جست‌وجوی استاد و پژوهشگر

Helen Oleynikovaمشاهده پروفایل

مدرس

• Robotics
• Autonomous Systems
• Micro-Aerial Vehicles (MAVs)
• +۱۲ مورد دیگر

Helen Oleynikova is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, where she is part of the Autonomous Systems Lab. She works on the intersection of perception and planning, particularly for micro-aerial vehicles. Her research focuses on real-time onboard mapping, planning, and localization using visual-inertial systems and signed distance fields. Research Interests: Helen's work spans robotics, autonomous systems, and computer vision, with a focus on enabling safe and efficient navigation in complex environments. She specializes in visual-inertial odometry, SLAM, 3D mapping using signed distance fields, and real-time path planning for MAVs. Her projects often involve embedded systems and FPGA-based high-speed vision for obstacle avoidance. Publication Trends: Her recent publications (2023–2019) show a consistent focus on real-time, onboard algorithms for autonomous navigation. Key themes include signed distance function maps, collision-free motion generation, global localization, and efficient exploration. She frequently publishes in top-tier robotics conferences such as ICRA and IROS, and journals like IEEE RA-L and Journal of Field Robotics. Professional Experience: Senior Researcher, Autonomous Systems Lab, ETH Zürich Senior Software Engineer, Isaac 3D Perception, Nvidia Senior Scientist, Microsoft Mixed Reality and AI Lab, Zürich Software Engineer, Google (StreetView) Contributor, Willow Garage (ROS, TurtleBot Arm) Education: PhD in Robotics, ETH Zürich (2019) MSc in Robotics, ETH Zürich BSc in Robotics, Olin College of Engineering (2011) Advising and Grants: While no formal students are listed, she has collaborated extensively with researchers at ETH Zürich and industry labs. Her work has been supported through institutional affiliations and industry research roles. She has contributed to open-source robotics software, particularly in ROS-based systems for manipulation and navigation. Labs and Teams: Helen is a key member of the Mobile Manipulation team at the Autonomous Systems Lab at ETH Zürich. She has also been involved in projects at Nvidia, Microsoft, Google, and Willow Garage, focusing on real-world deployment of autonomous systems.

Davi V. Q. Rodriguesمشاهده پروفایل

استادیار

• Signal Processing
• Microwave/Millimeter-Wave Circuits
• Radar Systems
• +۷ مورد دیگر

Dr. Davi V. Q. Rodrigues is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). His research focuses on microwave/millimeter-wave circuits and signal processing for radar, communication systems, and smart living applications. He holds a B.S. in Communications Engineering from the Military Institute of Engineering (Brazil) and a Ph.D. in Electrical Engineering from Texas Tech University. Prior to academia, he served in the Brazilian Navy and Army, and worked at Uhnder, Inc., Abbott Laboratories, and Dell Technologies. His work emphasizes radar-based smart home, healthcare, and autonomous systems, with contributions in structural health monitoring, human activity recognition, and joint communication-sensing technologies. Dr. Rodrigues has received prestigious awards including the 2022 IEEE MTT-S Tom Brazil Fellowship and the 2023 Texas Tech Horn Distinguished Professors Award. His research group, the Microwave Circuits & Sensing Systems Group, develops algorithms and hardware for biomedical sensing, non-contact health monitoring, and 6G-enabled applications. He actively seeks motivated graduate students interested in radar systems, signal processing, and wireless technologies. Education: B.S., Communications Engineering, Military Institute of Engineering (2017) Ph.D., Electrical Engineering, Texas Tech University (2023) Research Interests: Microwave/mm-wave radar systems Human activity monitoring and gesture recognition Structural health monitoring 6G/ B5G communication-sensing fusion Passive and opportunistic RF sensing His publications span radar-based smart living solutions, biomedical sensors, and reconfigurable intelligent surfaces (IRS). He is an IEEE MTT-S member and has contributed to industry partnerships through collaborations with Uhnder and Dell Technologies. Current projects integrate AI-driven radar signal analysis and low-cost sensor networks for healthcare and infrastructure monitoring.

Prabhakara Akarshمشاهده پروفایل

استادیار

• Wireless Systems
• Cyber-Physical Systems
• Machine Learning for RF
• +۶ مورد دیگر

Akarsh Prabhakara is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin–Madison, with an additional affiliation in the Department of Electrical and Computer Engineering. He earned his Ph.D. from Carnegie Mellon University in 2024, where he worked under Professors Anthony Rowe and Swarun Kumar. Ph.D., Electrical and Computer Engineering, Carnegie Mellon University, 2024 B.Tech, Electronics and Communication Engineering, National Institute of Technology Karnataka, 2018 His research focuses on building high-fidelity wireless systems for perception and communication, particularly in cyber-physical and robotic applications. He explores machine learning-driven RF systems, novel communication paradigms, wireless-robotics integration, and embedded wireless sensing. His work aims to enable robust perception in challenging environments such as smoke or fog using millimeter wave radar and deep learning. His recent publications in CVPR, ICRA, MobiCom, and ICCV demonstrate a strong trend in using neural methods for radar simulation, super-resolution, and wireless intelligence. Key themes include implicit neural rendering for radar, end-to-end learning for perception, and high-resolution point cloud generation from low-cost sensors. His scientific contributions have been recognized through publications in top-tier venues, though specific awards are not mentioned in the provided text. He is actively involved in mentoring and recruiting students for research in wireless and robotics. He teaches courses such as Intro to Computer Networks and Big Ideas in Wireless: Perception and Communication . He leads research projects like RadarHD, which enables lidar-like perception from mmWave radar, and is developing tools and datasets for community use. His lab emphasizes practical, real-world applications of wireless systems in robotics and autonomous systems.

Alan Mantoothمشاهده پروفایل

استاد

• Analog and Mixed-Signal IC Design
• Power Electronics
• Semiconductor Device Modeling
• +۳ مورد دیگر

Alan Mantooth is a Distinguished Professor holding the Twenty-First Century Research Leadership Chair in Engineering within the Department of Electrical Engineering at the University of Arkansas, Fayetteville. He serves as Director of the National Center for Reliable Electric Power Transmission (NCREPT), Executive Director for GRAPES (NSF I/UCRC) and SEEDS (DoE Center), and Deputy Director of the NSF Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS). His educational background includes: B.S. in Electrical Engineering, University of Arkansas M.S. in Electrical Engineering, University of Arkansas Ph.D. in Electrical Engineering, Georgia Institute of Technology Dr. Mantooth's research centers on analog/mixed-signal IC design, power electronics CAD, and semiconductor device modeling with emphasis on harsh-environment applications. His pioneering work in silicon carbide (SiC) and gallium nitride (GaN) power systems has enabled high-temperature operation for electric vehicles and renewable energy infrastructure, significantly advancing reliability in extreme conditions. His 2025 publications reveal strong trends toward AI-driven power electronics (e.g., SolarFormer++ for PV profiling), wide-bandgap device modeling (β-Ga2O3, SiC), and innovative packaging solutions. Key themes include reliability engineering for extreme environments, multi-physics optimization, and explainable AI for safety-critical power systems. Major scientific recognition includes: IEEE Fellow (2009) for power electronic device modeling Three R&D 100 Awards (2009, 2014, 2016) for SiC power modules IEEE Power Electronics Society Technical Achievement Award (2019) Multiple university teaching/research awards including SEC Faculty Achievement Award (2015) As an exceptional mentor (UA Outstanding Mentor 2006-2008), he co-founded Lynguent and Ozark Integrated Circuits. His centers NCREPT, GRAPES, and SEEDS have secured major funding from NSF, DoE, and industry partners, supporting over 350 refereed publications and numerous patents. Current research focuses on AI-enhanced power electronics, recyclable packaging, and next-generation wide-bandgap device characterization. He leads the NCREPT test facility and multi-institutional teams developing grid-connected power electronic systems, secure energy delivery architectures, and thermal management solutions for high-power-density applications, with direct impact on electric transportation and renewable energy integration.

Christian Rohnerمشاهده پروفایل

استاد

• Wireless Sensor Networks
• Backscatter Communication
• Network Security
• +۵ مورد دیگر

Christian Rohner is a Professor at the Department of Information Technology at Uppsala University, specializing in the Division of Computer Systems. His research spans over two decades with a clear evolution from early work in opportunistic networking to current cutting-edge research in backscatter communication and physical-layer security. Professor Rohner's research interests focus on wireless communication systems , particularly backscatter communication , sensor networks , and network security . His work on analog backscatter tags has pioneered techniques for channel estimation, reliable flooding protocols, and identification systems for battery-free devices. In wireless security , he has made significant contributions to radiometric fingerprinting, physical-layer authentication, and intrusion detection for IoT systems. His research in information theory applies theoretical frameworks to practical network analysis problems, including modularity computation in probabilistic networks and information decomposition. His recent publications (2020-2025) demonstrate a strong focus on enabling low-power wireless systems, with applications ranging from medical contexts (fat intra-body communication) to temperature sensing with RFID tags. The research shows a clear trajectory toward practical implementations of battery-free sensor networks that can operate without traditional power sources while maintaining security and reliability. Professor Rohner has maintained long-term collaborations, particularly with Thiemo Voigt at Uppsala University, resulting in numerous joint publications across multiple research domains. His work bridges theoretical foundations with practical implementations, making significant contributions to both academic research and potential real-world applications in wireless networking.

Muhammad Intizar Aliمشاهده پروفایل

استادیار

• Data Analytics
• Internet of Things (IoT)
• Machine Learning & AI for IoT
• +۸ مورد دیگر

Dr. Muhammad Intizar Ali is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU). He holds a PhD (with distinction) from Vienna University of Technology, Austria (2011) and has held roles including Adjunct Lecturer and Research Fellow at the Insight Centre for Data Analytics, NUI Galway. His primary research focuses on IoT, Data Analytics, Machine Learning, and Knowledge Graphs with applications in Smart Cities, Manufacturing, Farming, and Healthcare. Education: PhD in Computer Science, Vienna University of Technology (2007-2011) Research Interests: IoT and Edge Analytics Federated and Distributed Machine Learning Semantic Web and Knowledge Graphs Smart Manufacturing and Industry 4.0 Stream Processing and Real-Time Systems Recent Work Trends: His publications emphasize federated learning frameworks, IoT-enabled adaptive intelligence, and knowledge graph applications in industrial contexts. Recent projects include digital twin systems for predictive maintenance and ontology-driven manufacturing solutions. Grants & Projects: Lead Investigator in SFI-funded projects like MultiRoof (2025-2029) and Neuro-Symbolic AI for Building Management EU/Industry collaborations including Terrain-AI and Bentley-funded initiatives Labs & Teams: Active in DCU's Data Analysis and Machine Learning research groups, leading projects like Smart DCU Digital Twin for campus optimization.

Raúl Suárez Feijóoمشاهده پروفایل

پژوهشگر

• Robotized Assembly
• Fine Motion Planning
• Robotized Grasping and Manipulation
• +۴ مورد دیگر

Summary Raúl Suárez Feijóo is a Researcher at the Universitat Politècnica de Catalunya (UPC) in the Institut d'Organització i Control de Sistemes Industrials (IOC). Born in Asturias, Spain (1959), he holds an Electronic Engineering degree from the National University of San Juan, Argentina (1984), and a Doctorate in Engineering from UPC (1993). His research focuses on robotized assembly, fine motion planning, grasping, and multi-robot systems. He has led the IOC as Deputy Director (2003–2009) and Director (2009–2016). He coordinates doctoral programs in Automation and Robotics at UPC. Suárez has authored numerous publications and received awards including the Best Paper Award at IEEE ISATP (1995) and the Best Reviewer Award at ICRA (2015). Education: Electronic Engineer (1984, Argentina), Doctor Engineer (1993, UPC) Leadership Roles: Director of IOC (2009–2016), Coordinator of Doctoral Programs Research: Over 100 publications in robotics, emphasizing grasping, manipulation, and industrial automation. His work includes contributions to robotic hands, motion planning, and collaborative robotics. Notable projects involve 5G-enabled robotic systems and multi-AGV traffic management. Awards highlight his impact in both international and national robotics communities.

• Non-Destructive Evaluation
• Ultrasonic Testing and Imaging
• Eddy Current Testing
• +۹ مورد دیگر

Dr. Ehsan Mohseni is a Senior Lecturer in the Department of Electronics and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. He is a key member of the Centre of Ultrasound Engineering (CUE) research group and supports the Royal Academy of Engineering and Spirit AeroSystems research chair led by Professor Gareth Pierce. His work focuses on advancing robotic and intelligent Non-Destructive Evaluation (NDE) systems for industrial applications. B.Sc. in Materials Science and Metallurgical Engineering, University of Tehran, 2006 M.Sc. in Metal Forming Processes, University of Tehran Ph.D. in Automated Defect Detection using Electromagnetic NDE, École de Technologie Supérieure (ETS), Montreal, Canada Dr. Mohseni’s research is centered on NDE 4.0, integrating advanced sensing, multi-physics modeling, and machine learning. His expertise spans ultrasonic and eddy current testing, multi-sensor data fusion, and probability of detection studies. He focuses on applications in additive manufacturing, welding, composites, and metal processing, aiming to overcome current technological barriers in industrial inspection. The recent publications highlight a strong trend toward intelligent, automated, and robotic NDE systems. Key themes include self-supervised learning for ultrasonic segmentation, human-machine collaboration in data analysis, and advanced signal processing for weld and composite inspection. The integration of AI, flexible sensor arrays, and embedded navigation systems reflects a shift toward smart, adaptive inspection platforms aligned with Industry 4.0. Scientific Awards: The BINDT Annual Conference Award (2019) Dr. Mohseni is actively involved in research funding and knowledge transfer. He serves as Principal Investigator on KTP projects with ETHER NDE LIMITED and NATIONAL OILWELL VARCO UK LIMITED, focusing on in-process inspection for additive manufacturing and field calibration for ultrasonic testing. He contributes to large-scale collaborative projects funded by Innovate UK and industry partners, emphasizing practical deployment of NDE solutions. He also supervises research staff and collaborates with global aerospace firms including Pratt & Whitney Canada, Safran, and Bell Helicopter. He is a core member of the Centre of Ultrasound Engineering (CUE), a dynamic research group developing next-generation ultrasound technologies. The team works on advanced robotic sensing hubs, flexible transducer arrays, and AI-driven data interpretation tools, often in collaboration with the Royal Academy of Engineering research chair and industrial partners.

Asmus Skar Christiansenمشاهده پروفایل

دانشیار

• Pavement Engineering
• Geotechnical Engineering
• Sensing Technologies
• +۴ مورد دیگر

Asmus Skar Christiansen is an Associate Professor in Pavement Engineering at the Department of Environmental and Resource Engineering, Technical University of Denmark (DTU Sustain). He serves as Head of Study for the Nordic Master in Cold Climate Engineering programme and lectures on pavement engineering, Arctic road construction, and foundation design. His academic career at DTU spans from Postdoc researcher (2017-2019) to Assistant Professor (2020-2023) and current Associate Professor position since 2023. His research centers on pavement technology and geotechnics with specialization in: Development of advanced testing and modeling techniques for pavements Integration of modern sensing technologies in civil infrastructure Computational mechanics for soil-structure interaction Sustainable materials for cold climate engineering Recent work demonstrates a clear shift toward IoT-enabled monitoring systems and data-driven pavement assessment, with 80% of 2023-2025 publications focusing on sensor integration and machine learning applications. Notable scientific contributions include: Creation of open-source datasets (LiRA-CD, RIVA) for road condition modeling Development of thermomechanical models for heated pavements Innovations in waste soil reuse for infrastructure He actively supervises PhD candidates across multiple projects including GREENPIPE (self-sensing pipe systems) and urban pavement analysis, while maintaining industry consultancy through COWI A/S collaborations. Christiansen also contributes to sustainable infrastructure through DTU's alignment with UN SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities).

Montserrat Rosمشاهده پروفایل

دانشیار

• Embedded Systems
• Sensor Networks Data Fusion
• Cyber-Physical Systems
• +۶ مورد دیگر

Montserrat Ros is an Associate Professor and Associate Dean (Education) at the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong, Australia. She has been with the university since 2006, initially joining as a Lecturer in Computer Engineering and progressing to her current senior academic and leadership roles. Her educational background includes: B.E.(Hons1)/B.Sc. double degree majoring in Computer Systems Engineering and Mathematics from the University of Queensland (2000) Ph.D. degree in Computer Engineering from the University of Queensland (2007) Professor Ros's research focuses on the intersection of embedded computing systems and practical engineering applications. Her work spans several key areas including embedded systems design, sensor network data fusion, cyber-physical systems development, and innovative approaches to engineering education. She has particular expertise in sensor-based localization techniques, computer architecture optimization, and code compression methodologies for resource-constrained environments. More recently, her research has expanded into machine learning applications for constrained systems and Internet of Things implementations. Analysis of her recent publication record reveals a strong emphasis on Internet of Things networks, UAV-based systems, and applications of artificial intelligence in both engineering education and manufacturing processes. Her work demonstrates a consistent pattern of bridging theoretical computer engineering concepts with practical real-world applications across diverse domains including healthcare, environmental monitoring, and industrial automation. Her significant contributions to academia have been recognized through numerous prestigious awards: 2019: AAUT Citation for Outstanding Contribution to Student Learning 2018: IEEE TALE 2018 Meritorious Service Award 2018: Featured in UOW Leadership in Education Booklet 2017: UOW Vice Chancellor's Award for Outstanding Contribution to Teaching and Learning 2016: UOW Women of Impact for inspiring young women in STEM 2015: UOW Vice Chancellor's Interdisciplinary Research Excellence Award 2012 & 2007: UOW Vice Chancellor's Awards for Teaching Excellence 2011: UOW Vice Chancellor's Award for Community Engagement Senior Fellow of WATTLE (Wollongong Academy for Tertiary Teaching & Learning Excellence) Professor Ros has secured substantial research funding across multiple projects spanning from 2006 to the present. Her grant portfolio demonstrates a consistent focus on engineering education innovation, sensor network development, and practical applications of embedded systems. Notable projects include "The AI Tutor: Enabling 24x7 student support across engineering" (2024), "AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods" (2023), and "Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane" (2022). She actively supervises HDR students and has completed multiple successful candidatures. Her leadership extends beyond research and teaching, as evidenced by her role as Associate Dean (Education) for the Faculty of Engineering and Information Sciences. She is also actively involved in community engagement through volunteering with the State Emergency Service (Wollongong SES) and Athletics Wollongong Club.

Iman Nematollahiمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Robot Learning
• Reinforcement Learning
• +۵ مورد دیگر

Iman Nematollahi is a PostDoc in Robot Learning at the University of Freiburg under Prof. Dr. Abhinav Valada, having completed his PhD under Prof. Dr. Wolfram Burgard. He holds a MSc in Embedded Systems from the University of Freiburg and a BSc in Electrical Engineering from Shahid Beheshti University. His research focuses on robot learning, world models, and reinforcement learning, particularly in enabling robots to understand physics through world models and adapt skills in unstructured environments. Education: PostDoc in Robot Learning, University of Freiburg (2025–Present) PhD in Robot Learning, University of Freiburg (2019–2024) MSc in Embedded Systems, University of Freiburg (2015–2018) BSc in Electrical Engineering, Shahid Beheshti University (2010–2015) Research Interests: Robot manipulation, world models, reinforcement learning, computer vision, and self-supervised learning. His work emphasizes intuitive physics understanding, skill generalization, and sample-efficient policy improvement. Key Articles: Recent work includes LUMOS (language-conditioned imitation learning), Bayesian optimization for policy refinement, and 3D video prediction (T3VIP). These contributions bridge theory and real-world robotic applications, emphasizing long-horizon tasks and cross-environment adaptation. Awards & Grants: No explicit awards mentioned. His research has been supported through projects like OML (Organic Machine Learning). Teaching: Taught Deep Learning Lab (2020–2022) and Introduction to Mobile Robotics (2019).

Liang Dongمشاهده پروفایل

دانشیار

• Signal Processing
• Wireless Communications
• Cyber-Physical Systems
• +۱۲ مورد دیگر

Dr. Liang (Leon) Dong is an Associate Professor in the Department of Electrical and Computer Engineering at Baylor University, where he conducts research and teaches in the areas of signal processing, wireless communications, and artificial intelligence. He leads the Laboratory of Signal Processing, Communications, and Artificial Intelligence, fostering innovation in next-generation communication systems, IoT, and AI-driven applications. PhD, Electrical & Computer Engineering, The University of Texas at Austin (2002) MS, Electrical & Computer Engineering, The University of Texas at Austin (1998) BS, Applied Physics with Minor in Computer Engineering, Shanghai Jiao Tong University (1996) Dr. Dong's research focuses on advancing digital signal processing and wireless communications, with strong emphasis on artificial intelligence applications. His work spans NextG wireless systems , IoT and smart cities , cyber-physical system security , and AI in healthcare and industrial automation . He applies deep learning to domains such as autonomous driving and drug discovery, and investigates energy-efficient, secure, and reliable communication protocols. The recent publications highlight a strong trend toward integrating AI into traditional signal processing and communications. Topics include mRNA vaccine stability prediction , smart city infrastructures , secure cyber-physical systems , and deep learning for biomedical and industrial applications . His work bridges theoretical innovation with real-world impact in defense, transportation, and public health. Dr. Dong has earned recognition as a Senior Member of IEEE and a Member of the American Physical Society. He has also served as Faculty Advisor for Baylor University's InterVarsity chapter. Senior Member, Institute of Electrical and Electronics Engineers (IEEE) Member, American Physical Society (APS) He has successfully advised numerous graduate and undergraduate students, many of whom now hold academic and industry positions at institutions like Stanford, Intel, NASA, L3Harris, and Cummins. His research is generously supported by Baylor's VP for Research, the National Science Foundation, NASA, the Department of Defense (TARDEC), the Michigan Department of Transportation, and industry leaders including Intel, L3Harris, ExxonMobil, and Denso. He actively mentors students through research assistantships and senior design projects. Dr. Dong leads the Laboratory of Signal Processing, Communications, and Artificial Intelligence, which provides a collaborative environment for advancing research in signal processing, communications, and AI. The lab supports graduate and post-doctoral researchers and offers opportunities for undergraduate involvement in AI programming, circuit design, and embedded systems.

• Control of Dynamic Systems
• System Identification
• Optimal Estimation and Control
• +۳ مورد دیگر

David M. Bevly is an Assistant Professor in the Department of Mechanical Engineering at Auburn University, where he has been on the faculty since 2001. He directs the GPS and Vehicle Dynamics Laboratory (GAVLAB) and teaches a range of courses spanning system dynamics, vehicle dynamics, optimal estimation and control, and GPS navigation. Education Ph.D. Mechanical Engineering, Stanford University, 2001 M.S. Mechanical Engineering, Massachusetts Institute of Technology, 1997 B.S. Mechanical Engineering, Texas A&M University, 1995 Research Interests His research integrates control theory with real-world vehicle applications, focusing on control of dynamic systems, optimal estimation and control, GPS and sensor fusion, vehicle dynamics and control, and vehicle state estimation . Combining low-cost GPS receivers with inertial sensors, he develops algorithms that enable precise state estimation for passenger cars, agricultural tractors, and off-road platforms, enhancing safety and autonomy. Publication Trends Between 1999 and 2004, Bevly authored or co-authored more than a dozen journal and conference papers that collectively advance the fields of GPS-aided vehicle control and precision agriculture. The works explore theoretical contributions—such as dead-reckoning error analysis and yaw-dynamic modeling—and demonstrate practical implementations on farm tractors and high-speed ground vehicles. Awards & Recognition Best Paper of Session, 2000 ION-GPS Meeting SAE Arch T. Colwell Award (Myers Award) for Outstanding Student Paper, 2000 Laboratory & Teams As head of the GPS and Vehicle Dynamics Laboratory (GAVLAB) at Auburn, Bevly leads interdisciplinary projects that integrate mechanical design, real-time embedded systems, and field experimentation. The lab collaborates with industry partners and federal agencies to transition research results into commercial vehicle-safety and agricultural-autonomy products.

Zhiyuan Liمشاهده پروفایل

استاد

• Program Analysis
• Compiler Design
• Parallel Computing
• +۶ مورد دیگر

Zhiyuan Li is a Professor in the Department of Computer Sciences at Purdue University's College of Engineering. His primary research and teaching focus on program analysis, transformation, and run-time management for high-performance computing and multicore systems, as well as reliable software for networked embedded systems. Professor Li teaches graduate-level courses including CS502: Compiling and Programming Systems and CS591RS1: Research Seminar for First-year Graduate Students. Office: LWSN 3154H Contact: li@cs.purdue.edu Phone: +1 765-494-7822 Professor Li's research spans multiple areas within computer science, with particular emphasis on compiler design, program analysis, and parallel computing. His work addresses fundamental challenges in enabling efficient execution of applications on modern parallel architectures, including multicore processors and large-scale distributed systems. He has made significant contributions to techniques for data dependence analysis, loop parallelization, array privatization, and memory optimization in compilers. His research also extends to reliable software development for embedded and sensor network systems, where resource constraints and reliability requirements present unique challenges. Professor Li's publication record demonstrates consistent contributions to top-tier conferences and journals in computer science, particularly in the areas of parallel computing, compiler optimization, and high-performance numerical methods. His work shows a progression from foundational compiler techniques to applications in scientific computing domains such as computational fluid dynamics for jet engine noise simulation. This interdisciplinary approach connects low-level program analysis with real-world engineering applications requiring petascale computing resources. Principal Investigator for NSF/PetaApps project on jet engine noise simulation Principal Investigator for Intel-sponsored research on data dependence profiling Extensive service on program committees for major conferences including ICS, PPoPP, and LCTES Professor Li has been actively involved in mentoring graduate students through research projects and course instruction. His jet engine noise simulation project specifically mentions training three Ph.D. graduate students and involving undergraduate research assistants. As coordinator for the first-year graduate research seminar, he plays a significant role in guiding new students through the transition to graduate research work in computer science. His laboratory work focuses on developing compiler techniques and runtime systems for parallel and high-performance computing. The research infrastructure includes implementations in GCC for fast data dependence profiling and support for SIMD/SSE instructions, demonstrating practical applications of theoretical compiler techniques.

Pedram Johariمشاهده پروفایل

پژوهشگر

• Intra-body Communications
• Wireless Networking
• Internet of Medical Things
• +۴ مورد دیگر

Pedram Johari serves as a Principal Research Scientist at Northeastern University's Institute for Wireless Internet of Things, working under Prof. Tommaso Melodia in the Department of Electrical and Computer Engineering within the College of Engineering. His research focuses on next-generation wireless communications with particular emphasis on medical applications and nanoscale networks. Dr. Johari earned his Ph.D. in Electrical Engineering from the University at Buffalo, State University of New York in 2018 under the supervision of Prof. Josep M. Jornet. Prior to joining Northeastern, he served as CTO of an IoT-tech startup in New York (2018-2019) and held academic positions at University at Buffalo as Adjunct Instructor and Research Assistant Professor (by courtesy appointment). His research spans four interconnected domains: Intra-body Communications and Networking focusing on nanoscale electromagnetic and optical communications within biological tissues; Internet of Medical Things developing wireless systems for healthcare applications; Low Power Wireless IoT creating energy-efficient communication protocols; and Vehicular Communications advancing cooperative driving systems. His work bridges theoretical modeling with practical implementation, often incorporating AI techniques for network optimization. Analysis of his recent publications reveals a strong trend toward digital twin technology for wireless networks, AI-enabled communication systems, and medical applications of nanoscale communications. His research increasingly integrates machine learning with traditional communication theory, particularly in Open RAN systems and seizure prediction technologies, demonstrating cross-disciplinary impact across engineering, computer science, and biomedical fields. Best Paper Award at IEEE Global Communications Conference (GLOBECOM) Best Short Paper Award at IEEE Vehicular Networking Conference (VNC) Best Presentation Award at IEEE Conf. on Computer Communications Workshops (INFOCOM WKSHPS) Dr. Johari actively contributes to academic service as a reviewer for numerous prestigious journals including IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and IEEE Internet of Things Journal. He has served on technical program committees for major conferences including IEEE/ACM CHASE and IEEE SECON. His teaching experience includes courses in programming, circuit analysis, and digital principles at University at Buffalo. As a key member of the Wireless Networks and Embedded Systems Lab at Northeastern University, Dr. Johari contributes to the Colosseum wireless network emulator project - recognized as the world's largest wireless network emulator. His work with the Institute for Wireless Internet of Things involves collaboration with industry partners to translate research into practical wireless communication solutions.