Yohan PETETIN is an Associate Professor at Telecom SudParis (Institut polytechnique de Paris) in the CITI Department. His research focuses on Bayesian filtering, Monte Carlo methods, hidden Markov models, and multi-object tracking. He has authored over 20 peer-reviewed articles since 2011, with notable contributions in IEEE Transactions on Signal Processing and other top venues. His work bridges statistical signal processing with machine learning applications. PhD: Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles Markoviens (2013, Telecom SudParis) HDR: Generative models for time series data (2023, Institut polytechnique de Paris) Research interests emphasize sequential Monte Carlo algorithms, particle filtering optimizations, and deep learning integration for time-series analysis. Recent work explores expressivity comparisons between recurrent neural networks and hidden Markov models. Teaching includes courses on probabilistic graphical models, Bayesian filtering, and deep learning across undergraduate and graduate programs at Telecom SudParis and affiliated institutions.
Neng Wan is a Professor of Geography and Director of the Utah Geo-Health Lab at the University of Utah's School of Environment, Society & Sustainability. His work bridges geography, public health, and spatial analysis to address critical healthcare access and environmental health issues. Previously, he served as an Assistant Professor of Geography at the University of Utah from 2014-2019. Dr. Wan earned his BS in Geodesy from Wuhan University in 2003, followed by an MS in Cartography and GIS from the same institution in 2006. He completed his PhD in Geography from Texas State University-San Marcos in 2011. His research focuses on applying GIS and spatial methods to understand public health and environmental health problems. Current research projects include mHealth-supported health behavior research, access to healthcare, health disparities, and health consequences of pesticide exposure. His work demonstrates how spatial patterns influence health outcomes, particularly in emergency surgical care, telemedicine utilization, and pandemic responses. Dr. Wan's approach integrates advanced geospatial techniques with public health questions to reveal patterns that might otherwise remain hidden in traditional analyses. Analysis of his recent publications shows a consistent focus on healthcare access disparities, with particular attention to how social vulnerability, geography, and infrastructure impact health outcomes. His work increasingly incorporates network analysis and advanced spatial statistics to model complex healthcare systems. Recent studies examine telemedicine adoption patterns, emergency surgical care networks, and spatial patterns of discrimination during the pandemic. Presidential Scholar Award (2023, University of Utah) Dr. Wan has secured multiple grants from the National Institutes of Health (NIH), American Cancer Society, and other organizations to support his research on healthcare access disparities, mobile health interventions, and spatial epidemiology. His teaching includes advanced GIS methods, GIS & Public Health, and Health-Global Pandemics courses, where he trains students in applying spatial methods to health problems. As Director of the Utah Geo-Health Lab, Dr. Wan leads a research team that develops and applies innovative geospatial methods to address pressing public health challenges, particularly those related to health disparities and access to care. The lab's work has direct implications for healthcare system design, resource allocation, and policy interventions aimed at reducing geographic health disparities.
Dr. Alexei Vernitski is a Senior Lecturer in the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex. His research focuses on applying artificial intelligence (including reinforcement learning and deep learning) to mathematical problems in knot theory, algebra (e.g., braid theory), and universal algebra. He also explores mathematics education, particularly enhancing student motivation. Previously, he worked in the financial sector as a programmer and as a computer science lecturer. His research interests span AI-driven knot theory, algebraic structures (semigroups, groups), and mathematics education. Notable areas include the application of neural networks to braid untangling, automated reasoning in knot diagrams, and cognitive studies on math anxiety using EEG. He has supervised PhD students in mathematics education, universal algebra, and computer science applications of mathematics. His recent work demonstrates trends in combining machine learning with topological and algebraic problems, emphasizing practical AI solutions for abstract mathematical challenges. His articles reflect interdisciplinary approaches, merging computer science techniques with pure mathematics. Dr. Vernitski has advised multiple PhD students, contributing to diverse fields from knot theory to educational technology. His work bridges theoretical mathematics with real-world applications, such as optimizing data transmission and enhancing learning systems through neuroadaptive methods.
Professor Ling Li is a faculty member at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), within the Faculty of Science and Engineering. Their research focuses on interdisciplinary applications of machine learning, computer vision, and deep learning in structural engineering and materials science. Notable contributions include advancements in structural health monitoring, blast loading prediction, and 3D displacement measurement using monocular vision. Professor Li has authored numerous peer-reviewed articles and collaborates on projects involving civil infrastructure resilience, smart materials, and AI-driven solutions for engineering challenges. They hold an office in the New Technologies Building at Curtin Perth and can be reached at L.Li@curtin.edu.au.
János Kertész is a Professor at the Department of Network and Data Science at Central European University (CEU) since 2012, and previously held the position of Professor at the Budapest University of Technology and Economics (1992–2018). He obtained his PhD in Physics from Eötvös University (1980) and DSc from the Hungarian Academy of Sciences (1989). His research spans statistical physics applications, complex networks, and financial analysis. He has authored over 280 papers and served on editorial boards of journals like Journal of Physics A and Physical Review E . His research focuses on interdisciplinary topics including social network dynamics, systemic risk in economic systems, and algorithmic bias in digital environments. Notable awards include the Széchenyi Prize (Hungary’s highest scientific honor) and the Finland Distinguished Professorship. He has led projects such as SAI (Socially Explainable AI) and HUMANE-AI-NET, addressing algorithmic bias and AI ethics. His work bridges physics-based modeling with real-world social and economic systems, emphasizing computational approaches to corruption, opinion formation, and innovation diffusion. Key contributions include modeling cascading failures in interdependent networks and analyzing attention dynamics on platforms like Sina Weibo during the pandemic. He advises on systemic risk mitigation strategies and collaborates internationally, with visiting roles in Germany, the U.S., France, Italy, and Finland.
Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.
Vaibhav Garg is a Collegiate Assistant Professor of Computer Science at the Virginia Tech Innovation Campus. Previously, he worked as a teaching and coding instructor at Outco Company in California. His research focuses on applying computational methods to societal challenges such as detecting inciting speech on social media and auditing rogue mobile apps for misuse. He holds a Ph.D. in Computer Science from North Carolina State University (2024), an M.S. from Indraprastha Institute of Information Technology, Delhi (2019), and a B.S. from The LNM Institute of Information Technology (2017). Research Interests: Applied Natural Language Processing Social Media Analytics Cybersecurity AI for Social Good Responsible Computing Educational Technology His recent work includes publications on inciting speech detection, trauma narratives analysis, and mobile app misuse audits. These studies emphasize ethical AI applications in addressing societal issues like online harassment and privacy violations. Awards & Honors: Carla Savage Award (2023) for outstanding Ph.D. research at NC State Grants & Funding: National Security Agency (NSA) research grants during Ph.D. National Science Foundation (NSF) research grants during Ph.D. His current work explores interdisciplinary solutions at the intersection of NLP and social responsibility, with a focus on developing technologies that promote ethical online environments.
Dr ASM Kayes serves as Senior Lecturer in Cybersecurity and Cyber Curriculum Lead at La Trobe University's Department of Computer Science and Information Technology, where he shapes cybersecurity education programs including Master's, Bachelor's, and Double Degrees. His academic journey began with a PhD from Swinburne University of Technology in 2015, followed by postdoctoral research at La Trobe before joining as Lecturer in 2019 and promotion to Senior Lecturer in 2022. His research spans critical cybersecurity domains including data security, privacy preservation, context-aware access control, malware/ransomware defense, and IoT/fog/cloud security leveraging AI/ML techniques. Dr Kayes has established himself as a leading voice in blockchain security frameworks, privacy policy analysis, and cyber incident response through publications in top-tier venues like ACM Computing Surveys, IEEE Internet of Things Journal, and Computers & Security. His recent publications reveal a strong trajectory toward integrating AI with traditional security frameworks, particularly in blockchain risk assessment (2025), cross-domain access control (2025), and IoT behavior prediction (2024). The research demonstrates consistent focus on practical security solutions addressing ransomware mitigation, privacy breaches, and emerging threats in decentralized systems. Over $880,000 secured as Chief Investigator for cybersecurity projects Australian Government Department of Social Services grant (2023-2026) for cyberbullying prevention AustCyber research funds with industry partners (2020-2023) SmartSat CRC and ASCRIN PhD scholarship grants (2021) Dr Kayes has successfully supervised 5 PhD candidates to completion and currently mentors 5 doctoral students across diverse topics including AI-driven threat hunting, satellite network security, and blockchain risk frameworks. His collaborative network spans UK, USA, Europe, and Asia, with active industry partnerships through Westpac, BHP, and Quantum Victoria. He serves on editorial boards for leading cybersecurity journals and has examined HDR dissertations globally, reflecting his significant standing in the academic community.
Engin Erzin is a Professor at Koç University's College of Engineering, leading the KUIS AI Lab and Multimedia, Vision and Graphics Lab . His research focuses on AI-driven human-centric systems, affective computing, and multimodal interaction analysis. He has contributed extensively to robotics, speech processing, and human-robot interaction through over 70 peer-reviewed publications since 2008. Research interests include: Affective computing and emotion recognition from speech/gestures Human-robot interaction and socially engaging agents Speech-driven animation and gesture synthesis Multimodal data fusion for interaction analysis Deep learning applications in robotics and biomedical engineering Recent work emphasizes: Developing adaptive pHRI controllers for manufacturing tasks Creating engagement measurement frameworks for human-machine interfaces Advancing Turkish speech recognition through self-supervised learning Designing multimodal databases for interaction studies Labs: KUIS AI Lab : Focuses on AI applications in robotics and human-computer interaction Multimedia Lab : Specializes in vision, graphics, and audiovisual analysis
Professor David Stone is a faculty member in the School of Electrical and Electronic Engineering at the University of Sheffield, where he holds the position of Professor in Electrical Engineering and serves as Theme Lead for Electrical Machines. He earned his BEng (Hons) in Electronic Engineering from the University of Sheffield (1982–1985) and a PhD in Automatic Weld Penetration Control from Liverpool University (1989). Since joining the University of Sheffield in 1989, he has been affiliated with the Electrical Machines and Drives (EMD) Group, advancing to full professorship in 2013. His research focuses on power electronics , energy storage systems , and their applications in electric vehicles (EVs) and renewable energy integration . Notable work includes leading the CREESA initiative, which operates the UK's largest grid-connected lithium titanate battery storage system. Current projects explore hybrid energy storage, EV battery second-life applications, and high-frequency resonant converters for industrial heating. Research interests span: Simulation of energy systems and hybrid storage technologies Electric vehicle energy consumption modeling using dashcam telemetry Smart battery management for EVs and grid support High-efficiency wireless EV charging systems Advanced control strategies for power converters His publications (2020–2025) reflect contributions to: Energy storage system design and control Electric machine fault detection and diagnostics Grid integration of renewable energy sources Piezoelectric transformer-based power supplies Microgrid optimization with hybrid energy storage He collaborates with industry through initiatives like the EPSRC Prosperity Partnership in Offshore Wind and the TransEnergy project linking road-rail energy exchange. His work addresses challenges in sustainable energy systems and smart grid technologies.
Prof. Helmut Grabner is a Professor at the Zurich University of Applied Sciences (ZHAW), leading the Visual Intelligence and Applications Group and the Entrepreneurship initiatives within the School of Engineering. His work bridges computer science, medical technology, and visual communication, with a focus on Extended Reality (XR), surgical training simulations, and AI-driven decision making. Education: PhD in Computer Science (Graz University of Technology, 2008), Master's in Computer Science (2008), and a Certificate of Advanced Studies in Higher Education (ZHAW, 2021). Prior to academia, he held roles including CTO at Logitech and co-founder of upicto, applying computer vision in industry and startups. Research spans augmented reality medical training tools, NMR spectrum analysis via deep learning, and understanding visual engagement in advertising. Awards include the prestigious Koenderink Prize (2018) for contributions to computer vision. Projects include Immersive Education frameworks, bias-mitigation in venture capital algorithms, and surgical proficiency measurement systems. Teaching includes courses on Visual Computing, Machine Learning, and Deep Learning. His work integrates academic research with practical applications in healthcare, education, and entrepreneurship.
Prof. Serkan Simsek is a Professor at the Department of Electronics and Communications Engineering, Faculty of Electrical and Electronics Engineering, Istanbul Technical University. He holds a PhD in Telecommunication Engineering from the same institution. His academic roles include serving as Vice Dean (2020-2023) and Program Coordinator of the ITU-NJIT Joint Degree Program. His research focuses on Optics, Photonics, Electromagnetics, and Microwave/Antenna Technologies. Education: PhD in Telecommunication Engineering (Istanbul Technical University, 2008), MSc in Electronics Engineering (Istanbul Technical University, 2003), and BSc in Electrical-Electronic Engineering (Istanbul University, 2001). Research interests span antenna design, electromagnetic bandgap structures, microwave imaging, and slow-wave structures. Notable projects include developing breast cancer imaging systems and optimizing antenna performance using metamaterials. He has been awarded the Leopold B. Felsen Award for Excellence in Electromagnetics (2009). His 14 advisees include students working on topics like 5G amplifiers, low-profile antennas, and ultrawideband arrays. Prof. Simsek contributes to academic administration and has coordinated multiple international joint degree programs. His lab focuses on advanced antenna systems and microwave engineering applications.
James R. Green is a Professor in the Department of Systems and Computer Engineering at Carleton University , where he has been a faculty member since 2005. He holds a PhD from Queen's University and is a licensed Professional Engineer (P.Eng.) and Senior Member of IEEE. His work integrates machine learning, biomedical informatics, and high-performance computing. His educational background includes: B.A.Sc. in Systems Design Engineering, University of Waterloo (1998) M.Sc.(Eng.), Queen's University (2000) PhD, Queen's University (2005) Dr. Green's research focuses on machine learning challenges in biomedical informatics , particularly class imbalance and rare event prediction. Key areas include protein structure, function, and interaction prediction; microRNA detection in unique species; non-contact neonatal monitoring; and accelerating scientific computing via parallel architectures like the Cell BE processor. His lab has developed several widely used bioinformatics tools such as PIPE, ProtDCal, and PCI-SUMO. His recent publications reflect a strong trend in computational biology and machine learning , with applications in proteomics, genomics, and medical diagnostics. He has published over 100 peer-reviewed papers and secured funding from NSERC, CIHR, CFI, ORF, OCE, MITACS, and IBM. Scientific and teaching recognitions include: Three teaching awards NSERC Best Project Award (twice: 2006-2007 and 2007-2008) Multiple student projects resulting in conference papers (e.g., CMBEC) He has supervised numerous undergraduate capstone projects in areas such as assistive technologies, robotic systems, and bioinformatics. His teaching portfolio includes courses in Pattern Classification, Machine Learning, Computer Architecture, and Biomedical Engineering. He leads an active research group that bridges computer engineering and life sciences, fostering interdisciplinary collaboration. Lab and research team initiatives include: Development of open-access web servers for protein analysis Collaborations with biologists and clinicians Integration of hardware and software for medical applications
Professor J. Debus is a distinguished academic in Radiation Oncology at Heidelberg University's Medical Faculty, with extensive research focused on particle therapy, medical physics, and cancer treatment optimization. His work spans clinical trials, radiobiology, and technical innovations in radiation delivery systems. Primary Affiliation: German Cancer Research Center (DKFZ), Heidelberg Research Focus: Particle therapy, radiation oncology, medical physics Key Collaborations: Mein S., Liew H., Tessonnier T., and other leading researchers in radiation oncology Professor Debus' research interests center on advancing particle therapy techniques including proton, carbon ion, and emerging modalities like helium and oxygen ion therapy. His work addresses critical challenges in radiation oncology such as normal tissue sparing, hypoxia-induced radioresistance, and precision treatment delivery. He has made significant contributions to understanding the biological effects of different radiation types and optimizing treatment protocols for various cancer types including head and neck cancers, brain metastases, and prostate cancer. His recent publications demonstrate leadership in clinical trials (GUARD, ESTRON, PROBASE) and technical innovations in radiation delivery systems, particularly in the emerging field of FLASH radiotherapy and ultra-high dose rate treatments. Professor Debus has published extensively on treatment planning optimization, radiation-induced biological effects, and imaging techniques for precise radiation delivery. Leading clinical trials in particle therapy Developing novel techniques for normal tissue protection Advancing understanding of radiation biology across different modalities Professor Debus has secured significant research funding for his work in radiation oncology and particle therapy. His research has contributed to clinical implementation of advanced treatment techniques at the Heidelberg Ion-Beam Therapy Center (HIT), one of the world's leading facilities for particle therapy. He supervises numerous doctoral students and postdoctoral researchers in the radiation oncology field. His laboratory and research team focus on translational research bridging basic radiobiology with clinical applications, with particular emphasis on optimizing treatment protocols for challenging tumor types and improving patient outcomes through precision radiation therapy.
Anna Monreale is an Associate Professor in the Department of Computer Science at the University of Pisa and a key member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group with the Information Science and Technology Institute of the National Research Council (ISTI-CNR) in Pisa. Her academic career is rooted in the University of Pisa, where she completed her Bachelor's, Master's, and Ph.D. in Computer Science. Her research focuses on privacy-preserving data analytics, with core interests in big data analytics, social network analysis, spatio-temporal mining, and explainable AI. She is particularly known for her work on privacy-by-design in data mining and evaluating privacy risks in analytical processes. Her research bridges technical innovation with ethical and legal considerations in data science. Her recent publications reveal a strong trend toward explainable AI, privacy in federated learning, and risk assessment in mobility and health data. She actively contributes to developing methods for explaining black-box models, assessing privacy exposure, and balancing privacy, utility, and fairness in AI systems. Privacy by Design Ambassador (2014) ISTI-CNR Young++ Researcher Award (2014) Monreale has advised and co-chaired several international workshops, including PriSMO, PinSoDa, and MoKMaSD, and serves on editorial boards such as Transactions on Data Privacy. She teaches advanced data mining, big data ethics, and database systems across multiple graduate and undergraduate programs. She is involved in major EU projects like SoBigData, XAI, TAILOR, and HumMingBird, reflecting her leadership in data science and AI ethics. She is affiliated with the KDD-Lab, a prominent research group focused on knowledge discovery, social mining, and big data analytics, contributing to both theoretical advances and real-world applications in privacy-aware data science.
