جست‌وجوی استاد و پژوهشگر

Jie Xiongمشاهده پروفایل

دانشیار

• Wireless Sensing
• Mobile Health
• Wireless Networking
• +۳ مورد دیگر

Jie Xiong is an Associate Professor at the University of Massachusetts Amherst, affiliated with the Department of Computer Science within the College of Information and Computer Sciences. He holds a PhD from University College London (2015) and has been a faculty member since 2018. His research focuses on wireless sensing, mobile health, and IoT, with notable contributions to sensor-free systems and long-range sensing. He leads the Center for Smart and Connected Society and has been recognized with awards like the SIGMOBILE Test-of-Time Award (2024) and MobiCom Best Paper Award (2024). Education: PhD in Computer Science, University College London (2015) Research Interests: His work spans wireless sensing (e.g., acoustic, RF, LoRa), mobile health monitoring, and IoT applications. He explores sensor-free techniques, long-range through-wall sensing, and leveraging ambient signals for novel applications. Grants & Awards: NSF CAREER Award NIH R01 Grant (Smart and Connected Health) Google European Doctoral Fellowship BCS Distinguished Dissertation Award (Runner-Up) MobiCom '22 Best Paper Award (Runner-Up) Advising & Students: Supervises PhD students including Minhao Cui, Yuda Feng, Binbin Xie, and Dong Li. His students have received accolades like the Google Ph.D. Fellowship (Binbin Xie, 2022). Labs & Teams: Leads research in the Center for Smart and Connected Society, focusing on wireless systems and health applications. Collaborates with industry and academia on projects like EVLeSen (in-vehicle sensing) and SoilCares (agricultural monitoring).

Dina Katabiمشاهده پروفایل

استاد

• Digital Health
• Wireless Sensors
• Machine Learning
• +۳ مورد دیگر

Dina Katabi is the Thuan and Nicole Pham Professor of Electrical Engineering and Computer Science at MIT, leading the Katabi Lab and directing the MIT Center for Wireless Networks and Mobile Computing. Her research bridges AI, wireless systems, and digital health, focusing on non-invasive health monitoring via wireless signals and machine learning. She is a MacArthur Fellow and holds the Andrew & Erna Viterbi Professorship. Key research areas include emotion recognition (EQ-Radio), sleep posture monitoring (BodyCompass), and through-wall human pose estimation. Her lab develops AI systems for biosensors, leveraging RF signals to detect diseases like Parkinson's and Alzheimer's. Notable awards include the ACM Prize in Computing and SIGCOMM's Lifetime Achievement Award. Publications span wireless networks, computer vision, and health tech, with impactful work in CVPR, ECCV, and Nature Medicine. She advises over 20 students/postdocs and collaborates on technologies like in-body backscatter communication and AI-driven drug development monitoring. Labs: Katabi Lab (MIT CSAIL) and the MIT Wireless Center. Ongoing work explores digital biomarkers, self-supervised learning, and scalable health monitoring systems for chronic diseases.

Ajit Jhaمشاهده پروفایل

دانشیار

• Photonic sensing
• Robotics
• Machine Learning
• +۶ مورد دیگر

Ajit Jha is an Associate Professor at the Department of Engineering Sciences , University of Agder , Norway, with research expertise in photonic sensing, robotics, machine learning, and sensor fusion. His work bridges theoretical advancements with real-world applications in autonomous systems, industrial automation, and biomedical imaging. Research Areas: Photonic sensing, Robotics, Machine Learning, Computer Vision, Sensor Fusion, Mechatronics Recent Publications demonstrate innovative applications of deep learning to thermal imaging (gesture recognition), reinforcement learning for drone landing, and sensor fusion techniques for autonomous navigation. His interdisciplinary approach combines photonics, radar systems, and AI to solve complex engineering problems.

Bletsas Aggelosمشاهده پروفایل

استاد

• Wireless Transmission
• Wireless Relay Networks
• Software-Defined Radio
• +۳ مورد دیگر

Aggelos Bletsas is a Professor at the School of Electrical and Computer Engineering, Technical University of Crete. He holds a PhD from MIT (2005) and has expertise in wireless communication, backscatter networks, and RFID systems. His research focuses on scalable wireless networks, ultra-low-cost sensor technologies, and signal processing. Education: PhD, MIT Media Lab (2005) MSc, MIT Media Lab (2001) Diploma in Electrical & Computer Engineering, Aristotle University of Thessaloniki (1998) Research Interests: His work spans wireless transmission techniques, backscatter sensor networks, and RFID systems. Key areas include: Ultra-low-cost sensor deployment RFID localization and multi-static systems Energy-efficient hardware implementations Probabilistic inference in distributed networks Awards: IEEE Marconi Prize Paper Award (2008) Technical University of Crete Research Excellence Award (2012-2013) Multiple best paper awards at RFID-TA, ISWCS, and SENSORS Academic Contributions: He advises students who have won IEEE best thesis awards and leads projects funded by ERC grants. His laboratory focuses on practical implementations of wireless sensor networks and backscatter systems. Labs & Affiliations: Director of the Telecommunications Laboratory and affiliated with the Telecommunication Systems Institute (TSI).

• Cyber-physical systems
• Green and sustainable computing
• Mobile and pervasive computing
• +۷ مورد دیگر

Sandeep Gupta is a Professor and Director of the School of Computing and Augmented Intelligence at Arizona State University's Ira A. Fulton Schools of Engineering. He also serves as a Senior Global Futures Scientist. His work bridges computer science, engineering, and healthcare applications with a focus on creating reliable cyber-physical systems that interact safely with humans. Education: Ph.D. from The Ohio State University (1995) Research Interests: Professor Gupta's research spans cyber-physical systems, green and sustainable computing, mobile and pervasive computing, and parallel and distributed computing. His work increasingly focuses on human-in-the-loop systems where AI and humans collaborate safely, particularly in healthcare contexts. Recent research integrates large language models with cyber-physical systems to enhance safety and operational effectiveness in critical applications like medical monitoring and industrial automation. Research Trends: Analysis of Professor Gupta's recent publications reveals a strong focus on operational safety in human-AI collaborative systems, particularly in healthcare applications. His work combines physics-guided models with machine learning to detect "unknown-unknowns" in safety-critical systems, develops LLM-based approaches for medical image classification, and creates frameworks for ethical human-AI collaboration. The research increasingly addresses real-world challenges in diabetes management, epilepsy diagnosis, and industrial automation. Professional Service: IEEE Communication Letters Editorial Board, Area Editor (2008-Present) IEEE Journal on Special Areas in Communications - Issue on Body Area Network, Co-editor (2006-Present) Elsevier COMNET - Computer Network Journal, Reviewer (2008-Present) Technical Advisory Committee Chair for Networks (2005-Present) Advising and Grants: Professor Gupta has secured numerous research grants from NSF, NIH, Intel, Raytheon, and other organizations, totaling millions of dollars. His research portfolio includes projects on smart stadiums, mobile ECG sensing, power-aware scheduling, medical device verification, and sustainable data center management. He actively advises PhD and Master's students through thesis courses and research supervision, focusing on cyber-physical systems and healthcare applications. Labs and Research Groups: Professor Gupta leads research in cyber-physical systems with applications in healthcare, sustainable computing, and mobile networks. His work involves interdisciplinary collaboration across engineering, computer science, and medical domains, particularly through ASU's Global Futures initiatives.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Petar Popovskiمشاهده پروفایل

استاد

• Wireless Communication
• Internet of Things
• Ultra-Reliable Low Latency Communication
• +۱۲ مورد دیگر

Petar Popovski is a Professor at the Department of Electronic Systems within the Technical Faculty of IT and Design at Aalborg University, Denmark. His research focuses on next-generation wireless communication systems, with a strong emphasis on ultra-reliable low-latency communication (URLLC), Internet of Things (IoT), multiple access, and 6G technologies. He leads several high-impact research projects, including the Classique - Center for Classical Communication in the Quantum Era funded by the Danish National Research Foundation and WATER (Wireless Architectures for intelligent and Trusted connectivity in the posT-5G ERa) supported by Villum Fonden. His research interests span key areas in modern communication theory and systems, including random access , non-terrestrial networks , satellite communication , and machine learning for reliable communication . He is actively involved in advancing the integration of sensing and communication, digital twin technologies, and quantum-era classical communication frameworks. The recent publications highlight a strong trend toward deterministic and reliable access in wireless networks, integration of sensing and communication for industrial automation, and novel physical-layer techniques using reconfigurable intelligent surfaces. These works are published in top IEEE journals such as IEEE Transactions on Communications , IEEE Transactions on Haptics , and IEEE Transactions on Vehicular Technology . Award highlights include the Best Student Paper Award (2021) , recognizing his mentorship and collaborative research excellence. Prof. Popovski serves as a principal investigator (PI) and supervisor in multiple research projects, securing significant funding from national and international bodies such as the Danish National Research Foundation and the European Space Agency (ESA). He hosts visiting researchers regularly and contributes to scientific leadership through editorial roles and conference participation. He is a key figure in the Connectivity section at Aalborg University and leads cutting-edge research in future wireless systems, contributing to both theoretical foundations and practical implementations in smart infrastructure, space communication, and dependable 6G networks.

Haining Wangمشاهده پروفایل

استاد

• Cybersecurity
• Networking Systems
• Cloud Computing
• +۵ مورد دیگر

Haining Wang is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on cybersecurity, networking systems, cloud computing, and cyber-physical systems. He holds a Ph.D. from the University of Michigan (2003). His work addresses critical challenges in network security, IoT device fingerprinting, drone navigation security, and 5G/6G infrastructure vulnerabilities. Notable contributions include developing frameworks for detecting deceptive reviews, securing industrial IoT devices, and enhancing geofencing systems with 6G technologies. Wang's IEEE Fellow award (2020) recognizes his contributions to network and cloud security. His research also explores cloud gaming security, data center thermal vulnerabilities, and DNS privacy risks. He actively publishes on topics like container registry typosquatting, acoustic indoor localization, and encrypted DNS censorship analysis. Education: Ph.D., University of Michigan, 2003 Awards: IEEE Fellow (2020) Key Research Areas: Cybersecurity, Network Measurement, IoT Security, 5G/6G Systems Wang's recent work emphasizes securing emerging technologies like drone navigation systems and optimizing sensor placements in indoor environments. His projects often bridge theoretical frameworks with practical implementations in real-world networks and cloud infrastructures.

Aftab Ahmadمشاهده پروفایل

استاد

• Machine Learning
• Cyber Security
• Public Health Prediction
• +۵ مورد دیگر

Aftab Ahmad is a Professor in the Department of Computer Science at the City University of New York (CUNY), specializing in cybersecurity and machine learning applications. He holds a Doctor of Science from George Washington University. His research focuses on developing machine learning algorithms for cyber threat intelligence (CTI) and public health prediction, along with designing secure generative deep learning models resistant to reverse-engineering. Key research areas include: Cybersecurity frameworks and privacy-preserving architectures Generative adversarial networks (GANs) with embedded security features Biomedical signal analysis and human body channel modeling Secure wireless protocols for critical infrastructure His publication trends emphasize: Privacy metrics and data protection mechanisms Smart grid and IoT security Neuroscience-inspired machine learning models Wireless network vulnerability assessments No scientific awards or grants were explicitly mentioned in the provided texts. He teaches advanced courses in computer security and network forensics at undergraduate and graduate levels. No advising relationships or lab affiliations were detailed in the available information.

Jin Luمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Bio-informatics
• +۶ مورد دیگر

Jin Lu is an Assistant Professor at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He earned his Ph.D. (2019) and M.S. (2019) in Computer Science and Engineering from the University of Connecticut. Prior to his current role, he served as an Assistant Professor at the University of Michigan–Dearborn (2019–2023). Educational Background: Ph.D. in Computer Science and Engineering, University of Connecticut, 2019 M.S. in Computer Science and Engineering, University of Connecticut, 2019 Research Interests: Dr. Lu focuses on machine learning, optimization, bio-informatics, and smart mobility. His work spans federated learning, healthcare applications (e.g., depression and BMI monitoring), IoT systems, and computer vision. Recent projects explore AGI's potential in medical and educational contexts, leveraging models like CycleGAN and reinforcement learning. Grants & Funding: Develop digital brains to advance portable diagnosis of neurological conditions (Google, 2025) Lab/Teams: While specific lab affiliations are not explicitly stated, his research involves collaborations in interdisciplinary areas such as health informatics and smart mobility.

Maxime Ferreira Da Costaمشاهده پروفایل

دانشیار

• Signal Processing
• Data Science
• Inverse Problems
• +۶ مورد دیگر

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

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.

Nico Palleitمشاهده پروفایل

پژوهشگر

• MIMO Communication Systems
• Channel Estimation
• Signal Processing
• +۳ مورد دیگر

Dr.-Ing. Nico Palleit is affiliated with the University of Rostock's Institute of Communications Engineering, part of the Faculty of Computer Science and Electrical Engineering. His research focuses on MIMO (Multiple-Input Multiple-Output) systems, channel estimation, and prediction techniques to enhance spectral efficiency. He holds a PhD titled Channel Prediction in Multi-Antenna Systems (2011) and has contributed to advancements in MIMO channel analysis, including frequency/time prediction and interference management. Research Interests: Nico's work addresses challenges in modern radio transmission systems, emphasizing the development of robust channel estimation strategies. Key areas include MIMO channel modeling, non-line-of-sight (NLOS) positioning, and optimizing transmitter-side channel state information. His research bridges theoretical frameworks with practical implementations in wireless communication systems. Publications Overview: His 15+ publications (2006–2012) span topics like MIMO channel prediction, antenna array design, and interference channel optimization. Recent work emphasizes frequency/time-domain channel prediction and power allocation strategies for maximizing system capacity. These contributions highlight interdisciplinary approaches combining signal processing with electrical engineering principles. Affiliations & Labs: As part of the Radio Communication Research Group, he collaborates on projects within the Institute's advanced wireless communication initiatives. His work supports next-generation radio systems through innovative solutions for MIMO-FDD and OFDM-based architectures.

Christian Haubeltمشاهده پروفایل

استاد

• Embedded Systems
• Cyber-Physical Systems
• Smart Sensors
• +۹ مورد دیگر

Christian Haubelt is a Professor at the Institute of Computer and Network Engineering, School of Engineering, University of Rostock, Germany. He is actively engaged in research and teaching in the areas of embedded and cyber-physical systems, smart implants, and IoT. His work is supported by multiple national and international projects including ELAINE (SFB 1270), SmILE (EU), 6G-Health (BMBF), and GenerIoT (BMBF). His research interests include: Embedded and Cyber-Physical Systems Smart Sensors and Smart Implants System-Level Design Methodologies SystemC-based Modeling and Verification Design Space Exploration and Multi-Objective Optimization Industrial Internet of Things and 6G for Healthcare His recent publications focus on real-time communication protocols, 5G/6G localization, smart implants, and secure IoT systems. Trends show a strong emphasis on integrating embedded systems with medical and industrial applications, particularly leveraging TSN, MQTT-SN, and OPC UA for reliable and secure communication. His work bridges theoretical modeling with practical implementation in safety-critical domains. Christian Haubelt has supervised multiple researchers including Michael Nast, Benjamin Rother, Nico Kalis, and Nico Graumüller. He leads several funded research projects such as ELAINE, SmILE, 6G-Health, and SUSTAIN, which focus on smart implants, secure IoT, and next-generation medical systems. These projects involve collaboration with DFG, EU, and BMBF. He is involved in the following research labs and teams: Embedded Systems and Cyber-Physical Systems Group Smart Implants Research Team (SmILE, ELAINE) 6G-Health Localization Team Industrial IoT Security (SUSTAIN, CargoAssist)

Kalle Åströmمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Geometry and Algebra of Multiple Views
• +۵ مورد دیگر

Kalle Åström is a Professor at Lund University's Centre for Mathematical Sciences within the Faculty of Engineering. He coordinates Lund University's Natural and Artificial Cognition profile area and the AI Lund network. His affiliations include ELLIIT (Linköping-Lund IT initiative), eSSENCE (e-Science Collaboration), Stroke Imaging Research group, and Computer Vision and Machine Learning research groups. His research spans computer vision, machine learning, and mathematical modeling with applications in medical imaging, autonomous systems, and cognitive vision. Key interests include geometry of multiple views, structure from motion using heterogeneous sensors, medical image analysis, and handwriting recognition. His work contributes to UN Sustainable Development Goals through AI applications in healthcare and engineering. Recent publications (2025) demonstrate strong trends in medical AI (Alzheimer's diagnostics, breast cancer classification) and autonomous systems (safety testing, sensor fusion). His work bridges theoretical mathematics with practical applications across healthcare and robotics domains. Best Nordic Ph.D. Thesis in Pattern Recognition (1995-1996) Innovation Cup 1991 for Autonomous Guided Vehicles EU IST Grand Prize 2003 (Decuma startup) Åström supervises graduate students and leads multiple active research projects including machine learning for Parkinson's disease analysis, audiovisual drone detection (Vinnova-funded), and Alzheimer's disease modeling. He co-founded startups Decuma (1999), Cognimatics (2003), Spiideo (2012), and Neuromathics (2015), and serves on boards of the Royal Swedish Physiographic Society and Swedish AI Society (SAIS). His research integrates mathematical rigor with real-world AI applications through extensive industry-academia collaborations.