contributes to teaching and research, particularly in reactor physics and radiation detection. His research focuses on nuclear reactor diagnostics , gamma noise analysis , and neutron spectroscopy . He actively develops mixed reality visualization tools for radiation detection data and participates in the European CORTEX project for reactor monitoring. Selected publications highlight his work in gamma-ray imaging , neutron noise simulations , and detector system validation using advanced statistical methods like bootstrapping and Welch's technique. Teaching activities include courses on Radiation biology, protection, and applications Radiation and reactor experiments He advises PhD student Saliba Michel and collaborates with international institutions such as CEA, KIT, and LRS (Laboratory of Reactor Physics and Systems Behaviour) at EPFL.

• Economic Development
• Regional Economics
• Applied Econometrics
• +5 more

Marco Letta is a Tenure-Track Assistant Professor at the Department of Social and Economic Sciences , Sapienza University of Rome. His research focuses on economic development , regional economics , policy evaluation , and applied econometrics , with a strong emphasis on climate change impacts, food security, and machine learning applications in economic policy. University: Sapienza University of Rome Department: Department of Social and Economic Sciences Email: marco.letta@uniroma1.it His recent work explores the climate migration nexus , household resilience , and policy targeting , often leveraging machine learning and empirical econometric methods . Publications span topics such as local inequalities during the COVID-19 crisis , temperature shocks in rural Tanzania , and machine learning applications in state aid regulation . Notable trends in his research include: Integration of machine learning with traditional econometric techniques Focus on climate resilience and migration patterns Analysis of policy impacts in developing economies Investigation of local mortality estimates during global crises Development of cross-country empirical frameworks Current projects include assessing agrifood system vulnerabilities and refining counterfactual policy evaluation methodologies.

Facundo M. FernandezView profile

Professor

• Analytical Chemistry
• Bioanalytical Mass Spectrometry
• Ambient Sampling/Ionization
• +4 more

Facundo M. Fernandez is a Regents' Professor and Vasser-Woolley Chair in Bioanalytical Chemistry at the Georgia Institute of Technology, where he leads the Fernandez Research Group within the School of Chemistry and Biochemistry in the College of Sciences. His research spans multiple cutting-edge areas of analytical chemistry with significant applications in medicine, forensics, and basic science. Dr. Fernandez earned his M.Sc. in Chemistry (1996) and Ph.D. in Analytical Spectrometry/Metallomics (1999) from the Facultad de Ciencias Exactas y Naturales at Buenos Aires University, Argentina. His research program focuses on Bioanalytical Mass Spectrometry with particular emphasis on Ambient Sampling/Ionization & Molecular Imaging, Ion Mobility Spectrometry, Metabolomics, and Pharmaceutical Forensics. His work has pioneered new approaches in ambient ionization techniques that enable direct analysis of complex samples without extensive preparation. His recent publications reveal a strong trend toward spatial metabolomics, particularly in traumatic brain injury and ovarian cancer research, with increasing integration of machine learning approaches for data analysis. His work also extends to pharmaceutical quality control, exercise physiology through the MoTrPAC consortium, and prebiotic chemistry investigations. The interdisciplinary nature of his research is evident in collaborations across Georgia Tech's campus and with external institutions. NSF CAREER Award (2007) 3M Non-tenured Faculty Award (2008) CETL/BP Junior Faculty Teaching Excellence Award (2009) Ron A. Hites Award for Outstanding Research Publication (2010) Sigma Xi (GT Chapter) Best Faculty Paper Award (2010) Vasser-Wooley Faculty Fellow (2012) Dr. Fernandez has secured significant funding for his research, including NSF CAREER support, and leads projects related to metabolomics for ovarian cancer detection, pharmaceutical forensics through the CODFIN network, and participation in the large-scale Molecular Transducers of Physical Activity Consortium (MoTrPAC). His laboratory develops advanced instrumentation for mass spectrometry applications and maintains strong collaborations with the Integrated Cancer Research Center, the College of Computing, and the Center for Chemical Evolution at Georgia Tech.

• Computer Vision
• Deep Learning
• Semantic Segmentation
• +3 more

Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.

Ibrahim KhalilView profile

Professor

• Security
• Privacy
• Federated Learning
• +4 more

Professor Ibrahim Khalil is a faculty member in the School of Computing Technologies at RMIT University, Melbourne, Australia. He holds a PhD in Computer Science from the University of Bern (2003) and has extensive industry experience in Silicon Valley focusing on secure network protocols. His research spans Security, Privacy, Federated Learning, Blockchain, Quantum Computing, and Distributed Systems. He leads high-impact projects funded by ARC grants (DP250100582, DP220100215, etc.) and international initiatives like the EU’s SELFY project. His work addresses challenges in secure AI data analytics, privacy-preserving systems, and critical infrastructure protection. Khalil supervises PhD/Masters students on topics ranging from federated learning security to quantum-enhanced machine learning. Education: PhD in Computer Science (University of Bern, 2003); prior roles at EPFL, Osaka University, and industry tech hubs. Research Interests: Privacy-Preserving Technologies Blockchain Applications in Healthcare and Supply Chains Quantum Computing for Machine Learning Secure Edge Computing and Federated Learning IoT Security and Critical Infrastructure Protection Grants & Collaborations: Over 10 major grants since 2017, including ARC Discovery/Linkage Projects and international partnerships (QNRF, EU). Notable projects include Privacy-Aware Digital Twins for Critical Infrastructure and Federated Learning frameworks for GenAI models. Advising & Labs: Active supervisor of 25+ research projects since 2013, focusing on anomaly detection, secure data analytics, and blockchain-based systems. Collaborates with industry partners on defense and healthcare tech.

• Distributed Systems
• Machine Learning
• Big Data
• +3 more

Jim Dowling is a distributed systems researcher at KTH Royal Institute of Technology, focusing on large-scale distributed systems, machine learning, and big data. His work emphasizes improving system dependability, performance, security, and scalability through middleware, peer-to-peer systems, and cloud-native solutions. He leads courses such as Advanced Course in Large Scale Machine Learning and Deep Learning and Scalable Machine Learning and Deep Learning , demonstrating his commitment to education in AI and distributed computing. His research spans topics like feature stores, Kubernetes integration, and AI-driven environmental analytics (e.g., ANIARA project for edge infrastructure automation and ExtremeEarth for Copernicus data analysis). He has contributed to scalable ML pipelines, cloud storage systems (HopsFS-S3), and hyperparameter optimization tools like Maggy. Key projects include the Hopsworks platform for machine learning operations and the development of cloud-native tools for big data analytics. His work bridges theoretical distributed systems research with practical applications in AI, healthcare, and environmental science. He has advised on numerous collaborative initiatives but no formal students are listed. His grants and lab activities are centered around Hopsworks and the ANIARA project, reflecting his focus on scalable, self-managing systems.

Mohsen Yoosefzadeh NajafabadiView profile

Assistant Professor

• Dry Bean Breeding
• Computational Biology
• Omics-Based Selection
• +5 more

Dr. Mohsen Yoosefzadeh Najafabadi is an Assistant Professor in the Department of Plant Agriculture at the University of Guelph, Ontario Agricultural College. He holds a PhD in Plant Breeding from the University of Guelph (2022), following M.Sc. and B.Sc. degrees from the University of Tehran. His research focuses on dry bean breeding, computational biology, and integrating omics technologies to enhance crop resilience and productivity. Key areas include developing stress-tolerant dry bean varieties, leveraging remote sensing for trait prediction, and optimizing genomic selection methods. He leads the Dry Bean Breeding & Computational Biology Program and has contributed to over 30 peer-reviewed publications since 2017. Education : PhD, Plant Breeding, University of Guelph (2022) M.Sc., University of Tehran B.Sc., University of Tehran Research interests emphasize computational tools development (e.g., AllInOne preprocessing framework), omics-based selection strategies, and non-Mendelian heredity mechanisms. His lab combines machine learning with field phenotyping to address agricultural challenges such as disease resistance and climate adaptation. Collaborative projects include soybean cold stress analysis and cannabinoid profile prediction in cannabis. Publications span genomic approaches to crop improvement, remote sensing applications, and transcriptomic studies. He teaches courses in plant breeding methodologies and actively engages in technology transfer initiatives. Lab activities include developing high-yielding dry bean cultivars resistant to biotic/abiotic stresses and advancing data-driven pipelines for crop breeding. Future work aims to synergize AI with multi-omics data to enhance crop resilience in diverse environments.

Yepang LiuView profile

Associate Professor

• Software Testing and Analysis
• Empirical Software Engineering
• AI for Software Engineering
• +5 more

Yepang Liu is a tenured Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Software Quality Lab and serves as director of the Trustworthy Software Research Center within the Research Institute of Trustworthy Autonomous Systems. His educational background includes a B.Sc. with honors from Nanjing University (2010) and a Ph.D. from the Hong Kong University of Science and Technology (2015), where he was supervised by Prof. Shing-Chi Cheung. Prior to joining SUSTech, he worked as a postdoc at HKUST's CASTLE Lab and Cybersecurity Lab. Liu's research primarily focuses on software testing and analysis, empirical software engineering, AI for SE, software security, and trustworthy AI. His work bridges traditional software engineering with cutting-edge AI technologies, particularly in automated testing, security analysis, and quality assurance for mobile, blockchain, and extended reality applications. Recent projects explore how large language models can enhance bug detection, improve testing automation, and address fairness issues in machine learning systems. His contributions have been recognized with three ACM SIGSOFT Distinguished Paper awards (ICSE 2021, ASE 2016, ICSE 2014) and one Distinguished Artifact award (ICSE 2019). He has also received the ACM SIGSOFT Service Award and Distinguished Reviewer Award for his extensive service to the software engineering community. Top-10 Most Active Early-Stage Software Engineering Researcher (2013-2020) Top-10 Most Popular Instructor Among 2024 Undergraduate Graduates at SUSTech Junior Faculty of the Year (2021) SUSTech Teaching Excellence Award (2021) Outstanding Mentor Award (2020, 2024) Liu actively serves on the editorial boards of Empirical Software Engineering (EMSE) and Journal of Computer Science and Technology (JCST). He has participated in over 80 conference committees including leadership roles in ICSE, FSE, ASE, and ISSTA. His research is supported by the National Natural Science Foundation of China, National Key Research and Development Program, and leading Chinese IT companies. He regularly mentors PhD and MSc students and has guided multiple national competition award-winning teams. The Software Quality Lab under Liu's direction focuses on innovative approaches to software testing, security analysis, and quality assurance across various platforms including mobile, blockchain, and extended reality applications. Current projects emphasize the integration of AI techniques with traditional software engineering practices to address emerging challenges in software quality.

Shahid RazaView profile

Professor

• Cybersecurity
• Internet of Things (IoT) Security
• AI/Machine Learning & IoT Security
• +3 more

Shahid Raza is a Professor of Cybersecurity at the University of Glasgow's School of Computing Science. He previously led the RISE Cybersecurity Unit in Sweden, establishing it as a leading research group. His expertise spans IoT Security, PKI, AI-driven cybersecurity solutions, and hardware/data security. Raza holds a PhD and Docentship from Uppsala University, alongside a Bachelor's with a Gold Medal for academic excellence. Education: B.Sc. (Computer Science, 3.99/4.0 CGPA, Gold Medal), Licentiate, PhD, and Docentship in Cybersecurity from Sweden. He leads EU-funded projects like H2020 CONCORDIA and Horizon Europe CUSTODES, coordinating initiatives such as the Cyber Node and Cyber Range. Active in cybersecurity policy, he serves on the EU SCCG, ECSO, and EARTO Security & Defence Research working groups. Research Interests Public Key Infrastructure (PKI) for IoT AIAgent-Driven Cybersecurity Solutions IoT Certification Standards Hardware Security for Low-Power Devices Grants & Projects Coordinator: Horizon Europe CUSTODES Technical Leader: H2020 Arcadian-IoT Founder: RISE Cyber Range (Sweden's largest cybersecurity test facility) Awards & Memberships IEEE Senior Member Gold Medal for Academic Excellence (Bachelor's)