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Zaigham SaghirView profile

Associate Clinical Professor

• Respiratory Medicine
• Lung Cancer Screening
• Pulmonary Nodule Analysis
• +3 more

Zaigham Saghir serves as a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen's Faculty of Health and Medical Sciences. His clinical role as a Consultant at Copenhagen University Hospital (Region H) is based at Gentofte Hospitalsvej 1 in Hellerup, with academic affiliation to the Blegdamsvej campus in Copenhagen. His dual appointment bridges clinical respiratory medicine with academic research, focusing on translational applications in thoracic oncology. Dr. Saghir's research centers on lung cancer screening optimization , pulmonary nodule management , and AI integration in thoracic imaging . His work explores the intersection of deep learning algorithms with clinical decision-making, particularly in risk stratification for pulmonary nodules and post-treatment surveillance. Recent publications demonstrate expertise in validating AI models against European screening data and developing safety mechanisms for out-of-distribution detection in medical imaging systems. His research program addresses critical gaps in lung cancer early detection through both technological innovation and population health approaches. Analysis of his 28 research outputs (2023-2025) reveals strong emphasis on translational respiratory oncology with three dominant threads: (1) AI-driven pulmonary nodule assessment (40% of recent work), (2) lung cancer screening program design (30%), and (3) post-treatment surveillance protocols (30%). His publications consistently bridge computational methods with clinical implementation challenges, particularly within the Danish healthcare context. Notable collaborations include the NELSON-POP consortium and SUPE_R Study Group, indicating leadership in European multicenter trials. His research is supported through clinical-academic channels at Copenhagen University Hospital and the University of Copenhagen, with evidence of participation in major European trials. The collaborative network spans radiology, respiratory medicine, oncology, and medical informatics departments across multiple Danish hospitals.

Brent PetersenView profile

Professor

• Digital Communications
• Antenna Arrays
• CubeSats
• +6 more

Brent Petersen is a Professor and Director of Graduate Studies in the Department of Electrical and Computer Engineering at the University of New Brunswick (UNB). He holds a PhD from Carleton University and has held academic roles since 1997. His research focuses on optical and wireless communications, with expertise in digital communications, antenna arrays, CubeSats, and electromagnetics. Education: B.Eng., Carleton University M.A.Sc., University of Waterloo Ph.D., Carleton University Roles: Director of Graduate Studies (2023–present; 2006–2009) Leadership in CubeSat NB initiative His research interests span communications systems, signal processing, and aerospace applications. Key contributions include work on MIMO systems, antenna optimization, and CubeSat technology. He has advised over 30 graduate students and holds patents in wireless communication and sensor systems. Awards include the CNSR 2006 Best Paper Award and IBM's Invention Achievement Award. Publications focus on topics like wireless network optimization, antenna design, and space communications. He actively engages in academic governance, supervising graduate programs and contributing to curriculum development.

Kamran KiasalehView profile

Professor

• Optical Communications
• Wireless Communications
• Biomedical Signal Processing
• +5 more

Professor Kamran Kiasaleh holds the position of Professor in the Department of Electrical & Computer Engineering at The University of Texas at Dallas (UT Dallas), within the Erik Jonsson School of Engineering and Computer Science. He is also a Professional Engineer licensed in Texas and directs the Optical Communications Lab. His research focuses on optical communications (including free-space and underwater systems), wireless technologies, biomedical signal processing, and tissue optics. Key areas include FSO channel analysis, quantum key distribution, microwave breast cancer detection, and particle swarm optimization for antenna design. Education: PhD in Electrical Engineering from the University of Southern California (USC). Awards include the NASA Group Achievement Award (1993) for contributions to the Galileo Optical Experiment. He holds multiple patents on optical receiver architectures, channel estimation, and communication systems. His work is supported by agencies like NSF, National Institute of Justice, Texas Instruments, and State of Texas programs. Research interests span optical beam tracking, turbulence effects, biomedical signal classification, and SDR implementations. Recent publications address underwater optical communication challenges, sepsis detection via ECG signals, and adaptive algorithms for OFDM systems. His lab explores innovative solutions in microwave imaging, antenna design, and real-time signal processing. Awards: NASA Group Achievement Award (1993), 7 USPTO patents. Grants: NSF, NIJ, Texas Emerging Tech Program, NxGEN Partners. Courses Taught: Optical Communication Systems, Detection & Estimation, Wireless Communications.

Andrew SingerView profile

Professor

• Signal Processing
• Underwater Acoustic Communications
• Biomedical Acoustics
• +4 more

Andrew Singer is the Dean of the College of Engineering and Applied Sciences and a Professor in the Department of Electrical and Computer Engineering at Stony Brook University. Previously, he held roles at the Grainger College of Engineering, University of Illinois Urbana-Champaign, including positions as Associate Dean for Innovation and Entrepreneurship. His research spans signal processing, underwater acoustic communications, biomedical acoustics, and entrepreneurship. He leads projects like the MAGIC Automated Acoustics Laboratory and Augmented Listening Technology, focusing on audio signal enhancement, wearable devices, and underwater communication systems. Research interests include adaptive signal processing, turbo equalization, and biomedical applications of acoustics. Notable contributions include high-data-rate ultrasonic communication through biological tissues and real-time video streaming using acoustic channels. His work integrates machine learning and physical models to address challenges in acoustics, communications, and biomedical engineering. Recent articles explore reliable measurement in unreliable systems, underwater source localization, and bio-inspired signal processing techniques. Awards and recognitions are not explicitly listed but his extensive publications and leadership roles highlight his contributions to the field. Advised students and collaborators span academia and industry, including roles at Apple, Amazon, and defense agencies. Current initiatives include developing mechatronic systems for acoustic research and advancing assistive listening technologies.

Charlotte LanglaisView profile

Associate Professor

• Signal Processing
• Digital Communications
• Wireless Communications
• +2 more

Charlotte Langlais is a Study Director and Researcher at IMT Atlantique in the Mathematical and Electrical Engineering department. She obtained her PhD in Electronics from INSA de Rennes in 2002, with research focused on turbo-equalization for wireless HF ionospheric transmission. Her current research explores signal processing and digital communications, particularly wireless communications, coding/modulation, MIMO systems, and multi-user transmission. Dr. Langlais teaches digital electronics using innovative flipped classroom approaches and coordinates project-based learning initiatives. She leads the coordination of project courses and industry partnerships involving over 50 economic partners. Her publications demonstrate expertise in mMIMO systems, NOMA techniques, and reconfigurable intelligent surfaces for 5G/6G applications. Recent work shows strong focus on railway communications, channel modeling, and efficient beamforming strategies for mmWave networks.

Pål OrtenView profile

Professor

• Telecommunications
• Underwater Acoustic Communication
• Maritime Communication Systems
• +4 more

Pål Orten is a Professor II at the University of Oslo's Institute of Transport Economics (ITS), holding a 20% part-time position. He is affiliated with the Section for Autonomous Systems and Sensor Technologies, focusing on advanced communication systems including underwater acoustic networks, maritime communication infrastructure, and millimeter-wave technologies. His research bridges theoretical signal processing with practical hardware implementations, particularly leveraging FPGA platforms for real-time systems. Key research areas include: (1) Underwater Acoustic Communication (channel modeling, MIMO systems, FPGA-based platforms), (2) Maritime Connectivity (5G applications, intelligent reflecting surfaces), and (3) Millimeter-Wave Systems (MIMO design, phased arrays). His work addresses challenges in autonomous systems, industrial automation, and aquaculture monitoring. Recent publications highlight innovations like the WindNet mobile base station for maritime use and experimental evaluations of high-frequency underwater communication systems. His contributions span IEEE conferences (e.g., IHTC, ITNAC, VTC) and journals like IEEE Journal of Oceanic Engineering and International Journal of Antennas and Propagation.

• Communication Theory
• Wireless Networks
• Signal Processing
• +3 more

Pap László is a Professor at the Budapest University of Technology and Economics (BME), affiliated with the Faculty of Electrical Engineering and Informatics (VIK) and the Department of Network Systems and Services. He has held roles since 1949, transitioning through departments including the Department of Information Technology (2000–2012) and currently leads the Department of Network Systems and Services (from 2013). He is also a core member and council member of the Doctoral School of Electrical Engineering (BME/EHBDT) since 2002. His research focuses on communication theory, wireless networks, and signal processing, with a strong emphasis on Non-Orthogonal Multiple Access (NOMA), MIMO systems, and LTE technologies. Recent work includes studies on channel estimation in NOMA systems, capacity analysis of multi-user scenarios, and synchronization methods for spread spectrum systems. He has collaborated with researchers like Zoltán Belső and Ádám Knapp on projects addressing wireless resource allocation and interference management. Pap’s publications span journals like INFOCOMMUNICATIONS JOURNAL and Wireless Networks , with conference contributions at events such as SoftCOM and IEEE EUROCON. His work bridges theoretical advancements in telecommunications with practical applications in modern wireless systems.

Saša V. NikolićView profile

Professor

• Digital Filter Design
• Signal Processing
• Electronic Circuit Design
• +2 more

Saša V. Nikolić is a Professor of Electronics at the Faculty of Electronic Engineering, University of Niš. He holds a permanent faculty position with extensive contributions to digital filter design and signal processing research. His educational background includes: Bachelor's Degree in Electronics (1991, University of Niš) Master's Degree in Electronics (1996, University of Niš) PhD in Electronics (2004, University of Niš) Nikolić's research focuses on advanced digital filter design techniques, particularly IIR notch filters, Chebyshev approximation methods, and linear phase systems. His work bridges theoretical circuit design with practical signal processing applications in telecommunications and electronic systems. Recent publications demonstrate expertise in optimizing filter magnitude responses while maintaining phase linearity—a critical challenge in real-time signal processing systems. His publication trends reveal sustained contributions to core electronics journals, with emphasis on non-iterative design methods, multi-notch systems, and thermal characterization of electronic components. Key venues include AEU - International Journal of Electronics and Communications (IF: 2.924) and Circuits, Systems and Signal Processing. Nikolić actively participates in national research projects and has co-authored a monograph of exceptional national significance titled 'On the Direct Design of Recursive Digital Filters' (2012). His textbook 'Signali i Sistemi' (2017) serves as foundational material for electronics students at the University of Niš. He maintains laboratory operations at Room 7 of the Faculty of Electronic Engineering (contact: +381 (18) 529-607) and contributes to curriculum development through departmental committees.

• Signal Processing
• Digital Communications
• Machine Learning Applications
• +3 more

Rafael Boloix Tortosa is an Associate Professor in the Department of Signal Theory and Communications at the University of Seville's School of Engineering, where he leads significant research in complex-valued signal processing and its applications. His academic career spans over two decades with continuous involvement in national and international research projects. His research focuses on advancing complex-valued signal processing methodologies, particularly complex Gaussian processes, kernel methods, and widely linear estimation techniques. These theoretical advances are applied to diverse domains including medical imaging (especially burn wound analysis), wireless communications (UWB positioning, OFDM systems), and art conservation. His work bridges fundamental signal processing theory with practical engineering solutions. Professor Boloix Tortosa maintains an active publication record with significant contributions in top IEEE journals, particularly in signal processing and machine learning. His recent work (2025-2019) demonstrates continued innovation in complex-valued machine learning methods, while his earlier publications established foundational work in blind source separation and multiuser detection. He actively participates in multiple research projects including EU-funded initiatives like COURAGEOUS (counter-UAS solutions) and ARIADNE (agricultural drones), alongside national projects in medical imaging and communications. His research group has received consistent funding through the TIC-155 consolidation incentives. His laboratory work centers around the Signal Processing and Communications Research Group, where he develops both theoretical frameworks and practical implementations for signal processing challenges. The group maintains strong connections between theoretical advances and real-world applications across multiple engineering domains.

• Communications
• Computer Engineering
• Signal and Image Processing
• +2 more

Zhaohui Wang is an Associate Professor at the University of Alberta's Faculty of Engineering, Department of Electrical and Computer Engineering. She joined in Fall 2024 after serving at Michigan Technological University since 2013, attaining tenure as Associate Professor in 2019. Education: PhD in Electrical Engineering (University of Connecticut, 2013) Dr. Wang specializes in underwater acoustic communications and networking, with expertise in channel modeling, machine learning applications, interference cancellation, and transceiver design. Her research spans under-ice environments, mobile communication systems, and field experiment-based testbed development. Her recent publications focus on deep-sea horizontal acoustic channels, energy-efficient communication using reinforcement learning, OFDM adaptations for underwater environments, and advanced signal processing techniques. These works emphasize machine learning integration, dynamic channel management, and robust transceiver design. Scientific Awards: NSF CAREER award (2017) Women of Innovation Award (2013) Outstanding Senior Women Academic Achievement Award (2014) Best Paper Award at ACM WUWNet (2021) Multiple Outstanding Reviewer recognitions from IEEE Journal of Oceanic Engineering Dr. Wang teaches courses including Wireless Communications (ECE 486) and Probability and Random Processes (ECE 502). She actively recruits motivated Master/PhD students for cutting-edge research in underwater/under-ice acoustic communication systems.

• Wireless Communication
• Wireless Sensor Networks
• Network Coding
• +3 more

Nadieh Moghadam is an Assistant Professor of Electrical Engineering at the University of San Diego (USD), affiliated with the College of Engineering. She holds a PhD from the University of Louisville (2017), an MS from Iran University of Science and Technology, and a BS from K.N. Toosi University of Technology. Her research focuses on wireless communication, network coding, sensor networks, and embedded systems. She has authored over 15 publications and served as a reviewer for IEEE Transactions journals since 2015. Education : PhD in Electrical Engineering, University of Louisville (2017) MS in Electrical Engineering, Iran University of Science and Technology BS in Electrical Engineering, K.N. Toosi University of Technology (Iran) Research Interests : Dr. Moghadam advances wireless communication systems through innovations in network coding, optimization algorithms, and embedded system design. Her work addresses challenges in multicast efficiency, queue stability, and energy conservation in IoT and sensor networks. She also explores blockchain applications in modern wireless standards like IEEE 802.11ax. Awards : Graduate Dean’s Citation Award (2017) University Fellowship (PhD period) Theobald Scholarship (PhD period) Industry Experience : She co-founded Ivani, a startup developing IoT wireless sensing systems using Bluetooth Low Energy (BLE) and Zigbee protocols. Her team designed low-power occupancy sensing solutions leveraging RF environments.

• Labor Economics
• Development Economics
• Family Economics
• +3 more

Yun Xiao is an Assistant Professor at the Department of Economics, University of Gothenburg (GU) and a Research Affiliate at the Centre for Economic Policy Research (CEPR). She holds a PhD from the University of Amsterdam (UvA). Her research focuses on empirical microeconomics with a specialization in labor, development, and family economics. Key themes include human capital formation, fertility decision-making, and socioeconomic inequality dynamics. She employs rigorous empirical methods to analyze policy impacts in contexts like China’s family planning policies and aid effectiveness in Afghanistan. Her education includes a PhD from the University of Amsterdam, complemented by advanced training in econometric methodologies. Her work bridges theoretical frameworks with real-world policy evaluation, particularly in developing economies. Yun Xiao’s research explores how social multipliers accelerate fertility transitions, the unintended consequences of aid fragmentation on corruption, and the long-term health impacts of environmental policies. Her studies often highlight how marginalized groups respond to policy interventions, such as ethnic minorities adapting to fertility restrictions or rural households benefiting from improved water access. Her publications appear in top journals such as the Journal of the European Economic Association and Journal of Environmental Economics and Management . She collaborates extensively with researchers like Pauline Rossi (fertility studies) and Li Li (health-environment linkages). Her advisory and grant activities are integral to her academic contributions, though specific grants are not detailed here. She maintains active research partnerships through her CEPR affiliation, contributing to policy-relevant economic analysis.

• Wireless Communication
• Signal Processing
• Information Theory
• +3 more

Rong-Rong Chen is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Utah's College of Engineering, where she has been employed since August 2003, progressing from Assistant Professor (2003-2011) to her current position (2011-present). Her educational background includes a BS in Applied Mathematics from Tsinghua University (1994), an MS in Mathematics from the University of Illinois at Urbana-Champaign (1996), and a PhD in Electrical Engineering from UIUC (2003). Dr. Chen's research focuses on wireless communication systems , with particular expertise in underwater acoustic communication, signal processing, information theory, and queueing networks. Her work bridges theoretical foundations with practical applications in modern communication systems. Her recent publication on OTFS and OFDM channel estimation demonstrates her continued contribution to advancing wireless communication technologies, particularly in comparing next-generation modulation schemes. Best Paper Award, IEEE Global Communications Conference (2021) NSF Faculty Early Career Development (CAREER) award (2006) University Fellowship for graduate study at UIUC M. E. Van Valkenburg Graduate Research Award for excellence in doctoral research at UIUC Dr. Chen has secured significant research funding including current projects on decentralized intelligent spectrum sharing in UAV networks (SWIFT/DISH-UNET), smart radio environments with reconfigurable intelligent surfaces (REFLECT-MMWAVE), and improving MRI technology through signal processing. She teaches courses including Engineering Probability & Statistics, Information Theory, and thesis research for PhD students.

Xiaohua LiView profile

Associate Professor

• Signal Processing
• Wireless Communications
• Cognitive Radio Networks
• +6 more

Xiaohua (Edward) Li is an Associate Professor of Electrical and Computer Engineering at the State University of New York at Binghamton. He holds a BS and MS from Shanghai Jiao Tong University and a PhD from the University of Cincinnati. His research focuses on signal processing for communications, cognitive radio networks, wireless security, and machine learning applications in networked systems. Education: Bachelor of Science, Control Engineering, Shanghai Jiao Tong University Master of Science, Biomedical Engineering, Shanghai Jiao Tong University Doctor of Philosophy, Electrical and Computer Engineering, University of Cincinnati Research Interests: Dr. Li's work spans adaptive signal processing, cognitive radio networks, blind equalization, and secure wireless communications. He has pioneered methods for integrating machine learning into signal processing frameworks and developed novel approaches for 5G imaging and communication systems. His research emphasizes practical applications, including anti-jamming designs, spectrum sharing, and secure physical-layer transmissions. Grants & Projects: AFOSR: Integrated 5G Imaging and Communication Surveillance (2021–2024) NSF: Intelligence Measure of Cognitive Radio Networks (2015–2017) AFRL: Jamming and spectrum mutation in cognitive radio networks (2013–2015) Awards: Best Paper Award at MMM-ACNS 2017 (Warsaw, Poland) Best Reviewer Award at ETRI Journal 2013 Teaching: Dr. Li teaches advanced courses including Digital Signal Processing II, Neural Networks and Deep Learning, and Wireless Communications. He advises students on projects ranging from cooperative networks to smart grid security.

• Underwater Acoustic Communications
• Signal Processing
• Communications Engineering
• +3 more

Qinghua Guo is an Associate Professor and Adjunct Associate Professor in the Department of Electrical, Electronic and Computer Engineering at the University of Western Australia . His primary affiliation is through the School of Engineering. He specializes in underwater acoustic communications, signal processing, and advanced communication systems. Research Interests: Underwater Acoustic Communications Channel Estimation and Detection Belief Propagation Algorithms OFDM Systems Sparse Bayesian Learning Multi-Mode Index Modulation Key Projects: Implementing a Reconfigurable Underwater Acoustic Communication Modem (2012) Cognitive Acoustics for Underwater Sensor Networks (2010) Bandwidth Efficient Underwater Acoustic Communication Systems with Turbo Processing (2010-2012) Cognitive Multi-hop Underwater Acoustic Communications (2009-2011) Factor Graph Receiver Design for Doubly Selective Channels (2012-2014) His research focuses on low-complexity solutions for underwater communication challenges, including moving transceivers and channel estimation. Recent work extends to sparse Bayesian learning and multi-mode modulation techniques.