جست‌وجوی استاد و پژوهشگر

Nilanjan Chakrabortyمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Dynamical Systems
• +۵ مورد دیگر

Nilanjan Chakraborty is an Associate Professor in the Department of Mechanical Engineering at Stony Brook University, where he has been since 2014. His research focuses on robotics, artificial intelligence, and dynamical systems, with particular emphasis on developing robust autonomy for robots operating in collaboration with humans or independently. He directs the Interacting Robotic Systems Laboratory, exploring algorithmic foundations for multi-robot systems, manipulation planning, and human-swarm interaction. Chakraborty holds a Ph.D. from Rensselaer Polytechnic Institute (2008) and previously served as a Postdoc/Project Scientist at Carnegie Mellon University’s Robotics Institute. His work includes pioneering contributions to contact dynamics-based algorithms, multi-robot coordination, and human-robot interaction. He is an IEEE member and has reviewed for top-tier robotics conferences/journals. Research interests span: (1) Manipulation planning with environmental interactions, (2) Safe whole-body robot control using complementarity constraints, (3) Human-guided motion planning via screw geometry, (4) Swarm robotics coordination under uncertainty, and (5) Agricultural automation with cobots. Recent trends in his publications emphasize real-time obstacle avoidance, deep learning for mechanism design, and provable search methods with imperfect sensors. He has received the 2013 Best Paper Award (HFES) and 2007 Best Student Paper (RSS). His lab investigates topics like LiDAR-based tripping hazard detection, time-optimized trajectory planning, and connectivity-resilient multirobot teams. Ongoing work addresses challenges in human-swarm interfaces and adaptive grasping systems.

William McMahonمشاهده پروفایل

پژوهشگر ارشد

• Sedimentology
• Clay Mineralogy
• Paleobiology
• +۵ مورد دیگر

William McMahon is a Research Fellow at the University of Cambridge's Department of Earth Sciences and a member of Clare Hall College. His research focuses on sedimentology, clay mineralogy, paleobiology, and planetary geology, particularly exploring how Earth's early environments and extraterrestrial landscapes evolved. He holds a PhD from Cambridge and has conducted postdoctoral research at Utrecht University and the University of Hull. His work investigates the interplay between biological evolution and sedimentary processes, such as the impact of early land plants on fluvial systems and the role of microbial mats in fossil preservation. Key projects include analyzing Martian sedimentary rocks, studying Devonian forest ecosystems, and examining Ediacaran macrobiota habitats. McMahon employs advanced techniques like SEM-EDS mineral mapping to study clay assemblages and their implications for Earth's geochemical history. Publications span topics from pre-vegetation river dynamics to the fossil record's chronostratigraphic fidelity. Collaborations with institutions globally highlight his role in interdisciplinary Earth science research, including contributions to the UK Space Agency's Mars Utah Rover Field Investigation (MURFI 2016). His research bridges sedimentology, geobiology, and planetary science, offering insights into Earth's deep-time ecosystems and extraterrestrial environments.

Christopher G. Fletcherمشاهده پروفایل

دانشیار

• Climate Modeling
• Climate Dynamics
• Snow Albedo Feedback
• +۵ مورد دیگر

Christopher G. Fletcher is an Associate Professor cross-appointed between the Department of Geography and Environmental Management and the Department of Applied Mathematics at the University of Waterloo, where he has been faculty since 2010. He serves on the governing body for the Waterloo Climate Institute and holds affiliations with the Computational Mathematics program. Previously, he was an Assistant Professor at Waterloo (2010-2017), SHARCNET Research Chair (2010-2012), and held postdoctoral positions at the University of Toronto. His education includes a PhD from University College London (2005), MSc from University of Reading (2001), and BSc from University of Manchester (2000). Fletcher leads a research group studying climate dynamics using global climate models to analyze variability from seasonal to centennial scales. Key research areas include: Arctic climate processes and snow-albedo feedbacks Machine learning applications to improve Earth System Models Extratropical teleconnections and land-ocean-atmosphere interactions Satellite remote sensing of snowfall and soil moisture His publications (87+ articles) demonstrate consistent focus on Arctic climate processes, model validation, and machine learning applications in climate science. Recent work emphasizes high-resolution snow modeling, climate tipping points, and geoengineering impacts. Honors include the SHARCNET Research Chair (2010-2012). Major grants support his research: NSERC Discovery Grant (2020-2024): 'Using statistical learning to build better Earth System Models' Microsoft AI for Social Good program funding Canadian Space Agency project on Arctic snowfall measurement (2018-present) NSERC Climate Change and Atmospheric Research networks (CanSISE, NETCARE) Fletcher actively advises graduate students, with 4 current PhD candidates and 21+ alumni from doctoral and master's programs. His lab develops novel observational datasets to constrain climate simulations, particularly for Canada's northern regions.

Gabriel Kerekesمشاهده پروفایل

پژوهشگر ارشد

• Geodesy
• Terrestrial Laser Scanning
• Engineering Surveying
• +۵ مورد دیگر

Gabriel Kerekes serves as a Postdoctoral Researcher and Group Leader of 3D Data Acquisition and Monitoring at the Institute of Engineering Geodesy Stuttgart (IIGS), part of the Faculty of Aerospace Engineering and Geodesy at the University of Stuttgart. He is actively affiliated with the Cluster of Excellence IntCDC (Integrative Computational Design and Construction for Architecture), contributing to cutting-edge research in computational construction methodologies. Dr. Kerekes specializes in terrestrial laser scanning (TLS), engineering surveying, and 3D data acquisition systems. His research addresses critical challenges in point cloud processing, stochastic modeling of geodetic measurements, and robotic total station networks for real-time construction monitoring. He develops innovative solutions for deformation analysis, geometric quality control of bio-based building elements, and integration of multi-sensor systems in architectural applications, with significant contributions to biomimetic shell construction and infrastructure monitoring projects. No scientific awards or fellowships were documented in the provided materials. Dr. Kerekes has supervised ten Master's and Bachelor's students since 2018, with research spanning TLS intensity analysis, atmospheric error modeling, and robotic positioning systems. His grant-funded projects include RP 16-2 (Spider Crane Robotic Platform) and AP 7 (Integrated Space-Time Point Cloud Modeling) under the IntCDC cluster. As leader of the 3D Data Acquisition and Monitoring team, he drives advancements in geodetic measurement technologies for next-generation construction processes.

Paul Marriottمشاهده پروفایل

استاد

• Geometric Statistics
• Differential Geometry
• Mixture Models
• +۶ مورد دیگر

Paul Marriott is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on integrating geometric principles, particularly differential and convex geometry, into statistical methodologies, with a recent emphasis on mixture models and information geometry. He has published extensively across diverse journals such as Biometrika, Annals of Statistics, and Psychological Medicine, bridging theoretical and applied statistics. Education: PhD, University of Warwick (1989) MA, University of Oxford (1984) Research Trends: His work explores geometric frameworks for statistical inference, mixture model parameterization, and robustness analysis. Recent publications highlight causal modeling, neural spike train analysis, and high-dimensional data applications. Contact: Office: Mathematics & Computer Building (M3) 4204, Phone: 519-888-4911 x35545, Email: pmarriot@math.uwaterloo.ca

• Machine Learning
• Computational Modeling
• Biomedical Engineering
• +۳ مورد دیگر

Stephen Baek is a faculty member at the University of Iowa, Department of Industrial and System Engineering, with a PhD from Seoul National University's School of Mechanical and Aerospace Engineering. His research spans interdisciplinary applications of machine learning in computational modeling, biomedical engineering, and geometric data processing. Current Affiliation: University of Iowa, Department of Industrial and System Engineering PhD Institution: Seoul National University Stephen's work focuses on physics-informed machine learning , multiscale modeling , and geometric deep learning . He develops algorithms that integrate physical principles with neural networks for applications in energetic materials , human pose estimation , and medical imaging . His research also includes federated learning and interpretable AI for constraint-based synthesis and text classification. Recent publications highlight a physics-aware deep learning framework (PARCv2) for spatiotemporal dynamics, graph convolutional networks for airway mesh smoothing, and prototype trajectory methods for explainable AI. This work bridges computer science with applied physics and healthcare domains. Stephen actively collaborates across disciplines, evidenced by co-authors from institutions like Iowa, Seoul National University, and Samsung Electronics, with publications in venues such as CoRR , J. Mach. Learn. Res. , and NeurIPS . His methodological innovations address challenges in model heterogeneity , 3D surface processing , and constraint satisfaction .

Mattia Stivalمشاهده پروفایل

پژوهشگر

• Health Inequalities
• Spatio-Temporal Modeling
• Sports Performance Analysis
• +۷ مورد دیگر

Mattia Stival is a Researcher at the Department of Economics , Ca' Foscari University of Venice , with a focus on social statistics (SSD: STAT-03/B). His work bridges Bayesian and computational statistics with applications in public health and sports science. Current statistician for the Planet4Health project Previously postdoctoral researcher for the Age-it project on multi-morbidity modeling PhD in Statistical Sciences (University of Padua, 2022) with thesis on Sports Performance Analysis with State Space Models Research Interests combine methodological innovation with real-world impact: Applied : Health inequalities, aging population dynamics, sport-for-health promotion, competitive sports analytics (talent identification, performance monitoring), diffusion models Methodological : Bayesian inference, computational statistics, spatio-temporal modeling, machine learning, Monte Carlo methods Publications demonstrate interdisciplinary trends across: Sports statistics (decathlon/heptathlon scoring, youth-to-elite transition) Bayesian spatio-temporal health modeling Missing data patterns in longitudinal athlete datasets Scientific Recognition : 2023 Honorable Mention for Best PhD Thesis in Applied Statistics by the Italian Statistical Society (SIS) Teaching includes statistics exercises for economics degrees and R coding in finance analytics. Office hours: Wednesday 10-12 by appointment.

• Machine Learning
• Optimization Algorithms
• Wireless Communication
• +۳ مورد دیگر

Yurii Malitskyi is an Assistant Professor at the Faculty of Mathematics , University of Vienna , affiliated with the Department of Mathematics . His research focuses on optimization algorithms, machine learning, and wireless communication systems.

• Cybersecurity
• Drone/UAV Security
• Wireless Network Security
• +۶ مورد دیگر

Ahmad Y. Javaid is an Assistant Professor in the Electrical Engineering and Computer Science Department within the College of Engineering at The University of Toledo. He serves as the founding director of the Paul A. Hotmer Cybersecurity and Teaming Research (CSTAR) lab and holds the position of Cyber Education and Cyber Threat Mitigation Faculty Fellow. Dr. Javaid received his B.Tech. (Hons.) Degree in Computer Engineering from Aligarh Muslim University, India in 2008 and his Ph.D. from The University of Toledo in 2015, where he was awarded the prestigious University Fellowship Award. Prior to joining the University of Toledo faculty, he worked for two years as a Scientist Fellow in the Ministry of Science & Technology, Government of India. Dr. Javaid's research spans multiple domains within cybersecurity and human-machine interaction. His primary expertise lies in cyber security of drone networks, smartphones, and wireless sensor networks. He has conducted extensive research on human-machine teams and applications of AI and machine learning to attack detection and mitigation. His work bridges theoretical security frameworks with practical implementations across various domains including UAV security, physical layer communications security, and multimodal human-computer interaction systems. His recent publications demonstrate a continued expansion into nuclear-renewable energy systems security and advanced cybersecurity education frameworks for K-12 students. Dr. Javaid has secured significant research funding, with collaborative proposals totaling $9.7M (including all partners along with UToledo), of which $1.4M has been allocated specifically to him. Approximately $4.1M has been allocated to the University of Toledo from these projects. His research has been funded by prestigious agencies including the NSF, AFRL, NASA-JPL, Department of Energy, and the State of Ohio. He has also played a critical role in cultivating private gifts to support the CSTAR lab. IEEE Graduate Student Member (2012) IEEE Member (2015) IEEE Senior Member (2019) University Fellowship Award As an educator, Dr. Javaid has developed innovative cybersecurity curriculum modules and educational tools. He has served as a reviewer for high-impact journals and as a member of technical program committees for reputed conferences. His work extends to cybersecurity education initiatives targeting K-12 students, with projects focused on developing interactive learning modules to improve cybersecurity awareness at the high school level. He has published more than 85 peer-reviewed publications across journals, conferences, and posters, demonstrating consistent scholarly productivity with significant output in recent years. Dr. Javaid leads the Paul A. Hotmer Cybersecurity and Teaming Research (CSTAR) lab, which focuses on cutting-edge research in cybersecurity, particularly in the areas of UAV security, physical layer security, and human-machine teaming. The lab has received support from both government agencies and private sources, enabling the development of novel security frameworks and educational tools. His interdisciplinary approach bridges computer science, electrical engineering, cybersecurity, and human factors research, making significant impacts in both academic and practical cybersecurity domains.

Juseong Leeمشاهده پروفایل

استادیار

• Predictive Maintenance
• Industrial Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Juseong Lee is an Assistant Professor at the Eindhoven University of Technology (TU/e), affiliated with the Department of Industrial Engineering and Innovation Sciences. His research focuses on integrating data-driven models and artificial intelligence into industrial processes, with a specific emphasis on predictive maintenance approaches for engineering systems. PhD in Aerospace Engineering from Delft University of Technology (2022) MSc and BSc in Aerospace Engineering from Korea Advanced Institute of Science and Technology (2018, 2016) His work employs methodologies such as digital twins, multi-objective optimization, and deep learning to enable real-time decision-making in business contexts. He aims to improve the reliability, efficiency, and sustainability of Industry 4.0 systems through his research. Recent publications highlight his expertise in predictive maintenance, with applications to aircraft systems, landing gear brakes, and turbofan engines. Key techniques include Gaussian process learning, adaptive sampling, deep reinforcement learning, and stochastic Petri nets. These studies often involve collaborations with researchers like Mihaela Mitici. Lee's research is supported by the European Union's Horizon 2020 program (Grant No. 769288). He teaches courses on quality and reliability engineering, maintenance optimization, and service logistics.

Pedro Felzenszwalbمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۲ مورد دیگر

Pedro Felzenszwalb is a Professor of Engineering and Computer Science at Brown University , with a research focus spanning computer vision, artificial intelligence, machine learning, and algorithms. Born in Rio de Janeiro, Brazil, he earned his BS in Computer Science from Cornell University (1999) and MS/PhD in EECS from MIT (2001/2003). He previously held a faculty position at the University of Chicago (2004-2011) before joining Brown in 2011. Education : PhD in EECS, MIT (2003) MS in EECS, MIT (2001) BS in Computer Science, Cornell University (1999) His research integrates computer vision and AI, emphasizing scalable algorithms for object recognition, image segmentation, and probabilistic modeling. Key methodologies include deformable part models, belief propagation, and dynamic programming. His work has significant applications in early vision tasks, scene understanding, and geometric constraints in 3D object recognition. Pedro’s publications demonstrate a trajectory from foundational graph/image algorithms (2004-2006) to advanced machine learning approaches (2010-2023), with recurring themes in optimization, clustering, and multiscale modeling. Notable journals include Journal of the ACM , IEEE Transactions , and Communications of the ACM . Scientific Awards : ACM Grace Murray Hopper Award IEEE Technical Achievement Award PASCAL Visual Object Challenge Lifetime Achievement Prize Longuet-Higgins Prize NSF CAREER Award He has received NSF funding for projects including Graph Cut Algorithms (2012-2015) and Object Recognition with Hierarchical Models (2008-2013). At Brown, he teaches graduate courses in machine learning, linear systems, and pattern recognition.

Shmuel Avidanمشاهده پروفایل

استاد

• Object Tracking
• 3D Object Modeling
• Computer Vision
• +۳ مورد دیگر

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

Chuang Ganمشاهده پروفایل

استادیار

• Computer Vision
• Graph Deep Learning
• Multimodal Learning
• +۵ مورد دیگر

Chuang Gan is a distinguished researcher holding dual positions as a Principal Research Staff Member at the MIT-IBM Watson AI Lab and an Assistant Professor at the University of Massachusetts Amherst. His work bridges academic research and industrial applications in artificial intelligence, with particular focus on advancing the frontiers of computer vision and multimodal learning systems. Dr. Gan's research interests span multiple interconnected domains within artificial intelligence. He specializes in video understanding, with deep expertise in representation learning, neural-symbolic visual reasoning, audio-visual scene analysis, and embodied intelligence. His work frequently integrates graph deep learning techniques with neuro-symbolic approaches to create more interpretable and robust AI systems. The recurring themes across his research portfolio include developing models that can understand physical dynamics from visual inputs, creating systems capable of embodied reasoning, and building bridges between symbolic and neural approaches to artificial intelligence. His publications reveal a strong trend toward increasingly sophisticated multimodal systems that integrate visual, auditory, and linguistic information. Over time, his work has evolved from basic video understanding tasks to complex embodied reasoning systems capable of physical simulation, 3D scene understanding, and multi-agent collaboration. A notable pattern is the progression from analyzing static scenes to understanding dynamic physical interactions and embodied agent behaviors in increasingly complex environments. Microsoft Fellowship Baidu Fellowship Dr. Gan's research has received significant recognition from major technology companies through prestigious fellowships and has been widely covered by leading media outlets including CNN, BBC, The New York Times, WIRED, Forbes, and MIT Tech Review. His work at the MIT-IBM Watson AI Lab provides him with access to substantial resources for cutting-edge AI research, while his academic position enables him to train the next generation of AI researchers. His collaborations with prominent researchers like Antonio Torralba demonstrate his integration within the top echelons of the computer vision and AI research community. At the MIT-IBM Watson AI Lab, Dr. Gan leads research initiatives focused on advancing video understanding and embodied intelligence. His work contributes to the lab's mission of developing AI systems that can perceive, reason about, and interact with the physical world in more human-like ways. His research group likely focuses on developing novel architectures for multimodal learning, creating benchmarks for physical reasoning, and building systems that can transfer knowledge between simulation and real-world environments.

Ganggang Xuمشاهده پروفایل

دانشیار

• Nonparametric and Semiparametric Statistics
• Functional Data Analysis
• Spatial Statistics
• +۳ مورد دیگر

Ganggang Xu is an Associate Professor (with tenure) in the Department of Management Science at the Miami Herbert Business School , University of Miami . He specializes in advanced statistical methodologies, particularly in nonparametric and semiparametric modeling, spatial statistics, and point process theory. Education: Ph.D. in Statistics, Texas A&M University (2011) B.S. in Statistics, Zhejiang University (2006) Research Interests: His research spans several key areas in modern statistics and data science. He has made significant contributions to nonparametric and semiparametric regression , particularly in the context of functional data analysis and spatial-temporal modeling . His work on point processes includes marked, multivariate, and clustered point processes, with applications ranging from neuroscience to social media behavior. He also explores Bayesian hierarchical models and model selection techniques, often integrating computational efficiency with theoretical rigor. Publications Overview: His recent publications (2023–2025) reflect a strong focus on machine learning-enhanced statistical modeling , including tree-based estimation of intensity functions, network autoregressive models, and quantized inference. He has also contributed to applied domains such as medical imaging and inventory control , demonstrating the broad applicability of his methodological work. Grants & Collaborations: While specific grants are not listed in the provided text, his extensive publication record with multiple co-authors across institutions suggests active collaboration and possible funding from NSF or NIH-equivalent bodies in statistics and data science. Labs & Teams: Though no specific lab is mentioned, his affiliations and co-authorships imply involvement in interdisciplinary research teams at the University of Miami, especially within the business analytics and statistical modeling domains.

• text processing algorithms
• classification of texts
• data processing and analysis
• +۳ مورد دیگر

Голуб Тетяна Василівна serves as an Associate Professor in the Department of Computer Systems and Networks at Zaporizhzhia National Technical University's Faculty of Computer Science and Technologies. With over 12 years of academic experience since joining the university in 2011, she specializes in computer electronics, computer circuitry, signal and image processing, and reliability of computer systems. Dr. Golub's research focuses on text processing algorithms, text classification, and data processing with particular emphasis on hardware acceleration using FPGA technologies. Her work bridges theoretical computer science with practical engineering applications, developing efficient methods for natural language processing tasks through specialized hardware implementations. She has pioneered approaches to optimize text classification speed while maintaining accuracy through innovative vector space modeling and stemming algorithms. Her publication record shows a strong trajectory from foundational work on text classification methods (2019) through hardware acceleration techniques (2020-2021) to current research on AI applications in education and neural network-based classification systems (2023-2025). The research demonstrates consistent focus on improving computational efficiency of text processing systems while expanding into educational technology applications. Dr. Golub maintains active scholarly presence with verified profiles on Scopus (ID: 57189328111), Web of Science (Researcher ID: G-9688-2019), Google Scholar, and ORCID (0000-0001-6024-008X), reflecting her commitment to academic transparency and international scholarly communication. She teaches core computer engineering subjects while conducting research that combines theoretical computer science with practical hardware implementation. Her laboratory work in computer electronics provides students with hands-on experience in circuit design and digital systems. Fluent in English, Ukrainian, and Russian, Dr. Golub operates from room 53b at the university's Zaporizhzhia campus (69063, Ukraine, Zhukovsky Street, 64).