جست‌وجوی استاد و پژوهشگر

Danfei Xuمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Robot Learning
• +۳ مورد دیگر

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 Nezamoddinمشاهده پروفایل

دانشیار

• Statistical Modeling
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

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 Xuمشاهده پروفایل

مدرس ارشد

• Computational Haemodynamics
• Cardiovascular Modeling
• Personalised Medicine
• +۸ مورد دیگر

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
• +۷ مورد دیگر

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 Caoمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain
• Wireless Sensing and Networking
• +۴ مورد دیگر

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
• +۵ مورد دیگر

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 Crainiceanuمشاهده پروفایل

استاد

• Databases
• Distributed Systems
• Cloud Computing
• +۱۱ مورد دیگر

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
• +۳ مورد دیگر

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 Roueffمشاهده پروفایل

استاد

• Statistical Signal Processing
• Time Series Analysis
• Stochastic Processes
• +۳ مورد دیگر

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
• +۵ مورد دیگر

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
• +۶ مورد دیگر

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 Huمشاهده پروفایل

دانشیار

• Handheld/Mobile/Smartphone/Spatial Computing
• Location-Based Services
• Web-Enabled Information Systems
• +۶ مورد دیگر

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.

Jim McCannمشاهده پروفایل

دانشیار

• Creativity Support Tools
• Real-time Systems
• Textiles Fabrication
• +۷ مورد دیگر

Jim McCann is an Associate Professor at the Robotics Institute of Carnegie Mellon University. He holds a PhD from Carnegie Mellon University (2010), advised by Nancy Pollard, and has held positions at Adobe Research and Disney Research Pittsburgh. His academic journey includes postdoctoral work and industry experience in game development before joining CMU's faculty in 2017. His research focuses on creativity support tools spanning real-time systems, textiles fabrication, machine knitting, and interactive design. Key themes include: Developing compilers and interfaces for machine knitting (e.g., 3D shape knitting, knitout semantics) Building accessible fabrication tools for textiles and soft objects Creating parameterized design spaces enhanced with machine learning Advancing physics-based animation and simulation tuning His publications demonstrate strong interdisciplinarity, with recent work emphasizing textiles computing (knitting compilers, fabric 3D printing), human-AI collaboration (design adjectives), and novel interfaces (infinity mirrors, RFID systems). Earlier contributions established foundations in gradient-domain editing, fluid control, and motion synthesis. He leads the Carnegie Mellon Textiles Lab and has advised 9+ graduate students on topics ranging from knit microstructures to robot design. His teaching includes courses on Algorithmic Textiles Design, Real-Time Graphics, and Game Programming.

Roy Edgar Hansenمشاهده پروفایل

استاد

• Synthetic Aperture Sonar
• Signal Processing
• Underwater Acoustics
• +۴ مورد دیگر

Roy Edgar Hansen is Professor II in acoustic imaging at the Department of Informatics, University of Oslo, with his primary position as Chief Scientist at the Norwegian Defence Research Institute (FFI). His work bridges academic research and defense applications in underwater acoustics and signal processing. Hansen's research focuses on synthetic aperture sonar (SAS), imaging techniques, sonar signal processing, array signal processing, and statistical signal processing. His work spans theoretical developments in signal processing algorithms to practical applications in underwater imaging, seabed mapping, and target detection. He has made significant contributions to interferometric SAS, artifact reduction techniques, and the application of machine learning to sonar image analysis. His recent publications demonstrate a strong trend toward integrating advanced signal processing with machine learning techniques for SAS applications. He has published extensively on interferometric SAS for bathymetric mapping, uncertainty quantification in deep learning applications for sonar, and the development of novel processing techniques for point scatterer detection and image enhancement. His work covers both fundamental signal processing theory and practical implementation challenges in underwater environments. Hansen teaches courses including IN5340 Statistical Signal Processing, IN3015 Ultrasound Imaging, and IN5450 Array Signal Processing at the University of Oslo. His research is associated with the Digital Signal Processing and Image Analysis (DSB) research group and the Centre for Innovative Ultrasound Solutions (CIUS) project.

• Mathematical Foundations of Machine Learning
• Online Convex Optimization
• Statistical Learning Theory
• +۵ مورد دیگر

Dr. Tim van Erven is an Associate Professor at the University of Amsterdam , affiliated with the Korteweg-de Vries Institute for Mathematics . His research focuses on the mathematical foundations of machine learning , particularly adaptive methods in online convex optimization, PAC-Bayesian concentration inequalities, and robust learning strategies. Research Interests : Mathematical machine learning, explainable AI, online learning, statistical learning theory, and adaptive optimization. Grants & Awards : VICI (2025), VIDI (2019), TOP grant (2016), NIPS Outstanding Reviewer (2014), Rubicon (2011). Teaching : Involved in graduate-level machine learning and statistical learning theory courses. His recent work explores explainable machine learning and robust optimization , with a focus on algorithmic recourse, gradient filtering, and theoretical guarantees. His publications span top venues like NeurIPS, COLT, and JMLR, often addressing the intersection of statistics and machine learning. He co-organized workshops such as the AI & Mathematics (AIM) initiative and contributed to the COLT program committee. His group develops MetaGrad and other adaptive algorithms for online learning.