Professor Geraint Jewell is affiliated with the University of Sheffield , serving as Director of the Rolls-Royce University Technology Centre in Advanced Electrical Machines (since 2006) and Director of the EPSRC Future Electrical Machines Manufacturing Hub (since 2019). He is a graduate of the university (BEng 1988, PhD 1992) and has held academic roles since 1994. EPSRC Advanced Research Fellowship (2000-2005) Royal Society Industry Fellowship at Rolls-Royce (2006-2008) Former Faculty Director of Research and Innovation (2008-2011) Former Head of Department (2013-2019) His research focuses on power-dense electrical machines for aerospace applications , including permanent magnet machines , switched reluctance machines , and linear actuators . He has supervised ~20 PhD students and led collaborations with Rolls-Royce on high-temperature devices (up to 800°C) and aero-engine starter-generators. Recent publications analyze stator insulation thermal degradation , eddy current control in additively manufactured materials , and magnetic loss prediction in silicon steel. His work spans electromagnetic modeling , core loss calculation , and advanced manufacturing techniques for electrical machines. EPSRC Advanced Research Fellowship (2000-2005) Royal Society Industry Fellowship (2006-2008) He has advised PhD students across topics like consequent-pole PM machines , doubly salient SynRMs , and core loss characterization . His Electrical Machines and Drives Research Group explores modular motor design and magnetic material optimization for aerospace and electric vehicles.
Professor Jon Barker is a faculty member at the University of Sheffield , where he holds a Personal Chair in the School of Computer Science . He leads the Speech and Hearing (SpandH) research group and co-founded the CHiME international workshop series on robust speech recognition. Education : PhD in Computer Science (University of Sheffield, 1999); BA in Electrical and Information Sciences (Cambridge University). Research Focus : His work bridges machine listening and human auditory perception , with key contributions to noise-robust speech recognition , speech intelligibility prediction , and hearing aid signal processing for speech and music. Recent projects include the Clarity Challenges and Cadenza Challenges , large-scale machine learning initiatives to improve accessibility for hearing-impaired users. Publication Trends : Recent articles emphasize machine learning for hearing aid optimization , dysarthric speech recognition , audio-visual integration , and music demixing algorithms . Collaborations span speech processing, psychoacoustics, and biomedical engineering. Scientific Awards : EURASIP Best Paper Award (2009) ISCA Best Paper Award (2008) Grants and Leadership : He has secured major EPSRC grants including EnhanceMusic (2022-2026) and Challenges to Revolutionise Hearing Device Processing (2019-2025). He co-led the TAPAS Marie Curie Training Network (2017-2022) and led projects like AV-COGHEAR (2015-2018) and CHiME (2009-2012). Labs and Teams : Barker collaborates closely with the Speech and Hearing Research Group and contributes to international initiatives like the CHiME Workshop . His lab develops open datasets such as the Clarity Speech Corpus and Audio-Visual Lombard Corpus .
Stephen E. Ralph is Professor and Glen Robinson Chair in Electro-Optics within Georgia Tech's School of Electrical and Computer Engineering, serving as Director of the Georgia Electronic Design Center (GEDC) and founder of the Terabit Optical Networking Consortium. His leadership spans cross-disciplinary research in electronics, photonics, and signal processing for revolutionary system performance. Educational background includes a BEE with highest honors from Georgia Tech (1980) and PhD in Electrical Engineering from Cornell University (1988), followed by postdoctoral work at AT&T Bell Laboratories and IBM Watson Research Center. His research integrates integrated photonics , machine learning , and aerospace applications to develop ultra-high-capacity optical communication systems. Current focus areas include photonic topology optimization, radiation-hardened space systems, and converged optical/mm-wave technologies, emphasizing the synergistic development of electronic-photonic components for next-generation interconnects. Analysis of 2024-2025 publications reveals dominant themes in foundry-compatible photonic design (topology optimization, inverse design), aerospace photonics (radiation testing, analog/digital signal transport), and machine learning applications for nonlinear equalization. The work bridges fundamental device engineering (grating couplers, waveguide bends) with system-level implementations for 5G/6G networks and space communications. Key recognition includes: Fellow of the Optical Society (OSA) Professor Ralph has mentored over 20 PhD students and secured significant research funding including the IUCRC Phase I EPICA project (2021) for aerospace photonic integration. His industry partnerships through the Terabit Optical Networking Consortium drive translational research in high-speed communications. He leads the Georgia Electronic Design Center's multidisciplinary team developing photonic-electronic co-design methodologies, with recent emphasis on topology-optimized devices for commercial foundries and radiation-tolerant systems for space applications.
Yves Comeau is a Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. With a Ph.D. in Environmental Civil Engineering from the University of British Columbia (1989), his career spans over three decades, focusing on wastewater treatment, nutrient removal, and water resource recovery systems. He directs the Environmental Engineering Laboratory and co-leads the Center for Research, Development and Validation of Water Treatment Technologies (CREDEAU). Education: B.Eng. (1980), M.A.Sc. (1984), Ph.D. (1989) from UBC Current research emphasizes biological/chemical nutrient removal, modeling, and phytotechnology for cold climates Key projects include steel slag filters for phosphorus removal, willow-based wastewater treatment, and landfill leachate management His 183 publications cover topics from activated sludge modeling to bioremediation of contaminated soils Recent work demonstrates the efficacy of willow vegetation filters in cold climates and microplastic characterization in wastewater. He has supervised 12 Ph.D. and 54 Master’s students, including Dominique Claveau-Mallet (2017) and Xavier Lachapelle-Trouillard (2017). Awards include the Environmental Network Distinction (2016) .
Samsung Lim serves as an Associate Professor of geographic information systems (GIS) in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW) Sydney. With expertise spanning data science, artificial intelligence, and machine learning, Lim applies geospatial technologies to critical real-world challenges in natural disaster management and public health research. Lim's interdisciplinary work bridges engineering, computer science, and public health domains to develop practical decision-making tools for emergency response and disease surveillance. Ph.D. in Aerospace Engineering and Engineering Mechanics, University of Texas, Austin, TX, USA M.A. in Mathematics, Seoul National University, Seoul, South Korea B.A. in Mathematics, Seoul National University, Seoul, South Korea Lim's research focuses on applying GIS to natural disaster management and public health challenges. Key areas include machine learning methods for bushfire susceptibility mapping, spatial clustering for landslide susceptibility analysis, city-scale evacuation management in flood scenarios, and social media-based natural disaster assessment. In public health, Lim investigates geo-correlations between environmental factors and asthma occurrence, computational approaches to avian influenza outbreaks, emerging hot spot analysis of COVID-19, and early detection systems for emerging infectious diseases. This work combines advanced spatial analytics with machine learning to address complex environmental and health challenges. The recent publication record demonstrates a clear interdisciplinary trajectory where geospatial science intersects with public health emergency response and natural hazard management. Lim's work consistently applies machine learning techniques to geospatial data, with particular emphasis on disaster susceptibility mapping, disease outbreak detection, and infrastructure monitoring. The research spans multiple continents and addresses both immediate emergency response needs and long-term environmental health challenges, reflecting a commitment to practical applications of geospatial science. Associate Editor of Geospatial Information Science National Delegate of Commission 3 of International Federation of Surveyors (FIG) National Representative of the International Cartographic Association (ICA) Commission on Sensor-driven Mapping Senior Member of Institute of Electrical and Electronics Engineers (IEEE) Lim actively contributes to the development of early warning systems for emerging infectious diseases through collaborations with public health researchers. The work on EPIWATCH demonstrates how AI can enhance surveillance capabilities for outbreak detection. Lim's research on cruise ship transmission of diseases and the spread of avian influenza through bird migration patterns and poultry trade networks shows strong engagement with real-world public health challenges. These projects often involve multidisciplinary teams spanning engineering, computer science, epidemiology, and veterinary medicine. Lim's work integrates multiple geospatial data sources and analytical techniques to address complex environmental and public health challenges. This includes developing frameworks for performance analysis of OpenStreetMap data, creating specialized road datasets for pedestrian navigation, and applying Persistent Scatterer Interferometry for land motion monitoring. The research combines traditional geospatial methods with cutting-edge machine learning approaches to extract meaningful insights from complex spatial datasets.
Iraklis Lazakis is a Reader in Maritime Operations and Maintenance at the Department of Naval Architecture, Ocean and Marine Engineering (NAOME), within the Faculty of Engineering at the University of Strathclyde. He joined the university as a PhD researcher in 2007 and began his academic career in 2011, establishing himself as a key figure in maritime systems research and education. His research interests span a broad range of topics including ship operations, systems maintenance and reliability, condition monitoring, risk and asset management, shipyard productivity, and offshore renewable energy systems (wind, wave, and tidal). His work bridges academic theory with industrial application, drawing from his 8 years of prior industry experience in maritime surveys, accident investigations, and ship repairs. The trends in his recent publications reflect a strong focus on data-driven and digital solutions for sustainable maritime operations. Key themes include the development of simulation and optimization tools, application of virtual reality for safety, cost reduction in offshore wind O&M, and decarbonization strategies such as onboard CO2 capture. His work increasingly integrates AI, digital twins, and human factors to enhance system performance and crew wellbeing. He has received numerous accolades, including: SNAME Faculty Advisor of the Year (2024) SNAME WES Best Paper Award (2023) Multiple Knowledge Transfer Partnerships Certificates of Excellence (2020, 2022) Laureate of the Franz Edelman Award (2012) ISSC Committee IV.2 Membership (2012–2015) Lazakis actively supervises undergraduate, postgraduate, and PhD students, and leads or contributes to a wide portfolio of research and knowledge exchange projects. His recent projects include decarbonizing UK shipping, structural surveys of vessels like Calmac and the Royal Yacht Britannia, and development of low-cost underwater gliders. He plays a strategic role in supporting colleagues with funding applications, publications, and industry collaboration. His work contributes to UN Sustainable Development Goals related to sustainable energy and industry innovation.
Professor Jennifer Tieman (Flinders University) is a leading Matthew Flinders Professor and Director of the Research Centre for Palliative Care, Death and Dying. As inaugural Dean (Research) of the College of Nursing and Health Sciences, she leads nationally/internationally recognized programs to enhance Australian health/aged care systems through digital knowledge tools like CareSearch , palliAGED , and ELDAC . Education: PhD (Flinders University, 2011), MBA (University of South Australia, 2008), BSc(Hons) (Melbourne University) Research Focus: Digital knowledge translation in palliative/aged care, evidence-based end-of-life resources, home care funding models, and death doula integration. She established Flinders Filters – a bibliometric research group. Teaching: Led the Dying2Learn MOOC (6,000+ enrollments) and developed palliative care training modules for aged care nurses (60,000+ completions). Scientific Awards: Innovation in Palliative Care award (2017, Dying2Learn MOOC) Finalist in 2024 Innovation in Palliative Care Awards Professional Engagement: Sits on National Advisory Group (Doctors Legal Knowledge Project), Advance Care Planning Australia committee, and Palliative Care Australia’s expert panel. Leads the CareSearch project and co-led ELDAC’s Knowledge Hub.
Oleg Lashinin is an active researcher in the field of Recommender Systems , with a focus on Machine Learning , Temporal Modeling , and User Behavior Analysis . He has contributed to 15 recent publications spanning 2021–2025, including conference papers at ECIR, SIGIR, RecSys, and workshops like KaRS@RecSys and ORSUM@RecSys. His work explores advanced techniques such as Self-Attention Models , Time-Aware Item Weighting , and Cost-Constrained Recommendations . Key research trends in his publications include Deep Learning for sequential recommendation tasks, Crowdsourcing for explanation evaluation, and Temporal Dynamics in user behavior. Notable projects include the GPT3RecBot Telegram chatbot and the RecBaselines2023 dataset for benchmarking recommender systems.
Cosimo Lacava is an Assistant Professor at the University of Pavia, Department of Industrial and Information Engineering. He works in the Integrated Photonics Laboratory (Floor F) and specializes in silicon photonics, integrated optics, and nonlinear optics for optical communications applications. His research focuses on developing advanced photonic integrated circuits for next-generation optical networks and signal processing systems. Dr. Lacava's primary research interests include: Silicon and silicon nitride photonic devices (design, fabrication and testing) Integrated electro-optic devices for telecommunications applications Design of highly spectral-efficient optical networks enabled by advanced modulation formats Digital signal processing (DSP) for telecommunication and data communication applications Nonlinear optics for all optical signal processing His recent publication record demonstrates significant contributions to integrated photonics, particularly in wavelength conversion technologies, nonlinear signal processing, and silicon nitride platforms. The research shows a clear progression from fundamental studies of nonlinear optical properties to practical implementations of photonic integrated circuits for optical communications systems. His work bridges theoretical understanding with practical device development for real-world applications. Dr. Lacava received his education at the University of Pavia, graduating with honors in Electronic Engineering in 2011 and completing his PhD in Optoelectronics and Electronic Engineering in 2014. Prior to his current position, he worked as a Senior Research Fellow at the Optoelectronics Research Centre, University of Southampton, and as a Postdoctoral researcher at the Quantum Electronics Laboratory at the University of Pavia working on the 'Fabulous' European Project. As an educator, he teaches Physics 1 for civil and environmental engineering students. His laboratory work in the Integrated Photonics Laboratory focuses on developing and testing photonic integrated circuits with applications spanning optical communications, signal processing, and emerging quantum information systems.
Montek Singh serves as an Associate Professor and Associate Chair for Academic Affairs in the Department of Computer Science at the University of North Carolina at Chapel Hill. His research focuses on high-performance and energy-efficient digital systems with particular emphasis on asynchronous and mixed-timing circuit design. Dr. Singh received his Ph.D. in Computer Science from Columbia University in 2002 and his B.Tech. in Electrical Engineering from IIT Delhi, India, in 1993. His primary research interests span high-performance and low-power digital systems, with specialization in asynchronous or clockless and mixed-timing integrated chip design. His work encompasses circuit design methodologies, CAD tools for automated synthesis, analysis and optimization techniques. He has also explored applications in energy-efficient mobile graphics hardware, secure chip design for computer security, and design challenges in emerging computing technologies. His research has practical applications in industry, with work transferred to companies including IBM, Boeing, and Handshake Solutions. Analysis of Dr. Singh's publications reveals a strong focus on asynchronous circuit design spanning two decades. His work covers fundamental pipeline architectures (MOUSETRAP), high-speed asynchronous systems, latency-insensitive design methodologies, and practical applications in graphics hardware and mobile devices. The research demonstrates consistent innovation in making asynchronous design more practical for real-world implementation while addressing performance, power efficiency, and testing challenges. His notable scientific achievements include: Best Paper Award at the 6th IEEE Intl. Symp. on Adv. Res. in Async. Circ. and Syst. (ASYNC-2000) Best Paper Finalist at the 8th IEEE Intl. Symp. on Async. Circ. and Syst. (ASYNC-02) Dr. Singh has secured significant research funding including participation in the DARPA CLASS Program (led by Boeing) in 2005, where he collaborated with Philips/Handshake Solutions to develop an industrial-strength automated synthesis flow for high-speed asynchronous systems. His research has strong industry connections and practical applications, with technology transferred to major companies including IBM and Boeing. He has also organized major academic events such as the International Symposium on Asynchronous Circuits and Systems 2009 (ASYNC 2009) at UNC Chapel Hill. Dr. Singh leads research in asynchronous systems design with connections to industry partners and practical applications. His work has been featured in prominent media outlets including The New York Times, International Herald Tribune, and Technology Review Magazine, highlighting the significance of clockless design approaches as traditional synchronous design approaches face limitations.
Lars G. Johansen is an Associate Professor at Aarhus University, affiliated with the Department of Electrical and Computer Engineering. His work bridges interdisciplinary domains, with a focus on signal processing and machine learning. Research interests include Audio engineering and acoustic signal analysis Biomedical signal processing Neuroscience applications in Parkinson's disease studies Recent publications highlight trends in audio engineering (e.g., loudspeaker distortion analysis) and biomedical signal processing (e.g., ECG-derived respiration techniques). Collaborative work spans neuroscience, Parkinson's disease treatment evaluation, and noise reduction systems. Contact: Email: lgj@ece.au.dk Phone: +45 41 89 32 74 Labs/Teams: Signal Processing and Machine Learning Laboratory at Aarhus University.
David Y.H. Pui is a Regents Professor in Mechanical Engineering at the University of Minnesota and a Member of the National Academy of Engineering (NAE) . He serves as Director of the Center for Filtration Research (CFR) and the Particle Technology Laboratory (PTL) . Aerosol and nanoparticle science and engineering Particle instrumentation development Filtration solutions for air pollution control Industrial applications of aerosol science and nanoparticle engineering Real-time detection of airborne biological particles Environmental sustainability (microplastics, microfibers) Key research initiatives include studies on the slip effect in nanofiber filter media , loading characteristics of ceramic wall-flow filters , and control of airborne viable virus-containing particles . His work bridges theoretical and applied engineering, focusing on both indoor and outdoor air quality challenges. Scientific Awards National Academy of Engineering (NAE) Member Labs & Collaborations As Director of the Center for Filtration Research (CFR) and Particle Technology Laboratory (PTL) , Pui leads interdisciplinary teams developing innovative filtration technologies for HVAC systems, ozone/VOC removal, and real-time airborne particle detection.
Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.
Johan Sidén is a Lecturer and Associate Professor at Mid Sweden University , employed in the Department of Computer and Electrical Engineering (DET) . His work focuses on RFID technology , antenna design , and printed/flexible electronics , with a particular emphasis on industrial IoT and welfare technology applications. Research Keywords : Radio Frequency Identification, Antenna Design, Flexible Electronics, Wireless Sensor Networks, Microwave Engineering, Electronic Design Key Projects : DRIVEN (data-driven industrial transformation), SmartArea (functional surfaces), Pressure (ulcer monitoring), MakeSense! (welfare technology) Publications : 15+ recent works on wearable antennas, smart packaging, UWB antenna design, and RFID sensor integration Collaborations include partnerships with industrial and academic institutions, focusing on sustainable electronics, sensor systems, and smart infrastructure. His technical expertise spans antenna optimization , printed circuits , and edge computing for harsh environments.
Igor Wojnicki is a Professor at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, where he serves as Vice-Dean of the Faculty of Cooperation and Education. His primary affiliation is with the Department of Applied Informatics, where he maintains an active research laboratory focused on knowledge engineering and smart systems. His research spans multiple domains with evolving focus: Early career: Deductive databases and rule-based inference engines (PhD thesis on "A Rule-based Inference Engine Extending Knowledge Processing Capabilities of Relational Database Management Systems") Mid-career: Graph-based knowledge representation and Tabular Trees (XTT predecessor) Current focus: Smart city applications, particularly energy-efficient lighting control systems and graph-based urban data integration His recent publications demonstrate a clear trajectory toward applied urban computing, with over 15 significant papers in the last five years addressing smart city infrastructure optimization. Key themes include dynamic street lighting control, graph-based computational methods for urban environments, and energy conservation in public infrastructure. Wojnicki actively contributes to academic-practical collaboration through initiatives like the Green AGH Campus Project and IBM academic partnerships. His technical leadership includes development of the ReDaReS system for relational database knowledge processing and the Jelly View technology for advanced database queries. His laboratory maintains strong industry connections, particularly with IBM through student internship programs and technology transfer initiatives. The team produces both theoretical frameworks and practical implementations, with notable outputs including the Osiris GUI system and Magellan GPS software for Poland.
