Find Academic Supervisors

Amber DumfordView profile

Professor

• Student Engagement
• Gender Issues in Higher Education
• Arts Education
• +6 more

Dr. Amber Dumford is a Professor at the University of South Florida in the College of Education , specializing in Higher Education and College Student Affairs . She holds a Ph.D. in Higher Education (with a cognate in Sociology) from Pennsylvania State University , a M.P.A. from Indiana University Bloomington , and a B.S. in Mathematics from the same institution. Teaches courses in Student Affairs Assessment, College Teaching, and Globalization in Higher Education Currently serves on doctoral dissertation committees Active in research on student engagement, gender issues, and data quality in higher education Reviews for 20+ peer-reviewed journals and serves on editorial boards of top journals Consultant for National Center for Educational Statistics and institutions in US/Canada Research Interests include: Factors influencing student engagement and learning Gender disparities in higher education Arts and engineering education outcomes Creativity in academic settings Quantitative reasoning and assessment Data quality in educational research Food insecurity among diverse college students Publications reflect trends in student engagement , career readiness , data-driven educational practices , and equity in higher education . Her recent works focus on socioecological factors affecting food pantry usage and validity of survey instruments in measuring student behaviors. Service Contributions : Active member of university committees and councils Board member of Research in Higher Education and Journal of College Student Development Statistical modeling consultant for National Center for Educational Statistics Teaching Experience spans traditional mathematics instruction (2002-2014) to higher education seminars and globalization studies at USF. She plans to develop data analytics and institutional research courses.

Andrés BruhnView profile

Professor

• Computer Vision
• Optical Flow
• Scene Flow
• +5 more

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.

• Reinforcement Learning
• Neural Network
• Deep Reinforcement Learning
• +5 more

Abhinav Verma is an Assistant Professor in Computer Science and Engineering, focusing on reinforcement learning, neural networks, and formal verification of control systems. His research bridges machine learning, program synthesis, and theoretical computer science to ensure interpretable and verifiable AI. Research Interests : Reinforcement learning, neurosymbolic AI, policy synthesis, formal guarantees in control systems. Key Contributions : Developed frameworks combining neural networks with formal methods for verifiable sequential decision-making, contributed to explainable AI through programmatic policy learning. Article Trends : Recent work emphasizes temporal logic constraints, counterfactual experience replay, and reduced-variance reinforcement learning. His publications highlight compositional approaches, differentiable programs, and neurosymbolic integration. Collaborations : Collaborates with institutions and researchers in computer science, with a focus on stochastic systems and interpretable machine learning.

Zhicheng JiView profile

Assistant Professor

• Biostatistics
• Bioinformatics
• Single-cell RNA sequencing
• +5 more

Zhicheng Ji is an Assistant Professor of Biostatistics & Bioinformatics at Duke University, affiliated with the Division of Integrative Genomics. His research focuses on developing computational methods for single-cell RNA-seq , spatial transcriptomics , and genomic data analysis . He teaches courses like BIOSTAT 824: Case Studies in Biomedical Data Science . Research Highlights : Statistical modeling of microbiome data Machine learning for cell segmentation T cell immunology in cancer Multi-omics data integration Spatially variable gene detection Foundation models for epigenetics Scientific Awards : NIGMS Grant (2024-2029) for spatial transcriptomics NIEHS Grant (2023-2028) on PFAS exposure NSF ERC PreMiEr (2022-2027) NCI Grant (2022-2027) on ferroptosis

Maurice van KeulenView profile

Associate Professor

• Artificial Intelligence
• Data Mining
• Process Mining
• +7 more

Maurice van Keulen is an Associate Professor affiliated with the University of Twente's research institutes including Datamanagement & Biometrics, Digital Society Institute, and TechMed Centre. His multidisciplinary work bridges computer science, healthcare, and social systems. Research Focus: Van Keulen specializes in artificial intelligence applications with emphasis on: Data management (quality, integration, probabilistic databases) Explainable AI and interpretable machine learning models Healthcare informatics (cancer prediction, medical imaging, outcome analysis) Natural language processing and social media analytics His recent work explores dynamic sparse training, meta-learning for data imputation, and ethical AI frameworks. Publication Trends: Recent articles (2023-2025) show strong focus on: Interpretable AI methods in healthcare diagnostics Robust machine learning under data corruption Meta-learning approaches for data preprocessing 3D medical imaging and reconstruction techniques Awards: Beste paper award (2018) for work on probabilistic data conditioning Supervision & Activities: Has supervised 14 research projects and serves on executive boards including EDBT (Extending DataBase Technology) and IFIP WG 2.6. Leads research on ethical dimensions of AI systems.

Vinh NguyenView profile

Assistant Professor

• Advanced Manufacturing
• Industry 4.0
• Human-Robot-Machine Interaction
• +5 more

Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.

Youngmoo KimView profile

Professor

• Machine Learning
• Audio Signal Processing
• Human-Computer Interaction
• +5 more

Youngmoo Kim is Professor of Electrical and Computer Engineering at Drexel University, where he directs the Expressive and Creative Interactive Technologies (ExCITe) Center. His leadership extends to the Music & Entertainment Technology Laboratory (MET-lab), focusing on machine listening, robotics for expressive interaction, and STEM education through music technology. Education: PhD, Media Arts & Sciences, MIT (2003) MS, Electrical Engineering, Stanford University MA, Music, Stanford University BS, Engineering, Swarthmore College BA, Music, Swarthmore College Research Focus: Kim's interdisciplinary work bridges audio signal processing, machine learning for music information retrieval, human-robot interaction, and acoustic synthesis. His lab develops novel interfaces including brain-controlled robotics, augmented instruments, and AI-driven music systems, while championing K-12 STEAM education through technological innovation. Publication Trends: Recent articles demonstrate strong convergence of deep learning with music technology, particularly in neural audio synthesis, instrument recognition, and robotic control systems. Emerging themes include differentiable physical modeling of instruments, neuroadaptive interfaces using fNIRS/tDCS, and computational approaches to creative collaboration. Awards & Honors: 2021 College of Engineering Inclusive Excellence Award 2013 Apple Distinguished Educator 2012 Lindback Foundation Distinguished Teaching Award 2012 Philadelphia Geek Awards Scientist of the Year 2007 NSF CAREER Award Leadership & Funding: Kim has secured research support from the National Science Foundation and Knight Foundation. His ExCITe Center serves as an interdisciplinary hub for creative technology development, while MET-lab advances audio-machine intelligence through collaborations across engineering, computer science, and performing arts.

Orphée De ClercqView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Readability Prediction
• +7 more

Orphée De Clercq is an Assistant Professor at Ghent University, specializing in language technology for educational applications. Her research focuses on leveraging Natural Language Processing (NLP) and Machine Learning (ML) to enhance computer-assisted language learning , readability prediction , and automated writing evaluation . She also explores sentiment analysis , emotion detection , and event coreference resolution in Dutch and multilingual contexts. Education : PhD in 2015 with groundbreaking work in readability prediction and fine-grained sentiment analysis for Dutch. Research Trends in her recent publications emphasize: Readability across domains and languages Emotion Detection using transformers and affect lexica Event Coreference in cross-document news Automated Writing Evaluation through NLP Implicit Sentiment Analysis in user-generated content Cross-Lingual Transfer with multilingual datasets She co-supervises four PhD students and contributes to interdisciplinary projects like Steunpunt Toetsen , SentEMO , and NewsDNA . Her teaching includes courses on digital communication and Computer-Assisted Language Learning .

• Machine Learning
• Molecular Modeling
• Drug Discovery
• +4 more

Sungsoo Ahn is an Assistant Professor at the Graduate School of AI, KAIST, where he leads the Structured and Probabilistic Machine Learning (SPML) Lab. His research focuses on developing machine learning algorithms for molecular science, particularly in drug discovery, material design, and generative modeling. He directs a team of 13 researchers (including 2 post-docs and 11 students) and maintains collaborations with institutions like Mila and industry partners. His core research integrates probabilistic machine learning , generative models , and AI for science , with applications spanning molecular dynamics simulation, language model reasoning, combinatorial optimization, and graph neural networks. Key methodologies include flow matching, diffusion models, GFlowNets, and equivariant neural networks applied to chemical and biological domains. Recent publications (2023–2025) demonstrate strong emphases on: (1) Molecular generation/optimization for drug design, (2) Enhancing reliability and reasoning in large language models, (3) Graph-based machine learning for scientific discovery, and (4) Efficient training paradigms for generative samplers. These appear predominantly in NeurIPS, ICML, ICLR, and ACL. He advises multiple PhD/master's students and post-doctoral researchers in the SPML Lab. Current research directions include torsion-aware molecular generation, causal AI safety, neural operators for quantum chemistry, and multi-agent systems for molecular optimization.

Kimin LeeView profile

Assistant Professor

• Safe and capable decision-making agents
• Physical AI
• Reinforcement Learning from Human Feedback
• +9 more

Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.

Tim Ruben DavidsonView profile

Researcher

• deep generative models
• agentic systems
• synthetic data
• +1 more

Tim Ruben Davidson is a Researcher at the Digital Life Lab (DLAB) within the School of Computer and Communication Sciences at EPFL. His current role is Doctoral Assistant, pursuing a doctoral program in Computer and Communication Sciences. His research focuses on advanced machine learning topics including deep generative models, agentic systems, synthetic data applications, and representation learning. His work bridges theoretical advancements in AI with practical implications, particularly in understanding AI agency, synthetic data generation, and latent space structures. Key research trends in his articles include exploring AI self-awareness, evaluating AI-driven peer review systems, and optimizing generative models through geometric and topological approaches. He has contributed to foundational studies on hyperspherical VAEs and reparameterization techniques on Lie groups, advancing mathematical foundations of neural networks. His work is published in top-tier venues and reflects interdisciplinary engagement between computer science, mathematics, and ethical AI considerations.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +3 more

Freja Stær Hincheli serves as a Lecturer at the Department of Computer Science , University of Copenhagen. Her work intersects multiple domains within machine learning, with a particular emphasis on quantum-inspired algorithms, medical imaging, and sustainable AI development. Keywords : Machine Learning, Quantum Computing, Medical Imaging, Natural Language Processing, Computational Biology Key Collaborations : SCIENCE AI Centre Her research spans quantum-enhanced neural networks, explainable AI for medical diagnostics, and energy-aware model design. Recent publications highlight applications in cross-cultural recipe adaptation, emotion-aware dialogue systems, and climate-conscious AI strategies. The Machine Learning Section at DIKU focuses on theoretical foundations and applications including medical image analysis , biological data modeling , and quantum computing , aligning with her contributions.

Xiaoli FernView profile

Associate Professor

• Machine Learning
• Data Mining
• Graph Learning
• +5 more

Xiaoli Fern is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. She holds a Ph.D. in Computer Engineering from Purdue University (2005) and dual degrees (B.S. and M.S.) in Automation and Computer Science from Shanghai Jiao Tong University (2000). Her research focuses on applied machine learning , graph learning , and explainability in AI systems , with applications in microbiome analysis , ecological monitoring , and human-computer interaction . Research Expertise: Unsupervised learning, clustering, correlation analysis, outlier detection, and scientific data mining. Collaborations: Active involvement in the IGERT Ecosystem Informatics program and interdisciplinary projects with ecologists, roboticists, and biologists. Awards: 2011 NSF CAREER Award for early-career excellence in research. Her recent work includes applying deep learning to microbiome data and developing interactive systems that bridge theory with real-world applications in biology and materials science. She mentors students across all academic levels and emphasizes the importance of collaborative, real-world problem-solving in her research lab.

• Non-parametric statistical methods
• Machine learning
• Statistical methods in genetics
• +3 more

Ambrosi Alessandro is an Associate Professor in Medical Statistics at Vita-Salute San Raffaele University. Holding a PhD in Statistics from the University of Padua (1999), he has contributed extensively to biostatistical methodologies in medical research, particularly in gene therapy, hematology, and computational biology. Degree in Statistics, University of Padua (1996) PhD in Statistics, University of Padua (1999) His research focuses on non-parametric statistical methods, machine learning applications in medicine, and statistical genetics. He has developed analytical frameworks for retroviral integration site mapping and biomarker discovery in preeclampsia and cancer prognosis. Recent work examines placental gene expression in pregnancy complications, orthodontic biomechanics, and cardiovascular risk prediction models. His publications span top-tier journals such as EMBO, Blood, and Nature Biotechnology. Fellow, Cochrane (2024-present) Fellow, Società Italiana di Statistica (2024-present) Fellow, SISMEC (2013-present) Ambrosi has served as a contract professor at the University of Padua and participates in doctoral colleges across Italy. His collaborations include international institutions like the University of Aarhus (Denmark) and the University of Oslo (Norway).

Pieter SimoensView profile

Assistant Professor

• Distributed Artificial Intelligence
• Edge Computing
• Algorithms for Neuromorphic Computing
• +2 more

Pieter Simoens is an Assistant Professor at Ghent University and affiliated with the imec research institute. He works at the intersection of distributed artificial intelligence, edge computing, and collective intelligence, with a focus on AI applications for resource-constrained environments and robotic systems. His research explores innovative approaches to machine learning deployment in heterogeneous infrastructures, task planning for IoT-integrated robotics, and modeling collective decision-making processes. He has contributed to frameworks like DIANNE for distributed deep learning and developed methods for cognitive modeling in reinforcement learning scenarios. With over 100 publications, his recent work spans adaptive neural networks, privacy-preserving surveillance, UAV hyperspectral data analysis, and computational fairness in AI systems. He leads research initiatives within the Internet Technology and Data Science Lab (IDLab) and contributes to educational programs in software engineering and applied machine learning. Responsible for courses on software engineering, mobile development, system design, and applied machine learning Active in edge computing and neuromorphic algorithms research Develops AI solutions for robotics, surveillance, and industrial IoT applications