جست‌وجوی استاد و پژوهشگر

• Video Streaming
• Virtual Reality
• Multi-Access Edge Computing
• +۳ مورد دیگر

Matti Siekkinen is a Lecturer in the Department of Computer Science at Aalto University. He is also affiliated with the Helsinki Institute for Information Technology (HIIT), the Professorship Di Francesco Mario, and the Computer Science Lecturers group. His research focuses on latency-critical mobile multimedia services, cloud-based virtual reality (VR), and edge computing solutions to optimize network performance and user experience. Education: Doctoral degree in Engineering and Technology (2007) from Université Côte d'Azur Master's degree in Engineering and Technology (2003) from Helsinki University of Technology Research Interests: His work emphasizes improving Quality-of-Experience (QoE) in mobile and cloud gaming, optimizing video streaming for VR applications, and deploying edge computing to reduce latency. Key topics include foveated rendering, dynamic resource allocation, and energy-efficient mobile service delivery. He has led projects such as CloudVR (2019–2020) and Latency-Critical Mobile Multimedia Services (2016–2019), which investigate network architecture and latency reduction strategies. Recent Research Trends: Recent publications highlight advancements in foveated compression techniques, head-pose prediction for remote VR, and neural network-based depth map packing. His work often bridges theoretical models with practical implementations in edge and cloud environments. Grants & Projects: Principal Investigator for CloudVR: High-Quality Mobile VR Using Cloud Acceleration (Business Finland) Principal Investigator for Latency-Critical Mobile Multimedia Services (Academy of Finland) Labs & Groups: He leads the Siekkinen Matti group, focusing on innovative solutions for mobile and cloud-based multimedia systems. Collaborations include Tsinghua University and industry partners like Elisa.

• Computer Architecture
• Microelectronics
• Nanoscale Technology
• +۹ مورد دیگر

Dr. Paul Beckett is an Honorary Associate Professor in the School of Engineering at RMIT University. His research focuses on cutting-edge areas such as computer architecture, nanoscale microelectronics, digital logic design, and photonics. He specializes in low-power circuit design, FPGA-based systems, and sensor technologies with applications in healthcare and environmental monitoring. Beckett's teaching interests include embedded systems, VLSI design, and hardware description languages (VHDL). His research spans disciplines such as electronics, communications engineering, and distributed computing, with notable contributions to null convention logic (NCL) circuits, plasmonic sensors, and spectral imaging systems. He has collaborated on innovative projects like the TIARA apnea monitoring system and wearable health technologies. His recent work emphasizes interdisciplinary applications, including healthcare technology and biomimetic optical systems. Notable publications include advancements in gas sensing (H2S detection), THz spectrum analysis, and machine learning optimization for embedded systems. Beckett actively supervises research projects in asynchronous computing architectures and nanotechnology.

David Waldmanمشاهده پروفایل

استاد پژوهشی

• Polymer Science
• Optical Engineering
• Data Storage Technologies
• +۴ مورد دیگر

David Waldman is a Research Professor in the Department of Polymer Science and Engineering at the University of Massachusetts Amherst, where he also serves as Director of the Center for Ultrafast Microscopy and Imaging Research (CUMIRP). He holds a PhD in Polymer Science & Engineering from the same institution (1990). His research focuses on advanced materials science, particularly in holographic storage systems, photopolymer materials, and optical engineering. Key contributions include the development of high-density holographic data storage media, phase-encoded data recovery systems, and novel applications of nanotechnology in manufacturing. Waldman’s work integrates interdisciplinary approaches to solve challenges in data storage, medical imaging, and renewable energy systems. Education: PhD in Polymer Science & Engineering, University of Massachusetts Amherst (1990). Research interests span holographic data storage systems, photopolymer chemistry, and the application of holography in medical imaging (e.g., lymphatic system analysis). He has pioneered materials like CROP photopolymers for optical data storage exceeding 100 bits/μm². His recent work explores solar energy systems using volume holograms and nanoimprint lithography for precision manufacturing. Publications highlight advancements in holographic media design, biometric imaging systems, and security device technologies. Waldman collaborates with the Sylvio O. Conte National Center for Polymer Research, contributing to cutting-edge polymer science initiatives.

Yiorgos Tsiatouhasمشاهده پروفایل

استاد

• Analog and Digital VLSI Design
• Integrated Circuit Reliability
• Secure Computing Systems
• +۴ مورد دیگر

Yiorgos Tsiatouhas is a Professor in the Department of Computer Science and Engineering at the University of Ioannina. He holds a B.Sc. in Physics (University of Athens, 1990), M.Sc. in Informatics and Telecommunications (University of Athens, 1993), and Ph.D. in Informatics and Telecommunications (University of Athens, 1999). His research focuses on VLSI circuit design, reliability engineering, and secure computing. He teaches courses such as 'Electronics' (MYY404), 'VLSI Circuits' (MYE018), and 'Reliable Integrated Systems' (Y2), emphasizing topics like CMOS technology, radiation-hardened electronics, and testing methodologies. His research interests span secure computing architectures, fault-tolerant systems, and aging monitoring in integrated circuits. Recent work includes developing radiation-hardened latches, PUF-based security solutions for SoCs, and novel testing algorithms for phase-change memories. He has also contributed to visible light communication (VLC) systems, addressing challenges like anti-reflective obstacle detection and adaptive signal decoding in non-line-of-sight (NLOS) environments. Teaching activities include supervising student projects, maintaining an active lab for SPICE-based circuit simulation, and integrating industry-standard tools like Cadence into coursework. His lab focuses on designing and testing analog/digital circuits, emphasizing practical skills in SPICE simulation and layout design.

• Mobile Systems
• Augmented Reality
• Human-Computer Interaction
• +۳ مورد دیگر

Fahim Kawsar is Professor of Mobile Systems at the University of Glasgow's School of Computing Science. His research focuses on mobile and pervasive systems with applications in augmented reality, human-computer interaction, and health monitoring. He supervises PhD students working on therapeutic music interventions and wellbeing sensing technologies. Research spans: Augmented reality collaboration systems Energy-efficient AI on microcontrollers Contactless vital sign monitoring Wearable computing for health Recent publications explore avatar placement in AR meetings, thermal management of AI accelerators, and neurocomputational models for personalized therapy. Work consistently addresses practical implementation challenges in ubiquitous computing.

Thomas Pfannschmidtمشاهده پروفایل

استاد

• Chloroplast Biogenesis
• Photosynthesis Regulation
• RNA Polymerase
• +۲ مورد دیگر

Thomas Pfannschmidt is Professor of Plant Physiology at Leibniz University Hannover's Institute of Botany. His research focuses on photosynthesis regulation, chloroplast biogenesis, and plant-environment interactions, with emphasis on plastid-encoded RNA polymerase and retrograde signaling mechanisms. Teaching covers Plant Physiology, Photobiology, Evolution, and Bioinformatics. Current research investigates evolutionary adaptations in chloroplast transcription machinery and the role of polymerase-associated proteins in biogenesis.

Stephen Goodمشاهده پروفایل

دانشیار

• Ecohydrology
• Remote Sensing
• Isotope Biogeochemistry
• +۳ مورد دیگر

Dr. Stephen Good is an Associate Professor in the Department of Biological & Ecological Engineering at Oregon State University (OSU). He holds a Ph.D. in Civil and Environmental Engineering from Princeton University (2013), an M.S. in Civil Engineering from Michigan Technological University (2008), and a B.S. in Mechanical Engineering from Carnegie Mellon University (2004). His research focuses on ecohydrology, remote sensing, and isotope biogeochemistry, with applications in understanding water cycling, vegetation dynamics, and environmental monitoring. Dr. Good has led major projects funded by NASA, NSF, and the Agricultural Research Foundation, addressing topics such as satellite-based water use efficiency, ecohydrological separation, and microbial hydrologic data encoding. His teaching includes courses like BEE 313 (Ecohydrology), BEE 433/533 (Irrigation System Design), and BEE 512 (Physical Hydrology). He advises students such as Dawn Urycki and Catherine Finkenbiner, contributing to their research in hydrology and environmental engineering. Dr. Good has received notable awards, including the U.S. Fulbright Scholar (2022) and the Young Investigator Award (2011). His work integrates machine learning, stable isotopes, and microbial genomics to advance hydrological understanding and sustainable resource management. Key research outputs include the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset and studies on river microbiome dynamics. He collaborates widely, with recent projects exploring precision irrigation, solar energy optimization over croplands, and continental-scale ecohydrological modeling.

• Visual Analytics
• Predictive Analytics
• Geographical Visualization
• +۷ مورد دیگر

Ross Maciejewski is an Ira A. Fulton Professor of Computer Science at Arizona State University (ASU), leading the School of Computing and Augmented Intelligence. He serves as Director of the Department of Homeland Security-funded Center for Accelerating Operational Efficiency. His research focuses on visual analytics, geographical visualization, and predictive analytics, with applications in homeland security, public health, dietary analysis, and the food-energy-water nexus. He holds affiliations with the Center for Biodiversity Outcomes, Water Institute, and Global Futures Scientists program. Education: PhD (2009), M.S. (2004), and B.S. (2001) in Computer Science/Engineering from Purdue University and the University of Missouri. He advises students through courses like CSE 792 (Research) and CSE 499 (Individualized Instruction), and has led over 20 funded projects including NSF grants and industry collaborations. Research Interests: Visual analytics, predictive modeling, and interdisciplinary problem-solving. His lab, VADER, develops tools for decision support in complex systems. Key innovations include FEWSim (food-energy-water nexus simulations) and LossLens (machine learning diagnostics). Awards: NSF CAREER Award (2014), Best Paper (EuroVis 2017), ACM CHI Honorable Mentions (2018, 2022) Grants: INFEWS/T2 (2016-2021), NSF CAREER (2014-2019) Labs/Teams: Visual Analytics and Data Exploration Research (VADER) Lab

Ozan Özdenizciمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Robustness and Safety in ML
• +۴ مورد دیگر

Assoc. Prof. Ozan Özdenizci is a faculty member at the Institute of Machine Learning and Neural Computation, TU Graz, Austria. His research focuses on robustness, safety, and efficiency in machine learning, particularly in adversarial robustness, spiking neural networks, and privacy-aware learning. He holds a PhD from Northeastern University (2016), and MSc/BSc degrees from Sabancı University (2010/2008). Previously, he served as a postdoc at TU Graz (2016–2020) and a research group leader at Montanuniversität Leoben (2020–2023). Affiliations: TU Graz (2023–present), Montanuniversität Leoben (2020–2023), TU Graz Postdoc (2016–2020) Education: PhD (Northeastern University, 2016), MSc/BSc (Sabancı University, 2010/2008) Research Interests: Developing ML systems with robustness guarantees, efficient deep learning (e.g., spiking networks), and privacy-aware mechanisms. Key areas include adversarial defense mechanisms, sparse network design, and neuromorphic computing applications. His work bridges theoretical foundations with practical implementations in computer vision and autonomous systems. Recent Contributions: Advances in privacy-aware lifelong learning (ICLR 2025), robust spiking networks (TMLR 2024), and weather-resistant vision models (TPAMI 2023). His research emphasizes trade-offs between model efficiency, robustness, and scalability. Awards: Top Reviewer at NeurIPS 2023, Outstanding Reviewer at ICML 2022/ICLR 2022 Labs/Teams: Member of Graz Center for Machine Learning (GraML) and ELLIS Unit Graz

Keary Engleمشاهده پروفایل

استاد

• Catalysis
• Organic Chemistry
• Sustainable Synthesis
• +۳ مورد دیگر

Keary Engle, PhD, is a Professor in the Department of Chemistry and Dean of Graduate and Postdoctoral Studies at Scripps Research Institute. His research focuses on developing efficient and sustainable catalytic methods for small molecule synthesis, particularly in the areas of alkene/alkyne functionalization and transition metal chemistry. Engle holds a D.Phil. from the University of Oxford (2013) and a Ph.D. from The Scripps Research Institute (2013), alongside multiple undergraduate degrees from the University of Michigan (2007). Key research interests include designing novel catalyst systems to address challenges in multi-step organic synthesis, emphasizing environmental sustainability and scalability. His lab has pioneered directed catalytic strategies for C-H activation and asymmetric synthesis, with applications in medicinal chemistry and materials science. Recent work highlights include the development of copper-catalyzed alkyne functionalization platforms (2025), palladium-mediated atropisomer formation (2025), and tungsten-catalyzed stereodivergent isomerization (2025). Over 30+ scientific awards underscore his contributions, including the NSF CAREER Award (2021) and Camille Dreyfus Teacher-Scholar Award (2019). Engle has trained over 20+ students/postdocs, with notable collaborations spanning institutions like the University of Pittsburgh and Caltech. His lab emphasizes interdisciplinary training, combining organic, organometallic, and computational chemistry approaches.

Feng Wangمشاهده پروفایل

استادیار

• Next Generation Internet
• Network Performance Measurement
• Big Data Verification
• +۶ مورد دیگر

Dr. Feng Wang is an Assistant Professor in the School of Engineering at Liberty University, specializing in network reliability, interdomain routing, and software-defined networking. His research addresses challenges in next-generation internet architectures, network performance measurement, and secure IoT device management. Education: B.E. in Electrical and Computer Engineering from Zhejiang University (China) M.S. in Electrical and Computer Engineering from Yanshan University (China) Ph.D. in Electrical and Computer Engineering from the University of Massachusetts, Amherst Research focuses on: - Detecting transient routing failures and improving network performance through real-time diagnostic systems (e.g., collaboration with AT&T). - Designing scalable addressing schemes for IoT and wireless sensor networks. - Developing intrusion detection systems (e.g., MOCA framework) and network anomaly mitigation techniques. His publications (2015-2023) emphasize network security, routing protocol optimization, and scalable internet architectures. Notable work includes BGP rerouting solutions, real-time routing failure diagnosis, and variable-length addressing for 6LoWPAN. No scientific awards were explicitly listed in the provided texts. Collaborations include Agilent, AT&T, and Intel, focusing on practical network reliability and security solutions. Dr. Wang has advised no listed students or managed grants in the provided texts. His research extends to lab implementations of lightweight routers (SoC-based) and stability-aware protocols for RPL networks.

Håkon Kvale Stenslandمشاهده پروفایل

دانشیار

• Machine Learning
• Multimedia Systems
• Real-Time Processing
• +۵ مورد دیگر

Håkon Kvale Stensland is an Associate Professor in the Department of Networks and Distributed Systems at the University of Oslo (UiO), affiliated with Simula Research Laboratory. His work focuses on multimedia systems, machine learning applications in medical imaging and sports analytics, distributed computing, and GPU optimization. He has contributed to projects like HyperKvasir (a gastrointestinal dataset) and SmartIO (PCIe networking for device sharing). Key research areas include real-time video processing, 3D convolutional neural networks for event detection in soccer, and energy-efficient multimedia workloads. He has co-authored over 50 publications in venues like ACM Multimedia, IEEE Transactions, and Nature Communications. His tools and datasets, such as Saga and Bagadus, emphasize collaborative machine learning and real-time sports analytics. Recent work (2024-2025) explores prompt generation for medical segmentation and multi-host device sharing in high-performance clusters. His research bridges theoretical computer science with practical applications in healthcare, sports, and distributed systems.

Maurício Breternitzمشاهده پروفایل

استادیار

• Machine Learning
• High Performance Computing
• Neuromorphic Computing
• +۷ مورد دیگر

Maurício Breternitz is an Invited Assistant Professor and Principal Researcher at ISTAR-IUL, ISCTE - University Institute of Lisbon. With a PhD in Computer Engineering from Carnegie-Mellon University and extensive industrial research experience at AMD, Intel, and IBM, he focuses on bridging academia and industry for practical innovation. His academic service includes leadership roles in conferences like IISWC and editorial responsibilities at IEEE Micro. Education: Electronics Engineer (ITA, Brazil), MSc in Computer Science (UNICAMP), PhD in Computer Engineering (Carnegie-Mellon) Research: Machine Learning acceleration, Neuromorphic systems, Cloud workloads optimization, Heterogeneous computing His work spans two decades of patents (56 issued, 55 pending) and projects like the Horizon 2020 DIVIDEND CHIST-ERA and the FCT-funded AIMHealth initiative for AI-based public health solutions. Recent publications emphasize weightless neural networks, edge computing, and federated learning applications. Key contributions include: GPU acceleration for Hadoop MapReduce APU code migration techniques Microcode compression algorithms saving $18M Founding the International Workshop on Architectural Support for Binary Translation He has advised 7 Master's theses and 1 ongoing PhD project at UNICAMP, while serving on editorial boards and program committees for top-tier conferences like ISCA and CGO.

• Computer Vision
• Machine Learning
• Multimedia Signal Processing
• +۲ مورد دیگر

Hossein Malekmohamadi is a Senior Lecturer in Computer Vision at De Montfort University, affiliated with the School of Computer Science and Informatics within the Faculty of Computing, Engineering and Media. His work bridges computer vision, machine learning, and energy systems. PhD, MSc, BSc in Computer Science or related fields Postgraduate Certificate in Higher Education (PGCLTHE) His research focuses on computer vision, machine learning, and multimedia signal processing, with applications in energy data analysis, Internet of Energy (IoE), and human-computer interaction via EEG signal processing. He has pioneered techniques like Gramian Angular Field (GAF) encoding for energy data visualization and edge computing solutions for real-time classification. Recent publications highlight his work on neural networks for aerodynamic modeling, EEG-based energy data analysis, and 3D feature compression methods. He holds a U.S. Patent for paper classification based on three-dimensional characteristics and is recognized as a Senior Fellow of the Higher Education Academy.

Jan Pieter Abrahamsمشاهده پروفایل

استاد

• Structural Biology
• Electron Microscopy
• Protein Crystallography
• +۵ مورد دیگر

Jan Pieter Abrahams is a Professor and Group Leader of the Nanodiffraction group at the Laboratory for Multiscale Bioimaging, Paul Scherrer Institute (PSI) in Switzerland. His work focuses on developing electron diffraction technologies for atomic-resolution imaging of frozen hydrated biological samples, leveraging PSI's detector expertise to advance structural biology beyond conventional microscopy limitations. Current applications target mitochondrial stress mechanisms in neurodegeneration and aging, with active collaborations across international institutions. Research Interests: Abrahams pioneers electron diffraction and cryo-EM methodologies, emphasizing computational-phasing innovations and machine learning integration. His group specializes in: Overcoming dynamical scattering for atomic-level cellular visualization Hybrid pixel detector applications (JUNGFRAU, EIGER) in electron microscopy Deep learning frameworks for diffraction data processing (e.g., DiffraGAN) Structural analysis of protein nanocrystals in disease contexts Mitochondrial protease mechanisms in aging Bacterial cell division and sporulation structures Publication Trends: Recent work (2020–2024) reveals heavy emphasis on AI-driven structural biology, including generative networks for diffraction phasing and lossless data compression. Instrumentation advancements (e.g., Boersch phase shifters) and disease-focused studies (Alzheimer’s amyloid-beta, malaria heme processing) dominate, showcasing interdisciplinary convergence of physics, computation, and biomedicine. Scientific Awards: No specific awards or fellowships documented in source text Advising and Collaborations: Abrahams has mentored PhD students including Thakkar, Pooja; Rheinberger, Jan; Schärer, Martin; and Wennmacher, Julian. His team collaborates with PSI's detector group on sensor development (GaAs/CdTe) and international labs for structural studies. Funding likely stems from PSI instrumentation projects and disease-focused research initiatives. Labs and Teams: He directs the Nanodiffraction group under PSI's Center for Life Sciences, comprising scientists (van Genderen, Latychevskaia) and postdocs (Blum). The lab integrates cryo-EM, electron diffraction, and computational modeling to visualize cellular processes at nanometer scales, with strong ties to detector engineering and pharmaceutical structural analysis.