Find Academic Supervisors

• Tort Law
• Health Law
• Public Authority Liability
• +7 more

Lynda Crowley-Cyr is an Associate Professor of Law at the University of Southern Queensland's School of Law and Justice and a member of the Centre for Health Research. She holds a PhD in Law from the University of Sydney (2016) and has been with UniSQ since 2011. Her expertise spans tort law, health law, public authority liability, and tourist behavior, particularly in relation to marine hazards like jellyfish. She actively contributes to policy consultations (e.g., Queensland recreational diving safety standards) and ethics review processes. Education: LLB (QUT, 1991), GCertEd (JCU, 1996), LLM (JCU, 1997), PhD (Sydney, 2008). Research focuses on legal and ethical dimensions of public health risks, including jellyfish hazard communication, tourism policy, and pandemic response. Her work bridges law, environmental science, and behavioral studies. Recent articles analyze ethics review processes in humanities/social sciences and the impact of risk communication on tourist behavior during crises like COVID-19. Awards: None explicitly listed, though her contributions to policy and ethics reflect professional recognition. Supervision: Currently guiding doctoral research on the National Code of Conduct for Health Care Workers. Previously supervised studies on student retention and mental health. Labs/Teams: Active in UniSQ's Centre for Health Research, focusing on interdisciplinary health and legal issues.

Ralf LämmelView profile

Professor

• Software Language Engineering
• Model-Driven Development
• Semantic Web
• +2 more

Ralf Lämmel is Professor and Head of the Software Languages group at the University of Koblenz. His research spans software language engineering, model-driven development, and semantic web technologies. He specializes in API analysis, data validation (SHACL constraints), and variability management for software systems. Recent work focuses on AI-assisted workflows for archaeology and trust analysis in large language models. Publications demonstrate growing interest in knowledge graph validation and semantic web applications. He has developed tools like the Virtual Platform for software variability and ProGS for property graph validation. Service includes program committee memberships for software engineering conferences and editorial roles. He leads research groups exploring API evolution and RDF validation techniques.

Ehsan Shareghi NojehdehView profile

Assistant Professor

• Natural Language Processing
• Large Language Models
• Deep Generative Models
• +5 more

Ehsan Shareghi Nojehdeh is an Assistant Professor in the Department of Data Science and Artificial Intelligence at Monash University and an affiliated lecturer at the University of Cambridge. He leads a research team focused on predictive models for language (text and speech), with a background in postdoctoral work at Cambridge and prior roles at UCL. His research interests include probing and augmenting Large Language Models (LLMs), reasoning in legal contexts, safety of multimodal models, speech/text translation, and self-supervised learning. Education: PhD in Computer Science (Scalable Non-Markovian Sequential Modelling) from Monash University (2017). Professional roles include Deputy Course Director for the Master of AI program at Monash, service on academic committees (ECA Committee, 2022–2023), and senior reviewing roles at major conferences (ACL2025, EMNLP2023). He teaches units like FIT5217 Natural Language Processing and FIT5212 Data Analysis for Semi-structured Data. Key projects include a Paul Ramsay Foundation-funded study on media narratives around disadvantage in Australia (2022–2023). His work addresses UN SDGs through advancing education and AI ethics. Research collaborations span global institutions, with a focus on knowledge-intensive tasks like biomedical domain applications, graph-to-text generation, and generative model disentanglement. Publications emphasize safety, multimodal capabilities, and logical reasoning with LLMs, with recent work appearing in ACL, EMNLP, and NAACL venues. His team investigates LLM shortcomings, including alignment with human judgment, speech-specific vulnerabilities, and tool-based reasoning strategies.

• Visual Analytics
• Information Visualization
• Computational Linguistics
• +3 more

Menna El-Assady is an Assistant Professor at ETH Zurich's Department of Computer Science , leading the Interactive Visualization and Intelligence Augmentation Lab (IVIA) . She works at the intersection of data analysis, visualization, computational linguistics, and explainable AI , focusing on combining machine learning with visual analytics for text data. Current affiliations: ETH Zurich (Department of Computer Science, AI Center) Previous roles: Research Fellow at ETH AI Center, Research Associate at University of Konstanz, Doctoral Student at OntarioTech University Research Interests El-Assady's work focuses on human-AI collaboration , particularly through: Interactive systems for topic model refinement and language model explainability Visual analytics frameworks for argumentation analysis and digital humanities Explainable AI solutions for medical decision support and political deliberation Trust calibration mechanisms in generative AI and recommender systems Scientific Contributions Developed explAIner - a visual analytics framework for explainable machine learning Created LingVis - integrating computational linguistics with visual analytics Designed VALIDA and VisArgue for analyzing political debates and rhetoric Co-organized workshops: ArgVis , Vis4DH , VisLR , and VISxAI Academic Recognition Best Paper Award: Honorable Mention (IEEE Trans. on Visualization and Computer Graphics, 2018) 2024 VGTC Visualization Significant New Researcher Award

• Electrical Engineering
• Robotics
• Signal Processing
• +7 more

Jewon Lee is a researcher with a focus on interdisciplinary domains spanning Electrical Engineering , Robotics , Signal Processing , and Computer Science . Their work includes collaborations with co-authors like Joon-Young Jung and Sang Woo Kim on fault diagnosis for permanent magnet synchronous machines, IQ data compression methods, and video streaming optimizations. Publications highlight expertise in sensor technology, telecommunications, and machine learning model efficiency. Research interests are diverse, encompassing sensor design , fault detection in electric motors , adaptive video streaming , and large language model (LLM) optimization . These areas are reflected in their contributions to journals such as IEEE Transactions on Industrial Electronics , Sensors , and IEEE Access , as well as conferences like ICTC and ICUFN. Their recent work on LLaMA-3.2-Vision and knowledge graphs indicates an expanding role in Computer Science and Open Science initiatives, aligning with global efforts like the National Research Data Infrastructure (NFDI) . Despite active contributions, specific awards, educational background, and student advising details are not publicly documented in the provided sources.

Ritambhara SinghView profile

Assistant Professor

• Machine Learning
• Data Mining
• Computational Biology
• +3 more

Ritambhara Singh is the John E. Savage Assistant Professor of Computer Science and Data Science at Brown University, affiliated with the Center for Computational Molecular Biology. Her research focuses on developing interpretable machine learning methods for biomedical and genomic applications. Prior to Brown, she was a postdoctoral researcher in the Noble Lab at the University of Washington, and earned her Ph.D. in 2018 from the University of Virginia under Dr. Yanjun Qi. Key research areas include machine learning, computational biology, health sciences, and genomics. She has pioneered methods like SCOT for single-cell multi-omics integration and developed tools such as LUNAR for glioma recurrence prediction. Her work bridges theoretical advancements in deep learning with practical biomedical applications. Awards include the NHGRI Genomic Innovator Award (2025), Brown’s Richard B. Salomon Research Award, and a teaching excellence award. Her lab emphasizes ethical AI in genomics and interpretable models for clinical decision support. Publications span topics like graph neural networks, multimodal learning, and optimal transport methods applied to genomics. Her research also addresses privacy risks in single-cell data and evolutionary signatures in olfactory systems.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +7 more

Fischer Andreas is a Professor at HES-SO (University of Applied Sciences and Arts Western Switzerland) , specifically affiliated with the School of Engineering and Architecture of Fribourg (HEIA-FR) . His research spans Pattern Recognition , Applied Machine Learning , Handwriting Recognition , and Graph-based Methods . He has secured significant grants from TAINA Technology , Swisscom , and the Hasler Foundation for projects including automatic handwriting recognition for tax forms , Swiss German translation , and graph-based keyword spotting in Vietnamese steles . Education : BSc in Computer Science from HEIA-FR. Research Highlights : Graph Neural Networks for Büchi automata classification Annotation-free alignment in historical documents Graph edit distance optimization for keyword spotting Hybrid deep learning for Vietnamese stele analysis Graph-based tumor budding analysis in digital pathology Collaborative Impact : Fischer's work bridges historical document analysis and medical imaging , demonstrated through frameworks like DIVA-DAF and tools like GammaFocus for histopathology. His 2024 conferences suggest ongoing exploration of LLM integration in document processing and OCR-free models for information extraction.

Anita LamprechtView profile

Lecturer

• AI Governance
• Digital Transformation
• Internet Governance
• +8 more

Anita Lamprecht is an international lawyer, AI governance expert, and legal researcher based in Geneva. Affiliated with DiploFoundation , she contributes to the Digital Watch Observatory and teaches at the DiploAI Campus on topics like futures literacy , UN 2.0 , and governance applications of emerging technologies. As a consultant and facilitator of AI capacity-building programs, she bridges law and technology through custom AI agents, legal chatbots, and immersive learning experiences for international organizations. Focus on digital transformation's legal dimensions Specializes in AI, digital twins, and metaverse governance Emphasizes ethical alignment with institutional mandates Her research spans AI governance, internet governance, access to justice through technology, and the intersection of digital policy with international law. Notably, she explores how digital twins and virtual environments can reshape urban planning, legal systems, and global development frameworks. Recent publications analyze metaverse governance, AI's implications for diplomacy, and mathematical foundations of AI governance. She advocates for humAInism —a human-centric approach to AI integration in institutional settings. Anita also develops apprenticeship-style AI learning programs to equip professionals with technical and ethical competencies for the AI era.

• Answer-Set Programming (ASP)
• Explainable AI
• Logic in Artificial Intelligence
• +3 more

Tobias Geibinger is a PreDoc Researcher at the Institute of Logic and Computation of Vienna University of Technology , supported by a DOC Fellowship from the Austrian Academy of Sciences. His work focuses on explainability in Answer-Set Programming (ASP), particularly for advanced language features and hybrid ASP systems. Education: BSc, Dipl.-Ing. (Master's equivalent) in Computer Science Projects: CD Laboratory for AI and Optimization (2017–2025), KIRAS-PrEMI (2019–2022), ARTIS (2017–2025) His research combines logic programming with constraint programming and hybrid methods to solve industrial scheduling problems. This includes automated test laboratory scheduling, photolithography job scheduling, and pandemic-era physician scheduling. Recent work investigates neurosymbolic integration and contrastive explanation frameworks. Selected publications analyze ASP optimization techniques for parallel machine scheduling, large-neighborhood search strategies, and nonmonotonic paraconsistent logics. These contributions align with broader trends in declarative AI and logic-based optimization. Scientific Recognition: ASAI Master Thesis Prize (2023)

• Extreme Multi-label Classification
• Machine Learning
• Algorithms
• +3 more

Erik Schultheis is a Doctoral Student and Visitor (Faculty) in the Department of Computer Science at the School of Science. His research focuses on extreme multi-label classification, algorithm design, and optimization challenges in machine learning. He holds a Master of Science in Physics from Georg-August-Universität Göttingen (2019). Key research interests include scalable classification systems, label correlation learning, and addressing long-tail and missing label problems in large-scale datasets. His work emphasizes practical solutions for commodity hardware limitations and memory efficiency in training models with millions of labels. Notable contributions include the Dismec++ software tool for extreme classification, and he has presented at conferences such as NeurIPS, ICML, and KDD. His 2021 NeurIPS Outstanding Reviewer Award highlights his peer-review contributions to the field. Recent publications explore dynamic sparsity in large output spaces, online algorithm generalizations, and label calibration in extreme classification scenarios. His research bridges theoretical algorithm development with practical implementation challenges in real-world systems.

• Machine Learning
• Reinforcement Learning
• Optimization
• +4 more

Kishan Panaganti Badrinath is a Postdoctoral Scholar Research Associate in the Computing and Mathematical Sciences department at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. Hosted by Prof. Adam Wierman and Prof. Eric Mazumdar, his work focuses on theoretical foundations of reinforcement learning algorithms and bridging simulation-to-real-world performance gaps. Education: PhD in Electronics and Communication Engineering & Mathematical Optimization (2023) from Texas A&M University Masters in Communication and Networks (2018) from Indian Institute of Science (IISc) Bachelors in Electronics and Communication Engineering (2017) from PES University Research Interests: Kishan’s work spans reinforcement learning theory , addressing robustness in multi-agent systems , imitation learning , and human feedback integration . His expertise includes high-dimensional probability , optimization , and stochastic theory , with applications to autonomous solutions and environmental uncertainties . Recent Articles (2025): His recent publications focus on robust sequential decision-making , including KL-regularization privacy , multi-agent tractability via behavioral economics , and distributionally robust LLM alignment . These contributions reflect his emphasis on theoretical rigor and practical adaptability. Scientific Awards: PIMCO Postdoctoral Fellow in Data Science ML and Systems Rising Stars 2025 awardee (selected from 150+ applicants) Current Status: Kishan is actively seeking full-time faculty or core industrial research positions starting in 2025, with recent speaking engagements at IIT Bombay, Microsoft Research, and the Theory CS Winter School at IISc.

• Artificial Intelligence Economics
• Behavioral Economics
• Empirical Finance
• +2 more

Jeremy Nguyen is a Senior Lecturer in Economics at Swinburne University of Technology's School of Business, Law and Entrepreneurship. He researches economic impacts of AI/machine learning with focus on bias, discrimination mitigation, and creative industries. Education: Ph.D. in Computable General Equilibrium Modeling from Australian National University Current projects examine AI's effects on business processes, writing practices, and education through randomized controlled trials. Work has been featured in New York Times, The Guardian, and Forbes. Professional background includes Reserve Bank of Australia and Brookings Institution fellowship. Awards: National teaching awards for educational innovation Publications recognized through early career research prizes Recent publications analyze anchoring bias in LLMs, NFT market discrimination, and economic impacts of diversity in creative industries. His interdisciplinary approach integrates economic modeling with behavioral experiments and machine learning techniques.

Alfredo GarciaView profile

Professor

• Game Theory
• Dynamic Optimization
• Electricity Networks
• +3 more

Dr. Alfredo Garcia is a Professor of Industrial & Systems Engineering at Texas A&M University, holding the Mike and Sugar Barnes Professorship III. His expertise lies in game theory, dynamic optimization, and their applications in electricity and communication networks. He has published extensively in top journals like IEEE Transactions and Operations Research. Education: Ph.D. (1997) in Industrial & Operations Engineering from the University of Michigan; DEA (1992) in Industrial Automation & Computer Science from Université de Toulouse III. Research focuses on distributed optimization algorithms, incentive-compatible market mechanisms, and AI-driven models for autonomous systems. Recent work explores active inference frameworks for driver behavior modeling and federated learning in networked systems. His research bridges theoretical advancements with real-world applications in energy markets, transportation systems, and materials science. Key achievements include developing iterative mechanisms for electricity market coupling and contributions to decentralized optimization on manifolds. Collaborations span academia and industry, addressing challenges in smart grids and autonomous vehicle safety.

• Law
• Legal Studies

Katherine Dunn is an Assistant Professor in the Law Department at Northumbria University. Her role focuses on legal education and research within the university's academic framework. Education: LLM in Law, Awarded: 1 July 1992 Dr. Dunn's research interests are primarily aligned with legal studies, though specific subfields are not explicitly detailed. No recent publications are listed in the provided information. Grants & Awards: No scientific awards or grants are mentioned in the text. Advising & Labs: No advisees, labs, or teams are documented here.

• Computational Genomics
• Machine Learning
• Natural Language Processing
• +3 more

Brian Ondov is an Associate Research Scientist at the Yale School of Medicine's Department of Biomedical Informatics and Data Science. He holds a PhD in Computer Science from the University of Maryland College Park, an MS in Bioinformatics from the Georgia Institute of Technology, and a BS in Computer Science from Rensselaer Polytechnic Institute. His research spans Computational Genomics, Machine Learning, Natural Language Processing, and Human-Computer Interaction. Current work focuses on leveraging Large Language Models (LLMs) to enhance interactions with biomedical literature. His publications demonstrate expertise in developing algorithms for biomedical text simplification, genomic sequence analysis, and innovative data visualization techniques. Research trends show consistent focus on: 1) Biomedical NLP and text accessibility, 2) Genomic data analysis tools, and 3) Perceptual aspects of data visualization. Recent work emphasizes patient-oriented language processing and epidemiological visualization. Active research areas include developing interactive systems for biomedical knowledge discovery and creating visual analytics tools for complex spatiotemporal data in public health contexts.