جست‌وجوی استاد و پژوهشگر

Don Roosanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Quantum Computing
• Healthcare Informatics
• +۳ مورد دیگر

Dr. Don Roosan is an Associate Professor in Data Science at Merrimack College, specializing in cutting-edge applications of artificial intelligence (AI), quantum computing, and mixed reality in healthcare. He holds a Ph.D. in Biomedical Informatics from the University of Utah and a PharmD from Purdue University. His research focuses on leveraging AI for improving patient outcomes, quantum computing for drug discovery and molecular design, and mixed reality applications for genomics and biosecurity. He has also contributed to nutrigenomics database development and telehealth security through post-quantum cryptography. Dr. Roosan’s teaching portfolio includes computational sciences, sleep and circadian rhythms, biostatistics, quantum computing, and human-computer interaction. His work integrates AI with health informatics, addressing challenges such as opioid abuse detection, electronic health record optimization, and healthcare equity in underserved populations. He pioneered the PGxKnow augmented reality application for pharmacogenomics education and developed frameworks for blockchain-based healthcare data security. His recent publications highlight advancements in quantum-aided molecular classification, AI-driven EHR exploration, and telehealth biosecurity. His articles emphasize interdisciplinary approaches at the intersection of quantum computing, AI, and healthcare, with a focus on transforming clinical decision-making and drug development. Dr. Roosan’s research also addresses health disparities in digital technology design and the ethical integration of nutrigenomics into clinical practice.

Eric Kellerمشاهده پروفایل

دانشیار

• Computer Networks
• Software-Defined Networking
• Network Security
• +۲ مورد دیگر

Eric Keller is an Associate Professor in the Department of Electrical, Computer & Energy Engineering at the University of Colorado's College of Engineering. His research focuses on computer networks, network security, and cloud/edge computing, with particular emphasis on software-defined networking and efficient resource allocation in distributed systems. Keller's research spans several interconnected domains including network virtualization, hardware acceleration, container-based computing, and security protocols. His work often bridges the gap between theoretical networking concepts and practical system implementations. Through his publications, Keller demonstrates consistent focus on optimizing network performance and security, particularly in cloud and edge computing environments. His recent work shows growing interest in hardware-level acceleration and AI-enhanced networking solutions.

• Quantum Physics
• Optics
• Quantum Optics
• +۵ مورد دیگر

Florian Marquardt is a Professor at the University of Erlangen-Nürnberg and Scientific Director of the Theory Division at the Max Planck Institute for the Science of Light in Erlangen, Germany. His research focuses on the intersection of quantum physics, optics, and machine learning, with a particular emphasis on neuromorphic computing and quantum technologies. He holds a PhD from the University of Basel (2002) and has held roles including Emmy-Noether Fellow (2007-2012) and Full Professor at FAU Erlangen-Nürnberg (2010-2016). His work bridges theoretical physics and applied AI, aiming to advance quantum control, topological photonics, and energy-efficient computing. Education: PhD in Theoretical Physics, University of Basel (2002) Diplom Physics, University of Bayreuth (1998) Research Interests: The Marquardt Group explores quantum optomechanics, neuromorphic computing, and machine learning applications in physics. Current projects include designing analog neuromorphic systems, optimizing quantum error correction, and leveraging deep learning for inverse design in photonics. Recent work emphasizes AI-driven strategies for quantum circuit discovery and topological material engineering. Awards: Walter Schottky Prize (2009) ERC Starting Grant (2011) Emmy-Noether Fellowship (2007-2012) Grants & Collaborations: Leads research networks including the Max Planck School of Photonics and the MPC for Extreme and Quantum Photonics. His lab collaborates on EU-funded projects and organizes workshops on topics like quantum metrology and neuromorphic computing. Labs & Infrastructure: Directs the Theory Division, which hosts interdisciplinary teams working on photonic neural networks, quantum machine learning, and topological transport. The group uses advanced simulation tools like dCG (differentiable connected geometries) for multi-domain optimization.

Chenchen Liuمشاهده پروفایل

استادیار

• High-Performance Computing
• Machine Learning
• Brain-Inspired Computing
• +۳ مورد دیگر

Dr. Chenchen Liu is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She leads the Computing Compass Laboratory and focuses on high-performance computing for machine learning, brain-inspired computing with hardware-software co-design, and novel non-volatile memory technologies. Education: Ph.D., Electrical and Computer Engineering, University of Pittsburgh, 2017 M.S., Electrical and Computer Engineering, Peking University, 2013 Research Interests: Her work emphasizes optimizing neural networks for efficiency, enhancing AI security against adversarial attacks, and advancing neuromorphic systems using memristor and ReRAM technologies. Recent publications address multi-tenant DNN inference, edge computing frameworks, and latency-aware GPU optimization. Her articles highlight innovations in neuromorphic hardware, federated learning, and defense against adversarial attacks. Ongoing efforts include designing robust AI systems for autonomous driving and edge environments.

Dragan H. Stojanovicمشاهده پروفایل

استاد

• Artificial Intelligence
• Edge Computing
• Smart City Systems
• +۳ مورد دیگر

Dr. Dragan H. Stojanovic serves as a Professor at the Faculty of Electronic Engineering, University of Niš, specializing in computer science since his appointment in 2015. His academic journey spans over three decades at the same institution, where he earned all his degrees and now leads research in cutting-edge computational fields. His educational background includes: PhD in Electrical Engineering and Computing (2004, University of Niš) MSc in Electrical Engineering and Computing (1998, University of Niš) BSc in Electrical Engineering and Computing (1993, University of Niš) Stojanovic's research centers on Artificial Intelligence and Edge Computing , with significant contributions to Smart City infrastructures, parallel processing algorithms, and neural network applications. His work bridges theoretical computer science with practical implementations in urban systems, demonstrated through collaborations on 1 national and 2 international research projects . Recent publications (2022-2025) reveal a strategic pivot toward resource-constrained AI deployment across edge-fog architectures, while earlier work (2014-2020) established foundations in geospatial data parallelization and social media analytics. His publication portfolio shows evolving expertise: 2014-2019: Focus on parallel computing (MPI/CUDA) for geospatial and trajectory data 2020-2022: Integration of IoT and social networks into Smart City business processes 2023-2025: Advanced Edge AI optimization for real-time object detection and prediction systems Stojanovic actively contributes to academic knowledge transfer through project leadership and has published 16 journal articles in impact-factor journals. His current research trajectory emphasizes sustainable computing paradigms for urban environments, with emerging work on competency-focused AI education frameworks.

Dan Marius Dobreaمشاهده پروفایل

استادیار

• Virtual Reality
• Medical Electronics
• Pattern Recognition
• +۵ مورد دیگر

Dan Marius Dobrea is an Assistant Professor at the Technical University of Iasi, specializing in interdisciplinary research at the intersection of Virtual Reality, Medical Electronics, and intelligent systems. His work bridges advanced computational methods with practical applications in transportation monitoring, sensor design, and brain-computer interfaces. University: Technical University of Iasi Academic Rank: Assistant Professor Research interests span UAV systems, deep learning algorithms, and neuro-fuzzy networks. Recent publications highlight real-time tracking solutions for drones, pothole detection in road infrastructure, and sensor-based gesture recognition systems. His focus on lightweight AI architectures for embedded platforms demonstrates a commitment to resource-efficient technologies. Article trends reveal expertise in: Unmanned Aerial Vehicles (UAVs) Real-time computer vision Deep learning for infrastructure monitoring Human-computer interaction via inertial sensors Signal processing for biomedical applications GPU-accelerated algorithm optimization

Tony Gengمشاهده پروفایل

استادیار

• Computer Architecture
• Machine Learning
• Generative AI
• +۶ مورد دیگر

Tony Geng is an Assistant Professor at the University of Rochester, jointly appointed in the Departments of Electrical and Computer Engineering and Computer Science. He directs the IntelliArch Lab and holds secondary affiliations with the Goergen Institute for Data Science. His research spans computer architecture, generative AI, graph intelligence, and high-performance computing, focusing on sustainable and efficient artificial general intelligence. His work integrates hardware-software co-design for accelerating machine learning workloads, with applications in scientific discovery, fintech, and social media. Recent projects explore dynamical systems for graph learning and diffusion models for scientific simulations. Notable recognitions include multiple DOE and NSF research awards, the Excellence in Graduate Teaching Award, and substantial GPU allocations from NERSC for generative AI research. He actively mentors PhD students and postdocs in sustainable AI development.

Konstantinos Michmizosمشاهده پروفایل

دانشیار

• Neuromorphic Computing
• Artificial Intelligence
• Computational Neuroscience
• +۴ مورد دیگر

Konstantinos Michmizos is an Associate Professor in the Department of Computer Science at Rutgers University, with affiliations to the Rutgers Center for Cognitive Science, Brain Health Institute, and Center for Computational Biomedicine Imaging and Informatics. His research focuses on neuromorphic computing, neuro-AI integration, and brain-inspired robotics, emphasizing systems that interact with brain activity across neural and behavioral scales. He leads the Michmizos Lab and ComBra Lab, pioneering projects like robotic nurses powered by neuromorphic chips and astrocyte-driven computational models. Key research interests include neuromorphic hardware-software co-design, spiking neural networks, and applications in neurorehabilitation. Notable achievements include securing grants from NIH, Intel, and VCRI, along with awards for best papers on Neuro-AI and computational astrocyence. His work bridges artificial intelligence with biological systems, aiming to translate neural mechanisms into advanced robotic and biomedical technologies. Michmizos teaches graduate courses like Brain-Inspired Computing and has advised students such as Ioannis Polykretis. His research spans neuromorphic SLAM systems, EEG decoding frameworks, and biohybrid robotic systems, with recent breakthroughs in calcium wave modeling for astrocyte-neuron interactions. Collaborations emphasize multi-scale computational biology and clinical applications in neurology.

• Computer Vision
• Machine Learning
• Rail Transportation Systems
• +۵ مورد دیگر

Yu Qian is a Professor in the Department of Electrical and Computer Engineering at the University of Southern California's Viterbi School of Engineering, with extensive research contributions spanning computer vision, machine learning, and transportation systems. Their work bridges theoretical advances with practical applications, particularly in railroad safety and ferroelectric computing architectures. Research interests focus on computer vision applications for transportation safety, including railroad inspection systems and pedestrian behavior analysis at grade crossings. Additional expertise includes ferroelectric computing-in-memory architectures, micro-expression recognition, and multimodal sentiment analysis. The research program demonstrates strong interdisciplinary collaboration across engineering disciplines. Recent publications (2023-2025) show a clear trend toward hardware-algorithm co-design for specialized computing, particularly using ferroelectric devices for optimization problems, alongside continued work in transportation safety applications. The research spans both theoretical contributions to neural network architectures and practical implementations for real-world problems. While specific awards aren't documented in the provided records, the extensive publication record in high-impact venues demonstrates significant scholarly contribution to the field. The research program appears well-funded, supporting numerous collaborative projects across multiple institutions.

• Computer Vision
• Deep Learning
• Neural Network Acceleration
• +۵ مورد دیگر

Kaisheng Ma is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With an extensive publication record spanning from 2013 to 2025, he has established himself as a leading researcher in computer vision, deep learning, and computer architecture. His work bridges theoretical advances with practical applications in neural network acceleration and hardware-software co-design. Professor Ma's research interests focus on advancing the frontiers of computer vision and deep learning systems. His work in knowledge distillation has significantly improved the efficiency of neural networks, while his contributions to hardware acceleration have enabled more powerful AI systems. His recent research demonstrates a growing emphasis on 3D vision, embodied AI, and the intersection of language and vision systems. He has pioneered techniques in frequency domain analysis for image processing and developed novel approaches for neural network acceleration through hardware-software co-design. Analysis of Professor Ma's recent publications (2023-2025) reveals a strong research trajectory with increasing focus on multimodal learning, 3D scene understanding, and efficient neural network deployment. His work spans both theoretical advances in machine learning (particularly knowledge distillation and continual learning) and practical hardware implementations for AI acceleration. The publications show consistent collaboration with a core group of researchers while expanding into new areas like brain-computer interfaces and language-grounded spatial reasoning. Professor Ma has successfully mentored numerous PhD students who have become active contributors to the field, including Linfeng Zhang, Runpei Dong, and Zekun Qi. His research has been supported by significant grants that enable cutting-edge work in neural network acceleration and 3D vision systems. His students have published in top-tier conferences including CVPR, NeurIPS, and ICCV, demonstrating the strength of his research group. Professor Ma leads a research group focused on the intersection of computer vision, deep learning, and computer architecture. His lab investigates efficient neural network deployment, hardware acceleration techniques, and novel computer vision algorithms. The team maintains strong collaborations with industry partners working on AI hardware and has developed several innovative approaches to neural network acceleration and efficient model deployment.

• Wireless Sensor Networks
• Machine Learning
• Deep Learning Methods
• +۵ مورد دیگر

Shagufta Henna is a Lecturer in Computer Science at the Department of Computing, Atlantic Technological University. Her research focuses on machine learning applications in wireless sensor networks, IoT systems, cybersecurity, and environmental monitoring. She contributes to UN Sustainable Development Goals through projects addressing environmental sustainability and health. Her expertise includes federated learning for malware detection in healthcare IoT, deep learning optimization for sensor networks, explainable AI for water quality prediction, and ensemble methods for human activity recognition. Recent work highlights include: Developing graph neural networks for IoT-based healthcare cybersecurity Accelerating environmental monitoring via deep learning in uncontrolled sensor networks Deploying SHAP-based XAI for water quality prediction interpretability No scientific awards explicitly listed. Research output includes 45 publications across journals and conferences, with recent contributions in Results in Engineering and IEEE proceedings. Active collaborations span international institutions focusing on AI-driven environmental and healthcare solutions.

Wei Niuمشاهده پروفایل

استادیار

• Real-time Machine Learning
• Artificial General Intelligence (AGI)
• Compiler Optimization
• +۵ مورد دیگر

Wei Niu is an Assistant Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. His research focuses on real-time machine learning systems, AGI on embedded devices, compiler optimization, and parallel computing. He holds a Ph.D. in Computer Science from William & Mary (2023) and a B.S. from Beihang University (2016). Prior to academia, he worked at Bytedance as a mobile software developer (2016–2018). Research Interests: Dr. Niu’s work emphasizes optimizing deep neural networks (DNNs) for real-time execution on mobile and embedded systems. His contributions include frameworks like SmartMem (memory optimization for DNNs on mobile devices), GCD² (compiler for mobile DSPs), and SoD² (static optimization for dynamic DNNs). He also explores applications in autonomous vehicles, healthcare, and edge computing. Recent Trends in Publications: His articles address DNN efficiency, compiler-driven optimization, and real-time systems. Recent work includes semantic segmentation for autonomous vehicles, food recognition, and lossy data compression for scientific computing. He prioritizes practical solutions for edge devices and embedded systems. Funding: He leads NSF-funded projects totaling $600K, collaborating with Gagan Agrawal. These include MITTEN (memory hierarchy optimizations for transformers) and CropDL (scheduling DNNs on HPC clusters). Teaching & Labs: He offers RA positions for Ph.D. candidates and maintains a lab developing real-time AI applications (e.g., style transfer, object detection). Videos of his work are available on YouTube and Bilibili.

Lakshmish Ramaswamyمشاهده پروفایل

استاد

• Big Data
• Pervasive Computing
• Internet of Things
• +۶ مورد دیگر

Lakshmish Ramaswamy is a Professor of Computer Science at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He holds a Ph.D. from Georgia Institute of Technology (2005), an M.S. from Indian Institute of Science (1999), and a B.E. from University of Mysore (1996). His research focuses on Big Data, IoT, Cloud Computing, and Environmental Informatics, with notable contributions to resilient machine learning systems, geospatial analytics, and sustainable sensor networks. He has led projects funded by NSF, Army Research, and industry, including initiatives like CyanoTRACKER for harmful algal bloom monitoring. Dr. Ramaswamy has received a Best Paper Award (2010) and secured grants such as an NSF/NIFA award for GeoAI and an Accenture grant for data harmonization. His work bridges distributed systems, edge computing, and environmental sustainability, with collaborations across disciplines like geography and forestry. He has advised numerous interdisciplinary projects and maintains an active presence in conferences and journals, contributing to both theoretical and applied computing advancements.

• Computer Architecture
• GPU Architecture
• Power and Performance Modeling
• +۵ مورد دیگر

Sohan Lal is a postdoctoral researcher at the Technical University of Berlin (TU Berlin), focusing on advanced modeling and runtime support for large-scale HPC clusters under a DFG-funded project. His PhD in Computer Engineering from TU Berlin (2019) explored power modeling and architectural techniques for energy-efficient GPUs. He contributed to EU-funded LPGPU projects on low-power GPU computing, leading tasks and collaborating across consortium members. Previously, he lectured at Shri Mata Vaishno Devi University and worked as an IT specialist in the Government of India. Education: PhD in Computer Engineering, TU Berlin (2019) Masters in Computer Science, IIT Delhi (2011) Bachelor in Computer Science and Engineering, GCET Jammu (2003) His research interests span GPU architecture, power/performance modeling, memory systems, and applied machine learning. Notable contributions include techniques like Selective Lossy Compression (SLC) for GPUs and entropy encoding-based memory compression (E²MC). He received HiPEAC travel/grants and was an ACM SRC semifinalist (2018). Grants & Collaborations: HiPEAC Collaboration Grant for joint work with TU/e DFG-funded postdoctoral research He actively teaches advanced computer architectures and multicore systems at TU Berlin, reflecting his passion for education developed during his early teaching career.

Yiren Zhaoمشاهده پروفایل

استادیار

• Large-scale GenAI Systems
• Hardware-Algorithm Co-design
• AI Security
• +۴ مورد دیگر

Dr. Yiren Zhao is an Assistant Professor (Lecturer) in Computer Engineering at Imperial College London, part of the Faculty of Engineering. He leads the DeepWok Research Lab and holds affiliations with the UKRI Artificial Intelligence for Engineering Biology Consortium and the University of Cambridge as a Visiting Researcher. His work focuses on Large-scale GenAI Systems, emphasizing hardware-algorithm co-design, efficiency, and security. He has secured over £5M in funding since 2022 and reviews for top conferences like ICLR, ICML, and NeurIPS. Dr. Zhao earned a BEng in Electrical and Electronic Engineering from Imperial College London (2016), followed by an MPhil and PhD in Computer Science from the University of Cambridge (2017–2022). His research spans GenAI acceleration, unstructured data processing (e.g., graphs), and system-level AI safety. Recent projects include MASE (unified ML system exploration) and ImpNet (imperceptible backdoor attacks). Awards: Apple Scholar in AI/ML (2020), Microsoft Research Award (2023), and Junior Research Fellowship at St John’s College (2021). Labs/Teams: DeepWok Lab (20+ members), involved in Imperial-X and AI-4-EB initiatives. Grants: Industry/government-funded projects totaling over £5M since 2022. His work bridges computer systems and AI, with notable contributions to quantization, adversarial attacks, and hardware-aware optimization. Collaborations include the Center for Spatial Computational Learning and CaRAML group.