Margarita Rumbullaku serves as a Contract Professor in the Department of Experimental Medicine (DIMES) at the University of Genoa, with dual affiliation at Istituto Giannina Gaslini. She currently teaches Clinical Pathology I (code 68677) for the Biomedical Laboratory Techniques degree program in both the 2024-2025 and 2025-2026 academic years. Her academic focus centers on Clinical Pathology and Biomedical Laboratory Techniques, emphasizing diagnostic methodologies and laboratory medicine essential for training biomedical technicians. This expertise directly informs her curriculum development for degree courses in medical diagnostics. Contact is facilitated via university email (margarita.rumbullaku@edu.unige.it) or Gaslini institutional address (margaritarumbullaku@gaslini.org), with office hours available by appointment through the latter channel.
Jingbing Xue serves as an Assistant Professor in the Department of Imaging Sciences at the University of Rochester School of Medicine and Dentistry. He is affiliated with the University of Rochester Medical Faculty Group (URMFG) and Accountable Health Partners (AHP), focusing clinically on vascular and interventional radiology. His educational background includes: MB from Norman Bethune University of Medical Sciences (China), 1995 MS from Zhejiang University School of Medicine (China), 1998 Radiology residencies at Sir Run Run Shaw Hospital and Zhejiang University School of Medicine (1996-2000) Fellowships in interventional radiology at Beth Israel Deaconess Medical Center (Harvard) and University of Rochester (2008-2010) Dr. Xue's research spans neuroscience, radiology, and sedation, with early work on vestibular system physiology (1999-2010) evolving into clinical interventional studies (2015-2019) covering pulmonary embolism, brachytherapy, and procedural safety innovations. His 12 publications demonstrate expertise in image-guided therapies and neural mechanisms. Publication trends reveal a shift from fundamental vestibular research to applied clinical radiology, emphasizing vascular interventions and patient safety technologies like video glasses. His awards include: Shu-Ren Lin Award for Radiology Resident Teaching (2011-2012) Distinguished Participant Certificates at the 13th International Academic Week (2017-2018 and 2018) No information is available regarding graduate student advising or research grants. Dr. Xue operates within the Vascular and Interventional Radiology division at UR Medicine, collaborating across the URMFG and AHP networks for patient care and academic initiatives.
Laurent Schmalen is a full professor at the Karlsruhe Institute of Technology (KIT), co-directing the Communications Engineering Laboratory (CEL). He holds a Dipl.-Ing. and Dr.-Ing. in Electrical Engineering from RWTH Aachen University (2005 and 2011). Previously, he worked at Alcatel-Lucent Bell Labs (2011–2019) and led the Coding for Optical Communications department at Nokia Bell Labs (2016–2019). His research focuses on channel coding, machine learning, optical communications, and high-speed data transmission. Education and Professional Experience: 1999: Abitur at Lycée Classique Diekirch (Luxembourg) 2005: Diploma in Electrical Engineering and Information Technology, RWTH Aachen University 2011: PhD (summa cum laude) in Electrical Engineering, RWTH Aachen University 2011–2019: Research roles at Bell Labs 2014–2019: Visiting Lecturer at University of Stuttgart 2019–present: Full Professor at KIT Research Interests: His work spans channel coding (e.g., LDPC codes), machine learning for communications, modulation formats, and optical transmission systems. Key areas include non-linear channel analysis, probabilistic constellation shaping, and FEC optimization for fiber optics. Awards and Recognition: IEEE Fellow (2023) ERC Consolidator Grant (2023) Best Paper Awards in IEEE journals (2016, 2018) E-Plus Prize for Doctoral Thesis (2011) Editorial and Leadership Roles: Associate Editor, IEEE Transactions on Communications TPC Co-Chair, International Symposium on Topics in Coding (ISTC 2025) General Co-Chair, ITG Conference on Systems, Communications, and Coding (2025) Labs and Teams: He co-leads the Communications Engineering Lab (CEL) at KIT, focusing on cutting-edge research in optical communications and machine learning applications.
Meng Yu is a Professor in the Department of Computer Science at Western Illinois University. He holds a Ph.D. in Computer Science from Nanjing University (2001) and completed a postdoctoral fellowship at the Cyber Security Lab at Penn State University. His research focuses on computer security, trustworthy AI, and AI-enabled security, with particular emphasis on cloud computing security and wireless network security. Dr. Yu has led or co-led over $1.2 million in NSF-funded grants, including projects on privacy-preserving cloud computing architectures and secure channel allocation in wireless networks. He has advised numerous graduate students, including notable advisees such as Naiwei Liu (2020), Jin Han (2019), and Min Li (2015). His work has been published in top-tier conferences and journals, addressing challenges in virtualization security, side-channel attacks, and distributed computing resilience. Teaching responsibilities include courses on operating systems, distributed systems, and cloud computing security. His research lab collaborates with institutions like Virginia Commonwealth University and George Mason University, contributing to advancements in data center security, wireless network protocols, and privacy-preserving technologies.
Boris Karanov is a post-doctoral research member at the Signal Processing Systems Group within the Department of Electrical Engineering at Eindhoven University of Technology since October 2020. His research focuses on applying deep learning techniques to digital signal processing in optical fibre communications. His academic background includes a Ph.D. in Electrical Engineering from University College London (awarded December 2020), where his research focused on developing new coding and detection methods for communication over the nonlinear dispersive optical fibre channel using deep learning. He also holds joint M.Sc. degrees in Photonic Networks Engineering from Aston University, Osaka University, and Technical University of Berlin (2016), and a B.Sc. in Telecommunication Engineering from Technical University of Sofia (2014). Dr. Karanov's research interests span optical fiber communications, digital signal processing, deep learning applications in communications, nonlinear fiber effects, and transceiver design. His work bridges theoretical communication principles with practical AI-driven solutions for next-generation communication systems. His recent publications demonstrate a strong trend toward applying neural networks and deep learning techniques to solve challenging problems in optical communications, radar signal processing, and speech enhancement. The research shows increasing focus on robustness of AI solutions under real-world constraints and imperfect channel conditions. Nokia 'Most innovative AI solution' award (2018) for pioneering work in machine learning applications to communication systems 'Best paper award' from IEEE/OSA Journal of Lightwave Technology (2021) Lombardi prize from Electronic and Electrical Engineering department at UCL (2021) for the most impactful thesis Dr. Karanov is actively involved in multiple research projects including RAISE SPS (Robust AI for Safe radar signal processing), RAIDAR (AI for RaDAR), and ICONIC (Increasing the Capacity of Optical Nonlinear Interfering Channels), collaborating with institutions across Europe. His work in the Signal Processing Systems Group at Eindhoven University of Technology continues to push the boundaries of applying AI to communication challenges.
Dr. Vahid Abolghasemi is a Senior Lecturer (Associate Professor) in the School of Computer Science and Electronic Engineering at the University of Essex. He holds a PhD from the University of Surrey (2011) and has held academic positions including Assistant Professor at Shahrood University of Technology and postdoctoral research at Brunel University. His research focuses on signal/image processing, compressive sensing, machine learning, and their applications in healthcare, agriculture, and environmental monitoring. He has received the IEEE Young Investigator Award (2018) and is a Senior Member of IEEE. He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and actively reviews for top journals. His grants include projects on AI-driven dashboards for retail IoT data, smart irrigation systems, and high-voltage battery management systems. Research interests include biomedical signal processing, deep compressive sensing, and anomaly detection. He supervises PhD students in areas like ECG-based coronary artery disease detection and wireless image transmission systems. Key projects involve developing AI solutions for plant disease detection, energy-efficient sensing networks, and fault diagnosis in electrical systems. His work bridges theoretical advancements with practical applications in critical sectors such as healthcare and agriculture.
Linda Ward is a Lecturer in Filmmaking at Plymouth University's School of Art, Design and Architecture (Faculty of Arts, Humanities and Business). She is also a practicing artist filmmaker and doctoral researcher exploring iconography, perception, memory, and maternal themes through experimental film. Her work bridges academic and industry contexts, with a focus on 360-film, essay film, and intersections between film and architecture. Education: Pursuing a practice-based PhD in Filmmaking at Plymouth University. Research interests include experimental film forms, film and water studies, and the application of legacy lenses with digital technology. Her award-winning films have screened at festivals such as the London Short Film Festival and Kerala Short Film Festival. Teaching: Leads modules like FILM613 Beyond Film (collaborating with industry professionals) and FILM512 Cinematic Crafts. Previously taught FILM403 Film Drama and FILM511 Filmmaker. Supports MA Film and interdisciplinary modules like History/Theory/Critical Context with architecture students. Industry Engagement: Board Director of Screen Devon, Royal Television Society committee member, and organizer of the BA Filmmaking Producers Conference connecting students with industry leaders like Working Title Films and Channel 4. Contributions: Co-developed the Plymouth Best Practice Code of Filmmaking during the pandemic, facilitated industry workshops via Royal Television Society partnerships, and pioneered Zoom-based long-form drama during lockdown.
Dr. Rafik Zitouni is a Research Fellow and Lead Software Engineer at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Institute for Communication Systems. His work focuses on wireless communication protocols, IoT security, vehicular networks, and adaptive networking solutions. He has contributed to advancements in 6TiSCH, LoRaWAN, and MQTT protocols, with an emphasis on security mechanisms, network optimization, and machine learning applications. Research interests include securing industrial IoT networks, vehicular emergency messaging systems, and low-power wide-area networks (LPWAN). He has pioneered techniques such as fuzzy logic-based detection of selfish nodes in 6TiSCH and Fuzzy C-Means clustering for LoRa parameter optimization. His work on FPGA-based sensor fusion for autonomous vehicles addresses real-time data processing challenges. Key contributions span 20+ peer-reviewed publications in journals like Wireless Networks and Computer Communications , and conferences including IWCMC and MENACOMM. He actively collaborates with industry and academia on open radio access networks (O-RAN) and underwater localization systems. Current research emphasizes machine learning-driven network automation and resilient IoT infrastructure.
Dr. Charlotte Edling is a Researcher in Molecular Biology at the University of Surrey's School of Veterinary Medicine. Her work focuses on cardiac extracellular matrix (ECM) dynamics, aging-related cardiac changes, and molecular mechanisms underlying cardiovascular diseases. She investigates how ECM composition shifts with age and contributes to arrhythmias, particularly in atrial tissues. Dr. Edling also explores biomarkers for coronary artery disease and employs proteomics to study genetic deficiencies affecting cardiac electrophysiology. Her research integrates murine models, in-silico predictions, and comparative cardiology to understand arrhythmia pathogenesis. Key themes include age-related collagen degradation, SCN5A mutation effects, and PGC-1α deficiency impacts on ventricular function. Collaborations with veterinary medicine and neurology highlight her interdisciplinary approach to cardiovascular and neurodegenerative disease intersections. Dr. Edling's publications span 20 years, emphasizing translational research in cardiac electrophysiology, miRNA biomarkers, and mitochondrial dysfunction. Her work bridges fundamental science with clinical applications, addressing arrhythmia mechanisms in aging and genetic disorders.
Dr. Jens-Dominik Mueller is a Professor in Computational Fluid Dynamics and Optimization at Queen Mary University of London (QMUL), within the School of Engineering and Materials Science. He holds roles as SEMS Academic Misconduct Officer, Head of the Professional Bodies Liaison Group, and Education Lead for the Centre for Intelligent Transport. His research focuses on adjoint methods, CAD-based shape optimization, and data-driven modeling, with applications in aerospace, biofluids, and energy systems. He has secured significant industrial and EU grants, including Horizon 2020 funding for projects like IODA and MADDOG. Education : - PhD (Aerospace Engineering, University of Michigan, 1996) - MSc (Aerospace Engineering, Von Karman Institute, 1990) - Dipl-Ing (Mechanical Engineering, Technical University of Munich, 1989). Research Interests : Development of multi-disciplinary modeling chains using adjoint sensitivity analysis, CAD integration in design optimization, and turbulence modeling. His work applies automatic differentiation tools to link CAD to objective functions, enabling robust optimization of complex systems like turbine blades and biomedical devices. Grants and Funding : Computational methods for active flow control (€179,947, EU Horizon 2020) MHI2: Mixing plane and nozzle vane design (€186,258, Mitsubishi Heavy Industries) CAD-based wing optimization for Airbus (€27,000, Airbus Defense & Space) MADDOG: Turbine design optimization (€127,046, EU Horizon 2020) Advising and Labs : Leads a research group focusing on adjoint-based optimization. Current projects include CAD-based wing-body junction optimization and LES-aided shape parametrization. Collaborates with industrial partners like Rolls-Royce and Airbus. Affiliations : Member of the editorial board of Aerospace , ASMO-UK, and the UK Fluids Network's Special Interest Group on Numerical Optimization in Fluid Dynamics.
Andrew Lance is a Senior Research Fellow at the International Centre for Radio Astronomy Research (ICRAR) at The University of Western Australia. His research focuses on quantum cryptography, quantum key distribution (QKD), and continuous variable quantum systems. He has contributed to advancements in error correction codes, noise mitigation in quantum communication, and quantum state sharing. Lance holds a prestigious Eureka Prize for Scientific Research (2006), recognizing his impactful work in quantum information science. His research explores cutting-edge topics such as finite-size effects in QKD protocols, design of robust codes for low signal-to-noise regimes, and integration of quantum random number generators. Key areas of interest include securing quantum communication channels, optimizing quantum protocols for real-world applications, and advancing fundamental understanding of quantum phenomena like entanglement and coherence. Lance’s work spans theoretical and experimental aspects of quantum technologies, with a particular emphasis on practical implementations of quantum cryptography. His publications highlight contributions to both foundational science and applied engineering solutions for quantum systems.
Kaiquan Wu is a doctoral candidate and postdoc researcher at Eindhoven University of Technology (TU/e), Netherlands, specializing in digital signal processing for fiber-optic communication systems. His research focuses on error correction codes, coded modulation, and channel modeling. PhD Candidate in Electrical Engineering (Signal Processing Systems) Postdoc in Electrical Engineering (ICT Lab) Member of the ICONIC , BIT-FREE , and DIGI-OPT research projects Research interests include combating signal impairments in optical systems through advanced DSP techniques, such as decision feedback equalizers (DFE), geometric shaping, and low-complexity detection algorithms. He has published extensively on FSO systems, IM-DD links, and energy dispersion analysis. Collaborations involve developing simplified FSO channel models, low-complexity architectures for data center applications, and patent innovations in amplitude shaping methods. His work contributes to increasing the capacity of optical communication systems while reducing complexity and error rates.
Mei Yang is a Professor and Chair in the Department of Electrical and Computer Engineering at the University of Nevada, Las Vegas (UNLV). Her research focuses on computer architectures, interconnection networks, machine learning, embedded systems, and wireless sensor networks. She leads the Networking and System Integration Laboratory (NSIL), advancing interdisciplinary projects at the intersection of hardware design and intelligent systems. Education Ph.D., Computer Science, University of Texas at Dallas, 2003 Research Interests Dr. Yang's work spans network-on-chip (NoC) architectures, thermal-aware task management, and deep learning applications in microscopy and environmental monitoring. She explores energy-efficient computing, cybersecurity in many-core systems, and educational strategies to engage girls in STEM through hands-on projects like IoT and embedded systems. Articles Trends Her recent publications emphasize hardware security (e.g., thermal covert channels), AI-driven biomedical image analysis, and optimizing multi-chiplet systems. She bridges theoretical contributions with practical implementations, such as FPGA-based NoC emulators and GAN-based protein localization tools. Labs & Teams Hosts the NSIL lab, fostering collaborations in photonic networks-on-chip, embedded systems, and cybersecurity. Active in IEEE and ACM conferences, organizing special sessions on high-performance computing and network coding.
Yu Sun is a Professor in the Department of Computer Science and Engineering at the University of Central Arkansas (UCA). He holds a Ph.D. in Computer Science & Engineering from the University of Texas at Arlington. His research focuses on Multimedia Computing, Video Compression and Communication, Image Processing, and Wireless Videos. His work emphasizes optimizing video coding standards like HEVC, AVS2, and SHVC, with contributions to algorithms for rate control, intra prediction, and scalable video techniques. Research interests include developing efficient algorithms for 360-degree videos, generative adversarial networks (GANs) for image classification, and real-time video transmission over wireless systems. His publications span over two decades, addressing challenges in video compression efficiency, bufferless rate control, and neural network applications in multimedia systems. Dr. Sun’s articles highlight trends in hybrid coding strategies, probability-based optimization, and spatial/temporal scalability in video coding. His work bridges theoretical advancements and practical implementations for emerging technologies such as virtual reality video streaming and collaborative robotics. His academic webpage is available at https://faculty.uca.edu/yusun/ .
Juan Núñez is a Research Professor at the Microelectronics Institute of Seville (IMSE-CNM), part of the Spanish National Research Council (CSIC). His primary research focus lies at the intersection of emerging semiconductor technologies and novel computing paradigms, with particular expertise in tunnel field-effect transistors (TFETs) and their applications in low-power circuit design. Institution: Spanish National Research Council (CSIC) Research Institute: Microelectronics Institute of Seville (IMSE-CNM) Specialization: TFET, Pipeline, Variability, Low-power, Emerging technologies, RTD, NDR, Emerging devices, Dynamic logic Professor Núñez's research portfolio demonstrates a strong evolution from traditional digital circuit design toward emerging computing paradigms. His early work focused on optimizing conventional CMOS circuits and exploring tunnel transistors as potential successors to traditional MOSFETs. More recently, his research has pivoted toward neuromorphic computing, particularly oscillatory neural networks and Ising machines implemented with emerging devices. His work bridges the gap between device physics, circuit design, and computational applications, with a consistent emphasis on energy efficiency. The trend in his recent publications (2020-2024) shows a significant shift toward neuromorphic computing applications, particularly using phase transition materials and oscillatory networks for solving complex optimization problems. His research on VO 2 (vanadium dioxide) based Ising machines represents cutting-edge work in non-von Neumann computing architectures that could potentially solve NP-hard problems more efficiently than conventional computers.
