Find Academic Supervisors

• Knowledge Representation
• Semantic Web
• Artificial Intelligence
• +4 more

Angelo A. Salatino is a researcher affiliated with the Knowledge Media Institute (KMi) at the Open University. His work focuses on advancing knowledge representation, semantic technologies, and AI applications in scholarly communication. He has contributed to over 70 publications since 2013, with recent work emphasizing large language models (LLMs), knowledge graphs, and their integration in research and industry contexts. Education: PhD from the Open University (KMi). Research interests include ontology engineering, scientific question answering, and AI-driven literature review methods. He has co-edited conference proceedings and contributed to projects like the Computer Science Ontology (CSO) and the AIDA knowledge graph. Key areas of research include: (1) knowledge injection strategies for LLMs in academia, (2) semantic analysis of ESG discourse in news, (3) health informatics applications of knowledge graphs, and (4) optimizing tourism data through NLP and graph technologies. His work bridges computational methods with interdisciplinary research challenges. Co-authored over 60 papers with collaborators like Enrico Motta and Francesco Osborne. His research often addresses practical challenges in organizing scholarly knowledge, improving metadata quality, and enhancing accessibility through neuro-symbolic AI approaches.

Christine HerlihyView profile

Adjunct Associate Professor

• Artificial Intelligence
• Machine Learning
• Health Informatics
• +5 more

Christine Herlihy holds the academic rank of Adjunct Associate Professor in the Department of Computer Science at the University of Maryland. She is also a PhD Candidate advised by John Dickerson, located in IRB 2108. Her research focuses on algorithmic fairness, health informatics, and natural language processing with applications in socially consequential domains like public health and agriculture. She explores optimization techniques such as restless bandits and develops tools for scientific model augmentation, including a Julia package named SemanticModels.jl. Education: Christine is pursuing her PhD in Computer Science at the University of Maryland under the guidance of John Dickerson. Earlier academic qualifications are not detailed in the provided information. Research Interests: Christine’s work bridges computer science and societal impact. She investigates how AI systems can improve health literacy through personalized smartphone applications and ensure fairness in algorithmic decision-making. Her studies also address challenges in clinical NLP, resource allocation for small farmers, and longitudinal analysis of LLM data contamination. She advocates for transparent and equitable algorithmic frameworks in domains ranging from healthcare to policy evaluation. Advising & Grants: Christine is advised by John Dickerson in her doctoral studies. No grants or independently advised students are explicitly listed in the provided materials.

Carlos M. CarvalhoView profile

Professor

• Bayesian Statistics
• Causal Inference
• Time Series Analysis
• +4 more

Carlos M. Carvalho is a La Quinta Centennial Professor in Business and Professor of Statistics at The University of Texas at Austin McCombs School of Business. He joined UT Austin in 2010 after serving on the faculty at the University of Chicago Booth School of Business. Carvalho also serves as the Executive Director of the Salem Center for Policy, which supports research, education, and dialogue around the impact of economic policies on markets and the free enterprise system. Dr. Carvalho's educational background includes: Ph.D. in Statistics, Duke University, 2006 His research focuses on Bayesian statistics in complex, high-dimensional problems with applications ranging from finance to genetics. Carvalho's work encompasses several key areas including causal inference , machine learning , policy evaluation , and empirical asset pricing . His research interests specifically include modern statistical tools for causal inference, dimensionality reduction in high-dimensional models, advanced statistics in asset pricing, and time series analysis. His work often bridges theoretical statistical methodology with practical applications in various domains. Analysis of Carvalho's recent publications reveals a strong focus on Bayesian methods, causal inference, and high-dimensional statistics. His work spans multiple disciplines including statistics, finance, medicine, and social science. A notable trend is the application of Bayesian techniques to complex real-world problems, particularly in causal inference and treatment effect estimation. His research shows increasing interdisciplinary collaboration, with applications in healthcare, finance, and social policy. Dr. Carvalho holds the prestigious La Quinta Centennial Professorship at UT Austin, recognizing his significant contributions to the field of statistics and business. While specific awards aren't detailed in the provided text, his extensive publication record demonstrates recognition by his peers. As an academic leader, Carvalho directs the Salem Center for Policy, which facilitates research on economic policies and market impacts. His work appears to involve collaboration with researchers across disciplines, particularly in finance, healthcare, and social sciences, though specific lab structures aren't detailed in the provided text.

Sonia ChernovaView profile

Associate Professor

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

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

Fabio TamburiniView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Speech Processing
• +3 more

Fabio Tamburini is a Full Professor at the University of Bologna, where he teaches Computational Linguistics and Applied Linguistics across the Faculty of Arts and Humanities and the Faculty of Mathematics, Physics and Natural Sciences. He holds a degree in Computer Science (1992) and a PhD in Electronics and Computer Science Engineering (2004) from the same university, focusing on speech processing and computational methods. His research integrates interdisciplinary approaches, emphasizing automatic decipherment of ancient scripts, linguistic biomarkers for cognitive disorders, and advanced NLP techniques like transformer models and quantum computing-inspired methods. He has extensive experience in corpus development, notably overseeing projects like CORIS/CODIS and BoLC, and has contributed to tools such as AnIta-Lemmatiser and Semgrex-Plus. Tamburini frequently collaborates with interdisciplinary teams, including medical professionals for speech-based cognitive decline detection and computational linguists for ancient script analysis. He has edited volumes like CLUB Working Papers in Linguistics and organized key conferences such as CLiC-it 2021 and LREC workshops. His work frequently explores the intersection of computational methods and traditional linguistic analysis, with a focus on applying NLP to real-world challenges in healthcare and historical linguistics. Tamburini’s publications span over two decades, reflecting sustained contributions to both theoretical and applied computational linguistics.

Alex LeitchView profile

Lecturer

• Human-Computer Interaction
• Digital Humanities
• Social Media Analysis
• +3 more

Alex Leitch is a Lecturer at the University of Maryland's College of Information Studies (INFO College) and a PhD candidate. Their work bridges technology and art, focusing on interaction design, creative technology education, and feminist design principles. They co-founded Site 3 Colaboratory and Dames Making Games Toronto, promoting inclusive maker spaces in Canada. Leitch's research explores digital humanities, human-computer interaction, and the societal impacts of social media algorithms. Education: Pursuing a PhD at University of Maryland. Previous roles include technical lead on web projects and exhibiting artist at venues like Toronto International Film Festival and Burning Man. Research Interests: Social media analysis and ethics AI in art and education Feminist approaches to technology Makerspace community dynamics Articles highlight themes like AI art generation, social media discourse analysis, and post-truth media landscapes. Their 2025 studies on cross-platform violence detection and GenAI evaluation frameworks show current focus areas. Awards: No specific honors listed. Teaching and advising: Currently mentoring through their INFO College courses and collaborative maker initiatives. Labs/Teams: Affiliated with the Human-Computer Interaction Lab (HCIL) and involved in Canadian not-for-profit arts organizations.

Gustavo Bulhoes Carvalho da Paz FreireView profile

Assistant Professor

• Asset Pricing
• Option Pricing
• Financial Economics
• +2 more

Gustavo Bulhoes Carvalho da Paz Freire is an Assistant Professor in Econometrics at Erasmus School of Economics, Erasmus University Rotterdam. His research centers on financial market modeling and computational finance. Research Focus: Freire develops machine learning-enhanced econometric methods for asset pricing, volatility forecasting, and derivatives valuation. His work bridges theoretical finance with empirical analysis of high-frequency data. Publications: Recent articles explore neural network-based option corrections, tail risk measurement, and nonparametric pricing, emphasizing innovation in quantitative methodologies. Academic Engagement: Organizes the 'Financial Econometrics meets Machine Learning' conference, fostering interdisciplinary dialogue.

Gavin SuddreyView profile

Researcher

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +3 more

Dr Gavin Suddrey is a Researcher at the Queensland University of Technology (QUT) Centre for Robotics, specializing in Artificial Intelligence and Electrical Engineering. His research focuses on human-robot interaction, natural language instruction for robots, and the development of autonomous systems. He holds a Bachelor of Information Technology (Honours) from QUT. Key research interests include social robotics, wellbeing applications via robotic interventions, natural language processing for task execution, and vision-based industrial robotics solutions. His work emphasizes practical applications such as automated laboratory tours, healthcare robotics, and industrial automation. Dr Suddrey has contributed to over a dozen publications since 2014, exploring topics ranging from path algebra for mobile robots to robot-delivered wellbeing training. His projects include the Mini Autonomous Vehicles initiative and competitive robotics entries like Team ACRV's OCRTOC participation. He develops software systems for human-robot interaction, including HTML/JavaScript-based interfaces on Pepper robots, and investigates user perceptions of robots through scales like the Robot Self-Efficacy measure. While no specific awards are listed, his work aligns with QUT's commitment to interdisciplinary robotics innovation.

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +7 more

Sandra Carberry is a Professor in the Department of Computer and Information Sciences at the University of Delaware with a joint appointment in Linguistics. Her research integrates artificial intelligence, natural language processing, and user modeling to develop intelligent systems for digital libraries, medical decision support, and educational applications. She leads the NLP/AI laboratory and teaches core AI and data mining courses. Her primary research focuses include: Intelligent Interfaces : Developing systems that recognize user goals and generate tailored responses through advanced user modeling Digital Libraries : Pioneering methods to extract meaning from information graphics using perceptual effort analysis and Bayesian reasoning Medical Informatics : Creating TraumaCASE for generating clinical training scenarios and improving trauma care decision support Educational Systems : Building adaptive medical tutoring systems that dynamically adjust case complexity Dialogue Systems : Advancing transformation-based learning for dialogue act recognition and cooperative response generation Her publication trend (2002-2007) reveals a strategic shift toward information graphics understanding, where she established that 61% of graphics convey unique information not present in text. This work combines cognitive psychology with machine learning to recognize graphic intentions through perceptual effort metrics and communicative signals, significantly advancing digital library capabilities. Key recognitions include: James Chen 2006 Award for Best Paper (UMUAI Journal) Best Paper Award at Diagrams Conference (2006) Springer Best Paper Award at User Modeling Conference (2007) Funded by NSF grant IIS-0534948, her digital libraries project demonstrates strong grant acquisition capability. Her extensive co-authorship with junior researchers indicates active mentorship, though formal student lists aren't published. University service includes departmental committees and professional activities documented in her vita. She directs the NLP/AI laboratory, which maintains active research in natural language processing, artificial intelligence applications, and multimodal information systems, with current work extending graphic understanding to complex visual representations.

• Natural Language Processing
• Computational Linguistics
• Information Extraction
• +3 more

Gábor Recski is a computational linguist and postdoctoral researcher at TU Wien's Faculty of Informatics, affiliated with the Data Science Research Unit. He holds a PhD and previously served as an assistant professor at the Budapest University of Technology. His research focuses on natural language processing (NLP), particularly computational semantics and transparent information extraction. He teaches courses such as Natural Language Processing and Information Extraction, and supervises students at BSc, MSc, and PhD levels. His work includes over 50 peer-reviewed publications and projects like BRISE-Vienna (2019–2025) and Honeypot LLM: Creation of a Scam Conversation Dataset. Roles: PostDoc Researcher, Substitute Member of Faculty Council, Substitute Member of Curriculum Commission for Business Informatics Education: PhD (previous role at Budapest University of Technology) Research Interests: Transparent information extraction, semantic modeling, legal text analysis, and explainable AI His research projects span legal corpus construction (e.g., BRISE-plandok), offensive text detection, and AI applications in legal research. He actively contributes to conferences like ACL, EMNLP, and SIGIR. Supervised students have explored topics ranging from explainable prediction models to graph-based relation extraction.

• Multimodal Interaction
• Ambient Intelligence
• Human-Computer Interaction (HCI)
• +6 more

Yacine Bellik is an Associate Professor in Computer Science at the Université Paris-Saclay, affiliated with the IUT d'Orsay and the LISN laboratory. He holds the 'Habilitation à Diriger des Recherches' (HDR) and leads the AMI Group (Architecture and Models for Interaction). His research focuses on multimodal interaction, ambient intelligence, and assistive technologies, particularly for visually impaired users. He has supervised numerous PhD students and contributed to projects like the IRoom Intelligent Room and ATRACO. Education: PhD in Computer Science (1995), Paris-South 11 University, LIMSI-CNRS HDR (2006), Paris-South 11 University, LIMSI-CNRS Research Interests: Bellik's work spans multimodal interaction architectures, adaptive systems in ambient environments, haptic and tactile interfaces, and accessibility solutions for disabled users. He has pioneered frameworks for spatial user interaction (SUIL) and developed assistive technologies like tactile wheelchair interfaces and multimodal navigation aids for the blind. Publications & Awards: Over 50 peer-reviewed articles in journals like *Journal of Ambient Intelligence and Smart Environments* and conferences such as CHI and INTERACT. Notable achievements include a 2nd prize in the SETAA 94 innovation competition and recognition for top-tier research in HCI. Grants/Responsibilities: Coordinator of the Digiteo IRoom project (2007–2009) Membre du comité scientifique of multiple ANR and EU projects Teaching roles include directing studies at IUT Orsay and leading modules on multimodal interaction at Supélec Labs/Teams: AMI Group at LIMSI-CNRS, contributing to the ATRACO and IRo om projects. Collaborates internationally with institutions like the University of Ulm and École de Technologie Supérieure (Montreal).

Fotios LygerakisView profile

Researcher

• Representation Learning
• Robotics
• Machine Learning
• +7 more

Fotios Lygerakis is a doctoral student and university assistant at the Department of Cyber-Physical Systems (CPS) at Montanuniversität Leoben, Austria. His research focuses on advancing machine learning and robotics, particularly in representation learning, visuotactile fusion, and reinforcement learning for robotic manipulation. He holds a Diploma in Electrical and Computer Engineering from the Technical University of Crete (2019) and has held roles including teaching assistant at the University of Texas at Arlington, research assistant at Demokritos (Athens), and research intern at Toshiba Research Europe. His research interests span representation learning, multimodal fusion, reinforcement learning, and healthcare robotics. Notable contributions include work on CR-VAE and M2CURL, earning a Best Student Paper Award at the 2024 Ubiquitous Robotics Conference. Lygerakis actively supervises theses in areas like human-robot interaction and self-supervised learning, and teaches courses on machine learning and deep learning. Lygerakis maintains active engagement in the scientific community through reviewing for journals/conferences (e.g., IROS, IJRR), organizing workshops, and leading the Neural Coffee Reading Group. He has presented invited talks at institutions like New York University and Technical University of Crete, and his work has been featured in outlets like Computer Vision News.

Waleed MeleisView profile

Associate Professor

• Combinatorial optimization
• Machine Learning
• Large-scale machine learning
• +7 more

Waleed Meleis is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University and serves as Vice Provost for Graduate Education. He holds an MS and PhD from the University of Michigan and a BSE from Princeton University. His primary research focuses on combinatorial optimization, machine learning, assistive technology, and large-scale social experimentation platforms like Volunteer Science. He pioneered the Enabling Engineering student group, which designs assistive devices for individuals with disabilities, supported by over $350K in external funding. His leadership roles include Interim Vice Provost for Oakland Campus and Associate Dean for Graduate Education, driving significant enrollment growth and program development. Notable awards include multiple Martin W. Essigmann Teaching Awards and the Eta Kappa Nu Professor of the Year Award. His work bridges technical innovation with societal impact, particularly in healthcare and education. Education: PhD, Computer Science and Engineering, University of Michigan, 1996 MS, Computer Science and Engineering, University of Michigan, 1992 BSE, Electrical Engineering, Princeton University, 1990 Research: Combines algorithm design for engineering problems (e.g., cloud computing, spectrum management), social science experimentation platforms, and assistive technology development for rehabilitation. Leadership: Vice Provost for Graduate Education since 2023 Interim Vice Provost and Academic Lead for Oakland Campus (2023–2024) Associate Dean for Graduate Education (2020–2022) Awards: Recognized for teaching excellence and innovation in education across multiple years, including the 2010 Eta Kappa Nu Professor of the Year Award. Publications: Over 50 peer-reviewed papers in areas spanning reinforcement learning, distributed systems, and biomedical engineering. Grants: Secured funding from NSF, Army Research Lab, and private foundations for projects including Volunteer Science and Enabling Engineering initiatives. His interdisciplinary Dialogue of Civilizations course explores scientific revolutions, blending historical and computational perspectives. Enabling Engineering has delivered 60+ projects with clinical partners, emphasizing inclusive engineering education.

Zid Niel MancenidoView profile

Lecturer

• Teacher Education
• Education Policy
• Educational Measurement
• +5 more

Zid Niel Mancenido is a Lecturer at Harvard Graduate School of Education and Senior Manager (Research and Evaluation) at Australian Education Research Organisation. He works with multidisciplinary teams to advance educational evidence generation and application. Ph.B. (Honours) – Australian National University MTeach (Secondary) – University of Melbourne Ed.M. in Education Policy and Management – Harvard University Ph.D. in Education Policy and Programme Evaluation – Harvard University His research focuses on: Teacher recruitment and career decision-making Summer learning program effectiveness Educational measurement and evaluation Responsive teaching analysis via machine learning Teacher preparation interventions Recent publications examine: How social networks influence teaching careers Quantitative impacts of summer math programs Automated teacher feedback systems He received: NSF grant #1920616 for teacher education research design

Johanne TrippasView profile

Research Fellow

• Conversational AI
• Interactive Information Retrieval
• Human-Computer Interaction
• +4 more

Dr. Johanne Trippas is a Vice-Chancellor’s Senior Research Fellow at the School of Computing Technologies, STEM College, RMIT University. Her research focuses on improving information accessibility through conversational systems, generative interactive information retrieval, and novel interfaces beyond traditional text-based search. She holds a PhD in Computer Science from RMIT University (2019), recognized with the Deputy Vice-Chancellor’s Higher Degree by Research Prize. Trippas has held postdoctoral roles at the University of Melbourne and Monash University, and currently collaborates with Ambulance Victoria on AI for cardiac arrest detection. She has coordinated and lectured courses in Data Science and Usability Engineering at RMIT since 2022, and previously taught at the University of Melbourne. Trippas actively serves on program committees for ACM SIGIR, ACM CHIIR, and CUI conferences, with leadership roles including General Chair (ACM CUI’24), Workshop Chair (ACM CHIIR’25), and Tutorial Chair (ECIR’24). She is also a member of the NIST TREC program committee and the ACM CHIIR steering committee. Her research employs multi-method analysis, emphasizing user-centric approaches to model human behavior in information-seeking tasks. Key projects include the CIRQL dataset (2024) analyzing conversational search interactions and the BIDD-1k dataset (2024) studying prompts for large language models. She leads the Fourth Strategic Workshop on Information Retrieval (SWIRL’25) to revise the IR research manifesto. Trippas’ work bridges academic and industry domains, collaborating with Google, Microsoft, Amazon, and Booking.com. She advocates for reproducible research as Vice-Chair of the ACM SIGIR Artifact Evaluation Committee. Her research aligns with UN Sustainable Development Goals 4 (Quality Education), 5 (Gender Equality), 9 (Industry, Innovation and Infrastructure), and 10 (Reduced Inequalities).