Find Academic Supervisors

• Artificial Intelligence
• Multimodal Interaction
• Natural Language Processing
• +4 more

Arash Eshghi is an Assistant Professor in the School of Mathematical & Computer Sciences at Heriot-Watt University, specializing in the Department of Computer Science. His research focuses on multimodal interaction, embodied AI, and dialogue systems, with applications in human-AI collaboration, vision-language models, and incremental processing. He leads projects like the EMMA and AlanaVLM frameworks, which explore embodied agents in 3D environments and egocentric video understanding. His work emphasizes ethical considerations in conversational AI, including dementia-friendly voice assistants and faithfulness in large language models. He collaborates internationally, with contributions to benchmarks like the BURCHAK corpus and the Block World repair framework. Eshghi’s research bridges computational linguistics, cognitive science, and robotics, addressing challenges in ambiguity resolution, spatial reasoning, and incremental dialogue processing. Key collaborations involve institutions like the University of Edinburgh and MIT, focusing on multimodal learning, dynamic syntax, and interactional semantics. His lab develops tools for grounded language learning and real-time interaction, with a focus on systems that adapt to human feedback and contextual dynamics.

Jared F. EdgertonView profile

Assistant Professor

• Conflict Analysis
• International Security
• Network Science
• +7 more

Jared F. Edgerton is an Assistant Professor at the University of Texas at Dallas, affiliated with the School of Economic, Political and Policy Sciences. His research focuses on how social relations and networks influence conflict dynamics, employing methodologies from network science and machine learning. He holds a Ph.D. (2021) and M.A. (2018) in Political Science from The Ohio State University. Key research interests include terrorism, international security, civil war, and data-driven approaches to conflict analysis. His work spans topics like media effects on violence (e.g., Rwanda's radio impact), extremist recruitment networks (Islamic State), and elite polarization in crises like the COVID-19 pandemic. Methodologically, he combines experimental designs, bipartite network analysis, and geospatial techniques. Notable contributions include analyzing genocide participation in Rwanda, resilience in cooperative state networks, and the socio-technical dimensions of conflict mobilization. His interdisciplinary approach bridges political science, computer science, and security studies. While no scientific awards are explicitly listed, his prolific output reflects sustained scholarly impact. Edgerton has advised no listed students but has engaged in quasi-experimental policy evaluations (e.g., prisoner recidivism programs) and applied geospatial analysis to post-conflict land safety in Cambodia. His research portfolio demonstrates a commitment to both theoretical innovation and real-world conflict mitigation strategies.

Kaidi XuView profile

Assistant Professor

• Trustworthy AI
• Formal Verification of Neural Networks
• Adversarial Attacks
• +7 more

Kaidi Xu is an Assistant Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics. His research focuses on Trustworthy AI, with expertise in formal verification of neural networks, adversarial attacks (especially in the physical world), and certified defenses. He actively publishes in top-tier conferences including NeurIPS, ICML, ICLR, CVPR, and AAAI, and leads the award-winning research team 'alpha-beta-crown'. PhD in Computer Science, Northeastern University (2021) MS in Computer Science, University of Florida (2017) BS in Computer Science, Sichuan University (2015) Dr. Xu's research spans critical areas in AI security and robustness. He investigates formal methods to verify neural network behavior, develops techniques to defend against real-world adversarial manipulations (such as the famous 'Adversarial T-shirt'), and explores model compression and explainability. His work bridges theoretical guarantees with practical applications in healthcare, material science, and autonomous systems. His recent publications reflect a strong trend toward certified robustness, interdisciplinary applications, and formal verification across vision, language, and multimodal systems. He has consistently published at NeurIPS, ICML, CVPR, and ACL, demonstrating sustained impact in both machine learning and computer vision communities. Winner of VNN-COMP'21 with highest score Three-time VNN-COMP champion (2021–2023) with team alpha-beta-crown Faculty Research Excellence Award, CCI@Drexel (2024) Recipient of multiple Carleone Faculty Awards (2025) NSF grant recipient for projects on transit systems and material synthesis Dr. Xu advises PhD students, including Jinhao, and has secured significant external and internal funding, including multiple NSF grants and Drexel internal awards. He is actively recruiting motivated students with strong machine learning backgrounds. He also contributes to the academic community as an Area Chair for NeurIPS 2025, organizer of workshops like GenAI4Health@AAAI 2025, and frequent program committee member. He leads the 'alpha-beta-crown' research team, known for its leadership in neural network verification and repeated success in the VNN-COMP competitions. The team focuses on developing scalable, sound, and complete verification tools for deep learning models, pushing the frontier of AI safety and reliability.

Michael KrauthammerView profile

Professor

• Clinical Data Science
• Translational Bioinformatics
• Machine Learning in Healthcare
• +3 more

Michael Krauthammer is a Professor of Medical Informatics and Chair of the Department of Quantitative Biomedicine at the University of Zurich, affiliated with the University Hospital of Zurich. His lab focuses on Clinical Data Science and Translational Bioinformatics, leveraging AI and machine learning to address healthcare challenges. Key areas include cancer genomics, federated learning, and automated medical imaging analysis. Education and affiliations: Krauthammer leads an interdisciplinary team supported by major funding agencies. His research spans bioinformatics, clinical decision support systems, and multimodal data integration. Notable projects include AI-assisted diagnosis in rheumatology and prime editing efficiency prediction. Recent work emphasizes longitudinal cfDNA analysis, drug interaction modeling, and personalized oncology. The lab collaborates across disciplines, with projects funded by Swiss and international grants. Students and postdocs work on topics like machine learning for radiology reports, longitudinal disease trajectories, and protein design. Key projects include the NTCIR-18 RadNLP challenge, prime editing prediction models (Nature Biotechnology 2024), and vision transformers for capillaroscopy analysis. The lab advocates for reproducible data science and ethical AI in healthcare.

Christoph BenzmüllerView profile

Professor

• Automated Theorem Proving
• Computational Metaphysics
• Artificial Intelligence
• +5 more

Prof. Christoph Benzmüller is a Full Professor at the University of Bamberg (Chair for AI Systems Engineering) and an adjunct professor at Freie Universität Berlin's Department of Mathematics and Computer Science. He is a leading researcher in automated reasoning, computational metaphysics, and formal logic systems. His work focuses on integrating higher-order logic into AI to achieve transparent and ethically grounded systems. Research Interests: His research spans automated theorem proving, formal ontologies, and normative reasoning in AI. Notably, he has formalized Gödel's ontological argument using computational methods and developed the Leo theorem provers for higher-order logic. He emphasizes the use of symbolic reasoning for ethical and legal AI frameworks. Grants & Projects: He leads projects like PetraKIP (AI portfolios for teacher education) and NFDIxCS (National Research Data Infrastructure). His work is funded by DFG, EPSRC, and the Volkswagen Foundation. He also collaborates with institutions globally, including Stanford and Cambridge. Awards: Recipient of the Central Teaching Award (FU Berlin) for his Computational Metaphysics course and a DFG Heisenberg Fellowship. His research on Gödel's argument gained international media attention. Education: Studied at Saarland University, where he earned his PhD (1999) and habilitation (2006).曾是专业长跑运动员，后转向学术研究。

Erik RodnerView profile

Professor

• Machine Learning
• Computer Vision
• Data Science
• +5 more

Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)

Hao YangView profile

Assistant Professor

• Trustworthy Machine Learning
• Transportation Safety
• Transportation Equity
• +4 more

Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.

• Model-Based Testing
• Automata Theory
• Machine Learning
• +5 more

Bernhard Aichernig is an Associate Professor at the Institute of Software Engineering and Artificial Intelligence. His work bridges formal methods, model-based testing, and artificial intelligence, with a focus on automata learning, digital twins, and AI-assisted programming. Institution: Institute of Software Engineering and Artificial Intelligence Key Research Areas: Model-Based Testing, Automata Learning, AI-Driven Verification His research explores the integration of machine learning into formal verification, enabling scalable testing of complex systems like IoT devices and reinforcement learning agents. Recent projects include AI-Augmented DevOps frameworks (AIDOaRT) and digital twin validation (LearnTwins). Notable scientific awards include multiple best paper recognitions at SEFM (2020, 2021) and the TAYSIR Competition first place (2023). His publications emphasize hybrid approaches combining genetic programming, SMT solving, and neural networks for system modeling. 2025 : AI-assisted programming, timed automata via domain knowledge 2024 : Stochastic environment modeling, Git system learning 2023 : Reinforcement learning under partial observability, digital twins for VPN servers He actively contributes to testing frameworks like AALpy and investigates explainable AI for fault diagnosis in cyber-physical systems.

Minh Hoai NguyenView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Human Activity Recognition
• +5 more

Minh Hoai Nguyen is an Assistant Professor in the Department of Computer Science at Stony Brook University. He received his PhD in Robotics from Carnegie Mellon University and a Bachelor of Engineering from the University of New South Wales. Prior to Stony Brook, he was a post-doctoral research fellow at Oxford University and a Kurti Junior Research Fellow at Brasenose College. Education: PhD in Robotics, Carnegie Mellon University Bachelor of Engineering, University of New South Wales His research focuses on computer vision , machine learning , and time series analysis , particularly in developing algorithms for human action recognition , gesture detection , and expression analysis in video data. Applications include video surveillance , human-computer interaction , and medical diagnosis of behavioral disorders . His work integrates computer vision for video processing, time series analysis for modeling human behavior, and machine learning for training complex algorithms. Notable awards include: CVPR 2012 best student paper award Winner of PASCAL VOC 2012 Challenge for Human Action Recognition He teaches courses such as Video Analysis (CSE 594) and Introduction to Robotics (CSE 525) .

• Information Access
• Information Retrieval
• Natural Language Processing
• +3 more

Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction

Shuangping LiView profile

Assistant Professor

• Probability Theory
• High-Dimensional Statistics
• Theoretical Machine Learning
• +5 more

Shuangping Li is an Assistant Professor in the Department of Statistics and Data Science at Yale University. She was previously a Stein Fellow in the Department of Statistics at Stanford University (2022–2025). Her research lies at the intersection of probability theory, high-dimensional statistics, theoretical machine learning, and the theory of algorithms. Ph.D. in Applied and Computational Mathematics, Princeton University (2022) B.Sc. in Mathematics, University of Hong Kong Her research interests include probability theory , high-dimensional statistics , theoretical machine learning , and theory of algorithms . She investigates foundational aspects of random constraint satisfaction problems, neural networks, spectral methods, and phase transitions in high-dimensional models. Her work often draws from statistical physics and combinatorics to explain algorithmic behavior. The recent articles highlight a strong focus on binary perceptrons , clustering in network models , and algorithmic phase transitions . Keywords across publications include probability, theoretical computer science, machine learning, and statistical inference. Subfields reveal deep engagement with spin glass theory, discrepancy minimization, spectral embedding, and information-computation gaps. Scientific awards include: Stein Fellow, Department of Statistics, Stanford University (2022–2025) She has advised and taught at both Stanford and Yale, including courses such as Advanced Probability , Theory of Probability , and Stochastic Processes . She has organized seminars at Stanford and has delivered invited talks at institutions including Cornell, Duke, UC Berkeley, and Princeton. Her collaborative research involves prominent scholars such as Allan Sly, Emmanuel Abbe, and Tselil Schramm. There is no mention of external grants, but her postdoctoral fellowship suggests research funding support. She is involved in academic service through organizing the Stanford Statistics and Probability Seminars. She maintains an active research presence with publications in top venues like STOC, FOCS, COLT, ICLR, and journals such as Annals of Probability and Annals of Statistics .

Tushar SharmaView profile

Assistant Professor

• Software Engineering
• Software Quality
• Code and Design Quality
• +10 more

Tushar Sharma is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada. His research focuses on software engineering, particularly software quality, refactoring, technical debt, and the application of machine learning in software engineering (ML4SE). He leads the SMART Lab and is actively involved in projects related to Green AI and sustainable software development. PhD : Software Engineering, Athens University of Economics and Business, Greece (2019) MS : Computer Science, Indian Institute of Technology-Madras, India His research interests span software design and architecture, code and design quality, refactoring, technical debt, mining software repositories, and applied machine learning for software engineering. He is particularly interested in sustainable AI, green software engineering, and the use of large language models for code. His work bridges empirical studies with practical tool development to improve software maintainability and quality. His recent publications highlight a strong trend in code smell detection, refactoring automation, energy-aware AI, and the reliability of large language models in software engineering. He has developed tools like Designite and DPy and contributed datasets such as MaRV and DACOS, emphasizing empirical validation and reproducibility in software engineering research. Dean's Research Excellence Award Best Artifact Award, SCAM 2023 IEEE Senior Member Tushar Sharma has secured significant research funding, including an NSERC Discovery Grant for DevQOps, Mitacs Accelerate grants with industry partners, and contributions to the $154M Canada First Research Excellence Fund project. He actively mentors students and collaborates with industry. He leads the SMART Lab at Dalhousie and has organized workshops such as SATToSE 2018. He is also a founding developer of Designite, a widely used software design quality assessment tool.

Anders Kalsgaard MøllerView profile

Associate Professor

• Learning Design
• Computational Thinking
• Artificial Intelligence in Education
• +8 more

Anders Kalsgaard Møller is an Associate Professor in the Department of Culture and Learning at Aalborg University's Faculty of Humanities and Social Sciences. He is actively engaged in research and innovation in learning design, digital technologies, and artificial intelligence in education. His work is centered around the L-ILD (IT and Learning Design), Green Society, and MASSHINE Xlab – Design, Learning and Innovation research environments. His research interests span Learning Design , Computational Thinking , Artificial Intelligence in Education , Human-Robot Interaction , and Environmental Literacy . He investigates how emerging technologies can be integrated into educational practices to enhance collaborative learning, literacy development, and sustainable thinking. His work often involves participatory and co-design methods with educators and children. The recent publications of Anders Kalsgaard Møller reflect a strong trend toward the application of generative AI, robotics, and digital tools in language and primary education. His scholarly output emphasizes interdisciplinary collaboration, technological innovation, and real-world educational impact, particularly in K-12 and higher education contexts. Principal Investigator, 'Using artificial intelligence in English teaching at upper secondary schools' (2023–2026) Co-PI, 'Co-Designing Robot-Assisted Learning for Children' (ongoing) Co-PI, 'Labor market-oriented AI skills at cand.it.' (2024–2026) Co-PI, 'Understanding and fostering future consumers' environmental literacy' (2024–2025) Anders Kalsgaard Møller has been involved in media outreach, including coverage on children's interactions with social robots and discussions on AI in education. He has also contributed to academic leadership through conference organization and editorial roles, such as in the DLI conference series. He is affiliated with key research labs including: L-ILD – IT and Learning Design Green Society MASSHINE Xlab – Design, Learning and Innovation These labs focus on digital innovation, sustainability, and human-centered design in educational contexts.

Philippe SchwallerView profile

Assistant Professor

• Artificial Intelligence in Chemistry
• Machine Learning for Molecular Discovery
• Sustainable Chemistry
• +6 more

Philippe Schwaller is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Basic Sciences, within the Institute of Chemical Sciences and Engineering. He leads the Laboratory of Artificial Chemical Intelligence (LIAC), a research group focused on leveraging artificial intelligence to accelerate molecular discovery and sustainable chemistry. He is also a core Principal Investigator of the NCCR Catalysis, a national Swiss research center. His research lies at the intersection of chemistry, materials science, and computer science, with a strong emphasis on developing machine learning models for molecular design and synthesis. LIAC's work is driven by real-world sustainability challenges, aiming to reduce the time and cost of discovering new functional molecules and materials. The recent publications and projects from his lab highlight a strong trend in generative AI for chemistry, including memory-augmented models, hypergraph neural networks, and large language models tailored for scientific discovery. These efforts are complemented by educational initiatives such as the 'AI for Chemistry' course and practical programming resources for chemists. He actively supervises a diverse group of PhD students and contributes to multiple doctoral programs at EPFL, including EDCH and EDPY. His teaching portfolio includes courses on computational chemistry, AI applications in chemistry, and scientific machine learning. Philippe Schwaller is deeply involved in advancing AI-driven scientific discovery through both research and education, positioning his lab at the forefront of artificial chemical intelligence. The lab maintains active open-source contributions on GitHub, fostering collaboration and transparency in scientific AI development.

Siegfried HandschuhView profile

Professor

• Semantic Technologies
• Information Linguistics
• Information Extraction
• +4 more

Professor Siegfried Handschuh holds dual academic appointments as Associate Professor at the University of Passau and Ordinary Professor for Data Science at the University of St. Gallen. His research spans semantic technologies, information linguistics, and artificial intelligence applications in the humanities, with significant funding from the EU, Science Foundation Ireland, and Enterprise Ireland. His research focuses on making big data workable for humanities scholars through innovative AI approaches. Handschuh's work addresses critical challenges in digital humanities including inconsistent and non-integrated data with varying quality. His research interests encompass semantic technologies, information linguistics, information extraction, and distributional semantics, with particular emphasis on applying AI to art history and cultural heritage. Handschuh has coordinated numerous international research projects including the Digital Aristotle project initiated by Microsoft founder Paul Allen, which aimed to develop AI-powered educational tools. His Neoclassica project teaches machines to recognize neoclassical objects through formal language processing, while the PACE initiative establishes the Passau Centre for eHumanities as a research hub. Other significant projects include EU-Project MARIO for healthy aging with care robots and various studies on human-machine interaction. His scientific contributions focus on developing new tools for humanities researchers that leverage artificial intelligence to uncover novel contexts and relationships in cultural data. Handschuh's work bridges computer science with humanities disciplines, creating practical applications for semantic technologies in cultural heritage analysis.