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Lianhua ChiView profile

Lecturer

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +7 more

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

Mariusz KlećView profile

Researcher

• Music Information Retrieval
• Machine Learning
• Deep Neural Networks
• +5 more

Mariusz Kleć is a Researcher and faculty member at the Polish-Japanese Academy of Information Technology (PJATK) , affiliated with the Faculty of Information Technology and the Department of Multimedia . He is currently completing his PhD in Computer Science, focusing on music processing with deep neural networks. His work bridges computer science, music engineering, and machine learning, emphasizing practical applications in recommendation systems and healthcare. Education: PhD in progress (2014–present): Research on music classification and organization using DNNs. Postgraduate Sound Engineering (2014–2015): Project on equalizer and dynamic processor usage in music production. MSc in Computer Science (2005–2010): Thesis on sound similarity for music recommendation systems. High School of Visual Arts (2000–2005): Focus on visual identity design. Technical Expertise: Full-stack web development (Java, JavaScript, React, NodeJS, MongoDB), machine learning, graphic design (Adobe Suite), and audio engineering. Research Interests: Mariusz explores intersections of music processing, AI, and multimedia systems. His projects include: Music recommendation systems leveraging personality traits and neural networks. Automated genre recognition using deep learning and wavelet transformations. Applications of AI in healthcare for early symptom detection. Professional Experience: Administrator of Recording Studio (PJATK, 2012–present): Manages equipment and event coordination. Internship at Sony Stuttgart (2013): Developed an audio classification DNN in MATLAB. Web developer (2008–2011): Front-end and WordPress projects, SharePoint system administration. Labs/Teams: Active contributor to the CLARIN-PL project (2019–2021) for speech tool development. Collaborates with the Multimedia Department on sound design and data mining.

Kaushik SinhaView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Optimization
• +5 more

Kaushik Sinha is an Associate Professor at the School of Computing, Wichita State University, part of the College of Engineering. His research focuses on machine learning, optimization, algorithms, and their applications in health informatics. He holds a PhD in Computer Science from Ohio State University and completed a postdoctoral fellowship at UC San Diego under Sanjoy Dasgupta and Kamalika Chaudhuri. His professional activities include serving as an Associate Editor for Neurocomputing (2015–2017) and Technical Program Committee member for conferences like KDD, ICML, and AAAI. He is an affiliated researcher at the Institute for Foundations of Machine Learning (IFML). Dr. Sinha actively mentors PhD students in theoretical and applied machine learning. He currently offers two PhD positions for Fall 2025 focusing on algorithm analysis and applied machine learning, requiring strong mathematical or programming backgrounds. His work emphasizes both foundational research and practical applications, including federated learning, nearest neighbor search, and health data analysis.

• Statistics
• Data Science
• Biostatistics
• +3 more

Prof. Katrijn Van Deun is a Full Professor in the Department of Methodology at the Tilburg School of Social and Behavioral Sciences, Tilburg University. Her research focuses on statistical methodology, data science, and their applications in health and behavioral sciences. She leads projects such as the WaTCh study, exploring determinants of quality of life in thyroid cancer patients. Her work bridges methodological innovation with practical applications in fields like HR analytics, rheumatoid arthritis treatment, and biostatistics. Key research interests include multivariate analysis, sparse PCA, meta-analyses, and health outcomes research. She has published extensively on topics such as structural equation modeling, infection risks in rheumatoid arthritis, and predictive analytics in HRM. Her contributions address complex data challenges, integrating high-dimensional data with traditional methodologies to uncover meaningful patterns. Prof. Van Deun collaborates across disciplines, involving teams in psychology, medicine, and computer science. Her work emphasizes methodological rigor and translational research, aiming to improve clinical decision-making and patient outcomes. She is actively involved in developing statistical tools and frameworks to handle big data and interdisciplinary research challenges.

• Machine Learning
• Control Systems
• Computer Vision
• +3 more

Jian Sun is a researcher affiliated with the Chinese Academy of Sciences' Institute of Computing Technology. His work spans interdisciplinary fields including machine learning, control systems, robotics, and signal processing. He collaborates with institutions globally to advance theoretical and applied research in computational methods and their real-world applications. Research interests focus on machine learning for healthcare diagnostics, optimization algorithms for complex systems, and sensor fusion technologies for autonomous systems. Recent work emphasizes applications in cognitive impairment detection, UAV-based communication systems, and crystal structure prediction. Publications highlight advancements in adaptive control methodologies, cybersecurity in control systems, and 3D reconstruction techniques. His contributions bridge theoretical foundations with practical implementations in robotics, aerospace, and environmental monitoring.

Xiao GuView profile

Researcher

• AI for Healthcare
• Biomedical Signal Processing
• Pervasive Healthcare
• +3 more

Xiao Gu is a Researcher at the Computational Health Informatics Lab, Department of Engineering Science, University of Oxford. He holds a PhD in Computing from Imperial College London (2023), an MRes in Medical Robotics and Image Guided Intervention (2019), and a BEng in Electronic Engineering from Fudan University (2018). His research focuses on integrating pervasive sensing and deep learning for healthcare applications, addressing challenges in generalization and data scarcity. He has been recognized with awards including the BMVC Outstanding Reviewer (2024) and IEEE ICRA Student Travel Grant (2023). Key research interests include AI-driven healthcare solutions, biomedical signal processing, and the development of robust models for real-world medical applications. His work spans datasets like CFP for biomedical sensing and contributions to workshops on wearable intelligence. Xiao Gu has authored over 30 peer-reviewed publications, with recent work focusing on medical imaging, domain adaptation, and vision-language models. He actively contributes to academic activities, including editorial roles and conference reviewing.

Jorma LaaksonenView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Jorma Laaksonen is a Senior Lecturer at the Department of Computer Science, Aalto University (School of Science). His research focuses on artificial intelligence, machine learning, computer vision, and multimedia systems, with notable contributions to cross-lingual evaluation, hyperspectral imaging, and audio-visual attention mechanisms. He holds a Doctoral degree in Engineering and Technology from Helsinki University of Technology (1997). Education: Doctoral degree, Engineering and Technology, Helsinki University of Technology (1997) Licentiate degree, Engineering and Technology, Helsinki University of Technology (1991) Master's degree, Engineering and Technology, Helsinki University of Technology (1990) His research interests span AI-driven visual computing, human-computer interaction, and multimedia systems. Key projects include USSEE (audio-visual scene understanding) and ARTISDIG (AI for forest spectral analysis). He has published over 280 papers and supervised multiple theses. Awards include the IEEE Best Paper Award (2017) and Microsoft's Best Practice Award (2016). Recent work includes cross-cultural language modeling, efficient text-to-video retrieval, and open-world video instance segmentation. He leads the Lecturer Laaksonen Jorma group and actively participates in editorial roles and doctoral thesis evaluations.

Tarique AnwarView profile

Lecturer

• Data Science
• Machine Learning
• Artificial Intelligence
• +3 more

Tarique Anwar is a Lecturer in the Department of Computer Science at the University of York, UK, since 2021. He holds a PhD in Data Science from Swinburne University of Technology, Australia. As Programme Leader for both MSc and BSc Data Science programs, he oversees admissions and curriculum development. His academic roles include Data Science Admissions Tutor and membership in the Artificial Intelligence Group. Research interests span Data Science , Machine Learning , Artificial Intelligence , and Social Media Analysis , with a focus on solving real-world problems using structured/unstructured data. Notable areas include mental health detection via social media, network analysis, and explainable AI. His work addresses challenges in: Depression severity assessment using attention mechanisms Rumor source detection in social networks Eating disorder classification through multimodal learning Geographic-social-temporal pattern mining He supervises MS/PhD students in Data Science and Analytics, emphasizing interdisciplinary research. His publications (2020–2025) demonstrate expertise in AI applications for health, social networks, and optimization. Labs/Teams: Artificial Intelligence Group at the University of York, collaborating with industry and academic partners globally.

• 3D Face/Head Modeling
• Computer Vision for Self-Driving Vehicles
• Robot Manipulators
• +4 more

Professor Nick Pears is a faculty member in the Department of Computer Science at the University of York. He holds roles including Deputy Head of Department (Research) and Chair of the Departmental Research Committee. His academic career spans postdoctoral research at the University of Oxford, a Fellowship at the University of Cambridge, and progressive roles at York from Lecturer to Professor (since 2022). He holds a BSc in Engineering and a PhD in Robotics from 'Dunelm' (likely Durham University). His research focuses on 3D facial modeling, computer vision for autonomous systems, robotic manipulation, and medical imaging. He leads the Vision, Graphics and Learning research group and has contributed to advancements in morphable models, gaze estimation, and medical anomaly detection. His work bridges theoretical computer vision with practical applications in healthcare and robotics. Recent publications emphasize generative models for medical imaging, 3D face reconstruction techniques, and adaptive robotics. His research often intersects AI ethics, emphasizing safe and explainable systems for autonomous vehicles and surgical applications. He actively collaborates with clinicians and engineers to translate vision algorithms into real-world solutions. As a leader in academic research governance, he oversees York's REF submissions and departmental strategy, fostering interdisciplinary innovation. His lab maintains an open-access repository of 3D morphable models and benchmark datasets for robotic motion planning.

• Data Science
• Computer Graphics
• Data Visualization
• +3 more

Professor Mark Jones is a faculty member at the School of Mathematics and Computer Science , Swansea University , specializing in Computer Science . He serves as a full-time professor and is available for postgraduate supervision. His research interests span Data Science , Computer Graphics , Data Visualization , Time-Series Analysis , Machine Learning , and 3D Rendering . He has contributed to advancements in photon mapping , ray tracing , global illumination , and volume rendering . His recent publications focus on point cloud completion , text-based person re-identification , graph neural networks , and deep learning for time-series data . These works highlight trends in 3D reconstruction , neural network optimization , and medical imaging compression . Professor Jones has secured significant research grants, including EPSRC Grant EP/N028139/1 (2016–2019) and Leverhulme Grant RPG-2013-190 (2013–2015), and has held roles such as Member of the Editorial Board for Computer Graphics Forum (2014–2017). He supervises PhD students in areas like 3D medical image compression , face reenactment , and urban modeling visualization , and has contributed to Computational Foundry , a research facility at Swansea University.

• Machine Learning
• Causal Reasoning
• Mechanism Design
• +7 more

Dr. Shanfeng Hu is an Assistant Professor in the Department of Computer and Information Sciences at Northumbria University, UK. He obtained his PhD from the same department in 2020 and joined the faculty as a Lecturer immediately after. During his PhD (2018-2020), he worked as a Senior Data Scientist in the financial sector. His research focuses on machine learning, causal reasoning, and mechanism design, with funded projects in advanced manufacturing (Innovate UK KTP), supply chain optimization (Digital Catapult, Innovate UK ISCF), and digital health (EU Erasmus+, EU MSCA). Education: PhD in Computer Science (2020) from Northumbria University, specializing in Metric Representations for Shape Analysis and Synthesis. Research interests include computer vision applications (e.g., defect detection, pose estimation), predictive maintenance, inventory management, health monitoring systems, and human-in-the-loop machine learning. He has collaborated on projects involving 3D printing (KTP-funded), steel surface analysis, and elderly health monitoring. His recent work addresses challenges in digital transformation for manufacturing, integrating predictive maintenance with inventory systems, and developing explainable AI for control systems. Projects are supported by Innovate UK, EU programs, and Digital Catapult. Lead researcher on Innovate UK KTP project: 3D printed Models Contributions to UN Sustainable Development Goals through education and healthcare projects Labs/Teams: Collaborates with industry partners in manufacturing, healthcare, and robotics sectors through funded initiatives.

Honglei LiView profile

Assistant Professor

• Smart Cities
• Virtual Communities
• Electronic Business
• +5 more

Honglei Li is an Assistant Professor at the Computer and Information Sciences Department of Northumbria University. He holds a PhD in Information Systems from the Chinese University of Hong Kong (2007), an MPhil in Information Systems from Nanjing University (2000), and a BSc in Mathematics from Nanjing University (1997). His research focuses on virtual communities, smart cities, electronic business systems, and AI applications. Key themes include loyalty development in health communities, peer-to-peer energy trading platforms, and social media business models like TikTok's livestream commerce. He explores behavioral psychology in online environments and blockchain for e-government security. Recent work highlights include developing LSTM-based personality recognition models (2025), analyzing object localization in VQA systems (2024), and studying mobile health community dynamics in China (2024). His 2018 Science and Technology Progress Award recognizes contributions to systems analysis methodologies. He chairs the UK Academy for Information Systems and supervises PhD projects on AI-driven smart city applications. Current research integrates machine learning with energy market analysis and digital health interventions.

Stef De SabbataView profile

Associate Professor

• Geographical Artificial Intelligence (GeoAI)
• Urban Analytics
• Generative Artificial Intelligence
• +3 more

Stef De Sabbata is an Associate Professor of Geographical Information Science at the University of Leicester, holding dual affiliations with the School of Geography, Geology and the Environment and the Institute for Digital Culture. She serves as Chair of the Geographic Information Science Research Group at the Royal Geographical Society and previously chaired GISRUK 2018. Her research focuses on GeoAI, urban analytics, and cultural informatics, with notable contributions to graph neural networks in geodemographic classification and urban form analysis. Education includes a PhD in Geography from the University of Zurich (2013), and BSc/MSc in Computer Science from the University of Udine. Prior to Leicester, she held roles at the Oxford Internet Institute (OII) including Researcher (2013-2015), Junior Research Fellow at Wolfson College (2014-2015), and Research Associate (2015-2021). Her work explores spatially-explicit approaches to urban systems and the application of large language models in geographic contexts. Professional roles include membership in the International Cartographic Association's Commission on Location-Based Services and leadership in interdisciplinary initiatives like the GeoNet project examining Sub-Saharan Africa's knowledge economies. No specific awards are explicitly listed in the provided materials.

• Machine Learning
• Signal Processing
• Artificial Intelligence
• +3 more

Akila Hewa Thondilege is a Postdoctoral Research Fellow at Queensland University of Technology (QUT), affiliated with the School of Electrical Engineering & Robotics. She is part of the Signal Processing, Artificial Intelligence, and Vision Technologies group. Her qualifications include a PhD from QUT and a B.Sc.Eng(Hons) in Computer Science and Engineering. Her research focuses on applying machine learning principles to solve complex problems in communications, defense, healthcare, sports, and infrastructure. Her work spans radar signal processing, medical imaging, and robotics applications. Recent contributions include developing radar spectrum detection datasets and multi-task learning frameworks for signal characterization. Earlier research explored semantic segmentation in multimodal images and aspect-based analysis of restaurant reviews. Akila’s publications reflect her expertise in machine learning, signal processing, and computer vision. She collaborates with teams addressing challenges in defense systems, healthcare monitoring, and robotic vision. While no academic awards are listed, her research consistently bridges theoretical advancements with practical applications in emerging technologies.

Vladimir PuzyrevView profile

Research Fellow

• Deep Learning
• Geophysics
• Numerical Modeling
• +3 more

Vladimir Puzyrev is an Adjunct Research Fellow at Curtin University’s School of Earth and Planetary Sciences (EPS) and a Senior Research Scientist at Schlumberger-Doll Research. His work bridges geophysics, numerical modeling, and machine learning. He specializes in deep learning applications for geophysical inversion, electromagnetic exploration, and seismic facies classification. Research interests include: deep learning for geophysical data analysis, 3D modeling and inversion, isogeometric and finite element methods, and sparse linear solvers. His interdisciplinary approach integrates computational geosciences with advanced numerical techniques. Publications focus on applying neural networks to mineralization prediction, seismic facies classification, and electromagnetic inverse problems. Key contributions include frameworks for generative adversarial networks in geophysical modeling and Monte Carlo dropout for uncertainty quantification in geochemical data. Affiliations: Curtin University (Adjunct Research Fellow), Schlumberger Limited (Senior Research Scientist). Active in professional societies like SEG and EAGE. His work spans academic and industrial sectors, emphasizing real-world applications of computational geoscience.