Find Academic Supervisors

Jacob KauffmannView profile

Researcher

• Unsupervised Learning
• Explainable AI
• Clustering
• +3 more

Dr. Jacob Kauffmann is a Postdoctoral Researcher at the Technical University of Berlin, specializing in research areas such as Unsupervised Learning, Explainable AI, and Anomaly Detection. His work focuses on enhancing model interpretability in unsupervised techniques and uncovering biases like the Clever Hans effect in machine learning systems. Education: M.Sc. Computer Science, Technische Universität Berlin (2017) Research Interests: Jacob’s research bridges theoretical foundations and practical applications of explainable AI (XAI) in unsupervised learning. He investigates clustering explanations, anomaly detection mechanisms, and kernel-based methods to improve model transparency. His recent work explores dataset shifts via Wasserstein distances and neural network-driven cluster analysis. Publications: Dr. Kauffmann’s articles emphasize XAI advancements, such as explainable anomaly detection and model bias analysis. Key themes include algorithmic transparency, dataset transport phenomena, and deep learning applications in unsupervised tasks. Advising & Grants: No formal advisees listed. Research supported by grants related to XAI and unsupervised learning methodologies (specific grants not detailed).

Thomas SchnakeView profile

Researcher

• Explainable AI
• Natural Language Processing
• XAI for Quantum Chemistry
• +3 more

Thomas Schnake is a postdoctoral researcher at the Machine Learning Lab of the Technical University of Berlin and the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a Ph.D. in Machine Learning from TU Berlin and prior degrees in Mathematics and Scientific Computing from Humboldt University of Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing, and the mathematical foundations of machine learning. He has also gained industry experience at ebuero AG and GFaI e.V. in Berlin. Education: B.Sc. Mathematics & Philosophy, Humboldt University Berlin (2014) M.Sc. Mathematics, Humboldt University Berlin (2018) M.Sc. Scientific Computing, Technical University Berlin (2018) Ph.D. Machine Learning, Technical University Berlin (2024) Research Interests include: Explainable AI for complex domains like quantum chemistry and histopathology Graph neural network interpretability through walk-based explanations High-resolution data synthesis with minimal input Unsupervised anomaly detection in text and energy systems His recent publications (2021-2025) demonstrate contributions to XAI frameworks, graph neural network explanations, and transformer model interpretability. He is affiliated with two prominent institutions and maintains active research in interdisciplinary areas combining mathematics and machine learning.

Laura DietzView profile

Associate Professor

• Information Retrieval
• Natural Language Processing
• Knowledge Graphs
• +3 more

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire. She leads the TREMA lab and focuses on text-based machine learning, information retrieval, and environmental data science. Her academic journey includes postdoctoral roles at Mannheim University and the University of Massachusetts, alongside a PhD from the Max Planck Institute for Informatics. Education: PhD in Computer Science (Max Planck Institute, 2011), followed by postdoctoral research at UMass (2012–2015) and Mannheim (2015–2016). Tenure-track appointment at UNH since 2016. Research Interests : IR+NLP: Integrates knowledge graphs, entity retrieval, and machine learning to automatically generate comprehensive articles. This work is supported by an NSF CAREER Award (2019–2023). Watershed Data Science: Analyzes storm event impacts using time-series forecasting, with grants from the Northeast Big Data Hub. Machine Learning: Develops methods for sequential data analysis across domains like social media and environmental science. Grants & Awards : NSF CAREER Award (Utilizing Fine-Grained Knowledge Annotations) Elite Postdoc Program of BW-Stiftung (2016–2019) Best Paper at JCDL 2018 for Entity-Aspect Linking Service & Leadership : Organized HIPstIR Workshop on non-mainstream IR research Coordinated TREC Complex Answer Retrieval Track (2017–2019) Keynote speaker at ECIR, NAACL, and SPIRE Labs & Teams : Head of TREMA Lab at UNH, focusing on query-driven knowledge synthesis and environmental data science. Collaborates with faculty in Natural Resources and the Environment Department.

• French Language Practice
• Romance Studies
• Computer Vision
• +3 more

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

Dirk HovyView profile

Associate Professor

• Natural Language Processing
• Computational Social Science
• Ethics and Fairness in AI
• +3 more

Dirk Hovy is an Associate Professor in the Department of Computing Sciences at Bocconi University. He serves as Scientific Director of BIDSA’s Data and Marketing Insights (DMI) research unit and heads the MilaNLP lab. His work focuses on natural language processing (NLP) and computational social science, with a particular interest in ethics and fairness in AI, large language models, transfer learning, and computational methods for social science. He has organized major conferences such as EMNLP 2017 and workshops on abusive language detection and computational social science. Education: PhD in Computer Science from the University of Southern California (USC), Master’s in Sociolinguistics from Germany. Research interests span NLP techniques like reinforcement learning and adversarial learning, alongside societal impacts of AI. His recent work explores sociodemographic variables in NLP models and their integration with demographic information. Notable contributions include geolocation via social media text, gender bias in NLP systems, and hate speech detection on Twitter. Labs/Teams: Leads MilaNLP and directs BIDSA’s DMI unit. Grants: Received a 2020 ERC Starting Grant to study sociodemographic variables and NLP models.

Michele MancusiView profile

Researcher

• Deep Learning
• Source Separation
• Generative Models
• +3 more

Michele Mancusi is a Senior Research Scientist at Sony and holds a Ph.D. in Computer Science from Sapienza University of Rome, where he remains affiliated with the GLADIA research group under Prof. Emanuele Rodolà. He earned dual degrees in Physics (Quantum Information) from Sapienza, blending computational and theoretical expertise. His research focuses on deep learning applications in audio signal processing, including source separation techniques for music and ecological monitoring. Notable contributions include data-driven methods for marine biodiversity assessment via fish vocalization analysis and innovations in diffusion models for high-resolution audio generation. His work bridges academia and industry, with publications in top venues like ISMIR, ICASSP, and ICLR. Research interests emphasize generative models (autoregressive/diffusion), music technology, and interdisciplinary applications in environmental science. Recent efforts explore coherence-oriented audio representation learning (COCOLA) and inference-time optimizations for music mastering processors (ITO-Master). Publications reflect a trend toward real-world impact, such as accelerating transformer-based translation systems and developing latent diffusion techniques for timbre transfer. Collaborations include cross-disciplinary projects with marine biologists to advance biodiversity tracking through passive acoustic monitoring.

Jean VaillancourtView profile

Academic

• Probabilities
• Stochastic Equations
• Random Games
• +2 more

Jean Vaillancourt is an Affiliated Professor at the Department of Decision Sciences, HEC Montréal. He holds a PhD in Mathematics from Carleton University. His research focuses on stochastic processes, probability theory, and data mining applications in social media analysis. Key areas include martingale theory, random walks in random environments, and social network dynamics. He has contributed to methodologies for estimating multi-modal histograms and predicting network restructuring following node removals. His work integrates mathematical rigor with computational techniques, appearing in venues like Stochastic Analysis and Applications and Neural, Parallel and Scientific Computations . Active on platforms like Academia.edu and ResearchGate, his research bridges theoretical probability with practical applications in data science and network analysis. Education: PhD in Mathematics, Carleton University Research Interests: Probabilities, stochastic equations, social media analysis, data mining, and network science. His work emphasizes statistical methods for pattern extraction, centrality-based network prediction, and measure-valued processes. Articles Overview: Recent publications explore martingale convergence theories, random walk periodicity detection, and cascading removals in social networks. These studies highlight his dual focus on foundational probability theory and applied data-driven analysis. Grants & Advising: Supervision activities span the last five years, though specific grants or advisees aren't detailed here. His work on road database automation and edge detection showcases interdisciplinary impact. Labs & Teams: Collaborates across disciplines, integrating mathematical modeling with computational tools. Active in HEC's research ecosystem, contributing to formal concept analysis and SAR imagery processing initiatives.

Mete AhishaliView profile

Researcher

• Machine Learning
• Remote Sensing
• Medical Imaging
• +4 more

Mete Ahishali is a Postdoctoral Researcher affiliated with the School of Forest Sciences at the University of Eastern Finland's Faculty of Science, Forestry and Technology. His research focuses on advanced machine learning techniques applied to remote sensing, medical imaging, and environmental monitoring. He is a core member of the Global Ecosystem Health Observatory (GEHO) research group since January 2024. His technical expertise spans hyperspectral image analysis, SAR image classification, domain adaptation networks, and medical image restoration. Key contributions include developing neural network architectures for tree mortality mapping, anomaly detection in industrial systems, and COVID-19 classification using X-ray images. Recent work demonstrates innovations in autoencoder-based band selection for hyperspectral imagery, self-attention fusion networks for cardiac diagnostics, and operational U-nets for wildfire detection. His research bridges theoretical machine learning advancements with practical applications in environmental science and healthcare. Notable technical skills include deep learning frameworks, remote sensing data processing, and medical imaging modalities. Current projects focus on scalable solutions for large-scale Earth observation data analysis and cross-modal medical imaging systems.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +5 more

Ethem Alpaydin is a Professor of Computer Science at Özyeğin University in Istanbul, Turkey. He is a prominent researcher in machine learning and artificial intelligence, with affiliations including membership in The Science Academy, Turkey, and Academia Europaea. He also holds a fellowship with the Asia-Pacific Artificial Intelligence Association. His research focuses on foundational aspects of machine learning, including statistical methods, neural networks, and deep learning. He has contributed to areas such as generative adversarial networks (GANs), decision pathways in neural networks, and distributed decision trees. His work bridges theoretical advancements with practical applications in domains like natural language processing and computer vision. Alpaydin has authored influential textbooks such as Introduction to Machine Learning and Maschinelles Lernen , which are widely used in academic curricula. His publications emphasize model interpretability, regularization techniques, and cross-lingual learning for languages like Turkish. Awards include recognition from leading scientific institutions for his contributions to AI and data science.

Edith Alice KovácsView profile

Associate Professor

• Multivariate Statistics
• Probability Graph Models
• Artificial Intelligence
• +8 more

Edith Alice Kovács is an Associate Professor at the Department of Differential Equations, Budapest University of Technology and Economics (BME). She holds a PhD from the West University of Timisoara (2005) and has taught at multiple institutions, including the General College of Entrepreneurship (ÁVF) and universities in Arad. Her research focuses on multivariate statistics, probability graph models, information theory, AI/ML, network reliability, and mathematical modeling in finance/medicine. She has received the BME Innovation Award 2020 for anomaly detection work. Kovács has presented at conferences worldwide, including the European Conference on Operational Research and the IEEE SACI conference. Her teaching spans subjects like economic modeling, operations research, and data science across BME and ÁVF programs. Education: BSc and MSc in mathematics from the West University of Timisoara (1994), PhD in Mathematics (2005). Professional roles include full-time employment at BME since 2015 and prior experience at Aurel Vlaicu and Vasile Goldis Universities (1994–2000). Research interests include decision-making under uncertainty, metaheuristic algorithms, neural halos, and social network modeling. Recent work explores copula-based anomaly detection and vine copula structures. Kovács has contributed to projects like stochastic inventory modeling for TESCO and VNF fingerprinting for Nokia.

Hien NguyenView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Biomedical Image Analysis
• +9 more

Hien Nguyen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Houston's College of Engineering. His research focuses on machine learning, artificial intelligence, and their applications in biomedical image analysis, computational medicine, and drug discovery. He specializes in developing AI systems to enhance radiology workflows, improve diagnostic accuracy, and model radiologists' cognitive processes. His work spans domains like medical imaging segmentation, domain adaptation, and interpretable AI for clinical trust. Nguyen's research integrates multimodal data from LIDAR, satellite imagery, and histopathology slides to address challenges in urban mapping, kidney disease classification, and cellular analysis. He has contributed to frameworks like MIRAM for breast lesion risk prediction and ItpCtrl-AI for radiologist intent modeling. His team explores meta-learning, self-supervised learning, and diffusion models for tasks ranging from apoptosis forecasting to seismic data interpolation. Key technical strengths include domain invariant learning, medical dataset synthesis (e.g., MorphSet), and explainable AI systems. His work bridges human expertise with machine intelligence, emphasizing clinical adoption through collaboration between radiologists and AI algorithms.

Cynthia MatuszekView profile

Assistant Professor

• Robotics
• Machine Learning
• Human-Robot Interaction
• +5 more

Cynthia Matuszek is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She directs UMBC’s Interactive Robotics and Language lab, focusing on enabling robots to learn from human interaction using natural language. Her research spans robotics, machine learning, human-robot interaction (HRI), and natural language processing (NLP). Notably, she was recognized in IEEE's 2018 '10 to Watch in AI' list. Dr. Matuszek holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Washington (2014, 2010) and a B.S. in Computer Science from the University of Texas at Austin (2000). Her work bridges theoretical and applied domains, addressing challenges in grounded language learning, multimodal data fusion, and ethical AI deployment. Her recent publications emphasize cybersecurity (e.g., malware detection using compact features), neurosymbolic reasoning, and the integration of large language models into robotics. The lab’s work often explores VR-based human-robot collaboration scenarios, leveraging photogrammetry and simulation for reproducible studies. Key thematic trends in her articles include: Advancing grounded language learning through cross-modal alignment and interactive labeling Developing ethical AI systems capable of moral reasoning and command rejection Pioneering domain-specific cybersecurity solutions using tensor factorization and vector stores Expanding multilingual and multimodal capabilities for robots in unstructured environments Her research also addresses societal impacts, including studies on gender stereotypes in AI datasets and privacy implications of pervasive robotics. The lab’s work often intersects with NSF-funded initiatives, such as the CAREER award for inclusive robotic learning environments.

• Artificial Intelligence in Healthcare
• Medical Imaging
• Dermatology
• +3 more

Ludovic Amruthalingam is a Lecturer and Senior Researcher at the Lucerne School of Computer Science and Information Technology, Lucerne University of Applied Sciences, Switzerland. He holds a PhD in Artificial Intelligence from the University of Basel, a Master of Science in Computer Science from ETH Zürich, and a Bachelor of Science in Computer Science from EPFL. His professional activities include co-founding the startup Intelligent Orthopaedics in Chennai, India (2017–present), and leading research projects such as the Health Innovation Transfer Study (HIT) , Derma consultation documentation with AI , and the HSLU Medtech Joint Venture . His work focuses on leveraging AI for medical applications, particularly in dermatology, teledermatology, and medtech innovation. Research interests include medical anomaly detection, self-supervised learning, hyperbolic neural networks, and overcoming data scarcity in healthcare. He has pioneered projects addressing skin condition analysis, automated anatomical mapping, and diversity-inclusive medical imaging datasets. His recent publications highlight advancements in AI for dermatological diagnosis (e.g., vulvar lichen sclerosus pilot studies), hyperbolic space applications for medical anomalies, and benchmarking frameworks for image data cleaning (e.g., CleanPatrick ). He also contributes to foundational AI tools for scalable digital dermatology and equitable healthcare access through global skin imaging initiatives. Ludovic’s work bridges academic research and industry, driving innovations in medtech through collaborations like the ABIZ MedTech Expansion and Counteracting Data Scarcity in MedTech projects. His research emphasizes clinical translation of AI systems for real-world healthcare challenges.

Gemma MoranView profile

Assistant Professor

• Bayesian Statistics
• Machine Learning
• Deep Generative Models
• +4 more

Gemma Moran is an Assistant Professor in the Statistics Department at Rutgers University. Her office is located at Hill Center 475, 110 Frelinghuysen Road, Piscataway, NJ 08854. She teaches courses including MSDS597 Data Wrangling with Python, MSDS534 Statistical Learning for Data Science, and STAT588 Data Mining. Dr. Moran received her PhD in Statistics from the University of Pennsylvania, where she was advised by Edward George and Veronika Rockova. Prior to joining Rutgers, she was a postdoctoral researcher at the Columbia Data Science Institute working with David Blei. Dr. Moran's research focuses on developing flexible Bayesian models for analyzing high-dimensional data. Her recent work includes developing identifiable and interpretable deep generative models, particularly variational autoencoders, and creating improved tools for Bayesian model criticism. Her methodological contributions bridge statistics, machine learning, and data science, with applications spanning computer vision, biomedical imaging, and materials science. Analysis of Dr. Moran's publication record reveals a strong emphasis on Bayesian methodology, particularly in variable selection (spike-and-slab approaches), deep generative modeling, and model criticism. Her work consistently addresses the challenge of maintaining statistical rigor while developing practical machine learning solutions, with a growing focus on model interpretability and identifiability in complex models. Dr. Moran actively mentors students at multiple levels. Prospective PhD students are encouraged to apply to the Rutgers Statistics PhD program and mention her name. For Masters of Data Science students, she supervises capstone projects requiring independent revisions of group work using different statistical methods or datasets. She also encourages students interested in research to take her MSDS-534 Statistical Learning for Data Science course as preparation for research projects.

• Computer Graphics
• 3D Visualization
• User Interfaces
• +3 more

Tamara Munzner is a researcher in computer science with a focus on visualization and interactive 3D graphics. She holds a B.S. in Computer Science from Stanford University and pursued a PhD at Stanford's Computer Science Department. She was part of the senior technical staff at the Geometry Center, University of Minnesota, where she contributed to projects like Geomview, WebOOGL, and the Outside In animation. Her work integrates visualization with the World Wide Web, emphasizing user interfaces and interactive systems. Notable achievements include developing the Triangle Tiling exhibit with the Science Museum of Minnesota and contributions to the Siggraph Electronic Theater. Her research spans scientific visualization, web integration of 3D graphics, and collaborative visualization tools. Education: B.S. in Computer Science, Stanford University, 1991 PhD candidate in Computer Science, Stanford University (as of 1995) Research Interests: Munzner's work emphasizes interactive systems, 3D graphics visualization, and the integration of web technologies with visualization tools. She pioneered projects like Cyberview and WebOOGL, which enabled distributed 3D visualization via the web. Her focus on user interfaces and accessibility has shaped modern approaches to visualization design. Professional Contributions: Co-author of Geomview, a widely used 3D visualization system Animator/director of the Outside In mathematical animation Key contributor to the Triangle Tiling exhibit