Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Knowledge Graphs
• +5 more

Dr. Unai Zulaika Zurimendi is an Associate Researcher at DeustoTech - Deusto Institute of Technology, affiliated with the Faculty of Engineering at the University of Deusto and part of the DEUSTEK research group . He completed his doctoral thesis in 2022 titled Towards more interpretable graphs and Knowledge Graph algorithms , focusing on Artificial Intelligence and explainability in graph-based models. His academic background includes a Bachelor's and Master's degree in Computer Engineering from the University of Deusto (2017 and 2019 respectively). Research Interests : Artificial Intelligence in healthcare for rare cancers Explainable AI for Knowledge Graphs Natural Language Processing algorithms Tensor factorization and graph analytics Smart city technologies Recent Publications highlight applications of machine learning in clinical contexts, tensor decomposition for knowledge graphs, and context-aware food search systems. His work emphasizes model interpretability and practical AI implementations across domains like healthcare, urban technology, and user behavior modeling.

Slobodan VuceticView profile

Professor

• Artificial Intelligence
• Data Science
• Applied Machine Learning
• +3 more

Slobodan Vucetic is a Professor in the Department of Computer and Information Sciences at Temple University's College of Science and Technology and serves as Director of the Center for Hybrid Intelligence (CHI), formerly the Center for Cognitive Computing. His work bridges theoretical machine learning advancements with practical applications across public health, biomedicine, and social sciences, focusing on enhancing human capabilities through intelligent software systems. His research spans Human Factors in Artificial Intelligence, Data Science, Applied Machine Learning, Biomedical Informatics, and Bioinformatics. He develops novel algorithms to solve real-world data science problems in diverse domains including healthcare, education, traffic engineering, and social sciences. His work emphasizes human-AI collaboration, particularly in assistive technologies for neurodiverse populations and biomedical applications requiring interpretability. Analysis of his recent publications reveals dominant themes in AI-driven healthcare solutions (medical coding, protein structure prediction), human-in-the-loop systems for neurodiverse individuals, and sports analytics. His work consistently integrates domain-specific constraints with machine learning innovation, showing increasing focus on ethical AI applications for social good and accessibility. His scientific achievements include: CAREER award from National Science Foundation Outstanding paper at IJCAI 2013 (AI and Computational Sustainability Track) Leadership in top-performing CAFA teams across multiple assessment cycles Best protein disorder predictor at CASP competitions 5-7 Vucetic has advised 20 graduate students (11 current, 9 graduated), with alumni securing positions at Google, Facebook, Fox Chase Cancer Center, and academia. His extensive research portfolio includes 12 major NSF-funded projects, NIH grants for medical claims analysis, and industry collaborations with Yahoo! and ExxonMobil, totaling over $15M in funding for initiatives ranging from autism therapy customization to wireless localization security. He directs Temple's Center for Hybrid Intelligence, which pioneers human-AI collaboration frameworks. Current initiatives include NSF's Future of Work project for neurodiverse IT careers and ONR-funded additive technology research, with active workshops like KDD's Data Science with Human-in-the-Loop fostering community innovation.

Fang ChenView profile

Professor

• Artificial Intelligence
• Machine Learning
• Human-Centered Computing
• +7 more

Fang Chen is a Professor and Executive Director of the Data Science Institute at the University of Technology Sydney . With a career spanning academia, industry, and government, she has held leadership roles including Dean at Beijing Jiaotong University and senior positions at Intel, Motorola, and CSIRO. Research Interests include: Artificial Intelligence and Ethical AI Human-Computer Interaction and Cognitive Modeling Structural Engineering and Infrastructure Analytics Digital Transformation and Cybersecurity Optimization Algorithms and Multimodal Learning Article Trends demonstrate expertise in AI fairness frameworks, structural optimization, digital twins for transportation systems, causal reasoning in LLMs, and cybersecurity applications. Her work bridges theoretical innovation (e.g., ASM framework, SPFP algorithm) with real-world deployments (rail networks, illicit marketplace detection). Scientific Awards include: 2018 Eureka Prize for Excellence in Data Science 2021 NSW Premier's Science and Engineering Prize Women in AI Award (Australia & New Zealand) Intelligent Transport Systems Australia National Awards (2014-2018) iAwards (2017-2024) Leadership & Grants : She has supervised over 60 PhD students and leads major funded projects including ImpleMATE Responsible AI , ChatECG for cardiac monitoring, and Beihive Health Data for agricultural productivity. Her 400+ publications and 30+ international patents reflect her global impact across eight countries.

Chirag ShahView profile

Professor

• Information Retrieval
• Generative AI
• Machine Learning
• +5 more

Professor Chirag Shah at the University of Washington Information School (iSchool) is a leading researcher in intelligent information systems and generative AI. With adjunct appointments in Computer Science & Engineering and Human-Centered Design & Engineering, he bridges technical and ethical dimensions of AI. His work focuses on transparency, fairness, and bias mitigation in information access systems. PhD, Information Science - University of North Carolina (2010) MS, Computer Science - University of Massachusetts (2006) MTech, Computer Science - Indian Institute of Technology (2002) BE, Computer Engineering - Dharamsinh Desai Institute (2000) Shah's research spans task-oriented search, conversational systems, and fairness-aware AI. His work addresses critical questions about technology's societal impact, advocating for responsible AI development through his Center for Responsibility in AI Systems & Experiences (RAISE). Recent publications highlight multimodal interaction models, fairness frameworks, and cognitive trust metrics. These works examine generative AI's reasoning capabilities, bias detection in image search, and collaborative information seeking dynamics. Major Awards: 2025 SIGIR Academy Inductee 2024 ASIS&T Research in Information Science Award 2024 IEEE Pioneering Leadership in Trustworthy AI 2023 Outstanding Contributions to Information Behavior Research 2021 Karen Spärck Jones Award As founding director of the InfoSeeking Lab, Shah mentors students working at the intersection of AI and human values. His collaborations span Microsoft, Amazon, and Spotify, translating research into practical AI solutions.

Tiphaine ViardView profile

Lecturer

• Artificial Intelligence
• Data Analysis
• Sociotechnical Systems
• +4 more

Tiphaine Viard is a Lecturer in the Digital Technologies, Organization and Society (DTOS) team at Télécom Paris , part of Institut Polytechnique de Paris . Her work bridges artificial intelligence, data analysis, and sociotechnical systems, focusing on the interplay between technical concepts and societal implications. Current research centers on AI ethics , regulation , and environmental/human costs of AI through the ANR-funded CHAI project , which maps the social landscape of AI actors and institutions. Her methodological expertise includes temporal network analysis , graph theory , and clique enumeration in interaction streams , applied to domains like IP traffic, social networks, and public sector AI systems. Publications highlight interdisciplinary trends: 2025 work explores meso-scale ethical frameworks , while 2024 studies map socio-semantic variations in AI concepts. Earlier contributions (2018–2016) formalized stream graphs for temporal interaction modeling. She collaborates with institutions like the Interdisciplinary Institute of Innovation (i3) and has advised PhD candidates in computational social sciences . Her analyses of 75+ ethical charters reveal tensions between vague industry standards and concrete regulatory needs, emphasizing democratization of AI discourse and power asymmetries in global data practices.

• Data Science
• Machine Learning
• Natural Language Processing
• +7 more

Luca Cagliero is an Associate Professor (L.240) at the Department of Control and Computer Science (DAUIN) at Politecnico di Torino . He serves as Coordinator of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory and Scientific Advisor for the Partnership Agreement with TIERRA. His research spans Data Science , Machine Learning , and Natural Language Processing , with a focus on Financial Data Mining , Generalized Pattern Mining , and Legal AI . Scientific Branch: IINF-05/A - Information Processing Systems (Area 0009 - Industrial and Information Engineering) ERC Sectors: PE6_7 (Artificial Intelligence), PE6_9 (Human Computer Interaction), PE6_11 (Machine Learning), PE6_10 (Web and Information Systems) His scientific awards include the GiovedìScienza Award (2013), Working Capital PNI (2011), and Optime. Recognition of Merit in the Study (2008). He is a Fellow of ACM (2010-) and Effective Member of IEEE (2010-). As an Associate Editor for journals like Expert Systems with Applications and Machine Learning with Applications , he contributes to academic publishing. His conference roles include Program Committee memberships at ACM SIGMOD 2023, IEEE ICDM 2021, and ACM CIKM 2018. Luca supervises PhD students in areas such as AI-driven Cybersecurity , Conversational AI , and Neural Explainers , including Aurora Gensale, Giuseppe Gallipoli, and Irene Benedetto. His commercial research contracts involve projects like AI for Trend Analysis (Intesa Sanpaolo), Predictive Maintenance , and Legal Document Processing . Key research trends include Retrieval Augmented Generation for visually-rich documents, Bias Mitigation in speech models, and Shapley Value Estimation for model explainability. His work bridges Natural Language Processing with Cybersecurity in automotive systems.

• Deep Learning
• Brain-Computer Interfacing
• Neurotechnology
• +3 more

Robin Tibor Schirrmeister is a researcher at the University of Freiburg's Faculty of Engineering, specializing in deep learning applications for brain-computer interfacing and neurotechnology. His work bridges engineering and medical domains through affiliations with BrainLinks-BrainTools, the Neurorobotics Lab, and the Department of Presurgical Epilepsy Diagnostics. His doctoral research (2024) focused on EEG signal decoding under advisors Frank Hutter and Tonio Ball. Key research areas include: Deep learning for EEG pathology classification Real-time brain-signal decoding systems Human-robot interaction via BCI Medical diagnostics using neural networks Publication trends since 2018 reveal consistent advancement in EEG decoding methodologies, with recent work (2024-2025) focusing on brain-age dynamics, small-data prediction models, and Riemannian geometry applications. His research demonstrates strong clinical translation potential, particularly in epilepsy diagnostics and assistive robotics. Award: 2018 Poster Prize for µECoG research Collaborative work spans multiple labs including BrainLinks-BrainTools and the Epilepsy Center, with significant contributions to clinical EEG dataset development and intracranial signal processing. Current projects focus on scaling behavior in EEG classification and brain-age modeling. Research infrastructure includes the Neurorobotics Lab for BCI-robot integration and the Epilepsy Center for clinical validation, enabling translational work from algorithm development to medical applications.

Pepijn Van De VenView profile

Professor

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

Pepijn Van De Ven is a Professor in the Department of Electronic and Computer Engineering at the University of Limerick, with affiliations to multiple research centers including the Centre for Research Training in Foundations of Data Science, Data-Driven Computer Engineering Research Centre, and Lero – the Research Ireland Centre for Software. His work bridges artificial intelligence with healthcare applications, particularly focusing on mental health interventions, mobile app development, and ambient assisted living technologies. PhD in AI Techniques for Marine Vehicles (UL, 2005) MSc in Electronic Engineering (Eindhoven University of Technology, 2000) His research interests center on leveraging machine learning and mobile ICT to enhance mental health care, with a specific emphasis on eMental Health, treatment prediction models, and context-aware intervention systems. He also explores computer vision applications in waste classification, medical imaging, and Industry 4.0 safety systems. The trends in his recent publications highlight interdisciplinary work combining computer vision (e.g., instance segmentation, contamination detection), mental health (e.g., depression, diabetes comorbidity), and AI-driven clinical tools (e.g., interpretable models, Bayesian analysis). Subfields like ecological momentary assessment, graph-based learning, and human-AI trust mechanisms are recurrent. Pepijn has contributed to teaching through modules on Android programming and has held leadership roles such as Course Leader for the National MSc in Artificial Intelligence at UL and Past-President of the International Society for Research on Internet Interventions (ISRII). He leads significant research projects including the PROACTIVE (UK-MRC), PERSONAE (Innovation Fund Denmark), and BINSPECT (Science Foundation Ireland) grants. Current research group: D2iCE (Design, Intelligence, and Computer Engineering) Collaborations: King’s College London, University of São Paulo, University of Bristol

Gabriela MorgenshternView profile

Research Fellow

• Machine Learning
• Interactive Visualization
• Healthcare Data Analysis
• +4 more

Gabriela Morgenshtern is a Ph.D. candidate at the University of Zurich and a DSI Excellence Fellow, affiliated with the Faculty of Business, Economics and Informatics and the Department of Informatics. Her research focuses on interactive data visualization strategies to improve machine learning models in healthcare, particularly for recalibrating models through user feedback from practitioners and patients. Education : Hon. B.Sc. in Bioinformatics and Computational Biology, M.Sc. in Computer Science (University of Toronto), supervised by Prof. Anna Goldenberg and Prof. Fanny Chevalier. Research Affiliations : Vector Institute, Hospital for Sick Children, Canadian Bioinformatics Workshop. Research Focus : Human-model teaming applications, wearable sensor data analysis, uncertainty visualization in clinical settings, and interdisciplinary collaboration tools. Scientific Awards : DSI Excellence Fellow DeepMind Scholar Fellowship Projects include collaborative design for remote monitoring tools, innovations in digital health interventions, sensor data pipelines, and reflection promotion systems. She contributes to the DSI-Health community and leverages her industry experience to enhance user experiences in healthcare analytics.

Hazem TorfahView profile

Assistant Professor

• cyber-physical systems
• formal verification
• runtime monitoring
• +5 more

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.

Björn JohanssonView profile

Professor

• Cognitive Science
• Human-Computer Interaction
• Complex Systems
• +4 more

Björn Johansson is a Professor at Linköping University in the Department of Computer Science (IDA) , affiliated with the Human-Centered Systems (HCS) division. His research focuses on human interaction with complex systems, particularly in energy systems, crisis management, and autonomous systems, using simulation and game-based methods. Leading the "Att vända strömmen" project (funded by Swedish Energy Agency) to address sustainability challenges in energy systems Co-developing "megagames" for understanding societal polarization and climate change Collaborating with Saab on aviation interface design and autonomy research His recent publications span AI in smart grids, ERP cloud migration, and resilience in autonomous systems. He teaches courses in Cognitive Systems Engineering and Crisis Management, and co-leads the SkyLab research environment for future aviation concepts. Current grants include SEK 24.5 million from the Swedish Energy Agency and SEK 33.4 million from the Kamprad Family Foundation.

• Computational Linguistics
• Empirical Educational Science
• Intelligent Computer-Assisted Language Learning (ICALL)
• +10 more

Prof. Dr. Walt Detmar Meurers is a Research Professor at the Leibniz Institute for Knowledge Media (IWM) since April 2024, leading the Language and AI in Education research group. Previously, he served as a Full Professor of Computational Linguistics and head of the Theoretical Computational Linguistics group at the University of Tübingen (2008–2024). His career spans roles at The Ohio State University (Assistant to Associate Professor, 2001–2008), Universitet i Tromsø (Professor II, 2013–2016), and University of Tübingen (Lecturer, 1997–2000). His research bridges Computational Linguistics and Empirical Educational Science, focusing on Second Language Acquisition, Intelligent Computer-Assisted Language Learning (ICALL), learner corpora analysis, and linguistic complexity modeling for adaptive educational systems. Key projects include FeedBook (adaptive English learning), MERLIN (learner corpus analysis), and CoMiC (semantic comparison in education). He has contributed to formal syntax-information structure interfaces within the HPSG framework and advocated for integrating linguistic theory with educational technology. Scientific Leadership: Steering Board, LEAD Graduate School and Research Network Steering Committee, Heritage Language Consortium Scientific Advisory Board, CATALPA and SZHB Consultant, EFCAMDAT project Education: PhD in Computational Linguistics, University of Tübingen (2000) MA in Linguistics, Computer Science, Psychology, University of Tübingen (1994) ERASMUS exchange at IRIT, Université Paul Sabatier Impact: Co-authored Language and Computers (freely available as an open-access textbook) Organized workshops: AALL'09, IICALL Advisory roles for start-ups: cabuu, Prosodiya

Yi DingView profile

Assistant Professor

• Multimodal Machine Learning
• Human-Computer Interaction
• AI Ethics
• +5 more

Yi Ding is a tenure-track Assistant Professor of Computer Science at Georgia State University. He leads a research group focusing on multimodal machine learning and human-computer interaction, particularly in understanding human behavior through technology. His work emphasizes applications in healthcare, education, and community benefits. Before academia, he had extensive industry experience as an engineer and researcher. Education B.S. in Computer Science and Mathematics, University of Massachusetts Amherst (2011) M.S. in Computer Science, University of California Santa Barbara (2021) Ph.D. in Computer Science, University of California Santa Barbara (2022) Research Interests Dr. Ding explores how technologies can interpret human behavior and social signals to improve health, education, and community systems. His work addresses challenges in multimodal data fusion, label noise robustness, and ethical AI design. Key areas include: Multimodal machine learning frameworks Human-centric AI ethics Data augmentation strategies for noisy labels Gesture-based interfaces Bio-signal authentication systems Professional Activity His research bridges academia and industry, with contributions to VR/AR systems, health monitoring technologies, and adaptive user interfaces. He collaborates with interdisciplinary teams to address real-world challenges through innovative machine learning approaches.

Maryam GholamalitabarfirouzjaeiView profile

Assistant Professor

• Data Science
• Machine Learning
• Social Good
• +3 more

Maryam Tabar is an Assistant Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), College of Sciences. She holds a Ph.D. in Informatics from The Pennsylvania State University and M.Sc./B.Sc. degrees in Computer Engineering from Sharif University of Technology. Ph.D. in Informatics, Pennsylvania State University M.Sc. in Computer Engineering, Sharif University of Technology B.Sc. in Computer Engineering, Sharif University of Technology Her research focuses on Machine Learning and Data Science for Social Good, addressing grand challenges faced by vulnerable communities through computational approaches. The Society-Based Data Science Lab, which she directs, develops data-driven solutions for social equity, housing policy, agricultural resilience, and healthcare misinformation. Recent publications span topics like fairness in regression models, generative AI risks, eviction prediction, and locust movement forecasting. She has mentored 10 students, including 4 PhD candidates and 6 undergraduate researchers. She previously interned at Microsoft and The Washington Post, and teaches graduate courses in Data Science and Research Methods. Her work integrates technical rigor with social impact, leveraging interdisciplinary methodologies to address systemic inequities.

Dylan LoseyView profile

Associate Professor

• Human-Robot Interaction
• Machine Learning
• Control Theory
• +3 more

Dylan Losey is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech , part of the College of Engineering . He leads the Collaborative Robotics Lab (Collab) , focusing on developing learning and control algorithms for robots collaborating with humans. Education : Ph.D. & M.S. in Mechanical Engineering from Rice University (2018 & 2016), B.E. in Mechanical Engineering from Vanderbilt University (2014). Formerly a Postdoctoral Scholar at Stanford University (2019–2020) and Visiting Scholar at UC Berkeley (2017). Research Interests include Human-Robot Interaction , Machine Learning , and Control Theory , with applications in assistive robotics, healthcare, and user-centered design. His lab creates robots that personalize behavior to adapt to human needs, such as the Kiri-Spoon for robot-assisted feeding. Awards include the NSF CAREER Award (2024), Best Application Paper (IEEE Transactions on Haptics, 2024), and Outstanding New Assistant Professor (Virginia Tech, 2023). Advising & Grants : Advises students like Maya Keely and Heramb Nemlekar. His NSF CAREER grant supports research on bidirectional human-robot communication. Collaborates with institutions like The Virginia Home (Richmond) for user-centric design. Labs & Teams : Directs the Collaborative Robotics Lab , emphasizing interdisciplinary work with partners like Cornell University. Recent projects include assistive robotics for mobility-impaired users and haptic display systems.