Dr. Yücel Aydın is a Lecturer at the Department of Control and Automation Engineering , Faculty of Electrical and Electronics Engineering, Istanbul Technical University . His research spans interdisciplinary domains in automation, renewable energy, and cybersecurity for emerging technologies. PhD in Control and Automation Engineering from Istanbul Technical University (2007) MSc in Control and Computer Engineering (1987) BSc in Electronics and Communication Engineering (1982) Current research focuses on Cloud Computing Security , Internet of Things tracking frameworks, and Drone Swarm Authentication . His work addresses critical security challenges in next-generation wireless systems and autonomous aviation technologies. Recent publications demonstrate expertise in Anomaly Detection systems, Adaptive Control for MAGLEV applications, and Group Handover Protocols for drone networks. All work emphasizes practical implementations and mathematical modeling.
Stephen Hanly is a Professor in the School of Engineering at Macquarie University, specializing in Electrical and Electronics Engineering. He serves as Discipline Lead for the Electrical and Electronics Engineering teaching program and is part of the School's leadership team. His research is centered at the Future Communications Research Centre and Advanced Drone Systems Research Centre. His research focuses on next-generation wireless communications systems, with primary expertise in millimeter wave networks, UAV communications, satellite IoT systems, and beamforming technologies. Key investigation areas include distributed data collection using rechargeable UAVs, LEO satellite communications for IoT, millimeter wave beam alignment algorithms, and delay-Doppler domain waveforms for high-mobility scenarios. His work bridges theoretical communication theory with practical implementation challenges in dense wireless environments. His recent publication trends demonstrate strong emphasis on 6G-enabling technologies, particularly UAV trajectory optimization for IoT data harvesting, LEO satellite terminal protocols, and millimeter wave beam acquisition techniques. These works consistently address scalability challenges in ultra-dense networks while maintaining quality-of-service guarantees. IEEE Fellow INFOCOM Best Paper Award IEEE Information Theory Society and IEEE Communication Society Joint Paper Award IEEE Communications Society Tutorial Paper Award Professor Hanly actively supervises PhD students and postdoctoral researchers, with current projects including DP23: Enabling wide area mm-wave mobile broadband networks and LP20: Scaling Up Satellite Communications for the Internet of Things. His industry collaborations include the CSIRO Macquarie University Chair in Wireless Communications and the Macquarie University WiMed Research Centre. His research group develops novel algorithms for resource allocation, beam management, and network optimization in heterogeneous wireless environments. He leads the Future Communications Research Centre and Advanced Drone Systems Research Centre, focusing on practical implementations of theoretical communication models. Current research directions include UAV swarm coordination for wide-area coverage, LEO satellite IoT protocols, and millimeter wave beam alignment for mobile users in urban environments.
Dr Jonathan Roberts is a Professor and Head of the Sports Technology Research Group at Loughborough University, UK. He holds a PhD in Mechanical Engineering and has over 20 years of experience applying science, technology, and engineering to sports. His research focuses on athlete-equipment interactions, measurement technologies, and collaborations with leading sports brands like Callaway Golf, adidas, and FIFA. Roberts teaches on Sports Technology and Mechanical Engineering programs and holds administrative roles at the university. His work spans optimizing sports equipment, validating measurement systems, and studying athlete perceptions of surfaces and gear. Education: Bachelor’s in Mechanical Engineering, Nottingham University PhD in Mechanical Engineering, Loughborough University (2002) Research Interests: Subjective and objective evaluation of sports equipment Measurement science and sensor technologies Customization of equipment for athlete performance Analysis of sports surfaces and their impact on athletes Validation of commercial sports technology tools Collaborations: Global equipment manufacturers (adidas, HEAD, PING) Sports governing bodies (FIFA, ITF) Small and medium enterprises (SMEs) and coaches Labs/Teams: Sports Technology Research Group Sports Technology Institute at Loughborough University
Haluk Topcuoglu is a Full Professor in the Department of Computer Engineering at Marmara University's Faculty of Engineering. He holds a PhD from Syracuse University (1999) and has been at Marmara University since 1999, progressing through roles as Assistant, Associate, and Full Professor. His research focuses on multicore architectures, parallel algorithms, fault tolerance, dynamic optimization, and hybrid evolutionary algorithms. He has led multiple funded projects, including TUBITAK initiatives on reliability optimization and sensor placement. Education: PhD in Computer Science (Syracuse University, 1999), MSc and BSc in Computer Engineering (Boğaziçi University, 1993 and 1991). Research Interests: Task scheduling for multicore systems, reliability-aware computing, dynamic optimization, and applications of evolutionary algorithms in cloud/fog computing. His work emphasizes balancing performance, energy efficiency, and fault tolerance in parallel systems. Awards include the 2008 IBM Faculty Award, 2010 IEEE ISDA Best Paper Award, and Marmara University's 2012 Publication Impact Award. He has supervised 4 PhD and 19 MSc theses, with ongoing research in machine learning-assisted metaheuristics and dynamic monitor selection. Editorial roles include Cluster Computing Journal (SCI-Expanded) and Journal of Aeronautics and Space Technologies. He teaches courses on evolutionary computing, multicore computing, and parallel processing. Current projects include cross-layer reliability frameworks and scheduling algorithms for fog computing, reflecting his focus on industry-relevant computational challenges.
Johnson Thomas is a Professor in the Department of Computer Science at Oklahoma State University, with affiliations in both the Stillwater and Tulsa campuses. His research focuses on Quantum Computing, Machine Learning, and Computational Neuroscience, with notable work on quantum circuit optimization and spiking neural networks. He holds degrees from the University of Reading (PhD, 1995), University of Edinburgh (MSc, 1983), and University of Wales (BSc, 1982). His teaching spans advanced topics in databases, quantum computing, and programming languages. Recent courses include *Quantum Computing*, *Advanced Topics in Information Systems*, and *Discrete Mathematics for Computer Science*. He has also advised doctoral students and led research in distributed systems and security. Research interests include quantum algorithms, neurocomputational models, and causal inference. His work bridges theoretical foundations with practical applications, such as ROS security frameworks and NIRS-based forage quality prediction. Over 20 funded projects highlight his expertise in big data, autonomous systems, and sensor networks. Publications emphasize quantum circuit design, medical ML interpretation, and computational neuroscience. Collaborations include interdisciplinary projects with healthcare and agricultural sectors. His contributions advance both theoretical computer science and applied technologies.
Dr. Bhuvaneswari Ramachandran is a Professor of Electrical and Computer Engineering at the Hal Marcus College of Science and Engineering, University of West Florida. With over 15 years of experience in Power Engineering, she specializes in areas such as smart grid technology, electricity market strategies, and power system modeling. She holds a Ph.D., M.S., and B.S. in Electrical Engineering from Annamalai University, India, and has prior teaching experience at Florida State University and Annamalai University. Her research focuses on auction strategies in electricity markets , real-time power system simulation , and integration of distributed generation . Education: Ph.D. in Electrical Engineering, Annamalai University, India M.S. in Power Systems Engineering, Annamalai University, India B.S. in Electrical Engineering, Annamalai University, India Her recent work explores: Phasor Measurement-Based Grid Analysis Electric Vehicle Charging Management Smart Grid and Microgrid Economics Publications span impactful journals like International Journal of Electrical Power & Energy Systems and conferences including IEEE International Conferences. Her research bridges theoretical advancements with practical grid optimization challenges.
Salam Al Samman is a Doctoral Researcher affiliated with the Building Energy research group. Her work focuses on optimizing indoor climate sensor placement in open-plan offices under varying ventilation methods and layouts. She holds BSc and MSc degrees and is supervised by Professor Mahroo Eftekhari and Dr. Vanda Dimitriou. Her expertise spans indoor environmental quality analysis, particularly in thermal comfort and air quality, alongside airflow analysis, optimization techniques, machine learning, and energy modeling/simulation. No scientific awards, grants, or advised students are explicitly listed in the provided information. She collaborates within the Building Energy group to advance sustainable and efficient indoor climate solutions.
Matteo Scandella is an Assistant Professor at the University of Bergamo , Italy, since February 2024. Previously, he served as a post-doctoral researcher at Imperial College London (2020–2024). He holds a PhD in Control Systems (2019) and advanced degrees in Computer Science Engineering from the University of Bergamo (Bachelor 2014, Master 2016). His research focuses on kernel-based machine learning techniques applied to system identification , nonlinear dynamics , and control systems , with emphasis on aerospace applications and mechatronics. He has developed methods for stable nonlinear system modeling, continuous-time system identification, and data-driven control strategies like SelfMPC. Education: Bachelor Degree in Computer Science Engineering (2014) – University of Bergamo Master Degree in Computer Science Engineering (2016) – University of Bergamo PhD in Control Systems (2019) – University of Bergamo Research interests span kernel methods (e.g., manifold regularization, RKHS), stability analysis of nonlinear systems, and data-driven control . His work bridges theoretical advancements with engineering applications such as health monitoring of aerospace actuators and urban traffic optimization. Recent publications highlight innovations in automated MPC tuning and graph-based system identification techniques. Teaching includes courses like Automatica (6 CFU) and laboratory modules in sustainable industrial systems. His research has been published in top journals like Automatica and conferences such as L4DC and SYSID.
Jon Spangenberg is a Professor and Head of the Digital Building Technologies section at the Department of Civil and Mechanical Engineering , Technical University of Denmark (DTU). His work focuses on additive manufacturing, 3D printing technologies, and their applications in construction and materials science. He leads research on composite materials, computational fluid dynamics (CFD), and structural engineering innovations. Key research areas include: Additive manufacturing processes for construction materials (e.g., geopolymers, concrete) 3D-printed sensors and conductive materials Numerical modeling of material extrusion, pultrusion, and resin infusion Optimization of thermoset formworks and structural components Notable achievements include the Best Paper Award at Digital Concrete 2020 and recognition at the Solid Freeform Fabrication Symposium 2021 . He supervises multiple PhD projects on topics like topology optimization for AM, digital twins for sustainable 3D printing, and tomographic volumetric additive manufacturing. His work addresses UN Sustainable Development Goals related to affordable and clean energy and sustainable cities through advanced materials and construction techniques. Collaborations span academia and industry, focusing on process modeling, material characterization, and scalable manufacturing solutions.
Sara Kohtz is an Assistant Professor in the School of Systems Science and Industrial Engineering at Binghamton University. She holds a BS (2016) and MS (2017) in Industrial Engineering from Binghamton University and a PhD in Industrial Engineering from the University of Illinois at Urbana-Champaign (2024). Her research focuses on machine learning theory and applications in high-impact engineered systems, particularly physics-informed machine learning for energy systems, reliability engineering, and fault diagnosis in electrified systems. Education Background: PhD: Industrial Engineering – University of Illinois at Urbana-Champaign MS: Industrial Engineering – Binghamton University BS: Industrial Engineering – Binghamton University Her work bridges data science and engineering, addressing challenges in battery management, sensor placement, and optimal system design. Recent publications emphasize machine learning methodologies for prognostics, energy systems optimization, and physics-driven models. Professional memberships include ASME, IISE, and the Society of Women Engineers (SWE). No awards or grants are explicitly listed in the provided information.
Mohammad Javad Khojasteh is a Gleason Endowed Assistant Professor in the Department of Electrical and Microelectronic Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). He directs the Khojasteh Autonomous Systems Laboratory (KAS Lab) and is affiliated with RIT's Global Cybersecurity Institute (GCI) and Center for Human-aware AI (CHAI). Before joining RIT, he held postdoctoral positions at Scripps Institution of Oceanography (MPL), MIT’s Department of Mechanical Engineering and LIDS, and Caltech’s CAST, collaborating with NASA JPL’s Team CoSTAR. Education: B.Sc. in Electrical Engineering and Mathematics, Sharif University of Technology (2015) M.Sc. in Electrical and Computer Engineering, UC San Diego (2017) Ph.D. in Electrical and Computer Engineering (Intelligent Systems, Robotics, and Control), UC San Diego (2019) Research Interests: Dr. Khojasteh focuses on autonomous systems, applying machine learning and control theory to enhance robotics, marine autonomous systems, and quantum engineering. His work spans cybersecurity in cyber-physical systems, data-driven optimization, and statistical signal processing. He emphasizes practical applications in ocean science and quantum technologies. Key Contributions: His research has been recognized with awards such as the Tammy L. Blair Student Paper Award. He explores topics like particle flow filters, event-triggered control, and secure autonomous navigation. His lab’s projects include developing robust algorithms for underwater robotics and quantum sensor networks. Advising & Grants: While specific grants are not detailed, his work indicates a focus on multi-agent systems, safety-critical control, and adversarial learning. He advises students in robotics and control systems, though no named advisees are listed. Labs & Teams: Director of KAS Lab, collaborating with GCI and CHAI on human-AI interaction and cybersecurity challenges in autonomous systems.
Paris Mastorocostas is a Professor at the University of West Attica, specializing in computational intelligence, signal processing, and algorithmic data mining. His research focuses on neuro-fuzzy systems, deep learning applications, and their integration into domains like energy systems, transportation networks, and industrial automation. He has pioneered methodologies for short-term load forecasting, telecommunications fraud detection, and adaptive noise cancellation in medical signals. Key research themes include: Neuro-fuzzy modeling for dynamic systems Machine learning in energy and transportation Data warehouse development using Python/MySQL UAV-based inventory quantification Graph neural networks for urban metro flow His work demonstrates a strong interdisciplinary approach, combining algorithm design with practical industrial applications. Notable contributions include the ReNFuzz-LF model for electricity load forecasting and TMD-BERT for transportation mode detection. His publications span over 25 years, showing sustained innovation in computational intelligence techniques and their real-world implementation.
Sijung Hu is a Reader in Biomedical Engineering and Research Group Lead for Photonics Engineering & Health Technology at Loughborough University, UK. He holds senior positions including Visiting Professor at Shanghai Jiao Tong University and Chief Scientific Officer at Carelight Limited, a spin-out company commercializing his opto-physiological monitoring (OPM) technology. His career spans academic, industrial, and international collaboration roles, focusing on biomedical photonics, non-invasive physiological sensing, and health technology innovation. Education: PhD in Applied Science, Loughborough University (1996–1998) MSc in Environmental Management, University of Surrey (1993–1995) Research Interests: Dr. Hu specializes in opto-physiological monitoring systems, wearable sensors, and biomedical photonics. Key areas include: Non-invasive vital sign monitoring (heart rate, oxygen saturation) Development of OPM and DA 2 SD technologies Health technology commercialization via spin-outs like Carelight Respiratory pattern analysis for AAC (augmentative and alternative communication) Articles Trends: Recent work emphasizes real-time opto-physiological systems for motion-tolerant monitoring, adaptive signal processing algorithms, and integration of AI (e.g., GANs for PPG denoising). Applications span clinical diagnostics, sports science, and assistive technologies for speech-impaired individuals. Awards: 2014: Translation Research Award Nomination (SPIE) 2007: UK Health Technology Innovation Award 2011: Top 10 Article in BioMedLib Advising & Grants: Supervised numerous PhD students and coordinated multidisciplinary projects with academia/industry. Key collaborations include ACE Centre for AAC and Shanghai Jiao Tong University. Active in EPSRC grant reviews and international tech transfer initiatives. Labs & Teams: Leads the Photonics Engineering & Health Technology Research Group at Loughborough, and oversees Carelight’s R&D. Collaborates with global partners through initiatives like the UK-China Science Network and Shanghai International Medical Zone.
Dr. Qiuji Yi is an **Assistant Professor** in the **Computer and Information Sciences Department** at **Northumbria University**, focusing on applying artificial intelligence (AI) and machine learning (ML) to nondestructive testing (NDT) and structural health monitoring (SHM). His research develops AI-driven tools for NDT instruments like eddy current testing, thermography, and ultrasound, addressing challenges in composite materials such as carbon fiber composites. Yi holds a **PhD in Electrical and Electronic Engineering** (Awarded: 6 Apr 2021). **Research Interests**: Physics-informed ML for material characterization Defect detection in composites using clustering, matrix factorization, and deep learning Integration of AI with engineering needs in manufacturing and maintenance **Collaborations & Funding**: Collaborates with industry partners (Rolls-Royce, GKN Aerospace, National Composite Centre) and academic institutions (TU Delft, Newcastle University). Secured notable grants including the EU ITN project NDTonAIR (€3.8m), EPSRC grants (e.g., Certest: £6.9m), and the UK-Italy Trustworthy AI Visiting Researcher Award (2023). **Key Contributions**: Published 19 papers, including high-impact journals like NDT&E International and IEEE Transactions on Industrial Informatics . Notable work includes an automatic delamination detection framework using Kernel Principal Component Analysis. **Advising & Grants**: Accepting PhD students and actively pursuing funding for interdisciplinary projects combining AI and sustainable engineering solutions.
Deniz Erdogmus is COE Distinguished Professor at Northeastern University with joint appointments in Electrical & Computer Engineering and Bioengineering. He directs the Cognitive Systems Laboratory and serves as CTO of Kostas Research Institute. His research spans statistical machine learning, neural interfaces, and biomedical data analytics. Research integrates information-theoretic learning with applications in brain-computer interfaces, assistive technologies, and robotic systems. Recent work focuses on EEG-based intent recognition, sensorimotor neural processing, and adaptive human-robot collaboration. Honors include NSF CAREER Award, Distinguished Faculty Award, and Søren Buus Research Award. His 200+ publications demonstrate consistent innovation in machine learning for physiological systems and real-time signal processing.
