Find Academic Supervisors

Claudio LuccheseView profile

Professor

• Machine Learning
• Information Retrieval
• Data Mining
• +4 more

Claudio Lucchese is a Full Professor in the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. His research focuses on Machine Learning in Information Retrieval, specifically Learning to Rank, Adversarial ML, and Explainable AI. He has published over 100 articles and won awards including the 2015 ACM SIGIR Best Paper Award. He coordinates the Hospitality Innovation and e-Tourism degree program and leads the Data Mining and Information Retrieval Lab. Research Interests: Efficiency-Effectiveness Trade-offs in IR Adversarial Machine Learning Explainable AI Large-Scale Data Mining Recent Projects: Data Science for Mobility (Humco S.r.l., 2020) Flexymob (Currant s.r.l., 2022) Lucchese serves on editorial boards for ACM Transactions on Information Systems and Data Mining and Knowledge Discovery. His teaching spans Computer Science and Engineering Physics programs, emphasizing Massive Data Learning and High-Performance Computing.

Tharindu Rekha LiyanagunawardenaView profile

Researcher

• Educational Technology
• Massive Open Online Courses (MOOCs)
• Programming Education
• +7 more

Tharindu Rekha Liyanagunawardena is a researcher at the University of Reading, affiliated with the School of Systems Engineering. Her work focuses on educational technology, particularly Massive Open Online Courses (MOOCs) and programming education. Her research interests lie at the intersection of computer science and pedagogy, with a strong emphasis on making programming accessible to beginners. She explores game-based learning , learner engagement , dropout behaviors in MOOCs , and the use of open educational resources . Her projects often involve designing and evaluating online courses that use mobile game development as a vehicle for teaching programming fundamentals. The analysis of her recent publications reveals a consistent theme: understanding and improving the learning experience in large-scale online environments. Her work combines educational research , qualitative and quantitative data analysis , and practical course design , contributing to the broader discourse on accessible and inclusive digital education. Scientific Contributions: Investigated learner characteristics and perceptions in FutureLearn MOOCs Explored strategies for teaching programming to absolute beginners Contributed to understanding dropout patterns in online courses Advocated for clear audience targeting in MOOC design She has collaborated extensively with researchers such as Karsten Øster Lundqvist and Shirley Ann Williams. While no formal advising or grant information is provided in the text, her role as a corresponding author on multiple publications indicates active research leadership. She is involved in initiatives that bridge academic research with public-facing educational platforms like FutureLearn, contributing to both scholarly knowledge and practical pedagogical innovation.

Aviral KumarView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +3 more

Aviral Kumar is an Assistant Professor in the School of Computer Science at Carnegie Mellon University (CMU), jointly appointed in the Computer Science Department (CSD) and Machine Learning Department (MLD). He earned his PhD from UC Berkeley in 2023, where he received prestigious awards including the CV Ramamoorthy Distinguished Research Award, Apple PhD Fellowship, and Facebook PhD Fellowship. His research focuses on reinforcement learning (RL), particularly offline RL, scaling RL methods, and their intersection with foundation models. He leads the CMU AI & Reinforcement Learning (AIRe) lab, exploring core RL algorithms, robotics applications, and AI foundation models. Education: PhD in Computer Science from UC Berkeley (2023) Research Interests: Reinforcement Learning Offline Reinforcement Learning Robotics Foundation Models His lab emphasizes scalable RL techniques and real-world applications. Current advisees include Bhavya Kumar Agrawalla and Max Sobol Mark. Notable awards include the CV Ramamoorthy Distinguished Research Award for pioneering contributions to CS research. Lab activities and future work focus on advancing RL for autonomous systems and integrating foundation models into decision-making frameworks. The lab recruits PhD students annually from CMU SCS programs (CS, ML, RI). Undergraduates and MS students can apply via a dedicated form for research opportunities.

GuanNan WangView profile

Associate Professor

• Statistical Learning
• Large Scale Data Analysis
• Non-Parametric Statistics
• +7 more

GuanNan Wang is an Associate Professor in the Department of Mathematics at the College of William & Mary, where he teaches courses in probability, mathematical statistics, and data science. He holds a Ph.D. in Statistics from the University of Georgia (2015), and also earned two M.Sc. degrees in Computer Science and Statistics from the same institution. Ph.D., Statistics, University of Georgia, 2015 M.Sc., Computer Science, University of Georgia, 2015 M.Sc., Statistics, University of Georgia, 2010 His research focuses on statistical learning, large-scale data analysis, non- and semi-parametric statistics, spatial data analysis, and functional data modeling . He develops advanced statistical methodologies with applications in public health, environmental science, and data science. His work often involves spline-based estimation, varying coefficient models, and spatiotemporal modeling. The recent publications highlight a strong trend toward applied statistical modeling in epidemiology , particularly in the context of the COVID-19 pandemic. His research integrates anomaly detection, data fusion, and real-time forecasting, as demonstrated by his contributions to the COVID-19 Dashboard . The publications collectively emphasize nonparametric and semiparametric methods, spatial statistics, and robust inference. His scientific contributions include work published in top-tier journals such as Journal of the American Statistical Association , Biometrics , Statistica Sinica , and Journal of Nonparametric Statistics . Wang actively contributes to data-driven public health initiatives, advising on statistical modeling and leading efforts in infectious disease forecasting. He has not received any explicitly mentioned awards or fellowships. He leads a research group focused on spatiotemporal modeling and has developed tools for real-time risk analysis of infectious diseases. His lab is involved in the integration and analysis of multi-source public health data.

• Database Systems
• Natural Language Processing
• Data Mining
• +4 more

Hongrae Lee is a researcher specializing in database systems, natural language processing, and data mining. His work bridges structured data management with language models, focusing on tasks like natural language to SQL translation, similarity joins, and efficient data processing. His research interests include: Database query optimization and similarity search Language model applications for text generation and hallucination correction Web data curation and structured data ecosystems Cloud storage optimization and distributed systems Hongrae Lee's recent publications (2022-2023) highlight trends in large language models (LLMs) for dialogue applications, attributed text generation, and acronym disambiguation with weak supervision. Earlier work (2016-2007) established foundational techniques in database scalability, LSH-based similarity estimation, and geographical data thinning. He has collaborated extensively with researchers at institutions like Google, Seoul National University, and University of British Columbia on projects such as WebTables, LaMDA, and Google Fusion Tables. His contributions span both theoretical advancements (e.g., variance-aware query optimization) and practical systems (e.g., CloudRAMSort, T5-based disambiguation).

Piotr NapieralskiView profile

Associate Professor

• Computer Graphics
• Virtual Reality
• Computer Games
• +7 more

Piotr Napieralski serves as Associate Professor and Head of the Computer Graphics and Multimedia Department at Lodz University of Technology's Institute of Computer Science, where he has maintained continuous academic employment since 2000. His institutional leadership and research activities position him as a key figure in Poland's computer graphics research community. His research program centers on computer graphics, virtual reality, and game development with significant extensions into mobile systems and human-computer interaction. Core specialties include 3D graphics programming, computer animation, simulation/visualization techniques, and user interface design. Recent work increasingly integrates artificial intelligence methodologies while maintaining strong foundations in geometric algorithms and real-time rendering systems. Analysis of his 2021-2025 publications reveals a dual trajectory: continued innovation in graphics fundamentals (stereo vision consistency, 3D point cloud segmentation, procedural content generation) alongside strategic expansion into interdisciplinary applications including power electronics (solid-state transformers), transportation optimization (AI-driven ecodriving), and behavioral science (eating behaviors, mineral policy). This reflects deliberate diversification while preserving computer graphics as the unifying thread. Dr. Napieralski actively secures research funding through participation in numerous EU-funded projects and serves as a long-term expert for Poland's National Centre for Research and Development (NCBR). His teaching responsibilities include computer graphics instruction for both Polish and international students, though specific student mentorship details are not documented in available sources. As department head, he oversees research directions and educational programs in graphics, games, and multimedia technologies. His leadership fosters collaboration between theoretical computer science and practical industrial applications, particularly in virtual reality systems and mobile gaming platforms where his publications demonstrate consistent output.

Nazire KocView profile

Academic

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

Nazire Koc is an Associate Teaching Professor in the Department of Computer Science within the College of Arts and Sciences at the University of Missouri – St Louis (UMSL) . She maintains active office hours for Fall 2025 and is reachable by e-mail and phone, evidencing her continuing service to the university. Education MS Computer Science, Southern Illinois University – Carbondale, 2007 MS Mathematics, Southern Illinois University – Carbondale, 1998 Research Interests Nazire Koc pursues a highly interdisciplinary research agenda that spans core computer-science concerns and pressing societal questions. Her technical work includes the design and analysis of optimization algorithms—especially the Jaya family—and machine-learning methods for large-scale data analytics. She leverages cloud-based platforms and open-source ecosystems to tackle challenges in image classification, remote sensing, and bioinformatics. Simultaneously, she is deeply engaged in AI ethics and policy , exploring algorithmic accountability, data governance, and the philosophical underpinnings of trust in autonomous systems. A recurring theme in her scholarship is the ethical application of AI technologies . From reflections on digital personhood and data feudalism to critical analyses of facial-recognition systems and blockchain governance, her publications chart the evolving moral landscape of computing. Publication Trends Between 2020 and 2025, Koc has authored or co-authored more than forty peer-reviewed articles. These works collectively map onto four dominant clusters: (1) Optimization & Algorithms , where stochastic and semi-steady-state variants of the Jaya algorithm are rigorously analyzed; (2) Machine Learning & Data Science , featuring cloud-based classifiers, network-clustering techniques, and predictive models for Alzheimer’s disease; (3) AI & Society , investigating the normative dimensions of generative AI, algorithmic defendants, and digital remains; and (4) CS Education , introducing process-oriented visualizations and explanatory AI tools to enhance introductory programming courses. Awards & Recognition While the provided material lists no formal honors or awards, the breadth and impact of her recent publications signal significant recognition within the computer-science education and AI-ethics communities. Advising & Grants No specific doctoral or master’s advisees are named in the supplied text, and no grant details are disclosed. However, her heavy publication record across methodological and educational domains suggests active collaboration with graduate researchers and participation in sponsored projects. Labs & Teams Although no dedicated laboratory name is provided, Koc’s affiliation with the Department of Computer Science at UMSL implies access to departmental computing clusters and interdisciplinary research centers that support her work in algorithmic design, data analytics, and ethics research.

• Medical Imaging
• Computer Vision
• Mixed Reality
• +7 more

Andrzej Skalski serves as a Professor and Deputy Head of the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków. He actively participates in the Biomedical Engineering Discipline Council and maintains his primary workplace in Building B-1, Room 206, with contact via skalski@agh.edu.pl. His research spans Medical Imaging, Computer Vision, and Mixed Reality applications in healthcare, with concentrated expertise in medical image segmentation, surgical navigation systems, and 3D visualization techniques. Recent work demonstrates innovative integration of deep learning for vascular structure analysis, development of cloud-based diagnostic platforms like DECODE, and implementation of extended reality solutions for surgical precision and anatomy education. His scholarship bridges engineering principles with clinical practice to solve complex biomedical challenges. Analysis of his 2024-2026 publications reveals dominant trends in markerless surgical navigation, noninvasive vascular disease management, and educational technology for anatomy instruction. Key thematic clusters include: (1) Deep learning-driven segmentation of vascular and fracture structures in CT/X-ray data, (2) Mixed reality frameworks for surgical guidance and biopsy procedures, and (3) Systematic evaluations of digital versus traditional methods in medical education. These works consistently emphasize clinical applicability and technological innovation. Dr. Skalski leads collaborative initiatives including the DECODE platform for peripheral artery disease management and the PENGWIN 2024 Challenge for pelvic fracture segmentation benchmarking. His leadership in the Biomedical Engineering Discipline Council underscores institutional influence, while his extensive publication record indicates active supervision of graduate researchers despite no explicit student listings in available records. Current projects focus on WebGL-based medical visualization and mixed reality surgical navigation systems.

• Bioinformatics of neuropathic pain
• Genetics
• Transcriptomics
• +9 more

Georgios Baskozos is an Associate Professor and Bio-informatician at the Nuffield Department of Clinical Neurosciences (NDCN) at the University of Oxford. He is affiliated with the Neural Injury Group and works as part of David Bennett's research group. His academic credentials include an MSc and PhD. Dr. Baskozos specializes in the bioinformatics of neuropathic pain, with particular expertise in genetics, transcriptomics, functional genomics, and predictive modeling. His research focuses on transcriptional analysis of peripheral nerve injury in both humans and animal models of pain. He has developed customized computational pipelines for identifying novel long non-coding RNAs and gene model reconstruction using RNA-sequencing. Additionally, he has extensive experience in designing computational workflows for Whole Genome and Exome Sequencing data analysis, working closely with experimental biologists and medical doctors. His recent publications demonstrate a strong focus on machine learning applications in pain research, biomarker discovery for neuropathic conditions, and large-scale genomic studies of pain disorders. His work frequently utilizes UK Biobank data and spans from basic molecular mechanisms to clinical applications in diabetic neuropathy and other pain conditions. Dr. Baskozos is currently applying machine learning methods as the only directly appointed researcher for the Diabetes UK Project Grant 19/0005984 "Using machine learning to predict painful diabetic neuropathy" and serves as a researcher co-investigator for the "Partnership for Assessment and Investigation of Neuropathic Pain: Studies Tracking Outcomes, Risks and Mechanisms (PAINSTORM)" UKRI - Versus Arthritis APDP grant. He also provides statistical support, hypothesis testing guidance, and experimental design advice to colleagues within his research group.

• Machine Learning
• AI Safety and Robustness
• Deep Learning
• +4 more

Zico Kolter is a Professor and Director of the Machine Learning Department at Carnegie Mellon University . He also serves on the OpenAI Board of Directors as chair of the safety and security committee, co-founded Gray Swan AI (an AI security company), and acts as a Chief Expert at Robert Bosch, LLC . Research Focus: AI safety and robustness, LLM security, data impact on models, implicit models, and adversarial defense. Teaching: Offers graduate courses in artificial intelligence and deep learning systems. His work investigates robustness in deep learning, constraints in optimization, and security in foundation models. Recent publications address adversarial compression, diffusion models, and prompt engineering. Notable Awards: DARPA Young Faculty Award Sloan Fellowship Best Paper at NeurIPS, ICML (honorable mention), AISTATS (test of time), IJCAI, KDD, and PESGM.

Morgan SondereggerView profile

Associate Professor

• Phonology
• Phonetics
• Computational Linguistics
• +5 more

Morgan Sonderegger is an Associate Professor in Linguistics at McGill University and holds a Canada Research Chair (Tier 2) in Speech Variability. He co-leads the Montreal Computational and Quantitative Linguistics Lab (MCQLL), focusing on interdisciplinary research at the intersection of phonetics, computational modeling, and language variation. His research spans phonology, phonetics, computational linguistics, language variation and change, and quantitative methods. Sonderegger develops advanced statistical models for linguistic data analysis, particularly Bayesian regression techniques and computational approaches to speech variability. His work integrates large-scale corpus analysis with experimental methodologies to investigate sound change mechanisms, speech production, and perceptual patterns across languages. Recent publications reveal strong trends in quantitative phonetic analysis, computational modeling of speech variability, and cross-linguistic studies of phonological patterns. His research increasingly incorporates machine learning techniques and large-scale corpus approaches to address fundamental questions in phonetics and phonology. Canada Research Chair (Tier 2) in Speech Variability Sonderegger contributes significantly to linguistic methodology through software development and educational resources. He has created essential tools like the Montreal Corpus Tools (including PolyglotDB and Montreal Forced Aligner) that facilitate speech corpus analysis. His educational contributions include the textbook Regression Modeling for Linguistic Data and advanced course materials on Bayesian regression modeling. He leads the Montreal Computational and Quantitative Linguistics Lab (MCQLL), which develops computational frameworks for analyzing speech corpora and modeling phonetic variation. The lab's work bridges theoretical linguistics with practical applications in speech technology and language processing.

Shang MuView profile

Assistant Professor

• Connectomics
• Neuroscience
• Mouse Visual Cortex
• +5 more

Shang Mu serves as Assistant Professor of Research in Neuroscience at the Brain and Mind Research Institute, Weill Cornell Medical College since 2022. His work focuses on high-resolution neural circuit mapping using advanced connectomics techniques, particularly in mouse visual cortex and human brain disorders. His educational background includes: Ph.D. in Biomedical Engineering from University of Florida (2010) B.Eng. in Biomedical Engineering from Beijing University, China (2005) Dr. Mu's research spans connectomics, neural circuit architecture, and computational neuroscience. He investigates synaptic organization, inhibitory specificity, and wiring rules in cortical circuits using electron microscopy and machine learning. His work has significant implications for understanding neurological diseases including stroke and glioblastoma. Key methodologies involve large-scale image analysis, transcriptomic integration, and development of open-source tools for connectome annotation. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on mouse visual cortex connectomics, with emerging work on human brain disorders. Major themes include inhibitory circuit specificity, dendritic morphology mapping, and computational tool development (CAVE, NEURD). His research demonstrates consistent high-impact output in top journals like Nature and Cell , often featuring collaborative, interdisciplinary approaches combining neuroscience, genomics, and computer science. No scientific awards are documented in the provided materials. Information regarding student advising, grant funding, or specific laboratory teams is not available in the current dataset. His research is conducted within the Brain and Mind Research Institute infrastructure at Weill Cornell Medical College.

Philippe RigauxView profile

Professor

• Music Information Retrieval
• Database Systems
• Spatial Databases
• +6 more

Philippe Rigaux is a Professor at the Conservatoire national des arts et métiers (CNAM) in Paris, France, where he conducts research at the CÉDRIC laboratory (Centre d'études et de recherche en informatique et communications). His academic career spans over 25 years with continuous publication output from 1995 through 2024, demonstrating sustained research activity in computer science with a specialized focus on music information retrieval and database systems. Rigaux's research interests center on the intersection of music and computer science, particularly in music score processing, representation, and querying. His work explores graph-based approaches to music representation, topological querying of music scores, and quality assessment in digital music libraries. He has made significant contributions to the development of tools and frameworks for music information retrieval, including the GioQoso quality assessment tool and the FACETS exploration system for large symbolic music collections. His research often bridges theoretical database concepts with practical music applications, focusing on scalable solutions for music content search and pattern matching. Analysis of his recent publications (2018-2024) reveals a consistent research trajectory focused on improving music information systems through innovative database techniques, graph theory applications, and quality assessment methodologies. His work shows increasing emphasis on multimodal music sources, collaborative music digitization, and advanced representation techniques for rhythm and structure. Rigaux frequently collaborates with researchers across Europe, particularly on projects related to digital music libraries and music information systems. Rigaux has contributed to major projects including NEUMA (a collaborative digital score library), CollabScore (integrating optical recognition with multimodal music sources), and GioQoso (a quality assessment tool for music notation). His research has been published in prestigious venues including Data and Knowledge Engineering, International Journal on Digital Libraries, and proceedings of the International Society for Music Information Retrieval (ISMIR).

• Software Engineering
• Artificial Intelligence
• Cyber-Physical Systems
• +3 more

Mason Brown serves as a Visiting Assistant Professor of Computer Science at Hamilton College, based in Taylor Science Center 3005. His research integrates software engineering and artificial intelligence to advance cyber-physical systems across ground, air, and space domains, with emphasis on practical implementations for real-world applications. Educational Background: Ph.D. in Computer Science from Griffith University M. IT. from Griffith University B. I.T. from Charles Sturt University His doctoral dissertation focused on An Ilities Tool for Minisatellite Software Validation , developed in collaboration with Gilmour Space Technologies. Research expertise spans satellite systems validation, multi-robot coordination, and AI-driven cyber-physical solutions. His postdoctoral work at the University of Technology Sydney involved multi-robot target tracking and path planning under Professor Robert Fitch. Current investigations include aircraft visual inspection via machine learning and natural language interfaces for robotic systems, demonstrating strong interdisciplinary connections between space engineering and robotics. Publication analysis reveals a clear trajectory from foundational satellite software validation (2020-2021) toward advanced human-robot collaboration (2023-2024), with consistent focus on verification methodologies and real-world deployment challenges. Key thematic threads include ilities-based validation frameworks, multi-robot field experimentation, and machine learning applications in aerospace. Teaching includes Computer Science for All and Algorithms and Data Structures , with active supervision of student projects in mechatronics and computer science. He particularly encourages new students to engage in research. Professional activities include appointment to the Australian Computer Science faculty in 2025 and collaborations with laboratories at Griffith University (Professor Paulo de Souza) and University of Technology Sydney (Graeme Best).

• Software Engineering
• Software Evolution
• Knowledge Worker Productivity
• +4 more

Gail Murphy is a Professor in the Department of Computer Science and Vice-President Research & Innovation at the University of British Columbia (UBC). A co-founder of Tasktop Technologies Incorporated, she leads the Software Practices Lab and is affiliated with CAIDA (Centre for Artificial Intelligence Decision-making and Action) and ICICS (UBC Institute for Computing, Information and Cognitive Systems). B.Sc. (Honours), University of Alberta (1987) M.Sc., University of Washington (1994) Ph.D., University of Washington (1996) Her research focuses on software engineering , particularly improving knowledge worker productivity and tools for evolving large-scale systems. She combines lightweight design approaches with industrial evaluations, addressing structural degradation in software systems through: Techniques for understanding/imposing structure in existing systems Design approaches for component reuse and flexible architectures Her recent work includes developer discussion analysis for design recovery (2022), semantic-based task relevance detection (2022), and automated resource-task association (2023). Awards include SIGSOFT Outstanding Research Award (2023), ACM Fellow (2023), and Royal Society of Canada Fellow (2023). She has taught courses like Introduction to Software Engineering (CPSC 310) and Software Construction (CPSC 210).