Find Academic Supervisors

Julaine ClunisView profile

Assistant Professor

• Library Science
• Knowledge Organization
• Metadata
• +5 more

Julaine Clunis is an Assistant Professor in the Department of STEM Education & Professional Studies at Old Dominion University (ODU), affiliated with the Darden College of Education & Professional Studies. Her research focuses on knowledge organization systems, metadata, linked data, semantic web technologies, health informatics, and data science applications in library and information science. Clunis holds a Ph.D. in Knowledge Organization (2022), and dual M.S. degrees in Library and Information Science (2016) and Health Informatics (2016), all from Kent State University. Her work bridges library science and healthcare through projects like semantic mapping frameworks for clinical coding schemes and pandemic-era health information exchange strategies. Recent publications address legislative impacts on reproductive health informatics and automated semantic integration in healthcare data systems. Clunis has received prestigious awards including the Eugene Garfield Dissertation Fellowship (2021) and multiple IMLS grants supporting LIS education and data science initiatives. Her expertise includes developing FAIR-compliant data systems, ontology design, and collaborative educational partnerships. Clunis' research emphasizes practical applications such as preschool-library collaborations for family engagement and spatial-temporal analysis of social media sentiment. She contributes to advancing interoperability standards in both library and healthcare domains through semantic web technologies.

Xiaomao LinView profile

Professor

• Agricultural Climatology
• Climate Change Impacts
• Bio-Atmospheric Interactions
• +3 more

Xiaomao Lin is a Professor of Agricultural Climatology and Bio-Atmospheric Interactions at the Department of Agronomy, Kansas State University. He leads the Office of the Kansas State Climatologist, focusing on climate data analysis, climate change impacts on agriculture, and environmental resource management. His research integrates in-situ observations, remote sensing, crop modeling, and greenhouse gas flux measurements to address challenges in agricultural sustainability and climate adaptation. Dr. Lin holds a PhD and teaches AGRON 850: Agricultural Climatology, emphasizing climate change and crop responses. His work spans crop-climate interactions, water resource management, and soil health, with particular attention to winter wheat and maize systems. He collaborates on projects related to irrigation efficiency, soil-borne disease control, and climate policy in the Great Plains region. His research outcomes inform agricultural practices, groundwater conservation strategies, and climate resilience frameworks. Recent studies include evaluating irrigation impacts on maize yields in the Ogallala Aquifer region, analyzing satellite-derived precipitation trends, and developing genotype-environment interaction models for crops. Dr. Lin’s outreach efforts include the Kansas Foundation Seed and Soil Testing Lab initiatives. He contributes to the state’s climatological assessments and advises on adapting agricultural practices to climate variability.

Antonio FerraraView profile

Assistant Professor

• Artificial Intelligence
• Cybersecurity
• Recommender Systems
• +7 more

Antonio Ferrara is an Assistant Professor at Politecnico di Bari, affiliated with the Information Systems Lab (SisInf Lab). His research focuses on Artificial Intelligence (AI), Cybersecurity, Recommender Systems, and Trustworthy AI. Key contributions include frameworks like Elliot for reproducible AI evaluation, federated learning approaches for privacy, and knowledge graph integration in recommendation systems. He has organized conferences such as ICWE 2022 and workshops on Critical Infrastructures Security, SWEET Semantic Web, and CyberChallenge.IT. His work addresses AI fairness, privacy in recommendation systems, and adversarial attacks on ML systems. He actively contributes to interdisciplinary research at the intersection of software engineering, big data analysis, and ethical AI. Recent publications emphasize reproducibility in AI research, bias mitigation, and application of AI in healthcare (e.g., Alzheimer's diagnosis, cancer radiotherapy). His lab's activities span semantic technologies, cyber-physical systems security, and user-centric data control mechanisms.

Antonio NorelliView profile

Research Fellow

• Artificial Intelligence
• Deep Learning
• Explainable AI
• +3 more

Antonio Norelli is a Research Fellow at Oxford University and a PhD student in Computer Science at Sapienza University of Rome, specializing in AI and deep learning within the GLADIA research group. His academic journey includes a BSc in Physics, an MSc in Computer Science, and the SSAS interdisciplinary honors program. He has held research internships at Spiketrap (San Francisco) and Amazon Lablet in Tübingen. His research focuses on bridging the gap between human and artificial intelligence, emphasizing symbolic reasoning, explainable AI, and multimodal learning. Notable contributions include the ASIF framework for unsupervised multimodal alignment and Olivaw, an Othello-playing AI that achieves expert-level performance without human knowledge. His key research interests span artificial scientific discovery, neural network explainability, and the theoretical foundations of intelligence. Recent work explores relative representation learning and zero-shot latent space communication, with applications in cross-modal retrieval and efficient model adaptation. Norelli’s interdisciplinary approach combines insights from philosophy of science (Popperian critical rationalism) with machine learning to develop systems capable of symbolic reasoning and hypothesis generation. Publications highlight advancements in: (1) Multimodal learning without training through coupled data alignment, (2) Game AI strategies for Othello using minimal resources, (3) Neuro-symbolic architectures for interpretable decision-making. His work has been presented at NeurIPS and IEEE Transactions on Games, among others. Current efforts aim to formalize intelligence as information-processing mechanisms grounded in symbol manipulation and semantic alignment.

• Temporal Information Extraction
• Computational Linguistics
• Natural Language Processing (NLP)
• +3 more

Marc Verhagen is a computational linguist and programmer affiliated with Brandeis University's Computer Science Department. He holds a PhD in Computer Science (2004) and MA degrees in Computational Linguistics (University of Utrecht, 1991) and Social Geography (1985). His roles include Senior Research Scientist (2007-2008) and Postdoctoral Fellow (2004-2006). He co-founded LingoMotors Inc. (1997-2002), leading software development for natural language processing (NLP) tools. Research Focus: Temporal information extraction, TimeML standards, and computational linguistics infrastructure. Key Projects: TARSQI toolkit for temporal processing, LAPPS Grid, ISO-Space spatial annotation, and Medstract biomedical NLP. He has expertise in programming languages (Python, Perl), database systems (Oracle), and tools like Dreamweaver. His work bridges academia and industry, emphasizing NLP tool development and semantic interoperability. Beyond technical roles, he coordinated large-scale events (300–800 attendees) and served as a free-lance DJ and theater participant. Research interests span temporal semantics, event modeling, and spatial NLP, with contributions to TimeML, annotation frameworks, and semantic visualization. Over 30 publications since 1990, including his 2004 PhD thesis Times Between the Lines .

• Machine Learning
• Artificial Intelligence
• Blockchain
• +1 more

Dr. Ismail Alarab serves as a Lecturer in Computer Science at Bournemouth University, teaching core subjects including Artificial Intelligence, Databases, Big Data, and Programming. His research focuses on applied artificial intelligence with specialized expertise in blockchain technology and real-world machine learning applications, culminating in a December 2022 PhD from Bournemouth University. PhD in Novel Machine Learning Models Based on Uncertainty Estimation and Sequential Predictions for Blockchain Networks, Bournemouth University, 2022 MSc in Signal and Image Processing: Applications and Methods, Université Grenoble Alpes, France Diploma in Telecommunication Engineering (equivalent to MEng), Lebanese University, Lebanon Alarab's research centers on machine learning for blockchain systems, particularly uncertainty estimation in neural networks and graph-based models for cryptocurrency transaction analysis. His work bridges theoretical AI frameworks with practical financial security applications, emphasizing adversarial robustness and sequential prediction capabilities in distributed ledgers. Key methodologies include graph convolutional networks and LSTM architectures adapted for anti-money laundering systems. Analysis of his 14 publications (2020-2024) reveals consistent focus on machine learning applications in blockchain environments, with evolving sophistication from foundational transaction analysis (2020) to advanced uncertainty-aware sequential prediction models (2024). Dominant themes include adversarial attack methodologies for graph neural networks, thermal comfort optimization extensions, and specialized techniques for imbalanced financial datasets. No major scientific awards are documented, though professional development includes Leadership and Management Training (AshorneHill, 2023), Deploying Machine Learning Models in Production certification (DeepLearning.AI, 2023), and Microsoft Azure Fundamentals (AZ-900, 2023). Current advising activities and research grants are not specified in available materials. Dr. Alarab serves as reviewer for IEEE Transactions on Knowledge and Data Engineering and Elsevier's Neural Networks journal, indicating active peer engagement within academic communities. His research operates within Bournemouth University's Department of Computer Science, primarily collaborating with Dr. Prakoonwit and Dr. Nacer on blockchain-AI integration projects. While no dedicated lab is mentioned, his work demonstrates strong cross-institutional partnerships in financial technology security research.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +5 more

Professor Michalis Vazirgiannis is affiliated with the Informatics Department at Athens University of Economics and Business (AUEB). He holds a PhD in Informatics from the National and Kapodistrian University of Athens (1994), with prior degrees in Physics, Robotics, and Knowledge-Based Systems. His research focuses on data mining, machine learning, clustering algorithms, and web mining. He has led EU-funded projects like SQO-OSS and NGWeMiS, and contributed to initiatives like DB-GLOBE and I-KnowUMine. Awards include ERCIM Post-Doctoral Scholarship (2001) and Marie Curie Fellowship (2006). He serves on editorial boards (e.g., IDA Journal) and program committees for conferences like IEEE/ICDM and ECML/PKDD. His work spans theoretical advancements and practical applications in distributed systems, semantic networks, and temporal graph analysis. Education: BSc Physics, NKUA (1986) MSc Robotics, NKUA (1988) MSc Knowledge-Based Systems, Heriot-Watt University (1989) PhD Informatics, NKUA (1994) Research interests include semi-supervised learning, page ranking, and distributed feature selection. His publications address clustering frameworks, peer-to-peer systems, and normalized PageRank for evolving graphs. He has contributed to patents and held visiting researcher positions at INRIA, Fraunhofer, Max Planck, and IBM India.

• Statistics
• Probability
• Multivariate Geostatistical Modeling
• +6 more

Marco Minozzo is an Associate Professor of Statistics at the University of Verona , affiliated with the Department of Economics within the School of Economics and Management. Previously, he served as a Researcher in Statistics at the University of Perugia. His academic credentials include a degree in Statistics and Economics from the University of Padova and a Ph.D. in Statistics from University College London. Research Focus : Modeling multivariate geostatistical data, Monte Carlo estimation algorithms, spatial and temporal statistical models, and applications in economics, finance, and environmental science. Teaching : Statistics and Probability at undergraduate, graduate, and postgraduate levels, including courses on data analysis with R, Python, MATLAB, and SAS. Third Mission : Active in public engagement and continuous education, with teaching assignments in professional master’s programs and specialized courses. Research Projects : FAIR-PLAY (marketing 4.0), Management Journal Relevance, Monte Carlo methods for geostatistics, and high-dimensional dependency models.

Moises SuditView profile

Professor

• Information fusion
• Military operations research
• Optimization
• +2 more

Moises Sudit is Professor in the Department of Industrial and Systems Engineering at the University at Buffalo's School of Engineering and Applied Sciences, where he serves as Chief Scientist for the Collaborative Institute for Multisource Information Fusion. His research focuses on information fusion, optimization methods, network analysis, and decision support systems for military and security applications. Educational background includes PhD and MS degrees from Purdue University and BS from Georgia Institute of Technology. Research develops mathematical frameworks for integrating heterogeneous information sources, optimizing sensor resource allocation, and enhancing situational awareness. Recent work explores graph machine learning for healthcare optimization, satellite tasking algorithms, and geospatial analytics for fleet tracking. Applications span defense, healthcare logistics, and critical infrastructure protection. Publications demonstrate sustained innovation in multi-source fusion methodologies, with current emphasis on spatiotemporal optimization, graph-based machine learning, and adaptive decision algorithms for complex domains including satellite resource management and organ exchange programs. Professional affiliations include INFORMS, IEEE, IIE, SPIE, and International Society of Information Fusion (ISIF).

Mario KrennView profile

Professor

• Artificial Intelligence
• Quantum Physics
• Machine Learning
• +7 more

Mario Krenn is a Full Professor (W3) of "Machine Learning in Science" at the University of Tübingen since June 2025, leading the Artificial Scientist Lab. His research bridges artificial intelligence , quantum physics , and experimental design , focusing on developing AI systems that act as "artificial muses" to inspire novel scientific discoveries. ERC Starting Grant recipient (2024) for ArtDisQ project Developed PyTheus, a framework for AI-driven quantum experiment design Created XLuminA, a JAX-based simulator for microscopy and photonics Co-inventor of SELFIES, a robust molecular string representation His work has led to experimental implementations of AI-designed quantum protocols (e.g., entanglement without pre-existing resources) and gravitational wave detector concepts. He explores scientific understanding in human-AI collaboration, nonlocal interference phenomena, and the philosophical implications of AI-generated discoveries. Recent projects include predicting research trends via knowledge graphs and developing virtual reality tools to visualize AI-conceived quantum experiments. Scientific awards include the ERC Starting Grant 2024 and International Quantum Technology Emerging Researcher Award (Highly Commended) 2020 . He serves on the editorial board of Machine Learning: Science and Technology and actively promotes open science through GitHub repositories and community-driven initiatives.

Sanorita DeyView profile

Assistant Professor

• Human-Computer Interaction
• Social Computing
• Crowd Computing
• +5 more

Sanorita Dey is an Assistant Professor in the Department of Computer Science and Electrical Engineering (CSEE) at the University of Maryland, Baltimore County (UMBC). She leads the Social Intelligence Lab and investigates human-computer interaction, social computing, and persuasive technology, with applications spanning STEM education, developmental-disability support, and equitable technology adoption. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2020) M.S. in Computer Science and Engineering, University of South Carolina (2013, Outstanding Thesis Award) B.E. in Computer Science and Technology, IIEST (formerly B.E. College) (2007) Research Interests: Dr. Dey’s work lies at the intersection of human-computer interaction , social computing , and crowd computing . She explores how technologically mediated persuasive systems influence spatial learning, STEM motivation, and community narratives. Ongoing projects examine AI agents’ contextual reliability, YouTube’s role in supporting parents of children with developmental delays, and barriers Black and Latinx students face in computing pathways. Publications & Trends: From 2017–2025, her 15 most-cited articles map a trajectory from early work on crowdfunding narratives and spatial-navigation tools to recent AI-bias and developmental-disability studies. Collectively, these works advance explainable AI , inclusive education technology , and computational social science , consistently appearing in top-tier venues such as CHI, CSCW, AAAI/AIES, and GROUP. Awards & Honors: Maria Pia Gratton International Fellowship (2019) Top-5 Paper Award, SUI (2019) UIUC Graduate College Dissertation Completion Fellowship (2018) CHI 2018 Best Paper Honorable Mention Adobe Digital Marketing Research Award (2016) Outstanding MS Thesis Award (2014) SPARC Graduate Fellowship (2013) Best Poster Runner-Up, HotMobile (2013) Advising & Funding: Dr. Dey currently mentors 3 Ph.D. students , 6 master’s students , 2 undergraduates , and 2 K-12 researchers in the Social Intelligence Lab. She recently received the COEIT Interdisciplinary Award (2024) to develop a summer workshop promoting computing role models among under-represented students. Labs & Teams: She directs the Social Intelligence Lab at UMBC, fostering interdisciplinary collaborations across HCI, AI ethics, education, and global-development domains.

• Complex Geometry
• Kähler Geometry
• Stability of Algebraic Varieties
• +5 more

Chi Li is an Associate Professor in the Department of Mathematics at Rutgers, The State University of New Jersey. Their research focuses on Complex Geometry, including canonical metrics in Kähler geometry, stability of algebraic varieties, and pluripotential theory. They hold an office in HLL-511 and can be contacted at cl1412@rutgers.edu. Research interests emphasize foundational aspects of geometric analysis with applications to algebraic structures. Recent work explores interdisciplinary applications in AI, such as anomaly detection, cross-lingual NLP, and large language model optimization. Key contributions include frameworks for domain-oriented model compression and dynamic routing in mixed LLMs. Publications span 2020–2025, addressing topics from turbulence forecasting to spatio-temporal recommendation systems. Notable trends include integration of geometric principles with machine learning methodologies. Extensive contributions to log anomaly detection (GLAD, Cat), time series meta-contrastive learning, and graph-based cross-lingual transfer. Active in developing interpretable imitation learning and uncertainty quantification techniques for in-context learning.

Carlos D. BarrancoView profile

Professor

• Computational Intelligence
• Fuzzy Logic
• Soft Computing
• +7 more

Carlos D. Barranco is a Professor at Universidad Pablo de Olavide , Spain, with over two decades of experience in Computational Intelligence , Fuzzy Logic , and Soft Computing . His work focuses on applying these techniques to Big Data , Flexible Querying , and Imperfect Information Management in databases. He teaches courses in Computer Science at both bachelor's and master's levels, including Advanced Programming , Big Data , and Data Analysis in Digital Humanities . Research interests include: Fuzzy Database Systems : Developing indexing strategies for possibilistic and fuzzy numerical/temporal data to improve query efficiency in traditional RDBMS. Big Data Applications : Leveraging fuzzy logic in bioinformatics, energy consumption forecasting, and medical image retrieval. Interdisciplinary Work : Bridging computational methods with Digital Humanities (e.g., medieval charter databases) and Medical Imaging . Collaborations span institutions across Spain and Europe, with publications in journals like Applied Soft Computing , International Journal of Intelligent Systems , and IEEE Transactions on Fuzzy Systems . His work has been presented at major conferences including IWBBIO , FUZZ-IEEE , and Flexible Query Answering Systems . Contact: cdbargon@upo.es

Michael GeneserethView profile

Professor

• Computational Logic
• Enterprise Management
• Computational Law
• +4 more

Michael Genesereth is a Professor of Computer Science at Stanford University and a professor by courtesy in the Stanford Law School. He serves as director of the Logic Group at Stanford and is the founder and research director of CodeX - the Stanford Center for Legal Informatics. Genesereth is renowned for his groundbreaking work on Computational Logic and its diverse applications in Enterprise Management, Computational Law, and General Game Playing. His academic journey includes an Sc.B. in Physics from M.I.T. and a Ph.D. in Applied Mathematics from Harvard University (1978). These foundational studies paved the way for his influential career at the intersection of logic, computer science, and law. Genesereth's research program spans from theoretical foundations of logic programming to practical implementations across multiple domains. His work on General Game Playing has established frameworks enabling AI systems to understand and play games based purely on logical descriptions of rules. In Computational Law, he pioneers approaches for representing legal rules in machine-processable formats to automate compliance checking and legal reasoning. His Logic For All initiative has successfully introduced logic education to high schools across the United States, with teachers reporting significant improvements in students' preparedness for college-level coursework. The publication pattern reveals a sustained research trajectory focused on logic foundations, with recent work expanding into practical applications in computational law, insurance portfolio management, and knowledge representation systems. His 2022-2025 publications demonstrate continued innovation in logic programming frameworks and their real-world implementations. Genesereth has founded multiple technology companies including Teknowledge, CommerceNet, Mergent Systems, and Symbium, translating theoretical work into practical solutions. His educational impact extends through widely adopted textbooks and his free massive open online course (MOOC) on Introduction to Logic, which has enrolled hundreds of thousands of students globally. His current initiatives include the International Logic Olympiad, launched in 2024, which engages high school students worldwide in logic problem-solving competitions. This effort builds on his observation that high school students consistently perform well in his university-level logic courses, demonstrating the readiness of secondary students for formal logic education.

Kexuan XinView profile

Research Fellow

• Knowledge Graphs
• Natural Language Processing
• Large Language Models
• +3 more

Dr. Kexuan Xin is a Research Fellow at the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University. She holds a PhD in Computer Science from the University of Queensland (2023), Master's in Computer Science from the University of Queensland (2018), and Bachelor's in Information Engineering from South China University of Technology (2016). Her research focuses on knowledge graphs, natural language processing, and large language models with applications in bioinformatics and health informatics. She conducted postdoctoral research at UIUC collaborating with the biology department, applying NLP techniques to biological domain challenges. Education: PhD in Computer Science, University of Queensland (2018–2023) MSc in Computer Science, University of Queensland (2017–2018) BEng in Information Engineering, South China University of Technology (2012–2016) Research Interests: Knowledge graph construction and alignment NLP applications in biomedical domains Entity alignment and disambiguation Temporal knowledge graph modeling AI-driven health informatics systems Recent Research Trends: Her work spans advancements in entity alignment techniques (e.g., TEA: time-aware entity alignment), bioinformatics applications (gene-metabolite prediction), and AI coaching systems for occupational safety. Her publications reflect expertise in scalable KG integration, graph embeddings, and context-aware modeling. Awards: Dean’s Award for Outstanding HDR Theses (2023) Best Paper Nomination at PERSUASIVE 2025 Dean’s Commendation for Academic Excellence (2017) Best Project Award (2017) Collaborations & Projects: Leads the Fujitsu Macquarie AI Research Lab (2023–2027), focusing on AI applications in health and technology. Actively collaborates across disciplines, including biology and urban informatics.