Find Academic Supervisors

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +7 more

Dr. Hilde Kuehne is a Professor at the University of Tuebingen and a key researcher at the Tuebingen AI Center. She holds affiliations with MIT-IBM Watson AI Lab and Goethe University Frankfurt, with a focus on computer vision, multimodal learning, and explainable AI. Her work bridges vision-language models, audio-visual alignment, and self-supervised methods. Co-organizer of the New Frontiers in Associative Memories workshop @ ICLR 2025 Member of the Scientific Advisory Board of the Carl-Zeiss-Foundation Contributor to CVPR 2025's UTD dataset for unbiased video benchmarks Her research addresses critical challenges in: Explainability for Vision Transformers (LeGrad) Fine-grained audio-visual alignment (CAV-MAE Sync) Zero-shot visual recognition automation (Meta-Prompting) Spatio-temporal grounding without annotations Recent collaborative work spans 15+ publications across CVPR, NeurIPS, ICCV, and ICLR, with emphasis on multimodal foundation models, dataset bias mitigation, and differentiable logic networks. She actively contributes to workshop organization and peer review as evidenced by her involvement in CVPR 2025 and ICLR 2025 program committees.

Julie BergnerView profile

Professor

• Homotopy Theory
• Algebraic Topology
• Algebraic K-Theory
• +3 more

Julie Bergner is a Professor in the Department of Mathematics at the University of Virginia. She is currently on sabbatical during 2024-25 as a Visiting Fellow at Clare Hall, University of Cambridge. Her research focuses on Homotopy Theory, Algebraic Topology, Algebraic K-Theory, and Category Theory, with a particular emphasis on higher category theory and homotopical algebraic structures. University of Virginia (Professor, Department of Mathematics) University of Cambridge (Visiting Fellow at Clare Hall, 2024-25) Her work spans 2-Segal objects, model categories, Waldhausen constructions, and applications to topological field theories. She has received NSF CAREER awards for her research on equivariant topological field theories and higher cluster categories. Her recent publications explore combinatorial models, enriched categories, and homotopy limits in the context of functor calculus and higher structures. Julie Bergner contributes to mathematical exposition through articles on writing papers in the mathematical sciences and homotopy theory applications. She is actively involved in the department's topology seminar and supports collaborative learning initiatives. Her research has implications in algebraic topology, category theory, and their interdisciplinary applications.

• Machine Learning
• Statistics
• Data Science
• +4 more

Lili Zheng is an Assistant Professor in the Department of Statistics at the University of Illinois. Her research focuses on statistical methodology, machine learning, and high-dimensional data analysis with applications in neuroscience and network science. Key areas of expertise include graphical models, stochastic processes, and algorithmic optimization. She collaborates extensively on projects involving functional connectivity analysis, neuronal data imputation, and interpretable machine learning frameworks. Her work bridges statistical theory and computational practice, addressing challenges in model inference, feature importance assessment, and low-rank tensor completion. Notable contributions include techniques for distribution-free inference, spectral clustering in patchwork learning, and Gaussian process parameter estimation using mini-batch stochastic gradient descent. Dr. Zheng's research emphasizes interdisciplinary applications, particularly in neuroimaging (calcium imaging, functional connectivity) and multi-modal data integration. She actively explores statistical challenges in big data contexts, emphasizing robust methodologies for real-world datasets.

Pontus StenetorpView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +3 more

Pontus Stenetorp is a Professor of Natural Language Processing at University College London (UCL) and holds a Specially Appointed Professorship at the National Institute of Informatics (NII) in Japan. At UCL, he leads the NLP Group, serves as Deputy Director of the Centre for Artificial Intelligence, and directs the Facebook Artificial Intelligence/UCL PhD Programme. His research focuses on the intersection of human language and computational systems, emphasizing empirical approaches to advancing and evaluating language processing capabilities. He is affiliated with UCL's Department of Computer Science and NII's Large Language Model Research and Development Center. Stenetorp prioritizes in-person collaboration for internships and research visits due to institutional bureaucratic constraints at UCL. He actively supervises MSc theses in Machine Learning and Data Science programs but restricts undergraduate and industry-oriented projects to exceptional cases. Communication preferences include structured emails sent to role-specific addresses: p.stenetorp@cs.ucl.ac.uk (UCL), pontus@nii.ac.jp (NII), and pontus@stenetorp.se (general). His research group, the NLP Group at UCL, pursues cutting-edge projects in computational linguistics and AI, often involving collaborative efforts with students and industry partners. While no specific awards are listed, his dual institutional roles underscore his significant contributions to the field of NLP and AI research.

Jingwen ZhangView profile

Associate Professor

• Health Communication
• Social Influence
• Digital Health Interventions
• +3 more

Jingwen Zhang is an Associate Professor in the Department of Communication at the University of California, Davis. Her work focuses on health communication, social influence, and innovative online interventions. She holds a Ph.D. from the Annenberg School for Communication at the University of Pennsylvania, with previous degrees from Zhejiang University and Clemson University. Her research explores digital health interventions, medical misinformation, and global health communication. Education Ph.D., Communication, University of Pennsylvania (2016) M.A., Communication, University of Pennsylvania (2013) M.A., English, Clemson University (2011) B.A., Media and International Culture, Zhejiang University (2009) Research Interests Zhang’s work bridges health promotion and technology, emphasizing: AI-driven chatbots for behavior change (e.g., sleep health, physical activity) Social media’s role in vaccine hesitancy and misinformation Cross-cultural health communication strategies Online networks for public health interventions Recent Article Trends Her recent work highlights: AI chatbots in health promotion targeting marginalized groups Fact-checking strategies and public engagement Cultural dynamics of vaccine hesitancy Media effects on health behavior during crises Awards Chinese Government Award for Outstanding Students Abroad (2016) Russell Ackoff Doctoral Student Fellowship (2015) Annenberg National Health Communication Study Grant (2013) Teaching & Engagement Zhang teaches courses on persuasion theories and health communication. Her work with UNICEF informs global health communication strategies. She actively designs interventions addressing HIV/AIDS, reproductive health, and pandemic response.

Andrew McCallumView profile

Professor

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +3 more

Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.

• Stakeholder Engagement
• Social Network Theory
• Project Management
• +4 more

Associate Professor Ken Chung is affiliated with the University of Sydney's School of Project Management, serving as Associate Head of Education. He holds a PhD (University of Sydney) and dual undergraduate degrees (B.Sc (Hons I), B.Com). His research focuses on social network theory applied to stakeholder engagement in projects, healthcare coordination, and complex systems. Notable awards include the 2021 Faculty of Engineering Dean's Award for Excellence in Teaching and a PMI grant for infrastructure stakeholder engagement research. He has collaborated with organizations like NSW Department of Planning, Google, and Western Sydney Airport. Teaching includes courses like ENGG1850 and PMGT5871. Current projects explore stakeholder analysis, community engagement in infrastructure, and healthcare networks. His expertise spans 20+ years, with international partnerships at Webster Vienna and University of Southern Denmark. Professional activities include editorial roles, ARC grant reviewing, and industry advisory roles. Education highlights: PhD in Information Technologies (2009, Sydney), prior lectureship at University of Wollongong. Industry experience includes Telstra Messaging Group and DoubleClick Australia. Research integrates social physics principles, examining network structures' impact on project success, healthcare outcomes, and organizational behavior. Grants include ARC DP-funded health research and PMI-funded infrastructure studies. Key themes include network-based models for stakeholder engagement, project management innovation, and social capital utilization.

• Communication Studies
• Interpersonal Communication
• Intercultural Communication
• +4 more

Professor Desiree Frieson teaches COMM 1000 Survey of Communication Studies at Brooklyn College during the Spring 2024 semester. She conducts synchronous classes on Tuesdays from 6:30-8:15 PM ET with virtual Zoom office hours held immediately afterward from 8:15-9:15 PM ET. Her expertise spans multiple communication domains: Interpersonal & Intrapersonal Communication Intercultural Communication Organizational Communication Gender Communication Nonverbal Communication Rhetorical Communication Conflict & Negotiation Professor Frieson's course examines how people use messages to generate meaning across various contexts, emphasizing communication's role in relationships, institutions, and society. Her pedagogical approach combines theoretical foundations with practical application through structured assignments. Course assessment includes: Bi-Weekly Connection Blogs (35%) requiring 500-750 word Wordpress.com posts with embedded multimedia Midterm Paper (25%) Discussion Boards (25%) Final Reflection Blog (15%) She encourages students to utilize the Learning Center/Writing Center for extra credit opportunities, emphasizing proper writing mechanics and research skills essential for communication studies.

Soroosh ShafieeView profile

Assistant Professor

• Optimization under Uncertainty
• Robust Optimization
• Optimal Transport
• +3 more

Soroosh Shafiee is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell University since July 2023. Before joining Cornell, he held postdoctoral positions at the Tepper School of Business (Carnegie Mellon University) and the Automatic Control Laboratory (ETH Zurich). He earned a B.Sc. and M.Sc. in Electrical Engineering from the University of Tehran and a Ph.D. in Operations Research from École Polytechnique Fédérale de Lausanne (EPFL). His research focuses on optimization under uncertainty, robust optimization, optimal transport, and their applications in machine learning and finance. Specific interests include designing algorithms for data-driven optimization, analyzing statistical and computational complexity, and exploring nonconvex optimization structures. Swiss National Science Foundation Early PostDoc Mobility Fellowship (2020) PhD Thesis Distinction Award, EPFL (2020) His work bridges theoretical foundations with practical applications, contributing to areas such as distributionally robust optimization, Wasserstein-based methods, and scalable algorithm development. His research has been published in top venues like Journal of Machine Learning Research and Operations Research .

Robert ÖstlingView profile

Professor

• Applied Microeconomics
• Behavioral Economics
• Political Economy
• +4 more

Robert Östling is a Professor of Economics at the Stockholm School of Economics (SSE), affiliated with the Department of Economics and the Karl-Adam Bonnier Center for Governance within the House of Governance and Public Policy. He holds a PhD from SSE (2008) and previously worked at Stockholm University's Institute for International Economic Studies until 2018. His research focuses on applied microeconomics and behavioral economics, particularly examining how wealth impacts attitudes, behavior, and life outcomes using Swedish lottery data. He teaches econometrics, behavioral economics, and economics for MBA students, and serves as program director for SSE's MSc in Economics. Key roles include board membership of the Swedish Economics Association and the Expert Group on Public Economics (ESO), and membership in the scientific advisory board of the Swedish Consumer Protection Agency. He co-founded the popular economics blog Ekonomistas , recognized as 'Swede of the Year' in 2017. Awards include the Assar Lindbeck Medal (2021) and the Arnberg Prize (2009). His research spans wealth effects on labor supply, health, child development, and political behavior. Notable projects include studying lottery winners' long-term well-being and portfolio decisions. Teaching innovations include using animated GIFs for econometrics instruction. He has contributed to policy debates on economic reforms, consumer protection, and pandemic responses. Outreach activities include public writing and policy advisory roles. His research has been featured in outlets like the New York Times and Swedish media. He remains active in academic networks, including the Network for Evidence-Based Policy.

Toni KotnikView profile

Associate Professor

• Computational Design
• Architectural Engineering
• Structural Design
• +4 more

Toni Kotnik is an Associate Professor in the Department of Architecture at Aalto University, Finland. He holds dual expertise in mathematics and architecture, with a Ph.D. from the University of Zurich and a second degree in architecture. His roles include principal of d’HKL , a Zurich-based experimental architecture firm, and adjunct lecturer at institutions like Harvard University and the Bartlett School of Architecture. His research focuses on integrating science, engineering, and computation into architectural design, emphasizing structural innovation and sustainability. Education: Ph.D. in Mathematics (Dr. sc. Nat.), University of Zurich, 1999 Second degree in Architecture Research Interests: Computational design and generative systems Structural aesthetics and neuroaesthetics Sustainable urban and architectural solutions Parametric modeling and material systems Key Contributions: Advances in graphic statics and form-finding Design strategies for high-density urban environments Integration of AI and generative tools in architectural practice Recent Work Trends: Recent articles emphasize computational methods for industrial layout optimization, AI’s role in architecture, and embodied perception in structural design. His work bridges theoretical research with practical applications, such as the Singapore Hawker Centre redesign and experimental structures like hypar-combined shells. Awards: Aalto University Doctoral Thesis Awards (2022) Best Presentation Award (2019) Oscari-Vilamo Award (2023) Advising & Grants: Supervised 4 theses, including recipients of awards like the Metex Award shortlist. Active in international academic networks, serving on conference committees (e.g., International Association for Shell and Spatial Structures) and editorial roles (e.g., Automation in Construction journal). Labs & Teams: Leads the d’HKL studio, collaborating globally on projects like the AA/ETH Pavilion and the Hypercube for Gardens by the Bay. Research focuses on experimental design and material systems.

Hendrik WeberView profile

Professor

• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• Regularity Structures
• +4 more

Prof. Dr. Hendrik Weber is a Professor of Mathematics at the University of Münster, leading the Workgroup for Stochastic Analysis. He holds the Bridging the Gaps Professorship and is affiliated with the Faculty of Mathematics and Computer Science. His expertise lies in stochastic analysis, particularly stochastic partial differential equations (SPDEs) and their applications in mathematical physics and statistical mechanics. Weber's research focuses on regularity structures, singular SPDEs, and the interplay between stochastic processes and nonlinear dynamics. Education and Career: Weber earned his PhD from the University of Bonn (2010) and held positions at the University of Warwick (2010–2018) and the University of Bath (2018–2022) before joining Münster in 2022. He has been recognized with awards including the ERC Consolidator Grant (2022), Philip Leverhulme Prize (2017), and Rollo Davidson Prize (2016). Research Interests: Weber's work addresses theoretical challenges in SPDEs, including invariant measures, phase transitions, and scaling limits. His projects span topics like singularities in PDEs, field theory randomness, and deep learning surrogate methods. Recent studies include the dynamic Φ⁴ model, stochastic quantization in non-commutative spaces, and a priori bounds for quasilinear SPDEs. Publications: Over 60 peer-reviewed articles, including high-impact contributions to Annals of Probability , Communications in Mathematical Physics , and Archive for Rational Mechanics and Analysis . His work emphasizes rigorous mathematical analysis of stochastic systems and their physical implications. Awards and Grants: ERC Consolidator Grant (2022), Royal Society Fellowship (2016), and multiple collaborative projects funded by the EPSRC and DFG. His research also bridges theoretical developments with applications in machine learning and feature engineering using regularity structures. Labs/Teams: Leads a dynamic research group comprising PhD students (e.g., Sophie Mildenberger) and postdoctoral researchers. Collaborations with global institutions like the University of Warwick and the University of Bath drive interdisciplinary advancements in stochastic analysis.

Tianzheng WangView profile

Associate Professor

• Database Systems
• Operating Systems
• Parallel Programming
• +5 more

Tianzheng Wang is an Associate Professor and Director of the Dual-Degree and Partnerships Programs at the School of Computing Science, Simon Fraser University. His research focuses on database systems, transaction processing, parallel and distributed computing, and embedded systems. He holds a PhD in Computer Science from the University of Toronto (2017) and a BSc in Computing from Hong Kong Polytechnic University (2012). Research Interests: Database systems optimized for modern hardware, parallel programming, synchronization, and distributed architectures. His work emphasizes high-performance transaction processing and efficient indexing techniques, with applications in cloud and embedded systems. Awards: ACM SIGMOD Best Paper Award (2025), IEEE TCSC Early Career Award (2019), and multiple distinguished reviewing recognitions (SIGMOD/VLDB 2021-2024). His research has been integrated into systems like Amazon Redshift and DragonflyDB. Teaching: Leads courses such as CMPT 454 (Database Systems II), CMPT 300 (Operating Systems), and special topics in databases. Actively mentors graduate and undergraduate students in research projects. Labs & Collaborations: Heads the Data-Intensive Systems Lab, part of SFU's Data Science and Systems groups. Collaborates on tools like PiBench for persistent memory benchmarking and contributes to open-source projects like CoroBase and Tabular.

• Computational Physics
• Electronic Structure Calculations
• Atomistic Simulations
• +4 more

Professor Stefan Goedecker is a distinguished faculty member in the Department of Physics at the University of Basel, Faculty of Science. He holds the position of Professor of Computational Physics and leads an active research group focused on developing advanced computational methods for materials science and quantum physics. Dr. Goedecker received his physics education at the Technical University Munich and the College of William and Mary, followed by a Ph.D. from EPFL Lausanne. His postdoctoral training included positions at Cornell University and the Max-Planck Institute in Stuttgart. In 2003, he was appointed Professor of Computational Physics at the University of Basel, where he has established himself as a leading researcher in computational methods development. His research interests center on computational physics with emphasis on electronic structure calculations, atomistic simulations, and the development of novel algorithms for materials science applications. His work has strong interdisciplinary connections spanning physics, mathematics, material sciences, chemistry, and computer science. Current research directions include machine learning applications in catalysis, fourth-generation neural network potentials for molecular chemistry, and methods for quantifying material synthesizability. Analysis of his recent publications reveals a strong focus on advancing computational methods for electronic structure calculations, with particular emphasis on machine learning potentials, molecular dynamics optimization, and accurate modeling of material properties. His work bridges theoretical physics with practical applications in materials science and nanotechnology, with increasing integration of artificial intelligence techniques into traditional computational physics frameworks. Machine learning for Catalysis (Ongoing) Fourth-Generation Neural Network Potentials for Molecular Chemistry (Completed) Towards Quantifying the Synthesizability of Materials (Completed) Professor Goedecker's research group operates within the Department of Physics at the University of Basel, which is part of the NCCR SPIN initiative focused on silicon-based quantum computing development. The department hosts over 20 research groups with more than 180 teaching staff members, creating a vibrant research environment for computational physics and quantum technologies.

• Medical Image Understanding
• Radiology Report Generation
• Deep Learning
• +3 more

Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.