Find Academic Supervisors

Cen LiView profile

Professor

• Data Mining
• Machine Learning
• Artificial Intelligence
• +5 more

Dr. Cen Li is a Professor in the Department of Computer Science at Middle Tennessee State University's College of Basic and Applied Sciences. He holds a PhD from Vanderbilt University (2000) with a dissertation on Bayesian approaches to temporal data clustering using Hidden Markov Models, an MS from Vanderbilt (1995), and a BS from MTSU (1993). Dr. Li's research spans multiple domains within artificial intelligence, including data mining, machine learning, computer graphics, and robotics. His work bridges theoretical foundations with practical applications in healthcare, education, and industrial systems. He has developed innovative approaches for temporal data clustering, personalized information retrieval, and educational technologies that enhance student learning in computer science. His recent publications demonstrate a focus on skeletal-based image processing techniques, hybrid deep learning approaches for medical diagnostics, and data-driven methods for predicting student retention. Dr. Li has also made significant contributions to educational technology through gamification and social learning environments that support peer review and collaborative programming among computer science students. Dr. Li actively mentors students at all levels, from undergraduate to PhD candidates, with a strong emphasis on involving students in research early in their academic careers. His work with undergraduate students includes multiple robotics summer camps that introduce high school students to programming and robotics concepts.

Yong ChenView profile

Professor

• Industrial Engineering
• Reliability Engineering
• Maintenance Optimization
• +4 more

Yong Chen serves as a Professor in the Department of Industrial and Systems Engineering within the College of Engineering at the University of Iowa. His academic appointment centers on advancing methodologies in industrial engineering with emphasis on system reliability and optimization across diverse applications. Chen's research spans Industrial Engineering, Reliability Engineering, and Maintenance Optimization, with significant contributions to Statistical Process Control, Bayesian Statistics, and Machine Learning applications. His work develops novel frameworks for condition-based maintenance, multi-component system optimization, and IoT-enabled industrial analytics, addressing critical challenges in manufacturing quality control and system reliability. The integration of stochastic modeling and data-driven approaches characterizes his methodological innovations. Analysis of his 15 most recent publications (2016-2025) reveals a dominant research trajectory in Markov decision processes for maintenance optimization (35% of publications), Bayesian modeling for process monitoring (27%), and IoT/data analytics applications (13%). His work demonstrates increasing interdisciplinary expansion from traditional manufacturing systems into healthcare (dementia care analysis) and renewable energy sectors, while maintaining core focus on reliability engineering fundamentals. Scientific awards: No scientific awards were mentioned in the provided text. Advising and grants: The available documentation contains no information regarding doctoral students, postdoctoral researchers, or research funding sources. His academic profile focuses exclusively on research outputs and methodological contributions without reference to mentoring activities or sponsored projects.

Toni KarvonenView profile

Assistant Professor

• Statistics and Probability
• Applied Mathematics
• Numerical Analysis
• +10 more

Toni Karvonen is an Assistant Professor of Applied Mathematics at LUT University's Computational Engineering Department, School of Engineering Sciences, since September 2024. His research focuses on statistics, applied mathematics, and computational methods with emphasis on Gaussian processes, uncertainty quantification, and kernel-based approximation. He has held postdoctoral positions at the Alan Turing Institute (2020–21) and the University of Helsinki (2021–24). Research Areas: Statistics and Probability, Applied Mathematics, Numerical Analysis, Gaussian Processes, Positive Definite Kernels Key Trends: His recent work explores probabilistic numerics, Bayesian quadrature, kernel methods for uncertainty quantification, and approximation theory in reproducing kernel Hilbert spaces. Email: Toni.Karvonen@lut.fi

Danfei XuView profile

Assistant Professor

• Robotics
• Machine Learning
• Robot Learning
• +3 more

Danfei Xu is an Assistant Professor at the School of Interactive Computing, College of Computing, Georgia Institute of Technology, and a part-time Research Scientist at NVIDIA AI. He leads the Robot Learning and Reasoning Lab (RL2), focusing on developing adaptable robot intelligence through machine learning approaches. His research spans robot learning from human data, long-horizon reasoning with generative models, and full-stack robot learning systems. Education: Ph.D. in Computer Science, Stanford University (2015-2021) B.S. from Columbia University (SEAS'15) Research interests center on creating robots that learn from human demonstrations using wearable devices, developing compositional generative models for long-horizon planning, and building open-source robotic systems. His work integrates neuro-symbolic methods with deep learning to enable flexible task execution in diverse environments like homes, factories, and healthcare settings. Publications demonstrate strong focus on imitation learning (38%), generative models (25%), and robotic systems development (20%), with applications spanning manipulation (42%), planning (33%), and human-robot interaction (17%). Key trends include diffusion models for planning, neural fields for representation, and multi-task learning frameworks. Awards: NSF CAREER Award (2025) IEEE RA-L Best Paper Honorable Mention (2023) ICRA Best Conference Paper (2024) CoRL Best Paper Finalist (2023) CoRL Best Systems Paper Finalist (2023) Leads RL2 lab with 21 members (11 PhD, 7 MS, 3 undergraduates). Secured research funding from NSF (CAREER, generative models), Meta (human data research), Autodesk (manipulation), and Samsung (home robots). Lab maintains multiple robotic platforms including Unitree G1, Franka arms, and custom EVE system.

Nezamoddini-Kachouie NezamoddinView profile

Associate Professor

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +7 more

Nezamoddini-Kachouie Nezamoddin is an Associate Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology , with an affiliate appointment in the Department of Electrical Engineering and Computer Science , both within the College of Engineering and Science . Since joining Florida Tech in 2012, he has built an interdisciplinary research program bridging engineering, medicine, and biology. Education & Training BASc – Electrical & Computer Engineering MASc – Systems Design Engineering, University of Waterloo, 2008 PhD – Biomedical Engineering Postdoctoral Fellow – Harvard Medical School & Harvard School of Public Health, 2010-2012 Research Interests Dr. Kachouie’s work sits at the confluence of statistical modeling , machine learning , artificial intelligence , image processing , pattern recognition , and biostatistics . He leverages these tools to tackle grand challenges in cancer research , public health , and climate change , producing actionable insights from complex, high-dimensional data. Publication Trends With more than 120 peer-reviewed papers spanning 2003-2025, his recent output emphasizes deep learning (U-Net, CNNs), Bayesian spatiotemporal models , and generalized additive models applied to glacier recession , wildlife population monitoring , sea-level rise , and cancer genomics . These works collectively advance both methodological innovation and high-impact societal applications. Student Mentorship & Grants He has mentored 80+ undergraduates who presented at venues such as NCUR, JSM, and AMS. Seven PhD students have graduated under his supervision and now hold academic positions. He has served as PI of the NSF REU Site “Statistical Models with Applications to Geoscience” (2020-2024) and as mentor for the NSF Biomath REU Site (2015-2017). Currently, his group comprises 2 post-docs, 6 PhD, 2 Master’s, and 7 undergraduate researchers. Contact & Resources Email: nezamoddin@fit.edu Office: Crawford 335 (inside 328) Phone: (321) 674-7485 Profiles: Google Scholar , Research Website

Xu XuView profile

Senior Lecturer

• Computational Haemodynamics
• Cardiovascular Modeling
• Personalised Medicine
• +8 more

Dr Xu Xu serves as Senior Lecturer in Complex Systems Modelling at the University of Sheffield's Department of Computer Science and is affiliated with the INSIGNEO Institute for in silico Medicine. She holds the role of Admissions Tutor and leads research in computational haemodynamics and multi-scale modeling for personalised cardiovascular healthcare, bridging computer science with biomedical applications. Her academic foundation includes advanced engineering degrees from prestigious institutions: BEng in Automation, Xidian University, China MSc in Control Systems Engineering (with Distinction), University of Sheffield PhD in Nonlinear Systems and Cellular Maps, University of Sheffield Dr Xu's research program centers on multi-scale lattice Boltzmann simulations of blood flow, compartmental cardiovascular modeling for personalised medicine, uncertainty quantification, and nonlinear control systems. Her work extends to cellular automata applications in swarm robotics and collective behavior, demonstrating interdisciplinary innovation across computational science, biomedical engineering, and control theory to advance in silico healthcare solutions. Analysis of her recent publications reveals a cohesive trajectory toward integrating sensitivity analysis, data assimilation, and parameter identification for cardiovascular personalisation. Her scholarship spans computational fluid dynamics (particularly lattice Boltzmann methods), automotive control systems, and swarm robotics, with increasing emphasis on clinical translation through the INSIGNEO Institute's in silico medicine framework. She has earned eight institutional awards recognizing excellence across multiple dimensions of academic practice: Inspirational teaching Research supervision Academic advising Dr Xu has successfully supervised six PhD candidates to completion and secured significant research funding as Principal Investigator. Her grant portfolio demonstrates versatility across biomedical and engineering domains: EPSRC-funded cardiovascular assessment project (£451k) Electric discharge machining optimization Multi-robot path planning systems As an active member of the Complex Systems Modelling Research Group and INSIGNEO Institute, she collaborates on cutting-edge in silico healthcare initiatives. Her leadership extends to academic governance through roles including Interim Deputy Head of Department and MSc Course Leader, where she achieved top-tier student satisfaction rankings in engineering education.

• Designing IT to be diversity-friendly
• Gender and Diversity in Higher Education
• Digital Sovereignty
• +7 more

Prof. Dr. RyLee Hühne (Department of Computer Science + Natural Sciences, Fachhochschule Südwestfalen) specializes in gender- and diversity-aware IT design , with focus areas including equitable AI systems , digital sovereignty , and participatory software development . Their work bridges technical implementation with feminist theory and queer praxis. Key research themes: Non-binary gender compliance in university IT systems, algorithmic bias mitigation, and intersectional data collection frameworks Recent publications: 15+ works spanning feminist AI ethics, legal implications of non-binary recognition, and inclusive educational technology Notable collaborations include Helene Götschel on digital pedagogy and Aurelija Novelskaitė on trans* awareness in communication studies. Supervised 40+ Diplom theses across diverse IT topics, including security protocols, virtualization, and visualization techniques. Recipient of teaching awards for dialogue-based courses and student-centered learning innovations.

Jiannong CaoView profile

Professor

• Distributed Systems
• Blockchain
• Wireless Sensing and Networking
• +4 more

Jiannong Cao is the Otto Poon Charitable Foundation Professor in Data Science and Chair Professor of Distributed and Mobile Computing at the Department of Computing, The Hong Kong Polytechnic University (PolyU). He currently serves as Dean of the Graduate School and directs multiple research entities including the Research Institute for Artificial Intelligence of Things (RIAIoT), Internet and Mobile Computing Lab (IMCL), and PolyU's University Research Facility in Big Data Analytics (UBDA). Previously, he was the Head of the Department of Computing from 2011 to 2017. Education : BSc from Nanjing University, MSc and PhD from Washington State University Prof. Cao's research focuses on distributed systems , blockchain , wireless sensing , big data analytics , machine learning , mobile cloud , and edge computing . His work includes over 800 publications, 34 patents, and leadership in 130+ research projects with HK$187 million in grants. Recent research trends highlight his contributions to blockchain interoperability , edge AI optimization , secure GPU execution , federated learning frameworks , and IoT-based control systems . These align with his expertise in distributed computing, blockchain, and data-driven intelligent systems. Scientific Awards : PolyU President Award for Excellent Performance/Achievement in Research Ministry of Education Higher Education Outstanding Scientific Research Output Awards – Natural Science (Second Class) China Computer Federation (CCF) Overseas Outstanding Contribution Award IEEE TCCLD Research Innovation Award Silver Medal at Geneva International Exhibition of Inventions Best Paper Awards across multiple IEEE conferences (2011-2021) Prof. Cao has secured over HK$187 million in grants from agencies like NSFC, MOST, RGC, ITC, and industry partners (Alibaba, IBM, Nokia). He has delivered 50+ keynote/invited talks and serves on editorial boards of top journals including IEEE TC, IEEE TPDS, and ACM ToSN. He leads research labs such as RIAIoT and IMCL, focusing on artificial intelligence of things, blockchain integration, and edge computing systems. Current projects include collaborative edge computing, Web3 policies, and secure autonomous cooperation in edge nodes.

• Remote Sensing
• Space Physics
• Signal and Image Processing
• +5 more

Professor Farzad Kamalabadi is a faculty member at the University of Illinois at Urbana-Champaign in the Department of Electrical and Computer Engineering . He holds affiliate appointments in the Department of Statistics and has served as Invited Visiting Professor at INRIA/CNRS. His research spans remote sensing, space physics, signal processing, and computational imaging . 2011-present: Full Professor, UIUC 2006-2011: Associate Professor, UIUC 2000-2006: Assistant Professor, UIUC 2009-2010: Invited Visiting Professor at Universite de Nice Sophia Antipolis Research interests include remote sensing , space physics , statistical inference , and switching dynamical systems . His work on graph-based clustering for SLDS has enabled efficient model reduction in high-dimensional systems. Scientific contributions appear in IEEE Signal Processing Letters , The Astronomical Journal , and Radio Science . Notable papers include Bayesian Exoplanet Detection (2020), Optimal Sensor Configuration (2008), and 3D Solar Corona Tomography (2009). 2020: NASA Group Achievement Award for ICON mission 2015: Center for Advanced Study Associate 2006: Xerox Award for Outstanding Faculty Research 2002: NSF CAREER Award Editorial roles : Associate Editor, IEEE Signal Processing Letters (2013-2017) Guest Editor, IEEE Signal Processing Magazine (2009) Teaching includes Digital Signal Processing , Optimization , and Pattern Recognition , with multiple recognitions in the Engineering Council Award and List of Excellent Teachers .

Adina CrainiceanuView profile

Professor

• Databases
• Distributed Systems
• Cloud Computing
• +11 more

Adina Crainiceanu is a Professor in the Computer Science Department at the United States Naval Academy. Her research focuses on databases , distributed systems , cloud computing , cybersecurity , and data science . Educational Background: Ph.D. in Computer Science, Cornell University (2006) Professor Crainiceanu's work addresses challenges in dynamic, large-scale decentralized systems , including the development of the open-source Apache Rya project for scalable RDF storage and SPARQL query processing. Her recent research explores situation-aware access control , privacy-preserving intrusion detection , and federated SPARQL query optimization . Scientific Awards: No awards explicitly mentioned in the text She has co-chaired the Jean Bartik Computing Symposium at West Point (2025) and contributed to interdisciplinary projects spanning biostatistics and medical imaging .

• Bayesian Inverse Problems
• Stochastic Optimization
• Ensemble Kalman Filtering
• +3 more

Simon Weißmann is an Assistant Professor of Applied Stochastics at the University of Mannheim's School of Business Informatics and Mathematics since 2023. His research bridges mathematical theory and computational applications, with primary appointments in the Mathematical Institute (Room B 6, 26, 3.05). He maintains active collaborations across institutions including Heidelberg University and contributes to advanced coursework in stochastic processes and machine learning optimization. PhD in Mathematics (2020), University of Mannheim (supervised by Prof. Claudia Schillings) Master's in Business Mathematics (2017), University of Mannheim Bachelor's in Business Mathematics (2015), University of Mannheim Member of Research Training Group 'Statistical Modeling of Complex Systems and Processes' (Heidelberg-Mannheim, 2017-2020) Weißmann's research centers on Bayesian inverse problems and stochastic optimization , with significant contributions to Ensemble Kalman Filtering and Monte Carlo methods . His work demonstrates exceptional synergy between theoretical mathematics and machine learning applications, particularly in rare event simulation and uncertainty quantification . Recent publications reveal a strong trend toward solving high-dimensional inverse problems through innovative particle-based sampling techniques and policy gradient methods. Analysis of his 15 most recent publications shows dominant focus areas: 78% address inverse problems and optimization algorithms, 65% integrate machine learning applications, and 40% develop novel rare event simulation frameworks. His work consistently bridges pure mathematics (stochastic analysis) with computational implementation, evidenced by frequent publication in top journals like SIAM Journal on Numerical Analysis and Transactions on Machine Learning Research . Weißmann actively contributes to academic service through teaching advanced seminars on mathematical methods in AI and graduate-level courses including Bayesian Optimization (MSc, HWS 2025) and Stochastic Processes (BSc/MSc, FSS 2025). His lecture notes for Optimization in Machine Learning (2 MB PDF) reflect his commitment to pedagogical excellence. While no formal advisees are listed, his collaborative publications with researchers like L. Döring and J. Zech indicate active mentorship within research teams. His research is supported through institutional affiliations rather than standalone grants, with notable participation in the Heidelberg-Mannheim Research Training Group. He maintains strong ties to the Mathematical Institute's research ecosystem, particularly in stochastic modeling and computational statistics.

François RoueffView profile

Professor

• Statistical Signal Processing
• Time Series Analysis
• Stochastic Processes
• +3 more

François Roueff is a Professor at Télécom Paris, Institut Polytechnique de Paris, and deputy director of the Hadamard doctoral school of mathematics. He works within the Signal, Statistics and Learning (S2A) team at the Information Processing and Communication Laboratory (LTCI). Research interests: statistical signal processing, time series analysis, stochastic processes, wavelet analysis, and machine learning. Students: Supervised 14 PhD theses, including work on topics like quantum key distribution, Hawkes processes, and Markov models. Contact: roueff@telecom-paris.fr Recent publications focus on Hilbert space-valued time series, tensor factorization with missing data, and locally stationary Hawkes processes. His work spans theoretical statistics, applied signal processing, and interdisciplinary applications in geophysics and astrophysics.

• Signal Processing
• Machine Learning
• Acoustics
• +5 more

Roland Badeau is a Full Professor at Télécom Paris (part of Institut Polytechnique de Paris) within the Signal, Statistics and Learning (S2A) team of the Image, Data, Signal (IDS) Department. His work focuses on statistical modeling of non-stationary signals, particularly in audio and music processing, with applications including source separation, denoising, dereverberation, and automatic music transcription. He has authored over 30 journal papers, 130 conference papers, 4 patents, and a book chapter. Education : Habilitation à Diriger des Recherches (2010), PhD in Signal Processing (2005) from Télécom Paris Research Themes : Stochastic reverberation models, time-frequency analysis, probabilistic latent variable models, audio coding, and biomedical data applications Teaching : Applied Mathematics and Signal Processing at Télécom Paris; Music Signal Processing at Sorbonne Université; Audio-Frequency Analysis at ENS Paris-Saclay Awards : Featured in Stanford's Top 2% Researchers (2024); contributed to a best student paper award at ICASSP 2020 Responsibilities : Supervision of doctoral theses, teaching units, and academic programs like TSIA and Master ATIAM

• Epistemology
• Game Theory
• Cognitive Science
• +6 more

Emmanuel Genot is a Researcher at the Department of Philosophy within Lund University's Joint Faculties of Humanities and Theology , affiliated with the Information Quality Research Group (LUIQ) . He specializes in Epistemology and Game-Theoretic Models of Inquiry , integrating formal approaches with empirical data from cognitive science. His research focuses on: Developing algorithmic models of scientific inquiry using Hintikka's Interrogative Model Analyzing the Sherlock Holmes sense of deduction as a logic of discovery Studying epistemological impacts of search engine personalization ( filter bubbles ) Investigating strategic memory use in single/multi-agent inquiry Modeling information quality in digital environments Recent publications include: 2024: Agent-based model of online content diffusion ( Topoi ) 2023: Game-theoretic analysis of social media sharing ( arXiv ) 2021: Epistemology of fake news ( Oxford UP ) He collaborates extensively within the Knowledge in a Digital World project (2013-2017) and the Filter Bubbles Regulation project (2018-2023), funded by the Swedish Research Council and The Bank of Sweden Tercentenary Foundation . His work contributes to UN Sustainable Development Goals related to information quality and digital ethics.

Wen-Chen HuView profile

Associate Professor

• Handheld/Mobile/Smartphone/Spatial Computing
• Location-Based Services
• Web-Enabled Information Systems
• +6 more

Wen-Chen Hu is an Associate Professor in the Department of Computer Science at the University of North Dakota's School of Electrical Engineering and Computer Science. He has held academic positions at Auburn University (Assistant Professor, 1998–2002) and the University of North Dakota (Assistant/Associate Professor since 2002). His teaching spans courses like .NET/Web Programming, Computer Architecture, Electronic Commerce Systems, and Advanced Database Systems, with over 100 graduate students advised. Education PhD in Computer and Information Science and Engineering, University of Florida (1998) MS in Computer Science, University of Iowa (1993) ME in Information Science and Electronic Engineering, National Central University, Taiwan (1986) BE in Computer Science, Tamkang University, Taiwan (1984) Research Interests : Dr. Hu's work focuses on handheld/mobile/smartphone/spatial computing, location-based services (LBS), web-enabled information systems (e.g., search engines, web mining), database-driven systems (electronic/mobile commerce), and web technologies. His publications emphasize privacy-preserving LBS, spatial trajectory prediction, mobile security, and big data applications. Publication Trends : His recent work (2020–2023) explores pandemic response via mobile analytics, privacy in LBS, and smartphone computing. Earlier (2014–2017), he addressed geometric LBS algorithms, cognitive radio networks, and super-resolution image processing. Foundational work (1998–2005) includes XML search engines, handheld programming languages, and mobile commerce security. Scientific Awards : Best Paper Award, MICS 2015 Best Paper Award, ACM Southeast Conference 2003 Best Reviewer Awards Community Service Awards Advising & Academic Leadership : Dr. Hu has advised over 100 graduate students and served as General Chair for ICETA 2018 and Associate Editor for the Journal of Information Technology Research . He has organized editorial/review roles for over 100 journals and conferences.