جستجوی استاد راهنمای دانشگاهی

Gheorghi Guzunمشاهده پروفایل

دانشیار

• Data Management
• Machine Learning
• Data Compression
• +۴ مورد دیگر

Dr. Gheorghi Guzun is an Associate Professor in the Department of Computer Engineering at San José State University (SJSU), where he has served since 2017. He holds a Ph.D. in Electrical and Computer Engineering from the University of Iowa (2016) and a BSc in Electrical Engineering from the Technical University of Moldova (2010). His research focuses on scalable data management systems, machine learning, and hardware-driven software design, with applications in energy-efficient data centers, infrastructure monitoring, and predictive analytics. He is a recipient of the NSF CAREER Award for his work on efficient AI systems. Education background: Ph.D. in Electrical and Computer Engineering, University of Iowa (2016) BSc in Electrical Engineering, Technical University of Moldova (2010) Research interests span data compression, large-scale indexing, and algorithm optimization for machine learning. His projects emphasize scalability and performance in distributed systems, with recent work addressing energy efficiency and sparsity-driven methods for AI. He also develops educational technologies, such as competitive learning platforms to enhance student engagement. Notable achievements include the NSF CAREER Award and contributions to infrastructure monitoring systems, flood event modeling, and predictive university admission yield estimation. His lab's work is documented at https://g-guzun.github.io/ . Grants and advising: His NSF-funded CAREER project explores scalable sparsity-driven methods for AI systems. He collaborates on projects involving distributed data analytics and maintains an active research lab at SJSU.

Lu Tianمشاهده پروفایل

استاد

• Survival Analysis
• Semiparametric Modeling
• Restricted Mean Survival Time
• +۴ مورد دیگر

Lu Tian is a Professor of Biomedical Data Science in the School of Medicine at Stanford University, with a courtesy appointment as Professor of Statistics. He has been at Stanford since 2008, progressing from Assistant Professor to his current full Professor position in the Department of Biomedical Data Science. Sc.D. in Biostatistics, Harvard University (1998-2002) M.S. in Mathematics, Nankai University, Tianjin, P.R. China (1995-1998) B.S. in Mathematics, Nankai University, Tianjin, P.R. China (1991-1995) Professor Tian's research spans several critical areas in biostatistics and data science. His work focuses on developing innovative statistical methodologies with direct clinical applications. He has made significant contributions to survival analysis, particularly in restricted mean survival time methods, which offer clinically interpretable alternatives to traditional hazard ratios in clinical trials. His research in meta-analysis addresses limitations of existing methods, especially for studies with sparse data or few studies. In personalized medicine, he develops statistical frameworks for identifying patient subgroups that benefit most from specific treatments. His recent publications demonstrate a clear trend toward developing statistically rigorous methods with direct clinical interpretability. Rather than focusing solely on theoretical advances, his work emphasizes practical applications in oncology and cardiology clinical trials, where he develops methods that provide clear, actionable insights for clinicians. His research bridges the gap between complex statistical theory and real-world clinical decision-making, with particular emphasis on survival analysis techniques that clinicians can readily interpret and apply. Wangkechang Scholarship, Nankai University (1991-1998) Howard Hughes Fellowship (1999-2002) Robert B. Reed Award for Excellence in Biostatistics, Harvard (2000) Distinction in Teaching Award, Harvard School of Public Health (2000-2002) Professor Tian has served as Associate Editor for several prominent journals including Biometrics, Statistics in Medicine, and Biostatistics and Epidemiology, and as Book Editor for Advanced Medical Statistics. He was a Board Director of the International Chinese Statistical Association (2015-2018). His methodological contributions have been widely adopted in clinical research, particularly his work on restricted mean survival time which has changed how treatment effects are reported in oncology trials. His development of exact inference methods for meta-analysis has provided more reliable approaches for combining evidence from small studies.

Mac Schwagerمشاهده پروفایل

دانشیار

• Multi-Robot Systems
• Distributed Algorithms
• Autonomous Systems
• +۷ مورد دیگر

Mac Schwager is an Associate Professor in the Department of Aeronautics and Astronautics at Stanford University, with a courtesy appointment in Computer Science. He leads the Multi-robot Systems Lab (MSL), focusing on distributed algorithms for coordination, estimation, and learning in autonomous systems. His research spans multi-robot control, swarm intelligence, and perception-driven robotics. Schwager holds a PhD (2009) from MIT in Mechanical Engineering, following an MS (2005) and BS (2000) from MIT and Stanford, respectively. His academic career includes positions at Boston University and postdoctoral work at UPenn/MIT. Research interests emphasize distributed algorithms for autonomous teams, including aerial camera networks, quadrotor swarms, environmental monitoring, and human-swarm interfaces. Key projects include trustworthiness in multi-robot systems and autonomous drone racing. His lab develops tools like Gaussian Splatting for navigation and manipulation, integrating computer vision and reinforcement learning. Teaching includes advanced courses on state estimation, multi-robot control, and feedback systems. His work bridges robotics, control theory, and AI, addressing challenges in scalability, safety, and adaptability in complex robotic systems.

Ian Wassellمشاهده پروفایل

مدرس ارشد

• Wireless Communications
• Sensor Networks
• Cooperative and Relay Networks
• +۷ مورد دیگر

Dr. Ian Wassell is a University Senior Lecturer at the University of Cambridge's Department of Computer Science and Technology. He holds a PhD from the University of Southampton (1990) and BSc/BEng degrees from the University of Loughborough (1983). His research focuses on wireless communications, sensor networks, and electromagnetic propagation modeling. He has authored over 200 publications since 1999 and supervised 23 PhD and 6 MPhil students. Currently, he leads wireless communications research in the Digital Technology Group and is a Fellow of Churchill College. His research interests include broadband fixed wireless access (FWA), cooperative networks, MIMO systems, compressive sensing, and AI-driven radio propagation models. He teaches Digital Electronics and Hardware Practical Classes at the undergraduate level. Key contributions include developing data-driven propagation models, deep learning applications in wireless networks, and optimizing heterogeneous radio access networks. Dr. Wassell's work integrates theoretical advancements with practical systems, addressing challenges in high-speed rail communications, indoor/outdoor detection, and 6G IoT opportunities. His research bridges signal processing, machine learning, and wireless network design, emphasizing real-world deployment in infrastructure like tunnels and vehicles.

Fan Yangمشاهده پروفایل

استادیار مدعو

• Networking Protocols
• Internet Architecture
• Telecommunications
• +۳ مورد دیگر

Fan Yang is an Adjunct Assistant Professor at Boston University and the lead of Google’s networking protocol team. He specializes in internet architecture and protocol engineering, notably driving Google’s industry-first deployment of HTTP/3 over QUIC, which significantly improved latency for Google services. His research focuses on optimizing network protocols, satellite communications, and OFDM systems. Education: PhD in Computer Science from the University of Delaware (2015), where he worked in Professor Paul D. Amer’s Protocol Engineering Lab. Research Interests: Developing scalable internet protocols, enhancing TCP and multipath TCP performance, and applying advanced signal processing techniques to wireless communication challenges. His work bridges theoretical protocol design with real-world internet-scale deployment. Key technical contributions include QUIC protocol specifications, non-renegable selective acknowledgments (NR-SACKs), and channel estimation methods for OFDM systems. His publications span topics from HTTP/3 implementation to satellite-based TCP optimizations. Labs/Teams: Previously associated with the Protocol Engineering Lab at University of Delaware. Currently leads Google’s networking protocol team responsible for cutting-edge internet infrastructure projects.

Letty Koopmanمشاهده پروفایل

استادیار

• Psychometrics
• Statistics & Probability
• Education & Educational Research
• +۵ مورد دیگر

Dr. Letty Koopman is an Assistant Professor at the University of Groningen's Faculty of Behavioural and Social Sciences, Department of Educational Science. Her research focuses on psychometrics, statistics, and educational measurement, particularly in item response theory, multilevel data analysis, and Mokken scale analysis. She has contributed to advancing methodologies for clustered and two-level test data, addressing challenges in scalability coefficients and model fit evaluation. Her work bridges theoretical developments with practical applications in educational and psychological testing. Key research interests include nonparametric item response theory, inter-rater reliability, and statistical methodologies for complex data structures. Recent publications explore assumptions of two-level models and their applications in psychological and educational measurement. Koopman collaborates extensively with peers like B.J.H. Zijlstra and L.A. van der Ark, contributing to high-impact journals such as Psychometrika and Educational and Psychological Measurement . No scientific awards or grants are explicitly mentioned in the provided texts. Her work emphasizes methodological rigor in scaling techniques and improving test instruments for pedagogical practices. She maintains an active research agenda, with ongoing contributions to quantitative psychology and educational statistics.

Qianwen Xuمشاهده پروفایل

استادیار

• Power Systems
• Renewable Energy Integration
• Reinforcement Learning
• +۵ مورد دیگر

Qianwen Xu is an Assistant Professor at KTH Royal Institute of Technology and a Digital Futures Faculty member. She specializes in power systems, renewable energy integration, and control systems, with a focus on smart grids, hydrogen energy, and machine learning applications in energy infrastructure. Her work addresses challenges in sustainable energy systems, grid stability, and decentralized control mechanisms. Active research projects include the C3.ai DTI-funded initiative on data-driven control of smart converters using deep reinforcement learning, and the 'Autonomous coordination and control of smart converters' project. She explores topics like electric mobility systems, climate-resilient building designs, and cyber-physical system security. Her research spans technical areas such as reinforcement learning for power converter control, hybrid energy storage systems, and resilience of distribution networks against climate change. She collaborates with Digital Futures, a center focused on advancing digital technologies for societal impact, established jointly with Stockholm University and RISE Research Institutes.

Clive Pariniمشاهده پروفایل

استاد

• Microwave and Millimeter-Wave Antenna Measurements
• Compact Antenna Test Ranges
• Array Antenna Design
• +۵ مورد دیگر

Clive Parini is a Professor of Antenna Engineering at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science and leading the Antennas & Electromagnetics Research Group. He holds a BSc(Eng) and PhD from Queen Mary College, University of London. His career includes significant contributions to antenna design, measurement techniques, and metamaterials. He has authored over 300 papers and co-authored the book 'Principles of Planan Near-Field Antenna Measurements' (2008). His research focuses on microwave/millimeter-wave antennas, compact test ranges, array design, and body-centric communications. Prof. Parini has received notable awards, including the IEE Measurements Prize (1990) and BAE Systems Bronze Award (2002). He is a Fellow of the IET and Royal Academy of Engineering. His leadership roles include Director of Research for the School of Electronic Engineering & Computer Science and past Chair of the IET Antennas & Propagation Professional Network. Research highlights include metamaterials for antennas, on-body communication systems, and high-frequency imaging. His work bridges academic and industrial applications, with contributions to 5G, satellite antennas, and compact test range technologies. Recent articles emphasize advanced measurement techniques, compressive sensing, and drone-based systems for antenna testing. Teaching spans undergraduate and postgraduate levels, covering satellite systems, microwave electronics, and research techniques. His work integrates computational electromagnetics, antenna diagnostics, and emerging 5G/6G challenges, ensuring his research remains impactful across academia and industry.

Luo Xiaoمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Nonparametric Regression
• Biostatistics
• +۵ مورد دیگر

Luo Xiao is an Associate Professor in the Department of Statistics at North Carolina State University. His research focuses on statistical methodologies such as nonparametric regression, functional data analysis, and high-dimensional data analysis, with applications in neuroimaging, wearable computing, and genetic data. He leads the Functional Data Analysis Lab at NC State and teaches courses like Nonparametric Regression and Statistical Inference. Education: Ph.D. in Statistics from Cornell University (2012). Research interests emphasize statistical theory and applications, including: Nonparametric and semiparametric regression Functional data analysis Biostatistics Machine learning integration with functional data Medical imaging and single-cell genetic data analysis Recent publications highlight advancements in multivariate functional modeling, Bayesian inference for longitudinal data, and applications in Alzheimer's disease research. He has advised students such as Wanying Ma and Stephanie T. Chen. His work is supported by collaborations in medical and computational domains. Lab affiliations include the Functional Data Analysis Lab, with ongoing projects on wearable data analysis and multi-modal data integration. Teaching responsibilities include advanced statistical courses.

Thomas Trautnerمشاهده پروفایل

مدرس

• Visualization Hybridization
• Spatialization Cues
• Data Visualization
• +۳ مورد دیگر

Thomas Trautner is a University Lecturer at the Department of Informatics, University of Bergen, Norway, specializing in visualization techniques. He holds a PhD in Visualization (2022) and master's/bachelor's degrees in Visual Computing from TU Wien, Austria. Education: Bachelor's in Media Informatics and Visual Computing (2016), TU Wien Master's in (Media Informatics and) Visual Computing (2018), TU Wien PhD in Visualization (2022), University of Bergen Research Interests: Focuses on Visualization Hybridization—combining visualization techniques using spatialization cues to enhance data representation. Key areas include: Overplotting solutions for dense data (e.g., Honeycomb Plots, Sunspot Plots) Order-dependent rendering optimizations (e.g., Line Weaver) Interactive analysis of molecular dynamics simulations Publications: His work spans 2019–2022, emphasizing novel visualization methods for dense datasets and hybrid techniques. Notable contributions include integrating spatial encoding cues and addressing transparency order challenges in line charts. Labs/Teams: Member of the Visualization Research Group at UiB, collaborating on projects like Line Weaver and Honeycomb Plots . Active on GitHub with repositories for visualization tools.

• Nonlinear Model Predictive Control
• Offshore Drilling Automation
• Power Systems Control
• +۳ مورد دیگر

Lars Struen Imsland is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU) since 2009. He holds a PhD and MSc in Engineering Cybernetics from NTNU, with research focusing on nonlinear model predictive control, offshore drilling automation, and power systems. His career includes roles at Cybernetica AS, SINTEF ICT, and NTNU as a PostDoc and doctoral student. Research interests span control systems engineering, CO2 capture technology, and estimation in automotive applications. He chairs committees for international conferences like DYCOPS-CAB and ACOOG, and serves as Associate Editor for IEEE Transactions on Control Systems Technology. Key achievements include the Arch T. Colwell Merit Award (2008) and Automatica Outstanding Reviewer recognition (2004). He has advised over 10 PhD committees and contributed to pioneering work in MPC for wind farms, hybrid energy systems, and oil well modeling. Publications emphasize interdisciplinary applications of control theory, with recent focus on Bayesian optimization for wind farms, physics-informed neural networks for oil well monitoring, and stochastic MPC for renewable energy integration.

Shinjiro Suedaمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Animation
• Computational Biomechanics
• +۴ مورد دیگر

Shinjiro Sueda is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads the Aggie Graphics Group. His research focuses on physically-based modeling, biomechanical simulation, and computational fabrication. He also holds an adjunct appointment at Baylor University. Education: Ph.D. in Computer Science, University of British Columbia (2011) M.S. in Computer Science, Rutgers University (2006) B.Sc. (Hons) in Computer Science, University of British Columbia (2003) Postdoctoral Fellowships: Disney Research Boston & MIT (2011–2013) Research Interests: Computer Graphics and Animation Computational Biomechanics (musculoskeletal systems, muscle dynamics) Computational Fabrication (3D printing, mechanical design) Robotics and Haptic Interaction Virtual and Augmented Reality Applications Publications: His work spans 100+ peer-reviewed articles in top venues like ACM Transactions on Graphics (SIGGRAPH), with recent focus on neural simulation techniques, deformable physics, and robotics integration. Notable contributions include REDMAX (articulated dynamics), AutoConnect (3D-printable connectors), and Simit (physical simulation language). Awards: NSF CAREER Award (2019) Disney Inventor Award (2013) Best Paper Awards (SIGGRAPH Asia 2020, SWS 2023) Teaching & Service: Teaches courses on computer graphics, animation, and computational fabrication Program Chair: Motion, Interaction & Games (2020); Eurographics (2023) Associate Editor: ACM Transactions on Graphics (2022–present) Labs: Directs the Aggie Graphics Group, collaborating with industry partners like Disney Research and MIT. Active in developing open-source tools for physical simulation (Simit) and VR educational platforms (Anatomy Builder VR).

Ken M. L. Yiuمشاهده پروفایل

استاد

• Databases
• Query Processing
• Spatiotemporal Data
• +۷ مورد دیگر

Ken M. L. Yiu is a Professor in the Department of Computing at Hong Kong Polytechnic University , Faculty of Engineering. He received his PhD and Bachelor's degree from the University of Hong Kong in 2006 and 2002, respectively, and was previously affiliated with Aalborg University (2006–2009). He is a leading researcher in databases, with a focus on spatiotemporal data, query processing, and multidimensional data management. PhD, University of Hong Kong (2006) Bachelor of Computer Engineering, University of Hong Kong (2002) His research interests lie at the intersection of database systems and spatial analytics. He investigates efficient indexing, query optimization, and privacy-preserving techniques for large-scale spatial and temporal datasets. His recent work explores learned index structures, GPU-accelerated query processing, and high-dimensional data retrieval. He has made significant contributions to spatial query processing, trajectory analytics, and location-based services. The trends in his recent publications (2021–2025) reflect a strong focus on high-performance database systems, including GPU acceleration (GHive), perfect hashing on GPUs (GPH), and learned cardinality estimation. His work increasingly integrates machine learning with traditional database techniques, as seen in AlayaDB for LLM inference and learning-based query optimization. He also continues to advance core database problems such as spatial indexing, trajectory analysis, and similarity search. SSTD 2025 10-Year Impact Award Ken Yiu has successfully led multiple competitive research projects funded by the Hong Kong GRF, including grants on learned index structures (2024–2026), smart memory for vector data mining (2021–2023), and efficient spatial data management (2017–2019). He has supervised numerous PhD and MPhil students, many of whom now hold academic positions (e.g., Bo Tang at SUSTech, Yu Li at HDU) or work in top tech companies (e.g., Huawei, Alibaba). His professional service is extensive, including roles as PI for major grants, area chair (ICDE 2024), and program committee member for top conferences like SIGMOD, VLDB, and ICDE. He is actively involved in research groups and projects related to database systems, particularly in spatiotemporal data management and efficient query processing. His lab collaborates closely with students and co-supervisors like Bo Tang on topics such as trajectory mining, spatial indexing, and learned databases. The research group maintains strong ties with international institutions and contributes to major open problems in database performance and scalability.

• Assistive Robotics
• Rehabilitation Robotics
• Human-Robot Interaction
• +۷ مورد دیگر

Viviane Pasqui is a Lecturer and researcher at Sorbonne Université, affiliated with the Institute of Intelligent Systems and Robotics (ISIR), where she is a member of the ASIMOV research team. Her work focuses on assistive and rehabilitation robotics, particularly technologies for elderly support and mobility assistance. Her research interests include assistive robotics, rehabilitation engineering, human-robot interaction, smart walkers, postural stability, gait analysis, and gerontechnology. She has conducted extensive research on mobility assistance devices, including sit-to-stand support systems and intelligent walking aids, with an emphasis on user-centered design and clinical validation. The 15 most recent publications highlight a consistent focus on robotic assistance for rehabilitation and elderly care, with recurring themes in human motion modeling, transparency in control, real-time monitoring, and evaluation of assistive devices. Her work integrates robotics, biomechanics, and clinical insights to develop practical solutions for mobility challenges. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: There is no mention of students, advising roles, or research grants in the provided information. However, her long-standing collaboration with multidisciplinary teams suggests active participation in funded research projects, particularly in robotics and biomedical engineering. Labs and Teams: Viviane Pasqui is a core member of the ASIMOV team at ISIR, which specializes in assistive robotics and human-robot interaction. The team develops innovative robotic systems for rehabilitation and daily living support, combining engineering, medical, and human factors expertise.

• Electrical Power Engineering
• Renewable Energy Integration
• Power Quality
• +۱۲ مورد دیگر

Sarah Lyden is a Senior Lecturer in Electrical Power Engineering at the School of Engineering, University of Tasmania, where she also serves as Associate Head of Learning and Teaching. She is actively engaged in research, supervision, and teaching innovation, with a strong focus on renewable energy integration, power quality, and engineering education reform. Institution: University of Tasmania School: School of Engineering Department: Department of Electrical Power Engineering Email: Sarah.Lyden@utas.edu.au ORCID: 0000-0002-5364-6011 Her research interests span both technical and educational domains. In engineering, she focuses on electrical power systems, harmonics, renewable integration (especially agrivoltaics), hydrogen-diesel systems, and battery storage. In education, she investigates laboratory learning, assessment integrity in the age of generative AI, and sustainability integration in engineering curricula. Her recent publications reflect a dual focus: on one hand, technical advancements in power system monitoring, harmonic analysis, and renewable integration; on the other, transformative work in engineering pedagogy, particularly in response to AI-driven changes in assessment and learning. This interdisciplinary trend highlights her commitment to both technical innovation and educational excellence. Sarah Lyden has been involved in several significant funded projects, including the ARC Training Centre in Energy Technologies for Future Grids and projects on power quality, solar penetration in Tasmania, and isolated power systems. These grants underscore her leadership in energy research and collaboration with national and industry partners. She actively supervises doctoral and master’s students, with completed and ongoing projects in harmonic analysis, hydrogen-diesel engines, photovoltaic integration, and energy storage control. Her team includes researchers working on AI applications in power systems and sustainable energy systems.