جست‌وجوی استاد و پژوهشگر

David Weisblatمشاهده پروفایل

استاد

• Developmental Biology
• Evolutionary Developmental Biology (Evo-Devo)
• Molecular Phylogenetics
• +۴ مورد دیگر

David Weisblat is a Professor in the Department of Molecular and Cell Biology at UC Berkeley's College of Letters & Science. His research focuses on developmental and evolutionary processes in glossiphoniid leeches (e.g., Helobdella ), particularly their segmentation mechanisms and molecular phylogeny within Lophotrochozoa. He leads studies integrating embryological, genomic, and genetic approaches to understand how developmental pathways evolved across bilaterian animals. Research Interests: Weisblat’s work addresses how cell fates and signaling pathways (WNT, NOTCH, TGFβ) shape leech development, with emphasis on the evolutionary origins of segmentation. His lab combines microinjection techniques, transcriptome analysis, and CRISPR mutagenesis to explore stem cell dynamics and conserved developmental programs. Key Projects: Current efforts investigate grandparental stem cell divisions in segmentation and intercellular signaling during early embryogenesis. The lab’s genomic resources—including BAC libraries and EST databases—facilitate comparative studies across Lophotrochozoan taxa. Lab Contributions: The Weisblat lab pioneered Helobdella as a model organism, enabling breakthroughs in Evo-Devo. Their findings bridge developmental plasticity and evolutionary innovation, particularly in annelids versus arthropods/deuterostomes.

• Speech Technology
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Shashi Kumar is a doctoral student in the Doctoral Program in Electrical Engineering (EDDEE) at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the IDIAP Research Institute (LIDIAP) . He holds the role of Doctoral Assistant at LIDIAP, contributing to research in speech technology and machine learning. His work focuses on advancing automatic speech recognition (ASR), optimal transport frameworks, and variational autoencoders for speech enhancement and signal processing. Research interests include speech recognition systems , multimodal task unification , far-field speech processing , and machine learning applications in signal processing and computer vision. His publications highlight contributions to SLAM-ASR performance analysis, joint speaker change detection, and PCB defect classification using image segmentation techniques. Shashi's research is anchored at the IDIAP Research Institute , where he collaborates on projects involving deep learning, audio signal processing, and speech technology. While no awards or grants are explicitly listed, his work reflects active engagement in international challenges like the Interspeech DiCOVA competition.

Matt Garrattمشاهده پروفایل

استاد

• Robotics
• Swarm Robotics
• Unmanned Aerial Vehicles
• +۵ مورد دیگر

Professor Matt Garratt is a faculty member at the University of New South Wales (UNSW Canberra), School of Engineering and IT, serving as AI theme lead for the Defence Trailblazer Universities initiative with over $200 million in funding. His primary research focuses on sensing, guidance, and control for autonomous systems within robotics and unmanned aerial vehicles. Garratt's research spans robotics, swarm intelligence, and autonomous systems with emphasis on bio-inspired navigation techniques and adaptive flight control. His work addresses critical challenges including terrain following using vision systems, landing UAVs on moving platforms, and developing self-organizing swarms. He integrates artificial intelligence, computer vision, and machine learning to advance unmanned systems capabilities in complex environments. Analysis of his recent publications reveals strong trends in bio-inspired UAV navigation (particularly honeybee behavior modeling) and swarm robotics applications. His work increasingly incorporates deep learning for perception tasks while addressing real-world challenges like gas plume detection and adversarial robustness in 3D vision systems. The research demonstrates consistent progression toward practical implementation of autonomous systems in dynamic environments. Professor Garratt has secured over $7.7 million in external research funding as Chief Investigator on 33 grants. He actively mentors graduate students with scholarships available for Masters and PhD research in robotics and AI, focusing on: UAV path planning and adaptive control systems Swarm robotics collective motion optimization Bio-inspired autonomous navigation techniques Computer vision for robotic perception He co-founded the UNSW Canberra AIR (AI and Robotics) Group (AIR Lab), which drives research in trusted autonomy, swarm intelligence, and AI integration for defense applications. The lab develops practical solutions for autonomous systems operating in complex, real-world environments while maintaining ethical AI frameworks.

Markus Haltmeierمشاهده پروفایل

استاد

• Image Reconstruction
• Inverse Problems
• Deep Learning
• +۴ مورد دیگر

Markus Haltmeier is a Professor in the Department of Mathematics at the University of Innsbruck. His research focuses on inverse problems, image reconstruction, and deep learning with applications in medical imaging, photoacoustics, and computational mathematics. He leads a group dedicated to advancing theoretical and practical solutions for challenges in non-destructive testing and medical diagnostics. His work integrates mathematical analysis with machine learning, addressing issues such as high-resolution imaging in scattering media and automated segmentation of cardiac structures. Key research areas include regularization techniques for inverse problems, self-supervised learning approaches for limited data scenarios, and computational methods for photoacoustic tomography. His contributions span both theoretical developments (e.g., inversion formulas for Radon transforms) and applied solutions (e.g., algorithms for cylinder liner wear assessment and myocardial infarct segmentation). Publications highlight advancements in neural network-based regularization, 3D medical image synthesis, and unsupervised learning frameworks for segmentation and registration. His research emphasizes bridging the gap between mathematical theory and real-world applications in healthcare and engineering.

• Political Behavior
• Public Opinion
• Social Media
• +۳ مورد دیگر

Gregory Eady is an Associate Professor at the Department of Political Science, University of Copenhagen (Denmark), affiliated with the Faculty of Social Sciences. His research bridges political behavior, public opinion dynamics, social media's role in politics, and advanced statistical methodology. He examines how digital platforms influence political attitudes and representation, with a focus on electoral processes, foreign interference, and crisis impacts on governance. Key research foci include analyzing the ideological landscape via social media interactions, assessing post-pandemic political representation gaps, and exploring gender dynamics in political toxicity. His methodological contributions address challenges like measuring voter uncertainty and detecting misreporting in sensitive surveys. Eady's work spans cross-national studies and employs experimental designs to uncover causal mechanisms in political behavior. Notable projects include examining Russian disinformation campaigns in the 2016 U.S. election and the psychological effects of violent protests on party loyalty. His interdisciplinary approach integrates computational social science with traditional political theory, contributing to debates on democratic resilience in the digital age.

Oliver Deussenمشاهده پروفایل

استاد

• Visualization
• Computer Graphics
• Digitalization Processes
• +۴ مورد دیگر

Oliver Deussen is a Professor of Visual Computing at the University of Konstanz, recognized by the German Informatics Society (GSI) as a Fellow for his contributions to computer science. His research focuses on visualization, robotics, and environmental modeling, particularly in plant and landscape representation. He has pioneered methods in image manipulation and robotic painting, emphasizing digitalization's societal impacts. His work spans computational biology (e.g., schooling fish behavior) and AI-driven creative technologies. Research Interests: Visualization techniques, swarm behavior analysis, robotic creativity, and interdisciplinary applications of computer science. He explores how computational methods can model natural systems and enhance human-machine interaction. Awards: Fellow of the German Informatics Society (GSI) His research often bridges theory and practice, with contributions to SLAM frameworks, style transfer algorithms, and uncertainty visualization tools. Collaborations in robotics and biology reflect his commitment to applied computational research.

• Smart Cities
• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• +۱۰ مورد دیگر

Dr. Hung Cao is an Assistant Professor of Computer Science at the University of New Brunswick, where he directs the Analytics Everywhere Lab. His work focuses on interdisciplinary research in Cyber-Physical Systems (CPS), IoT, Edge/Fog/Cloud Computing, and Explainable AI, addressing societal challenges through data-driven solutions. Prior roles include PostDoc Fellow and Data Scientist at the People in Motion Lab, UNB, and Lecturer/Researcher at Vietnam National University. He holds a Ph.D. in Geomatics Engineering (specializing in Data Science) from UNB (2020), an M.Sc. in Computer Science from University College Dublin (2015), and a B.Eng. from Vietnam National University (2011). Research interests span Smart Cities, Embedded AI, TinyML, Federated Learning, and Real-time Systems. He has led projects with Cisco, NB Power, and other industry partners to develop scalable analytics frameworks for IoT applications. Dr. Cao actively contributes to technical communities (IEEE Smart City, Edge Computing, etc.), serving as a reviewer for journals and conferences, and a Topic Editor for Electronics Journal . His innovations include the Analytics Everywhere framework for spatio-temporal data analysis, MACeIP platform for smart cities, and energy-efficient IoT systems for environmental monitoring. Current work emphasizes human-centered AI for healthcare diagnostics and industrial inspection systems.

Kyle Bradleyمشاهده پروفایل

استاد پژوهشی

• Tectonics
• Seismology
• Geodesy
• +۷ مورد دیگر

Kyle Bradley is a research-focused academic affiliated with the College of Engineering at Cornell University, specifically within the Department of Earth and Atmospheric Sciences. His work centers on active tectonics, earthquake geology, and geodetic modeling, with extensive fieldwork and satellite-based analysis in Southeast Asia, particularly Indonesia and Myanmar. University: Cornell University School: College of Engineering Department: Department of Earth and Atmospheric Sciences Academic Rank: Research Professor His research interests lie primarily in understanding the mechanics of active fault systems, subduction zones, and their associated hazards. He investigates how tectonic and volcanic processes interact, particularly in regions like the Flores Thrust and Sumatran Fault Zone. His methodologies include GPS and InSAR geodesy, photogrammetry, drone-based mapping, and seismic source modeling to constrain fault geometries, slip rates, and rupture behaviors. The trends in his recent publications indicate a strong emphasis on interdisciplinary geophysics, combining structural geology, seismology, and geochemistry to assess seismic and tsunami hazards. His work often integrates high-resolution geospatial data with field observations to develop models of crustal deformation and earthquake cycles. Although no formal scientific awards are listed in the provided content, his consistent publication record in high-impact journals reflects significant scholarly contributions. There is no mention of formal grant funding, but his research scope suggests involvement in federally or institutionally supported projects. Kyle Bradley has not listed any students in the provided information, but his role as a research professor implies potential mentorship of graduate students and postdoctoral researchers. His work contributes to understanding earthquake triggering mechanisms, such as the role of wet rice cultivation in landslides during the 2018 Palu earthquake, demonstrating applied relevance to disaster risk reduction. He is actively involved in developing regional tectonic models using dense GNSS networks, focal mechanism catalogs, and bathymetric data. His research teams likely include collaborators from international institutions, particularly in Indonesia and Southeast Asia, given the geographic focus of his studies. Labs or research groups associated with his work may utilize geospatial analysis, satellite remote sensing, and computational modeling of tectonic processes.

• Robotics and Autonomous Systems
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Dr. Sen Wang is an Associate Professor in Robotics and Autonomous Systems at Imperial College London's Department of Electrical and Electronic Engineering, affiliated with I-X (Imperial's AI initiative), the Grantham Institute, and the Robotics Forum. He directs the Sense Robotics Lab and founded the MSc in Artificial Intelligence Applications and Innovation. His research focuses on advancing robotic autonomy through probabilistic and machine learning methods, addressing challenges in unstructured environments such as underwater infrastructure inspection and climate change solutions. Key projects include leading the £18M UKRI ORCA Hub, developing underwater robotics for offshore energy infrastructure inspection, and achieving the first autonomous wind farm foundation inspection at EDF's Blyth site. He holds editorial roles at IEEE Transactions on Robotics and other journals. Research interests span robotics, computer vision, SLAM, and AI applications, with recent work emphasizing underwater systems, sensor fusion, and safety-critical autonomy. His publications bridge theoretical advancements with real-world deployments in marine robotics and environmental monitoring. Awards: 2024 AI Most Influential Scholar Award Honourable Mention Grants: £18M ORCA Hub funding (UKRI) Labs: Sense Robotics Lab, I-X AI Initiative

Rohan Tabishمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Multicore Processors
• +۵ مورد دیگر

Rohan Tabish is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), specializing in real-time systems, embedded systems, and cybersecurity. His work focuses on developing predictable and secure software frameworks for multi-core and heterogeneous architectures. Education: Ph.D. in Computer Science and Engineering Master's in Computer Science and Engineering B.Sc. in Electrical Engineering with Telecommunications specialization Research Interests: Real-Time Task Scheduling Fault Tolerance in Embedded Systems Inter-Core Communication Frameworks Memory Bandwidth Management Cyber-Physical Systems Scratchpad-Centric Operating Systems Awards: Outstanding Paper & Best Paper Award (RTSS 2020) Outstanding Paper & Best Student Paper Award (RTSS 2020) Best Presentation Award (RTAS 2016) Nominated for Best Paper Award (ECRTS 2019) Teaching: CS 431: Embedded Systems (Instructor, 2015-2018) CS 424: Real-Time Systems (Instructor, 2019) CS 438: Communication Networks (TA, 2019) Labs/Teams: Active member of the Real-Time Systems Lab (RTSL) at UIUC, focusing on safety-critical embedded systems and real-time software frameworks.

Ruihui Liمشاهده پروفایل

استاد

• Deep Geometry Learning
• Generative modeling
• 3D Vision
• +۳ مورد دیگر

Ruihui Li - Academic Summary Professional Overview Prof. Ruihui Li is a faculty member at the College of Computer Science and Electronic Engineering, Hunan University , specializing in Deep Geometry Learning , Generative Modeling , and 3D Vision . His work focuses on advancing 3D reconstruction and generation with high controllability, particularly in applications like point cloud upsampling and neural rendering. Education & Career Dr. Li earned his Ph.D. in 2021 (oral defense passed in June 2021). His career includes significant contributions to research in geometric deep learning and 3D computer graphics. He actively collaborates with institutions like the Chinese University of Hong Kong and Tel Aviv University on projects such as PU-GAN and SP-GAN . Research Interests His research spans cutting-edge topics in: Neural Wavelet-Domain Diffusion for 3D Shape Generation Topology-Aware 3D Mesh Reconstruction Camera-Based 3D Scene Completion Point Cloud Analysis and Upsampling Professional Activities Dr. Li has delivered invited talks at prestigious events such as the Asia Graphics Webinar and Peking University . He serves as a reviewer for top conferences (CVPR, ICCV, ECCV) and journals (TPAMI, TVCG). His research is supported by grants from the National Natural Science Foundation of China and the Chinese Academy of Sciences. Technical Contributions Key projects include: PU-GAN : A point cloud upsampling adversarial network (ICCV 2019). SP-GAN : Sphere-guided 3D shape generation (SIGGRAPH 2021). Neural Wavelet-domain Diffusion : 3D shape generation and manipulation (TOG 2024). Labs & Teams He leads a research group at Hunan University, mentoring Ph.D./MPhil students, RAs, and postdocs. His team focuses on interdisciplinary projects combining AI, computer graphics, and geometric deep learning.

• Embedded Systems
• FPGA Technologies
• Edge Computing
• +۴ مورد دیگر

Professor Roger Woods is a prominent academic and researcher affiliated with Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, part of the Faculty of Engineering and Physical Sciences. He holds the rank of Professor and is actively involved in advancing research and innovation in embedded systems, FPGA technologies, and AI-driven solutions for industry challenges. His work emphasizes practical applications through close collaboration with industry partners. His research interests span novel computing architectures (e.g., multi-precision and edge computing), FPGA-based systems for data analytics, and secure IoT communication protocols. Notably, he co-founded and serves as Chief Scientist of Analytics Engines Ltd, a data analytics company. He has led significant initiatives like the Kelvin-2 Tier-4 High Performance Computing centre and contributed to semiconductor reviews through EFutures. Professor Woods has been recognized with prestigious awards, including the IET Northern Ireland Engineering Excellence Award and IEEE Fellowship. He has supervised numerous PhD students focusing on topics like FPGA-based image processing, secure wireless communications, and embedded AI platforms. His publications reflect interdisciplinary strengths in hardware acceleration, physical layer security, and structural health monitoring. Key Collaborations : Projects with industry and institutions on semiconductor design, bridge monitoring, and AI hardware. Grants : Principal Investigator for multiple research grants, including Core Equipment Awards for advanced instrumentation. Labs/Teams : Active in Queen's Advanced MicroEngineering Centre and the EFutures network.

Erik Linsteadمشاهده پروفایل

دانشیار

• Machine Learning
• GPU Programming
• Autism Spectrum Disorder
• +۵ مورد دیگر

Dr. Erik Linstead is an Associate Professor and Senior Associate Dean at Chapman University, affiliated with the Fowler School of Engineering, School of Pharmacy, and George L. Argyros College of Business and Economics. His expertise spans Machine Learning, GPU Programming, Autism Spectrum Disorder, Assistive Technologies, Predictive Analytics, and Virtual Reality. Education: Bachelor of Science, Chapman University Master of Science, Stanford University Ph.D., University of California, Irvine Dr. Linstead's research integrates machine learning with diverse domains, including autism treatment, environmental monitoring, and software engineering. His recent publications focus on coral reef health, land surface temperature trends, and embedded machine learning systems. His scholarly work includes collaborations in remote sensing, medical informatics, and neurodiversity support. Articles highlight his interdisciplinary approach, applying AI to ecological challenges (e.g., Red Sea coral reefs, Nile Basin droughts) and human-centered technologies (e.g., VR therapy for autism, medication adherence analysis).

• Multimedia Modeling
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Dr. Stevan Rudinac is a Researcher at the University of Amsterdam's Faculty of Economics and Business , Section Business Analytics . His work focuses on interactive learning systems and multimodal data analysis, particularly in urban contexts and multimedia modeling. Education: PhD in Multimedia and Information Retrieval from Delft University of Technology (2013). Research Interests: Stevan specializes in multimedia modeling , hypergraph learning , and interactive video search . He develops frameworks for scalable analysis of social networks, urban imagery, and large multimodal datasets, bridging machine learning with practical applications in city planning and financial social media. Recent Trends: His 2024-2025 publications highlight large language model optimization , diffusion model evaluation , and dynamic graph embedding for meme stocks. Collaborative projects include the CASTLE 2024 dataset and Exquisitor , a system for 100 million image exploration. Labs & Teams: He contributes to the Business Analytics group at UvA, collaborating with Prof. Marcel Worring and Dr. Björn Þór Jónsson. He co-organized the UrbanMM'21 workshop and participates in ACM Multimedia and MMM conferences.

Pengpeng Huمشاهده پروفایل

مدرس ارشد

• Fashion Technology
• Geometric Deep Learning
• 3D Human Body Reconstruction
• +۵ مورد دیگر

Dr Pengpeng Hu is a Senior Lecturer in Fashion Technology at the Department of Materials, The University of Manchester, UK. His research focuses on geometric deep learning, 3D human body reconstruction, point cloud processing, and smart textiles, bridging fashion technology with biomedical and engineering applications. Associate Editor: IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Automation Science and Engineering Academic Editor: PLOS ONE Editorial Board Member: Scientific Reports Programme Chair: 25th UK Workshop on Computational Intelligence Area Chair: 35th British Machine Vision Conference His work advances vision-based measurement systems, wearable technology, and 3D scanning for clothing and healthcare. Recent publications include innovations in MXene-based electronic textiles, 4D hand measurement extraction, and anthropometric analysis from depth images. Recipient of the Emerald Literati Award for an outstanding paper in 2019 Dr Hu accepts self-funded PhD students in areas like 3D human reconstruction, point cloud processing, and smart textiles. His editorial roles and conference leadership highlight his influence in computational intelligence and machine vision communities.