جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Deep Learning
• Computer Science
• +۱۲ مورد دیگر

Dr. José SantaMaría López is a Professor at the Department of Computer Science , University of Jaén, specializing in Machine Learning , Medical Imaging , and Evolutionary Computation . His work bridges Artificial Intelligence with practical applications in Healthcare and Security . Current research focuses on Trustworthy AI , Explainable AI , and 3D Modeling for forensic identification. His Google Scholar profile highlights publications on adversarial attacks, data fusion, and real-time diagnostics using Deep Learning . He employs Evolutionary Algorithms and Fuzzy Logic for optimization in medical imaging and vehicular networks. Key trends in his recent publications include Decentralized Learning , Security in AI , and Parallel Computing for healthcare applications.

• Machine Learning
• Deep Learning
• AI Compilers
• +۷ مورد دیگر

Alessio Burrello is a Researcher at the Department of Control and Computer Engineering (DAUIN) , affiliated with Politecnico di Torino . His academic career is oriented toward Machine Learning , Deep Learning , and Embedded Systems . Research Interests : Burrello focuses on AI compilers , neural network optimization for edge devices, distributed systems , and signal processing applications . His work spans interdisciplinary domains like energy management for IoT , neuromorphic computing , and biomedical monitoring . Recent Publications highlight his contributions to TinyML , low-power microcontrollers , and heterogeneous computing . Notable works include optimizing DNN inference on multi-accelerator SoCs and deploying compact transformers for seizure detection. Teaching : Burrello has been a course owner for 'Optimized Execution of Neural Networks at the Edge' in the Computer and Systems Engineering program since 2024/25. He also contributes to courses in Agritech Engineering and Aerospace Engineering . Supervision : He supervises Javier Jesus Poveda Rodrigo , a PhD candidate in Artificial Intelligence , and participates in commercial research projects like Efficient Active Inference Deployment .

• Heterogeneous computing systems
• High-level synthesis (HLS)
• Hardware specialization for machine learning
• +۳ مورد دیگر

Zhiru Zhang is a Professor at Cornell University in the School of Electrical and Computer Engineering , leading research at the Computer Systems Laboratory . His work focuses on algorithms, methodologies, and design automation tools for heterogeneous computing systems. Recent publications emphasize high-level synthesis (HLS), hardware specialization for machine learning, and programming models for software-defined FPGAs. Education: Ph.D. in Computer Science, UCLA B.S. in Computer Science, Peking University M.S. in Computer Science, UCLA Research Interests: Heterogeneous computing systems High-level synthesis (HLS) optimization FPGA-based hardware acceleration Sparse data format compilers Machine learning for electronic design automation Scientific Awards: IEEE Fellow Intel Outstanding Researcher Award NSF CAREER Award DARPA Young Faculty Award Multiple Best Paper Awards at ASPLOS, ISPD, FPGA, AutoML, FCCM Research Group: Mentors 12 current students including Jordan Dotzel, Jie Liu, and Grace Dinh, with 14 alumni now at institutions like AWS AI, NVIDIA, Google DeepMind, and Microsoft. His lab develops tools like UniSparse for sparse format customization, presented at OOPSLA'24 and IEEE CAL.

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۳ مورد دیگر

Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

Thor Alexander Bøje Simonsen is an Instructor at the Department of Computer Science , University of Copenhagen , focusing on interdisciplinary research at the intersection of machine learning, quantum computing, and real-world applications. He is affiliated with the SCIENCE AI Centre and contributes to projects in sustainability, medical data analysis, and quantum-enhanced algorithms. His research spans theoretical and applied domains, including: Quantum computing for biomolecular simulations Environmentally sustainable AI systems Medical data analysis and clinical decision support Image reconstruction and remote sensing Explainable AI for large language models Thor's work engages with cutting-edge challenges in machine learning, from hardware acceleration to ethical considerations in clinical contexts. He is part of the university's Machine Learning Section , which has access to a dedicated compute cluster and collaborates with initiatives like TreeSense for global tree resource monitoring.

Shyam Gollakotaمشاهده پروفایل

استاد

• Artificial Intelligence
• Computing + Biology
• Human-Centered Computing
• +۶ مورد دیگر

Shyam Gollakota is a Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His research spans Artificial Intelligence, Human-Centered Computing, and Wireless Systems , with interdisciplinary collaborations in Biology and Medicine. He leads the Mobile Intelligence Lab , co-founded Sound Life Sciences (acquired by Google), and developed battery-free computing technologies. Education: Ph.D. from MIT (2013), B.Tech from IIT Madras Awards: Infosys Prize 2024, Moore Inventor Fellow, NSF CAREER, ACM Grace Murray Hopper Award His research focuses on machine learning for mobile systems, battery-free sensing, and human auditory augmentation . Recent work includes proactive in-ear assistants (ACL 2025), spatial speech translation (CHI 2025), and sound bubbles for personalized audio (Nature Electronics 2024) . Publications span Human-Computer Interaction, Wireless Networking, and Medical AI . Scientific achievements include: Best Paper Awards: MOBICOM, SIGCOMM, UbiComp, SenSys, NSDI, CHI Breakthroughs: Battery-free phones, ambient backscatter, smartphone-based medical diagnostics His team co-developed underwater GPS (SIGCOMM 2023), AI-enabled ClearBuds (MobiSys 2022), and wireless blood clot testing (Nature Communications 2022) . Collaborations include the Washington Department of Agriculture for monitoring invasive hornets.

Chia-Lin Yangمشاهده پروفایل

استاد

• Low-power embedded systems
• High-performance processor architecture
• Massive data storage systems
• +۶ مورد دیگر

Chia-Lin Yang is a Professor in the Department of Computer Science and Information Engineering at National Taiwan University's College of Electrical Engineering and Computer Science. He currently serves as Director of the Delta-NTU Joint Research Lab and maintains active leadership roles in multiple research initiatives. Professor Yang's research focuses on low-power embedded systems, high-performance processor architecture, and massive data storage systems. His current work explores storage systems for big data analytics, compute-in-memory architecture design, and AI-enabled IoT. His lab, the Embedded Computing Laboratory, investigates emerging memory technologies including ReRAM and their applications in neural network acceleration and processing-in-memory architectures. His recent publication trends show a strong focus on memory systems for AI acceleration, with significant contributions to processing-in-memory architectures, ReRAM-based neural network accelerators, and storage optimization for machine learning workloads. His work bridges computer architecture, memory systems, and AI hardware acceleration, addressing critical challenges in performance, power efficiency, and reliability. Professor Yang has received numerous prestigious awards including the 2018 Outstanding Electrical Engineering Professor award, two IBM Faculty Awards (2010, 2005), and the 2009 IEEE/ACM ISLPED Best Paper Award. As an active researcher, Professor Yang serves on multiple editorial boards including ACM Transactions on Computer Architecture and Code Optimizations, ACM Transactions on Embedded Computing Systems, and IEEE Transactions on Computer-Aided Design. He maintains leadership roles in major conferences including DAC and ISLPED, demonstrating his significant contributions to the computer architecture community. His Embedded Computing Laboratory provides research opportunities in cutting-edge areas of computer architecture and memory systems, with connections to industry through the Delta-NTU Joint Research Lab and Taiwan AI Labs.

Mimi Xieمشاهده پروفایل

استادیار

• IoT Systems
• Non-Volatile Memory Optimization
• Energy Harvesting
• +۳ مورد دیگر

Mimi Xie is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), part of the College of Sciences. She holds a Ph.D. in Electrical and Computer Engineering from the University of Pittsburgh, with prior degrees from Chongqing University. Her research focuses on optimizing energy-efficient, secure, and intelligent IoT systems, particularly in non-volatile memory, energy harvesting, and tiny AI. She leads interdisciplinary efforts addressing challenges in data processing for IoT devices and sustainable computing solutions. Dr. Xie has received the prestigious NSF CAREER Award and co-PI roles in TranSET projects on traffic risk prediction and reinforcement learning. Her work appears in top venues like DAC, ICCAD, DATE, and IEEE journals. She advises multiple Ph.D./Master’s students (Sahidul, Jishnu, Wei, Shyamala) and actively serves on technical program committees for GLSVLSI, IEEE Cloud Summit, and ISVLSI. Key research themes include intermittent systems, energy-efficient AI inference, and secure embedded computing. She has developed frameworks like ELIXIR for self-powered IoT and Autotile for battery-less embedded systems. Over 60+ publications span smart healthcare systems (PPG-based health monitoring), environmental adaptive neural networks, and reinforcement learning for traffic networks. Her academic service includes organizing workshops (e.g., TinyML at DAC 2023), mentoring initiatives like the EDA Job Fair at ICCAD, and outreach efforts such as a virtual summer camp for students with disabilities. She balances technical leadership with educational advocacy, winning the GHC Faculty Award 2020.

Prabha Sundaravadivelمشاهده پروفایل

استادیار

• Internet of Things (IoT)
• Smart Healthcare
• IoT Architectures
• +۴ مورد دیگر

Dr. Prabha Sundaravadivel is an Assistant Professor in the Department of Electrical Engineering at the University of Texas at Tyler (UT Tyler). She joined UT Tyler in Fall 2018 and holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2018), an M.Tech in VLSI Design from Vellore Institute of Technology (2015), and a B.Tech in Electronics and Communication from SRM Institute of Science and Technology (2011). Her research focuses on IoT-driven smart healthcare systems, low-power VLSI design, and embedded systems for IoT applications. As Director of the Computer Engineering Research Laboratory (CERL), her team explores smart healthcare and smart cities initiatives, leveraging IoT technologies to enhance quality of life. Current projects include UAV-based agricultural monitoring, bio-inspired robotics, and edge-intelligent wearables for health monitoring. Dr. Sundaravadivel is an IEEE member and Faculty Advisor for the IoT Student Club. Her work emphasizes interdisciplinary innovation, integrating machine learning, hardware design, and environmental intelligence into practical solutions. Recent research highlights include autonomous UAV navigation systems, low-power memristor emulators, and FPGA-based intrusion detection for aerial robots. Her teaching philosophy incorporates project-based learning (PBL) to integrate sustainability and entrepreneurial skills into engineering education. Notable educational initiatives include video creation for developing entrepreneurial mindsets and blending entrepreneurship into senior-level courses.

Patricia Navaمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۱۰ مورد دیگر

Patricia Nava is a Professor in the Department of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP), with affiliations in Computational Science. Her work bridges theoretical and applied domains across Artificial Intelligence, Deep Learning, Digital Design, and Computer Architecture. Primary Research Areas: AI, Deep Learning, Computer Architecture, STEM Education Key Collaborations: UTEP Distributed Computing Lab, H-PIPE (High-Performance Computing Pipeline) project Her research explores Deep Learning Networks through analogies to biological systems, focusing on their implementation via programmable hardware. She has pioneered educational strategies in engineering pedagogy, particularly for Signals and Systems instruction, while maintaining a strong technical focus on neural network algorithms and hardware acceleration. Recent publications highlight her dual emphasis on STEM student success (e.g., graduate transition pathways) and technical innovations such as GPU clusters , quantized neural networks , and multi-core architectures . Her work spans biomedical applications like asthma classification and respiratory impedance modeling, demonstrating interdisciplinary breadth. Featured Scientific Support: $1M grant to increase graduate engineering participation She contributes to distributed computing infrastructure through projects like the Virgo 3-GPU upgrade and has developed fuzzy logic systems for sleep-stage and asthma classification. Her educational initiatives focus on transfer student integration and diversity in engineering.

Luca Pezzarossaمشاهده پروفایل

دانشیار

• Real-Time Systems
• Digital Microfluidics
• Compiler Optimizations
• +۷ مورد دیگر

Luca Pezzarossa is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on real-time systems, embedded computer systems, digital microfluidics, compiler optimizations, and hardware accelerators. He leads projects such as the Edu4Chip: Joint Education for Advanced Chip Design in Europe initiative and supervises PhD students in areas like compiler optimizations for neural networks and speech enhancement algorithms. His academic journey includes contributions to interdisciplinary fields, combining computer engineering with biomedical applications such as biochip design and PCR optimization. He actively engages in open-source tool development, particularly using the Chisel framework for hardware design education and research. Key research themes include: Real-time systems and time-predictable architectures Compiler-driven optimizations for constrained devices Digital microfluidics for lab-on-a-chip systems Edge computing and TinyML applications Recent publications highlight innovations in microplastic detection on edge devices, dynamic channel pruning for speech enhancement, and parallel execution engines for digital microfluidics. His work aligns with sustainable development goals through environmental applications and energy-efficient technologies. Current projects involve: PhD Supervision: Andrea Cerioli (Compiler Optimizations), Riccardo Miccini (AI-to-Neural Network Mapping), Ehsan Khodadad (Time-predictable Systems) Research Grants: EU-funded Edu4Chip (2023–2025), multiple industry-academia collaborations Labs and teams: Leads the Embedded Systems Engineering group at DTU, focusing on interdisciplinary hardware-software co-design for real-world applications. Active in developing open-source frameworks for education and research.

• Computer Vision
• Deep Learning
• Remote Sensing
• +۳ مورد دیگر

Trishna Barman is a Research Associate at Ulster University's School of Computing, Engineering and Intelligent Systems, affiliated with the Computer Science Research department. Her work focuses on interdisciplinary applications of artificial intelligence and computer vision in environmental technology, industrial automation, and remote sensing. Her research spans deep learning for hyperspectral imaging, image super-resolution techniques, and biomimetic engineering. Education: No specific qualifications listed in the provided text. Her research interests include developing algorithms for industrial visual inspection (e.g., solder quality assessment), optimizing e-waste recycling through AI-driven PCB segmentation, and enhancing remote sensing data through advanced super-resolution frameworks. She also explores motion-based gesture recognition and biomimetic prosthetics. Her publications reflect a progression from early work in prosthetic design (2013) to modern applications of deep learning in imaging (2025). Key themes include sparse representation techniques, GPU-accelerated processing, and attention-driven neural networks. Awards: None explicitly mentioned in the provided text. Advising & Grants: No student advisement or grant details found. Focus appears centered on technical research and development within her current role. She is part of the Computer Science Research team at Ulster University's Derry~Londonderry campus, contributing to innovations at the intersection of computing and engineering.

• Human-Robot Interaction
• Reinforcement Learning
• Artificial Intelligence
• +۶ مورد دیگر

Jamison Heard is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). His research focuses on adaptive human-robot teams, human-aware reinforcement learning, and real-time human state assessment. He teaches courses in artificial intelligence, biorobotics/cybernetics, and machine learning methodologies. He holds a B.S. in Electrical Engineering from the University of Evansville (2013), and M.S. and Ph.D. degrees in Electrical Engineering from Vanderbilt University (2016 and 2019). His work spans interdisciplinary areas including human-robot interaction dynamics, task recognition, and the integration of biomedical applications with robotic systems. His research publications emphasize advancements in human-robot collaboration, reinforcement learning techniques, and multimodal data analysis. He has explored topics such as stress impact on teleoperation performance, neural architecture search for GANs, and emotion estimation in human-robot interactions. Dr. Heard’s teaching responsibilities include courses such as EEEE-447 (Introduction to AI), EEEE-536/636 (Biorobotics/Cybernetics), and IDAI-710 (Fundamentals of Machine Learning). His work often bridges theoretical AI advancements with practical applications in healthcare and industrial systems.

Yukui Luoمشاهده پروفایل

استادیار

• Hardware-oriented Security
• FPGA Virtualization
• IoT Security
• +۳ مورد دیگر

Yukui Luo is an Assistant Professor in the Department of Electrical and Computer Engineering at Binghamton University, SUNY. He earned his PhD in computer engineering (specializing in hardware-oriented security) from Northeastern University in 2023. His research focuses on advancing security in cloud and edge computing, with expertise in FPGA virtualization systems, IoT security, AI acceleration, and hardware accelerators. Education: Bachelor's: Shanghai University of Engineering Science Master's: Illinois Institute of Technology PhD: Northeastern University His research interests emphasize hardware trustworthiness, including FPGA-based virtualization, IoT security, AI acceleration defenses, and secure database systems. Recent work includes frameworks like TBNet for neural network protection in trusted environments and AdAPI for efficient private inference in edge computing. Notable contributions include publications at DAC 2024, ICCAD 2024, and IEEE conferences addressing FPGA vulnerabilities, privacy-preserving machine learning, and adversarial attack mitigation. He advises PhD students focusing on FPGA acceleration, hardware security, and machine learning. His lab explores cutting-edge solutions for secure computing in multi-tenant environments and edge devices. Dr. Luo actively recruits PhD students (starting Fall 2026) with strong technical backgrounds in computer architecture, FPGA design, cybersecurity, or related fields. He emphasizes rigorous application requirements, including academic records and critical engagement with his research papers.

Luis Cezeمشاهده پروفایل

استاد

• Computer Architecture
• Programming Languages
• Machine Learning Systems
• +۴ مورد دیگر

Luis Ceze is a Full Professor at the University of Washington's Paul G. Allen School of Computer Science and Engineering, holding the Edward D. Lazowska Endowed Professorship. He leads research groups focusing on hardware/software systems (Sampa), machine learning systems (SAMPL), and DNA-based computing (MISL). Additionally, he co-founded and serves as CEO of OctoML, a company advancing AI infrastructure. His work bridges computer architecture, programming languages, machine learning, and biological systems to innovate computing paradigms. Education : PhD in Computer Science from UIUC (2007), MEng and BEng in Electrical Engineering from University of São Paulo (Brazil). Research Interests : Approximate computing, DNA data storage, molecular programming, and systems for machine learning. Key projects include TVM (deep learning compiler), APPROXBENCH, and molecular computing frameworks. Publications : Over 150 peer-reviewed papers spanning systems, architecture, and biology. Recent work emphasizes DNA storage scalability, neural network optimization, and hybrid molecular-electronic computing. Awards : ACM SIGARCH Maurice Wilkes Award (2020), multiple IEEE Micro Top Picks, and recognition for contributions to approximate computing and DNA storage. Grants & Industry : Supported by Microsoft, Intel, Google, and NSF. Active in startups and venture partnerships (Madrona Venture Group). Labs/Teams : Molecular Information Systems Lab (MISL), SAMPL/Sampa groups. Collaborates across UW, industry, and international institutions.