Find Academic Supervisors

• Corporate Finance
• International Finance
• Small Business Finance
• +5 more

Atreya Chakraborty is a Professor of Finance and the Director of the PhD Program & Academic Partnerships at the College of Management, University of Massachusetts Boston. His expertise spans corporate finance, international finance, small-business finance, risk management, executive compensation, and corporate governance. Education PhD, Boston College Research Focus Chakraborty’s research centers on how corporate governance mechanisms influence firm risk and performance, how executive compensation structures affect managerial risk-taking, and how corporate social responsibility initiatives interact with litigation and financial outcomes. He employs rigorous econometric techniques and large-scale data analysis, often integrating EVA (Economic Value Added) style metrics to evaluate investment strategies and firm value creation. Recent Scholarly Contributions Over the past five years his work has pivoted toward ESG-aware investing, the impact of COVID-19 on CSR disclosures, and the use of EVA frameworks to guide active portfolio management. These studies collectively highlight the importance of non-financial factors—such as governance quality and social responsibility—in explaining cross-sectional variation in stock returns and litigation risk. Honors & Awards College of Management Research Fellow (2007–2008) Dean’s Award for Distinguished Research (2005–2006) Betty Diener Award for Teaching Excellence (2006) Sloan Foundation Award (1998) Professional Service He serves on the Editorial Board of The Journal of Entrepreneurial Finance and has been instrumental in directing the PhD program, fostering academic partnerships, and mentoring doctoral students in empirical finance and governance research.

Ayan MukhopadhyayView profile

Researcher

• Multi-Agent Systems
• Cyber-Physical Systems
• Smart Cities
• +5 more

Dr. Ayan Mukhopadhyay serves as a Senior Research Scientist in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. Previously, he was a Post-Doctoral Research Fellow at Stanford Intelligent Systems Lab where he received the 2019 CARS post-doctoral fellowship. His academic journey includes a Ph.D. from Vanderbilt University's Computational Economics Research Lab with a doctoral thesis nominated for the Victor Lesser Distinguished Dissertation Award 2020. His research spans critical domains in smart infrastructure systems with particular focus on: Developing robust decision-making frameworks for cyber-physical systems under uncertainty Creating multi-agent solutions for emergency response optimization Designing machine learning approaches for urban mobility and energy management Building proactive incident detection pipelines using heterogeneous data sources Analysis of his recent publications reveals strong thematic continuity in applying artificial intelligence to real-world infrastructure challenges, particularly in transportation systems, emergency response, and energy management. His work consistently bridges theoretical AI advances with practical implementation in smart city contexts, demonstrating expertise in both algorithmic innovation and systems integration. Award highlights include: CARS Post-Doctoral Fellowship (2019) Best Paper Award at ICLR's AI for Social Good Workshop Victor Lesser Distinguished Dissertation Award Nomination (2020) Dr. Mukhopadhyay leads significant research initiatives through ScopeLab, focusing on creating deployable solutions for public transit, emergency response, and energy systems. His work on vehicle-to-building charging, traffic incident localization, and equitable transit network design demonstrates commitment to solving high-impact urban challenges through rigorous computational methods. Current projects involve developing simulation environments for non-stationary environments (NS-Gym) and explainable planning frameworks integrating formal logic with large language models.

Gian-Gabriel GarciaView profile

Assistant Professor

• Health Policy
• Personalized Medicine
• Medical Decision-Making
• +5 more

Gian-Gabriel Garcia is an Assistant Professor and Harold E. Smalley Early Career Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, with a dual role as Pillar 1 Co-lead in Data Science, Machine Learning, and Artificial Intelligence at the Children's Healthcare of Atlanta Pediatric Technology Center. His work bridges predictive analytics and decision-making frameworks in healthcare contexts. Education : PhD and MS in Industrial and Operations Engineering (University of Michigan), BS in Industrial Engineering (University of Pittsburgh) Research focuses on responsible AI for healthcare, emphasizing health equity , stochastic optimization , and machine learning applied to concussion management, opioids, maternal health, and cardiovascular disease. His methodologies integrate dynamic programming and simulation to address stakeholder acceptability. Key article themes include concussion diagnostics , responsible AI in diabetes care, and interpretable policies for chronic disease management. His work frequently leverages Markov decision processes and stochastic modeling . Scientific Awards National Science Foundation Graduate Research Fellowship INFORMS Bonder Scholarship for Applied Operations Research in Health Services SMDM Lee B. Lusted Prize INFORMS Minority Issues Forum Paper Competition First Prize INFORMS Minority Issues Forum Poster Competition First Prize Garcia also contributes to undergraduate mentorship and serves as Vice President of Communications (co-chair) for the INFORMS Minority Issues Forum, alongside council membership in the INFORMS Health Applications Society.

• Computer Science
• Databases
• Knowledge Representation & Reasoning
• +7 more

Dr. Yacine Sam is a Lecturer in Computer Science at the Polytechnic School of the University of Tours (EPU), affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT). His research focuses on databases, knowledge representation & reasoning, web services, and semantic web technologies. Doctorate in Computer Science, Paul Cézanne University Aix-Marseille 3 (2008) Master 2 Research in Computer Science, Claude Bernard University Lyon 1 (2005) Dr. Sam's research spans multiple subfields including: Trustworthy execution of adaptive business processes Privacy ontologies for Web of Things Deep learning applications in personalized service recommendations Linked open data frameworks for semantic integration Blockchain implementations in distributed systems Health analytics using collaborative IoT data Contact: yacine.sam@univ-tours.fr

Miltos AlamaniotisView profile

Associate Professor

• Applied Artificial Intelligence
• Nuclear Security
• Smart Grids
• +5 more

Miltos Alamaniotis is an Associate Professor and GreenStar Endowed Fellow in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA). His research focuses on applied artificial intelligence in nuclear security, smart grids, and radiation detection systems, with a particular emphasis on maritime nuclear applications and nonproliferation. Academic Appointments: Associate Professor (2023–Present), UTSA Education: Ph.D. in Applied Intelligent Systems, Purdue University Research interests include: Nuclear Security and Nonproliferation Smart Grids and Distributed Energy Systems Explainable AI for Radiation Detection Quantum Machine Learning Applications Intelligent Control of Nuclear Reactors Fuzzy Logic in Energy Management Recent publications highlight trends in AI-driven nuclear security systems, matrix profile methods for radiation anomaly detection, and quantum neural networks for thermographic image analysis. His work bridges nuclear engineering, cybersecurity, and smart city technologies. Scientific honors include: Top 0.5% ScholarGPS Ranking (2024) Best Paper Award at IEEE Texas Power and Energy Conference (2025) Luthcher Brown Fellowship (2023) GreenStar Endowment Fellowship (2023) NAE Frontiers of Engineering Symposium Selection (2023) UTSA President’s Distinguished Achievement Award (2022) He has supervised PhD students like Thanos Arvanitidis and secured over $15M in grants from DOE, NSF, and NRC for projects including the $25M NNSA Consortium. His AI Lab at UTSA collaborates with Argonne, Idaho, and Los Alamos National Laboratories.

Thi Phuong Khanh NguyenView profile

Researcher

• Prognostics and Health Management (PHM)
• Predictive Maintenance
• Condition-Based Maintenance
• +8 more

Dr. Thi Phuong Khanh Nguyen is a researcher at the Ecole Nationale d'Ingénieurs de Tarbes (ENIT) , affiliated with the College of Engineering and Department of Systems . Her work focuses on Prognostics and Health Management (PHM) , predictive maintenance, and industrial data analytics, combining machine learning with physics-informed modeling to address uncertainty in system degradation. Teaching: Mathematics for engineers, Probability, Statistics, Operating safety Research: Health indicators, diagnostics, prognostics, multimodal data fusion Methods: Data mining, physical and data-driven models, decision support systems Tools: FAST, Petri nets, UML, HMM, RNN, CNN, Transformer architectures Her recent publications highlight advancements in explainable AI , physics-informed neural networks , and multimodal learning for fault detection, battery RUL prediction, and robotic inverse dynamics. She also explores blockchain and federated learning for decentralized prognostics.

Nick BassiliadesView profile

Professor

• Semantic Web
• Ontologies
• Linked Open Data
• +7 more

Nick Bassiliades is a Professor at the School of Informatics , Aristotle University of Thessaloniki , Greece. His academic roles include serving as President of the Digital Governance Committee and the Digital Transformation of Greek Universities Committee, as well as Director of the Web, Data, and Knowledge Engineering Sector. Education: B.Sc. in Physics, Aristotle University of Thessaloniki (1991) M.Sc. in Applied Artificial Intelligence, University of Aberdeen (1992) Ph.D. in Parallel Knowledge Base Systems, Aristotle University of Thessaloniki (1998) His research focuses on Semantic Web , Ontologies , Knowledge Graphs , and applications in Artificial Intelligence , eGovernment , and Intelligent Agents . Recent publications emphasize ontological frameworks for requirements engineering, explainable AI, and electric vehicle knowledge graphs. He actively contributes to scientific communities as a Senior Member of IEEE and ACM , and serves as Co-Editor-in-Chief for the International Journal of Artificial Intelligence in Business and Management . His work involves collaborations with the Intelligent Systems laboratory and projects like XR4DRAMA for disaster management.

TCHUENTE DieudonnéView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Sustainability
• +7 more

Dr. TCHUENTE Dieudonné is an Associate Professor in the Department of Information, Operations and Decision Sciences at TBS Education. His research bridges artificial intelligence, sustainability, and operations management through practical applications in energy efficiency, supply chain resilience, and social media analysis. Department: Information, Operations and Decision Sciences University: TBS Education Email: d.tchuente@tbs-education.fr Research Focus : Dr. Tchuente's work explores AI-driven solutions for sustainable development, including building energy retrofits , real estate valuation , and vaccine supply chains . His studies on explainable AI provide actionable insights for corporate emissions reduction and aeronautics optimization. Publications Trends : His recent work (2022-2024) emphasizes AI ethics , climate accountability , and industry transformation across domains like real estate, aviation, and public health. Key methodologies include SHAP analysis , meta-learning , and crowdsourcing frameworks . Scientific Awards : FNEGE Grant - for research on connected vehicle infrastructure

Pontus EkbergView profile

Associate Professor

• Algorithms
• Real-Time Scheduling Theory
• Computational Complexity
• +3 more

Pontus Ekberg is an Associate Professor at Uppsala University's Department of Information Technology. His email address is pontus.ekberg@it.uu.se , and he can be reached at +46 18 471 73 41. He is affiliated with the Division of Computer Systems and holds the academic merit 'Docent' in real-time scheduling theory. Ekberg's research focuses on algorithms and computational problems in real-time scheduling theory. His work addresses NP-hardness in scheduling, fixed-priority algorithms, and formal verification of task feasibility. Current projects include applying pseudo-polynomial time analysis, combating butterfly attacks, and integrating deep learning for schedulability verification in safety-critical systems. His recent publications span topics like pseudo-polynomial time analysis (2025), optimistic period predictions (2024), and explainability in real-time schedulability (2023). He collaborates frequently with Sanjoy Baruah and others on uniprocessor and multiprocessor scheduling models. Although no formal awards are listed, his contributions to real-time scheduling theory are evident through 15+ publications in top conferences like RTSS and ECRTS.

• Artificial Intelligence in Healthcare
• Power Electronics
• Digital Control Systems
• +1 more

Anthony Kelly serves as a Postdoctoral Research Fellow in the Department of Electronic and Computer Engineering within the Faculty of Science and Engineering at the University of Limerick, Ireland, with his office located in E2-006. His affiliation spans both engineering and healthcare domains through interdisciplinary research initiatives. His research demonstrates dual expertise in artificial intelligence applications for healthcare and advanced power electronics. In healthcare AI, he develops interpretable mental health models, diabetes management chatbots, and comorbid condition interventions with emphasis on clinician trust and safety evaluation. In power systems, he pioneers digital control techniques for DC-DC converters, FPGA power management, and machine learning-integrated circuit designs. This bifurcated focus reveals a strategic transition from hardware-centric research (2005-2019) toward AI-health convergence (2024-2025). Analysis of his 15 most recent publications shows a pronounced shift toward healthcare AI since 2024, with 80% of current work addressing mental health modeling, diabetes chatbots, and comorbid condition management. Earlier publications (2009-2019) consistently focused on power electronics innovations including current-sharing algorithms, adaptive controllers, and FPGA-based systems, establishing foundational expertise later applied to healthcare technology development.

• Technology-Enhanced Learning
• Learning Analytics
• Artificial Intelligence in Education
• +5 more

Olga Viberg is an Associate Professor at KTH Royal Institute of Technology, specializing in Technology-Enhanced Learning within the Division of Media Technology and Interaction Design at the School of Electrical Engineering and Computer Science. With a PhD in Informatics from Örebro University (2015), she brings extensive experience from Dalarna University (2008-2016) as a lecturer in Media Technology and Learning Sciences. Current roles: Associate Professor, Docent, Course Coordinator Key research areas: AI in Education, Learning Analytics, Privacy & Ethics Leadership roles: Editor-in-Chief of International Journal of Learning Analytics , Vice-President of SoLAR Research Focus : Viberg's work bridges AI, learning analytics, and educational design through value-sensitive approaches. Her studies address: Privacy concerns in learning analytics Cultural alignment of AI systems Self-regulated learning frameworks Trust dynamics in AI adoption Responsible data practices in education Generative AI applications in assessment Scientific Contributions : Recognized through: 2024 Google Academic Research Award Multiple conference recognitions (LAK'24, LAK'23) Leadership in international initiatives like UNESCO's online education policy Educational Impact : Directly shaping academic programs through: Coordination of Bachelor's course in Media Technology Teaching PhD courses in Learning Analytics Organizing Nordic Learning Analytics Summer Institute

Ana LucicView profile

Assistant Professor

• Interpretable Machine Learning
• Artificial Intelligence
• Earth System Science
• +3 more

Ana Lucic is an Assistant Professor in Artificial Intelligence at the University of Amsterdam , with a joint appointment between the Institute for Logic, Language and Computation and the Informatics Institute . Her research focuses on interpretable machine learning applications for scientific discovery and societal impact. Formerly at Microsoft Research AI for Science and Partnership on AI PhD in Explainable Machine Learning from University of Amsterdam (2022) BSc/MSc in Mathematics from McMaster University Research Highlights: Develops mechanistic interpretability methods for deep learning architectures. Created Aurora , a foundation model for Earth system forecasting outperforming traditional operational models in air quality prediction and tropical cyclone tracking. Pioneers Clifford-Steerable CNNs for geophysical data analysis. Actively hiring PhD students for AI transparency research . Collaborative Networks: Contributions to ELLIS Summer School and ICML workshops . Collaborates with Microsoft Research AI for Science team on climate-related ML projects. Involved in organizing TerraBytes workshop at ICML 2025. Recent Advancements: Key role in publishing Aurora model in Nature (2025), demonstrating superior performance in Earth system forecasting. Supervises Ege Erdogan , new PhD student focused on mechanistic interpretability. Actively contributes to open-source AI development through GitHub repositories and technical discussions.

Caroline Margaux GevaertView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Caroline Margaux Gevaert serves as Associate Professor in the Department of Geo-information Processing at the University of Twente's Faculty of Geo-Information Science and Earth Observation (ITC), while also holding an appointment at the Digital Society Institute. Her academic career spans geospatial information systems, remote sensing, and the ethical applications of AI in geographical contexts. Dr. Gevaert earned her PhD in Informal Settlement Mapping with Unmanned Aerial Vehicles from the Faculty of Geo-Information Science and Earth Observation (ITC) in 2018, following Master's degrees in Geospatial Information Systems from Lund University (2014) and Remote Sensing from Universitat de Valencia (2013), and a Bachelor's in International Land and Water Management from Wageningen University & Research (2011). Her research focuses on the intersection of artificial intelligence and geospatial science, with particular emphasis on ethical dimensions of AI, algorithmic fairness in geo-intelligence workflows, and applications of machine learning for environmental monitoring. She has developed expertise in UAV applications for informal settlement mapping and flood vulnerability assessment, bridging technical innovation with societal impact. Analysis of her recent publications reveals a strong trend toward addressing ethical challenges in geospatial AI, with increasing focus on algorithmic fairness, explainable AI systems, and accountability frameworks. Her work demonstrates a consistent evolution from technical remote sensing applications toward socio-technical systems that consider both technological capabilities and societal implications. PhD Cum Laude (2018) Professor J.M. Tienstra Onderzoeksprijs 2020 Student Paper Competition Finalist (2017) Dr. Gevaert serves as Chair of De Jonge Akademie (2022-2027) and the International Society for Digital Earth (ISDE), while participating in the Frontier Technology Livestreaming Programme. Her external engagement includes consultancy for the World Bank and presentations on accountability in digital humanitarianism, demonstrating her commitment to applying geospatial intelligence for global development challenges. Her research activities center around geo-intelligence workflows, with particular focus on developing accountable and fair AI systems for spatial analysis. Current projects investigate causality frameworks for bias detection in flood vulnerability assessments and explainable workflows for ecological monitoring, positioning her at the forefront of ethical geospatial AI research.

• Explainable Artificial Intelligence Applications in Cyber Security
• Information Security
• Self-Protecting System
• +2 more

Dr. Sharmin Jahan serves as a tenure-tracked Assistant Professor in the Department of Computer Science at Oklahoma State University since August 2022. Her research centers on dynamic security assurance for autonomous systems (self-adaptive systems) through explainable AI models that interpret uncertain operational environments to enable autonomous security decision-making and compliance maintenance. Her educational background includes a Ph.D. and Master's in Computer Science from the University of Tulsa (2018-2021), and a B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (2007-2012). She teaches Introduction to Computer Security (CS 4243/5243) and leads Dr. Jahan's Lab focused on security for autonomous systems. Research interests span Explainable AI in Cyber Security, IoT Security, Self-Protecting Systems, and Micro-service Security. Her work develops frameworks that embed security awareness in dynamic systems, using XAI to interpret environmental uncertainty and maintain security compliance through autonomous adaptation. Current projects explore machine learning models for security analysis and XAI challenges in domain-specific security applications. Recent publications (2025-2020) demonstrate concentrated research on security assurance in self-adaptive systems, particularly for IoT and microservice architectures. Key trends include XAI-driven anomaly detection, security profile extraction from operational data, and risk-adaptive access control. Subfield specializations cover service mesh security, blockchain-based access frameworks, runtime trust evaluation, and autonomous threat containment. Scientific awards include: Principal Investigator for 2023 Arts and Sciences Summer Research Award on XAI-enhanced security awareness in autonomous systems Senior personnel on 2022 NSF RET Grant for Big Data and Machine Learning research experiences She advises M.Sc. student Masrufa Bayesh and teaches graduate/undergraduate security courses. Her lab actively investigates frameworks for security assurance in dynamic environments, with emphasis on IoT and microservice architectures requiring continuous adaptation to environmental changes while maintaining security compliance. Dr. Jahan's research team develops analysis and assessment models to determine security compliance degradation risks and optimal adaptation strategies, enhancing system resiliency through separate analytical frameworks integrated with her PhD-developed assessment methodology.

Fosca GiannottiView profile

Professor

• Mobility Data Mining
• Social Network Analysis
• Privacy-Preserving Data Mining
• +6 more

Fosca Giannotti is a Full Professor at Scuola Normale Superiore in Pisa, Italy, and leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Founded in 1994, the Pisa KDD Lab is one of the earliest research labs focused on data mining. Giannotti is a pioneering scientist in mobility data mining, social network analysis, and privacy-preserving data mining. Her educational background includes a Master Degree in Computer Science from the University of Pisa (1982) with 110/100 cum laude. She has held numerous visiting positions including at MCC in Austin, CWI Amsterdam, UCLA, and the Barabasi Lab at Northeastern University. Giannotti's research focuses on social mining from big data, encompassing smart cities, human dynamics, social and economic networks, ethics and trust, and diffusion of innovations. She has authored more than 300 papers and coordinated tens of European projects and industrial collaborations. Her current work increasingly centers on Explainable AI (XAI), as evidenced by her prestigious ERC Advanced Grant for the XAI project focused on "Science and technology for the explanation of AI decision making." Her recent publications reveal a strong emphasis on trustworthy AI, with research spanning privacy-preserving techniques, fairness in machine learning, human-AI collaboration frameworks, and medical applications of explainable AI. The breadth of her work demonstrates how data mining principles are being applied across diverse domains from social sciences to healthcare. ERC Advanced Grant for XAI project Premio Internazionale Tecnovisionarie 2021 Intelligenza Artificiale Giannotti has coordinated numerous significant projects including SoBigData (the European research infrastructure on Big Data Analytics and Social Mining), XAI, TAILOR (Foundations of Trustworthy AI), HumanE-AI-Net, and AI4EU. As former coordinator of SoBigData, she led an ecosystem of ten cutting-edge European research centers providing an open platform for interdisciplinary data science. She leads the Pisa KDD Lab, which serves as a hub for research on knowledge discovery and data mining. The lab has been instrumental in developing techniques for mobility data analysis, social network mining, and privacy-preserving data analytics, with applications ranging from smart cities to pandemic response.