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Christian Andersson NaessethView profile

Assistant Professor

• Approximate Statistical Inference
• Causality
• Generative Modeling
• +6 more

Christian Andersson Naesseth is an Assistant Professor of Machine Learning at the University of Amsterdam , affiliated with the Informatics Institute , the Amsterdam Machine Learning Lab (AMLab) , and the UvA-Bosch Delta Lab 2 . A former postdoctoral researcher at Columbia University and PhD graduate in Electrical Engineering from Linköping University (advised by Fredrik Lindsten and Thomas Schön), his research bridges generative modeling , uncertainty quantification , and approximate Bayesian inference with applications in AI for Science . Research Interests Generative Models (diffusions, flows, flow networks) Approximate Inference (variational and Monte Carlo methods) Probabilistic Modeling (natural sciences, computer vision, health) Uncertainty Quantification (E-values, conformal prediction) Recent Scientific Contributions Developed SDE Matching for simulation-free training of latent stochastic differential equations Advanced controlled generation via equivariant variational flow matching Proposed risk monitoring frameworks for test-time adaptation under unknown data shifts Awards & Recognition Best Workshop Paper Award at AABI 2025 Advising & Collaborations Lab manager of UvA-Bosch Delta Lab 2 Collaborates with institutions like Mila Montreal , Microsoft Research , and ECMWF Mentors students: Grigory Bartosh (PhD), Hany Abdulsamad (postdoc), and visiting PhDs from Antwerp, Manchester, Bielefeld, and DTU

Gustavo BatistaView profile

Associate Professor

• Machine Learning
• Data Mining
• Time Series Analysis
• +3 more

Associate Professor Gustavo Batista is a prominent researcher in the School of Computer Science and Engineering at the University of New South Wales, where he joined in 2018 after more than a decade at the University of Sao Paulo (USP). He previously served as a visiting researcher at the University of California, Riverside (2010-2012) working with Professor Eamonn Keogh. Education: Habilitation in Computer Science, University of São Paulo at São Carlos (2016) PhD in Computer Science, University of São Paulo at São Carlos (2003) MSc in Computer Science, University of São Paulo at São Carlos (1997) BS in Computer Science, São Paulo State University (1994) Professor Batista's research focuses on practical applications of Machine Learning, particularly in supervised machine learning, data mining, time series analysis, data streams, and imbalanced data. His work bridges theoretical computer science with real-world applications, especially in developing lightweight models for embedded devices and sensors. His research approach emphasizes identifying gaps in literature through challenging applications, leading to contributions in both Computer Science and application domains. His publication record demonstrates consistent contributions across time series analysis, data streams, and quantification, with notable emphasis on developing algorithms that function effectively in resource-constrained environments. His work on the UCR suite for time series matching under warping earned the KDD Best Research Award in 2012, while his more recent work on quantification algorithms received the Best Research Paper Award at DSAA-2020. Scientific Awards: Best Research Paper Award, IEEE International Conference on Data Science and Advanced Analytics (2020) Research Fellow, level 2, National Council for Scientific and Technological Development, CNPq (2017-2020) Research Fellow, level 2, National Council for Scientific and Technological Development, CNPq (2014-2017) Google Research Award in Latin America (2015-2016) Best Research Paper Award, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2012) Professor Batista has successfully secured significant grant funding as principal investigator, including a $500,000 USAID Combating Zika and Future Threats Grand Challenge award and multiple FAPESP and CNPq grants. He currently supervises PhD students Tiago Pinho da Silva (working on Election Forensics) and Antonio Parmezan (working on Hierarchical Classification of Data Streams), contributing to the next generation of data science researchers. His research group has developed innovative tools like EmbML, which converts scikit-learn and Weka classifiers into C++ code for low-power microcontrollers, demonstrating his commitment to practical implementations of machine learning in resource-constrained environments.

Desmond J. HighamView profile

Professor

• Numerical Analysis
• Stochastic Computation
• Artificial Intelligence
• +7 more

Desmond J. Higham is a Professor of Numerical Analysis in the School of Mathematics at the University of Edinburgh. His research spans the intersection of numerical analysis, stochastic computation, and artificial intelligence, with applications across network science, computational biology, and mathematical finance. He maintains active collaborations across multiple disciplines and leads significant research initiatives in AI security and robustness. University of Edinburgh, School of Mathematics Applied and Computational Mathematics research theme James Clerk Maxwell Building, The King's Buildings, Edinburgh EH9 3FD Higham's research interests focus on numerical methods for stochastic systems with particular emphasis on AI security, adversarial attacks, and robustness of machine learning models. His work bridges theoretical numerical analysis with practical applications in deep learning, network science, and computational biology. He investigates how stochastic phenomena affect computational models and develops methods to ensure reliability in AI systems. His recent publications reveal a strong focus on adversarial machine learning, examining vulnerabilities in diffusion models, language models, and computer vision systems. The research demonstrates how seemingly robust AI systems can be compromised through carefully designed perturbations, with implications for AI safety and security across multiple domains. Fellow of the Royal Society of Edinburgh Fellow of the Society for Industrial and Applied Mathematics Former Fellow of the Alan Turing Institute Editorial Board, SIAM Journal on Matrix Analysis and Applications Editorial Board, IMA Journal of Numerical Analysis Higham currently supervises multiple PhD students including Lucas Beerens, Aizhan Issagali, Alix Leroy, and Kevin Zhang. He has secured significant research funding including a 2025 Leverhulme Trust Research Fellowship on 'A Numerical Analysis Treatment of Adversarial Perturbations in AI' and a 2026-2030 European Research Council Advanced Grant for 'Numerical Analysis for Stable AI'. He serves on numerous advisory boards including the International Centre for Mathematical Sciences Management Committee and the UK Knowledge Exchange Hub for Mathematical Sciences Executive Team. He is actively involved in the Generative AI Lab at the University of Edinburgh and the Centre for Technomoral Futures, focusing on the mathematical foundations of trustworthy AI systems. His work addresses critical issues in AI safety through rigorous numerical analysis approaches.

• Electronic Design
• Lung Acoustics
• Biomedical Signal Processing
• +3 more

Yasemin P. Kahya is a Full-Time Professor in the Department of Electrical and Electronics Engineering at Boğaziçi University. She has held roles such as Department Chair (2012–2021), Deputy Dean (2003–2006), and has served on various administrative and scientific committees including TÜBİTAK and IEEE-EMBS. Her research focuses on electronic design, lung acoustics, and biomedical signal processing, with contributions to respiratory sound analysis and smart stethoscopes. Education: Ph.D. in Biomedical Engineering, Boğaziçi University (1987) M.S. in Electrical Engineering, Yale University (1981) B.S. in Electrical Engineering and Physics, Boğaziçi University (1980) Research Interests: Her work centers on respiratory sound analysis, lung acoustics, and the development of diagnostic tools like smart stethoscopes. Key areas include signal processing for disease detection (e.g., asthma, COPD), wavelet transform applications, and multi-channel biomedical instrumentation. Grants & Projects: "Smart Stethoscope System with Mobile Facilities" (TÜBİTAK TEYDEP, 2021–2023) "Wireless Respiratory Sound Acquisition System" (Boğaziçi University, 2006–2008) "Lung Acoustic Mapping" (TÜBİTAK, 1994–1998) Awards: Multiple TÜBİTAK fellowships and international scholarships Patents for smart stethoscope systems Editorial roles in Medical & Biological Engineering & Computing and TÜBİTAK Elektrik Dergisi Labs & Teams: Founder of the Lung Acoustics Laboratory (LAL) at Boğaziçi University, focusing on smart stethoscope development and interdisciplinary research with students and collaborators.

Jerzy StefanowskiView profile

Professor

• Machine Learning
• Data Mining
• Knowledge Discovery
• +9 more

Jerzy Stefanowski is a Full Professor at the Institute of Computing Science, Poznan University of Technology. He specializes in Machine Learning, Data Mining, and Knowledge Discovery, with a focus on imbalanced data, data streams, and explainable AI. His research addresses challenges in classifier learning from skewed datasets and has led to contributions like the BRACID algorithm. He has supervised multiple PhD students and holds grants such as the NCN-funded project on imbalanced data (2014–2017). A corresponding member of the Polish Academy of Sciences and Editor-in-Chief of the journal Foundations of Computing and Decision Sciences , he has organized numerous conferences and workshops in AI and data mining. Education: M.Sc./Eng. in Control Engineering (1987) Ph.D. in Computer Science (1994) Habilitation Thesis (2001) Research Interests: Machine learning, data streams, imbalanced classification, rough sets, medical informatics, and interpretable AI. His work emphasizes overcoming algorithmic biases in imbalanced datasets and developing robust classification strategies. Awards: Includes recognition from the Polish Information Processing Society, Foundation for Polish Science, and the Polish Academy of Sciences. He has also received medals and grants from national educational bodies. Grants: Led projects such as Learning Classifiers from Imbalanced and Evolving Data (NCN, 2014–2017) and Algorithms Transforming Data Representation for Machine Learning Systems (KBN, 2004–2007). Labs/Teams: Leads the Machine Learning Group at Poznan University of Technology and chairs the Scientific Council of the Institute of Computer Science, Polish Academy of Sciences.

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +3 more

Pietro Lovato serves as a Temporary Professor in the Department of Computer Science within the School of Science and Engineering at the University of Verona. His academic work focuses on Information Processing Systems (ING-INF/05), with active teaching responsibilities across multiple degree programs including Human Centered Medical System Engineering, Computer Science and Engineering, and Bioinformatics. Dr. Lovato's research centers on advancing beyond traditional text representation models, particularly through his BeBoW (Beyond the Bag of Words) project that examines structural and statistical perspectives in information processing. His work spans artificial intelligence, machine learning, pattern recognition, and information retrieval systems with applications in bioinformatics and medical text analysis. His research demonstrates a consistent focus on developing more sophisticated models that capture contextual and structural information beyond simple word frequency approaches. His publication record from 2010-2019 shows a progression from traditional information retrieval techniques toward more complex neural network architectures and structural analysis methods. This evolution reflects broader trends in the field moving from statistical models to deep learning approaches while maintaining a focus on practical applications in specialized domains. Dr. Lovato contributes to several research laboratories at the university including the Networked Embedded Systems (NES) Laboratory and the ALTAIR Laboratory, where his work intersects with electronic systems design and parallel computing. His teaching portfolio includes both theoretical coursework and laboratory components, indicating a hands-on approach to student education in artificial intelligence and machine learning.

• Neoplasm
• Ex Vivo
• DNA Methylation
• +5 more

Sjors Koppes is a Researcher in the Department of Pathology at Erasmus MC, Rotterdam, specializing in molecular diagnostics and tumor classification across multiple cancer types. His core research domains include: Neoplasm diagnostics using DNA methylation profiling Ex vivo surgical guidance techniques Immunohistochemistry for tumor markers Metastatic carcinoma characterization AI-driven histopathological analysis Molecular classification of head/neck and bone tumors Recent publications demonstrate convergence of genomic analysis with clinical pathology, particularly in DNA methylation-based tumor classifiers, MDM2 amplification in salivary tumors, and AI applications for melanoma assessment. His work increasingly integrates advanced imaging like autofluorescence for cholesteatoma detection and fluorescence lymph node mapping. Koppes maintains active collaborations across Erasmus MC departments and international institutions in surgical oncology, dermatopathology, and head/neck pathology research initiatives.

Luca DidaciView profile

Associate Professor

• Statistical Pattern Recognition
• Biometric Recognition and Security
• Semi-Supervised Learning
• +4 more

Luca Didaci is an Associate Professor of Computer Engineering at the University of Cagliari's Department of Electrical and Electronic Engineering (DIEE), within the Faculty of Engineering and Architecture. He leads research in statistical pattern recognition, biometric systems, and EEG signal analysis, focusing on applications in security, healthcare, and machine learning. His work includes developing EEG-based personal identification techniques, semi-supervised learning methods, and advanced tools for histopathological image analysis. With an h-index of 16, his research has been cited over 950 times. He teaches courses on programming, software development, and computer science fundamentals across undergraduate and graduate programs. Didaci is affiliated with the Pattern Recognition and Application Lab at DIEE and contributes to the PhD Program in Electronic and Computer Engineering. His research interests span multiple classifier systems, biometric authentication, and cybersecurity. Recent projects include studying ADaM/SDTM clinical data formats and investigating adversarial machine learning threats to biometric systems. He actively publishes in top-tier journals and conferences, emphasizing interdisciplinary applications of AI in engineering and medicine.

Giorgio GiacintoView profile

Professor

• Machine Learning
• Cybersecurity
• Network Security
• +6 more

Giorgio Giacinto is a Professor of Computer Engineering at the University of Cagliari, Italy. He coordinates the MSc program in Computer Engineering, Cybersecurity, and Artificial Intelligence. His research focuses on machine learning for malware analysis and detection, with over 180 international publications. Serves as Editor-in-Chief of the Security Engineering & Applications section in the Journal of Cybersecurity and Privacy. Member of managing committees for the Cybersecurity National Lab and the Artificial Intelligence & Intelligent Systems Lab (CINI consortium). Represents the Cybersecurity National Lab in the European Cybersecurity Organization (ECSO). Recognized as a Fellow of the IAPR and Senior Member of IEEE Computer Society and ACM.

Ioannis IvrissimtzisView profile

Associate Professor

• Computer Graphics
• 3D Modeling and Processing
• Surface Reconstruction
• +7 more

Dr. Ioannis Ivrissimtzis is an Associate Professor in the Department of Computer Science at Durham University. He holds a BSc in Mathematics from the University of Thessaloniki and a PhD in Mathematics from the University of Southampton. Before joining Durham, he was a lecturer at Coventry University's Department of Creative Computing, and previously conducted research at the Max-Planck Institut für Informatik and Cambridge University's Computer Laboratory. His research focuses on computer graphics, including subdivision surfaces, polygonal mesh encoding, and statistical learning methods for surface reconstruction from scan data. Key areas of interest include 3D model security (steganography/steganalysis), statistical surface reconstruction techniques, discrete geometry, and spectral graph theory. Notable contributions include work on 3D steganalysis, mesh quantization, and geometric algorithms. He has supervised multiple postgraduate students including Elizabeth Callander and Jindi Wang. His publications span conferences like ACM SIGGRAPH and journals such as IEEE TVCG and ACM TOMCCAP.

• Self-learning media search engines
• Reinforcement learning
• Bandit problems
• +6 more

Clement Leung is a Professor at the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. He holds a BSc (Hons) in Mathematics from McGill University, an MSc in Mathematics from Oxford University, and a PhD in Computer Science from University College London. His academic career includes prestigious roles such as Established Chair and Head of Department at the University of London, Foundation Chair in Computer Science at Victoria University (Australia), and full professorships at the National University of Singapore and Hong Kong Baptist University. Education: BSc (Hons) in Mathematics, McGill University MSc in Mathematics, Oxford University PhD in Computer Science, University College London His research focuses on self-learning media search engines , bandit problems in reinforcement learning , and performance of multiple sequential classifiers . He has authored five books and over 150 research articles in top-tier journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and ACM Transactions on Intelligent Systems and Technology. Prof. Leung is a Fellow of the British Computer Society and the Royal Society of Arts, and a Chartered Engineer. He contributed to international standards (MPEG-7) and served on editorial boards of ten journals. His professional roles include Program Chair for major conferences and leadership in the International Association for Pattern Recognition. Prof. Leung’s work emphasizes interdisciplinary research at the intersection of computer science, mathematics, and engineering, with applications in multimedia systems and intelligent algorithms. His patents and global academic contributions underscore his impact on advancing computational and AI-driven technologies.

Mingyan LiuView profile

Professor

• Cybersecurity
• Optimal Resource Allocation
• Sequential Decision Theory
• +5 more

Mingyan Liu Mingyan Liu is the Alice L. Hunt Collegiate Professor of Engineering and Associate Dean for Academic Affairs at the University of Michigan's College of Engineering. He holds a joint appointment in the Department of Electrical Engineering and Computer Science (EECS). His research focuses on cybersecurity, optimal resource allocation, incentive design, and network performance modeling, with emphasis on strategic interactions in cyber-physical systems. Liu has pioneered work on cyber insurance mechanisms, adversarial machine learning, and federated learning security. Education B.S. in Electrical Engineering, Nanjing University of Aeronautics and Astronautics, China (1995) M.S. in Systems Engineering, University of Maryland, College Park (1997) Ph.D. in Electrical Engineering, University of Maryland, College Park (2000) Research Highlights Liu's research group has developed technologies commercialized through QuadMetrics, Inc. (acquired by FICO), creating the first global enterprise cybersecurity ratings system. His work bridges game theory, machine learning, and network science to address challenges in cyber risk quantification, incentive mechanisms for secure software development, and fair AI systems. Awards & Recognition Awarded the NSF CAREER Award (2002), multiple Crosby Research Awards (2003, 2014), IEEE Fellow (201?), and best paper awards at IPSN (2012) and DSAA (2014). His book *Embracing Risk: Cyber Insurance as an Incentive Mechanism for Cybersecurity* (2021) synthesizes his theoretical and applied work in cybersecurity economics. Professional Contributions Liu served on editorial boards of IEEE/ACM Transactions on Networking and ACM Transactions on Sensor Networks. His current efforts include MURI projects on AI-driven edge networks and NSF-funded work on federated learning privacy frameworks.

• Machine Learning
• Computer Science
• Data Science
• +8 more

Haichao Wang is a researcher affiliated with multiple institutions, including Shanghai University of Electric Power, Henan Polytechnic University, Army Engineering University of PLA, and Huazhong University of Science and Technology . His work spans interdisciplinary applications in Machine Learning, Embedded Systems, Environmental Science, and Biomedical Engineering , with a focus on k nowledge distillation, rough set theory, and signal processing. Research interests include sleep staging using neural networks, defect detection in industrial settings, acoustic analysis of wind turbines , and environmental monitoring . Recent publications highlight trends in multi-modal learning, adaptive thresholding, and 5G-enabled geospatial modeling . The 15 most recent articles reflect a blend of deep learning architectures, remote sensing, and biomedical applications . His collaborations extend to institutions in China and international scholars, emphasizing interdisciplinary solutions for technical and environmental challenges.

• Temporal Reasoning
• Artificial Intelligence
• Temporal Constraint Networks
• +4 more

Roberto Posenato is a current Associate Professor in the Department of Computer Science at the University of Verona. His academic sector is INFO-01/A - Informatics, with research classified under ERC PE6_6 (Algorithms, Distributed/Parallel Algorithms) and PE6_7 (Artificial Intelligence). Office: Ca' Vignal 2, Floor 1, Room 1.84 Research Interests Temporal Reasoning with constraint networks BPMN Systems in Medicine Temporal Constraint Modeling for Healthcare Processes Algorithm Design & Computational Complexity Data Management Systems Teaching Activities Algorithms (12 credits, 2025/2026) Temporal Reasoning (6 credits, 2025/2026) Web Applications (6 credits, multiple academic years) Database & WEB courses (6-12 credits) Software Engineering (1-6 credits) Research Groups Algorithms group (focus on algorithm design, data structures, computational complexity, machine learning) Databases and Information Systems INdAM Research Unit at University of Verona

JingTao YaoView profile

Professor

• Data Science
• Data Analytics
• Artificial Intelligence
• +9 more

Dr. JingTao Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He joined the University of Regina in January 2002 and has been actively contributing to the academic community since then. His extensive academic career includes previous teaching positions at Massey University (New Zealand), National University of Singapore, The Open University (Singapore), and Xi'an Jiaotong University (China). Dr. Yao's research interests span multiple areas within computer science, with a strong focus on granular computing, rough sets, soft computing, data mining, three-way decisions, neural networks, computational finance, electronic commerce, and web intelligence . His work represents significant contributions to the theoretical foundations and practical applications of these areas. His recent publications demonstrate a consistent research trajectory focusing on three-way decision theory, game-theoretic rough sets, and their applications across diverse domains including text classification, intrusion detection, fraud detection, and biomedical applications. The publications show increasing integration of traditional rough set theory with modern deep learning techniques. Ranked as world top 2% scientist (top 0.92%) by Stanford University Three Highly Cited Papers according to Web of Science One Hot Paper according to Web of Science Dr. Yao coordinates the Rough Set Technology Lab and the Web Intelligence Consortium Canada Research Centre. He has served on numerous administrative committees at the departmental, faculty, and university levels, including as Chair of the Graduate Committee (Data Science) and member of various search and review committees. His professional activities include extensive editorial work as Area Editor of the International Journal of Approximate Reasoning and editorial board membership for several other journals. He has also been actively involved in organizing numerous international conferences in his field, serving as chair, program committee member, and steering committee member for major conferences in rough sets, granular computing, and web intelligence.