Abdourrahmane ATTO is a Professor at Polytech Annecy-Chambéry, part of Savoie Mont-Blanc University. His research focuses on advanced machine learning techniques, including deep learning theory, stochastic modeling of multi-fractal processes, and time series analysis of images. He specializes in applications such as SAR image processing, environmental monitoring, and geohazard prediction. His work integrates neural networks, wavelet analysis, and explainable AI methods. Research Themes: Deep Learning Theories (Analysis, Explainability) Multi-Fractality and Stochastic Modeling Time Series of Images & Video Analysis Convolutional Neural Networks SAR and InSAR Image Processing Key Contributions: Developed timed-image representations for action recognition in video sequences. Advanced fractional Brownian field models for texture synthesis and analysis. Created the ISSLIDE dataset for landslide detection using machine learning. Pioneered explainable AI methods for hydrological forecasting and SAR image classification. Labs & Affiliations: Active member of LISTIC laboratory, focusing on interdisciplinary research in signal processing and computer science.
Marzan Tasnim Oyshi is a Research Associate at the Leibniz Institute of Ecological Urban and Regional Development (IOER) and a Doctoral Candidate at the Chair of Computer Graphics and Visualization, Technische Universität Dresden, Germany. Her research focuses on immersive visualization, virtual reality, and data-driven decision-making for sustainability and environmental challenges. Education: Master of Science in Computer Science & Engineering, Daffodil International University, Bangladesh (2017–2019) Bachelor of Science in Computer Science & Engineering, Daffodil International University, Bangladesh (2013–2016) Erasmus Mundus Undergraduate Mobility Exchange, Information Technology, Lodz University of Technology, Poland (2015–2016) Her research interests include immersive visualization, virtual reality, IoT, machine learning, and environmental data analytics . She develops tools that support decision-making in climate resilience, flood risk, and urban sustainability using VR-based visual analytics. Her work bridges computer science with real-world environmental and societal challenges. The recent publications demonstrate a strong trend in immersive data visualization for environmental science and machine learning support . Oyshi’s work applies VR to visualize extreme weather, flood projections, and biological datasets, while also contributing to IoT-based monitoring and gamified education. The interdisciplinary nature of her research spans computer vision, sustainability, and human-computer interaction. Scientific Awards and Scholarships: 300 Army Scholarship (2017) Erasmus Mundus UG Mobility Scholarship (2015) Government District-Level Scholarship (2010) Best Debater, Transparency International Bangladesh (2010) Multiple Government Academic Scholarships (2005, 2008) Oyshi has actively contributed to academic advising, having supervised 2 Master’s theses, 2 Bachelor’s theses, 3 team projects (15 students), and 9 Hauptseminar students . She has also served as a reviewer for prestigious conferences including CHI, IEEEVIS, and ICAITA. Her research is supported by institutional affiliations with IOER and ScaDs.AI, focusing on systemic sustainability and data science. She is a key member of the Leibniz-Lab 'Systemic Sustainability' , where she contributes to developing dashboards that communicate science-society challenges related to climate change, biodiversity loss, and food security. Her projects like ExtremeWeatherVis, FloodVis, and VRCellLabeler exemplify her commitment to creating immersive, interactive tools for complex data understanding.
Qilin Li is a Lecturer in Computing at Curtin University, where he researches machine learning and computer vision applications for structural engineering. His work focuses on developing data-driven models for simulating physical processes in structural dynamics and health monitoring. Li integrates AI methodologies with engineering principles to improve infrastructure safety and reliability. His research interests include machine learning algorithms for structural analysis, computer vision-based monitoring systems, graph neural networks for physical simulations, and AI applications in safety engineering. He develops techniques for predicting structural responses to extreme events like explosions. Li's publications demonstrate innovation in combining computational mechanics with deep learning. His work on graph neural networks for blast fragmentation and transformer networks for structural segmentation advances predictive modeling for engineering applications.
Rohit Babbar is a Senior Lecturer in the Department of Computer Science at the University of Bath, specializing in Artificial Intelligence and Machine Learning. His research focuses on extreme multi-label classification, deep learning, and algorithmic optimization for complex performance metrics. He contributes to UN Sustainable Development Goals through his work in these technical domains. Dr. Babbar leads the NERC GW4+ project on leveraging deep learning to uncover hidden patterns in FDA adverse event data, demonstrating expertise in applying machine learning to real-world problems. His recent research emphasizes scalable solutions for high-dimensional classification tasks and improving algorithmic efficiency. His publications span conferences like ACL, KDD, and WWW, reflecting contributions to computational linguistics, neural network architectures, and online learning systems. He actively collaborates on international projects, such as FAERS data analysis and label correlation studies in extreme classification scenarios.
Jinbin Zhang is a Doctoral Researcher at the Department of Computer Science, Aalto University. Their work intersects machine learning and computational linguistics, focusing on extreme multi-label classification and text analysis. Research Interests : Large Language Models and Zero-shot Learning Extreme Multi-label Classification Historical Text Analysis and Genre Detection Publications : 2025: LLM-based zero-shot tagging 2024: Calibration in extreme multi-label classification 2022: Sequential genre change detection in historical texts
Rohit Babbar serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, leading a research group dedicated to advancing large-scale machine learning methodologies. His team specializes in tackling computational challenges inherent in extreme classification problems with massive output spaces while ensuring model robustness. His primary research domains encompass large-scale learning systems, extreme multi-label classification architectures, deep learning integration, sequential data processing, and robustness engineering. This work directly addresses industry pain points like computational inefficiency in massive label spaces and model vulnerability to distribution shifts, with applications spanning natural language processing, information retrieval, and recommendation systems. Publication trends reveal a strategic focus on algorithmic innovation for extreme classification, featuring breakthroughs in dynamic sparsity techniques, large language model integration for zero-shot scenarios, and calibration of extreme classifiers. Recent work demonstrates consistent emphasis on computational efficiency through optimized negative sampling, lightweight frameworks like InceptionXML, and specialized metrics for long-tail performance evaluation. Scientific recognition includes: Outstanding Reviewer Award at ACL 2021 Conference (July 2021) for exceptional contributions to computer science peer review As research group leader, Babbar directs collaborative efforts on next-generation classification systems while mentoring emerging scholars in machine learning. His team maintains active partnerships with industry leaders in search and recommendation technologies. The research group operates at the intersection of theoretical machine learning and practical deployment, developing frameworks that balance computational feasibility with predictive accuracy in extreme-scale environments. Current initiatives focus on integrating foundation models with specialized classification architectures while addressing real-world challenges like data sparsity and concept drift.
Erik Schultheis is a Doctoral Student and Visitor (Faculty) in the Department of Computer Science at the School of Science. His research focuses on extreme multi-label classification, algorithm design, and optimization challenges in machine learning. He holds a Master of Science in Physics from Georg-August-Universität Göttingen (2019). Key research interests include scalable classification systems, label correlation learning, and addressing long-tail and missing label problems in large-scale datasets. His work emphasizes practical solutions for commodity hardware limitations and memory efficiency in training models with millions of labels. Notable contributions include the Dismec++ software tool for extreme classification, and he has presented at conferences such as NeurIPS, ICML, and KDD. His 2021 NeurIPS Outstanding Reviewer Award highlights his peer-review contributions to the field. Recent publications explore dynamic sparsity in large output spaces, online algorithm generalizations, and label calibration in extreme classification scenarios. His research bridges theoretical algorithm development with practical implementation challenges in real-world systems.
Mehrdad Mohannazadeh is a postdoctoral researcher at the Department of Computational Hydrosystems (CHS) within the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. His work focuses on hydrological modelling , machine learning , and applied mathematics , particularly for analyzing and improving flood prediction models. Education : Ph.D. in Computer Sciences (2019-2023), Bielefeld University M.Sc. in Applied Mathematics (2016-2018), University of Applied Sciences Mittweida B.E. in Electrical Engineering (Electronics) (2007-2012), Shahid Beheshti University His research involves modifying hydrological models for better forecasting, working with projects like MOSES , HI-CAM II , 4DHydro , and SCENIC . His publications emphasize interpretable neural networks , probabilistic classification , and climate change impacts on water systems. Recent work includes high-resolution drought monitoring in Germany and seasonal hydrological forecasting for Andean-Amazonian basins. He contributes to UFZ Young Scientist Award winning initiatives and European forest model evaluations.
Dr. Boris Čule is an Assistant Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. He holds a Ph.D. in Computer Science from the University of Antwerp, followed by post-doctoral research there. His work focuses on data mining, machine learning, and AI, particularly sequential/temporal data, including pattern mining, anomaly detection, and recommender systems. Education: Ph.D. in Computer Science, University of Antwerp Research Interests: Sequential pattern mining, session-based recommendations, time series analysis, subspace clustering, spatial pattern discovery, and applications in environmental and economic forecasting. His contributions include novel algorithms for evaluating sequential patterns, sequence classification, and anomaly detection in dynamic systems. Publications Trends: Recent work emphasizes real-world applications like UAV path planning optimization (2024), news recommendation systems (2023-2024), and employment market forecasting (2023). He explores interdisciplinary challenges such as facial action analysis in gaming (2022) and wind farm response to storms (2021). Advising/Grants: While specific grant details are not listed, his prolific publication record suggests active research projects. Courses taught include Big Data analysis and cognitive science modules.
Cho-Jui Hsieh is an Associate Professor of Computer Science at the University of California, Los Angeles (UCLA) in the Henry Samueli School of Engineering and Applied Science. He also serves as a research scientist at Google. Previously, he was an Assistant Professor at UC Davis Computer Science and Statistics for three years and a visiting scholar at Google since summer 2018. Dr. Hsieh received his Ph.D. from UT Austin under the supervision of Prof. Inderjit Dhillon and his Master's degree from National Taiwan University under Prof. Chih-Jen Lin. His research focuses on developing new algorithms and optimization techniques for large-scale machine learning problems. His current work emphasizes improving model size, training speed, prediction speed, and robustness of deep learning models. He is particularly known for his contributions to neural network verification , adversarial robustness , and optimization algorithms for machine learning. Dr. Hsieh has co-authored the book "Adversarial Robustness for Machine Learning" with Dr. Pin-Yu Chen. His research trends show a strong emphasis on formal verification of deep neural networks, robustness certification, and optimization techniques for both traditional machine learning models and large language models. Recent work explores multimodal learning, neural network verification with branch-and-bound techniques, and certified training approaches. Frontiers of Science Award (2023) Okawa Research Award (2021) Outstanding Paper Award, ICLR (2021) Google Research Scholar Award (2021) NSF Career Award (2021) Samsung AI Researcher of the Year (2020) Dr. Hsieh has supervised numerous PhD students, both current and former, many of whom have gone on to become assistant professors at prestigious institutions or work at leading AI companies like OpenAI, Meta, and Mistral AI. His research has been supported by various grants including the NSF Career Award. He leads a research group focused on machine learning algorithms and has developed several widely-used software tools including Distributed Kernel SVM and Asynchronous Kernel SVM. His current research directions include advancing neural network verification techniques, improving the robustness of large language models, and developing more efficient training algorithms for deep learning models.
Zhaozhuo Xu is an Assistant Professor of Computer Science at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. He joined Stevens in 2024 after earning his Ph.D. in Computer Science from Rice University. His research focuses on machine learning, randomized algorithms, and efficient computing for large-scale AI systems. Xu has been recognized with the AAAI 2025 New Faculty Highlights award. His work emphasizes scalable and sustainable AI, including compression techniques for large language models (LLMs), efficient inference methods, and ethical considerations in AI deployment. Xu has served as an Area Chair for the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) and is a member of IEEE and AAAI. Key research trends in Xu's articles include optimizing LLM efficiency through sparsity, quantization, and adaptive architectures; probing ethical boundaries in AI (e.g., copyright compliance); and developing multi-agent systems for financial decision-making. His contributions span theoretical foundations (e.g., randomized algorithms) and applied systems (e.g., on-device LLM execution). Awards: AAAI 2025 New Faculty Highlights Grants/Advising: No explicit grants listed; advising status unclear due to no student listings Labs/Teams: No dedicated lab/team named in provided texts
Xi Niu is an Associate Professor at the College of Computing and Informatics, University of North Carolina at Charlotte. His research focuses on data and text analytics, knowledge discovery, search behavior, and interactive information retrieval with a strong emphasis on computational serendipity and cybersecurity applications. His work bridges theoretical advancements with practical applications, particularly in developing machine learning models for cross-domain recommendations, improving user experience through serendipity enhancement, and automating cybersecurity threat analysis. Notable contributions include frameworks for modeling user curiosity in recommender systems and methodologies for contradiction detection in text. Recent research trends show a focus on leveraging deep learning techniques for extreme multi-label classification, contrastive learning in recommendation systems, and integrating topological analysis for understanding complex text patterns. His work also explores human-centered design principles in crowdsourcing and active learning systems.
Philippe Cudré-Mauroux is a Full Professor in the Department of Computer Science at the University of Fribourg, affiliated with the Faculty of Science and Medicine. His research spans data management, big data systems, knowledge graphs, semantic web, and human-AI collaboration. His primary research interests include Data Management , Big Data Systems , Knowledge Graphs , Time Series Analytics , Database Systems , Human-AI Collaboration , and Machine Learning for Data Cleaning . His work integrates theoretical database research with practical applications in smart cities, social media, and healthcare analytics. The recent publications reflect a strong trend toward knowledge graph embeddings , large language models for data quality , time series benchmarking , and human-in-the-loop systems . His research combines symbolic and neural methods, emphasizing schema awareness, explainability, and real-world deployment. He actively supervises numerous PhD and Master’s students and collaborates widely across institutions. His group contributes to open-source tools and benchmarking frameworks for database and AI systems.
Javed Aslam is a Professor and Chief of Artificial Intelligence at Northeastern University's Khoury College of Computer Sciences. His academic role includes leadership in AI strategy and research across the university. He holds a tenured position with expertise spanning Artificial Intelligence, Data Science, and Natural Language Processing. Education: Details not explicitly provided in text, but his research trajectory suggests advanced training in computer science and information systems. His research focuses on AI applications in supply chain optimization, blockchain integration for logistics, recommendation systems, and robust machine learning methodologies. Notable projects include frameworks for blockchain adoption in energy and manufacturing sectors, and developing unbiased content recommendation strategies using bandit algorithms. As AI Chief, he advises on Northeastern's AI initiatives, including the development of critical thinking tools like the 'Claude' AI system. His work frequently bridges theory and practice, with emphasis on real-world applications in healthcare, energy, and logistics. Advising: Supervised 8 PhD students in areas like machine learning, cybersecurity, and information retrieval. Grants: Implied through active research projects but explicit details not provided in text. Labs/Teams: Oversee AI research teams at Northeastern, collaborating on interdisciplinary projects involving computer science, business analytics, and public policy.
Abdül Kadir GÖRÜR is a Lecturer in the Department of Computer Engineering at Çankaya University. He serves as Head of the Division (Data Analytics) and Vice Director (Institute of Graduate Studies). His research focuses on Information Retrieval, Text Categorization, and Search Engine Evaluation, with expertise in Machine Learning applications. Education: Bachelor of Engineering in Electrical and Electronics Engineering, Eastern Mediterranean University (1993) Master of Science in Electrical and Electronics Engineering, Eastern Mediterranean University (1997) Doctor of Philosophy in Computer Engineering, Eastern Mediterranean University (2004) Thesis: Text Categorization Using Inductive Learning Algorithm and Experiments with Document Representation Research Interests: Dr. GÖRÜR’s work spans: Search Engine Optimization for Turkish Language Documents Deep Learning for Multi-Label Text Classification Constraint Programming in Manufacturing Scheduling Data Mining in Healthcare Systems Inductive Learning Algorithms (ILA) Development Professional Contributions: His publications address topics ranging from workforce scheduling optimization to plagiarism detection systems in e-learning platforms. Recent work emphasizes neural network architectures for text categorization and real-time data processing frameworks. Laboratory & Teams: While specific lab affiliations aren’t detailed, his administrative roles indicate involvement in data analytics initiatives and graduate research coordination.
