جست‌وجوی استاد و پژوهشگر

Hristo Georgiev Papazovمشاهده پروفایل

پژوهشگر

• Machine Learning Theory
• Deep Learning
• Optimization Algorithms
• +۳ مورد دیگر

Hristo Georgiev Papazov is a Doctoral Assistant at the Theory of Machine Learning (TML) Laboratory under the Institute of Information and Communication Systems (IINFCOM) at EPFL. His primary affiliation is within the School of Computer and Communication Sciences (IC) at the prestigious École Polytechnique Fédérale de Lausanne. Additionally, he is enrolled in the Doctoral Program in Computer Science (EDIC) within the School of Engineering (ETU). His research interests span theoretical computer science, machine learning fundamentals, and applied deep learning in healthcare contexts. Notable focuses include algorithmic analysis of neural networks, optimization dynamics, and clinical text classification systems using SNOMED CT standards. His work also intersects with mathematical analysis, particularly in extending classical complex analysis techniques to elliptic frameworks. Papazov has contributed to publications addressing momentum-based training in linear networks, dimensionality effects in online algorithms, and interdisciplinary applications of machine learning in medical coding. His current position involves both research in the TML lab and doctoral studies, reflecting a dual commitment to advancing theoretical understanding and practical applications in computer science.

Anna Krauseمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Time Series Analysis
• +۷ مورد دیگر

Dr.-Ing. Anna Krause is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. She leads the Deep Learning for Dynamical Systems Group and has been actively involved in teaching at the university since 2019, including courses on Machine Learning for Time Series Analysis and Data Mining. Doctoral degree in Electrical Engineering (2019), University of Hannover Diploma in Electrical Engineering (2009), Technical University Dresden Her research focuses on Environmental Sensing and Time Series Analysis , particularly on enhancing physics-based models using machine learning techniques for meteorological applications and sparse sensor networks. She has made significant contributions to explainable AI, climate modeling, and fraud detection systems. Anna's recent publications demonstrate expertise in climate modeling (ConvMOS, ICLR 2024-2025), physics-informed neural networks (TaylorPDENet, ECMLPKDD 2023), and fraud detection (MIDAS workshops, ECMLPKDD 2020-2023). She actively contributes to conferences as organizer and PC member, including ECMLPKDD and ICLR workshops. Scientific Awards Best ML Innovation Award (2020) for Deep Learning in Climate Modeling Best Student Paper Award (2020) for Multi-Task Land Use Regression Best Paper Award (2020) for Financial Fraud Detection with INALU The DynaBench dataset introduced in 2023 provides benchmark tools for learning dynamical systems from low-resolution data. Her work combines theoretical advancements with practical implementations, including edge computing applications for beekeeping monitoring systems.

• Machine Learning
• Optimization
• Statistical Learning Theory
• +۵ مورد دیگر

Purushottam Kar is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He completed his PhD from IIT Kanpur in 2014 and his B.Tech. from the same institution in 2008. His academic career includes a post-doctoral research position at Microsoft Research India from July 2013 to August 2015, following a doctoral research internship there from May to August 2012. Dr. Kar's research focuses on the theoretical and practical aspects of machine learning. His primary interests include Machine Learning , Optimization , and Statistical Learning Theory . More specifically, he works on online and stochastic learning and optimization, multi-label classification, high dimensional statistics, kernel methods, and statistical learning theory. His work bridges theoretical foundations with practical applications in artificial intelligence. His recent publications demonstrate a strong focus on advancing machine learning methodologies, particularly in robust regression, non-decomposable performance measures, precision optimization, multi-label classification, and the theoretical foundations of online learning. His research spans both theoretical developments in statistical learning and practical implementations for complex classification problems. International Conference on Machine Learning (ICML) Reviewer Award (2015) Indian Unit of Pattern Recognition and Artificial Intelligence (IUPRAI) Doctoral Dissertation Award (2014) Yahoo! Key Scientific Challenges (Yahoo! KSC) Program Honourable Mention (2012) Microsoft Research (MSR India) PhD Fellowship (2010) Academic Excellence Awards by IIT Kanpur (2005, 2006) Dr. Kar teaches courses in Machine Learning, Online Learning, and Statistical Learning Theory. His research profile suggests he mentors PhD and Master's students in machine learning and related fields. His work with Microsoft Research indicates significant industry collaborations and grant funding in artificial intelligence research. His office is located in Room 509 of the CSE Extension building (Rajeev Motwani Building) at IIT Kanpur.

• Artificial Intelligence
• Data Mining
• Natural Language Processing
• +۴ مورد دیگر

Andrés Jonathan Abeliuk Kimelman is an Assistant Professor at the University of Chile's Faculty of Physical Sciences and Mathematics , affiliated with the Department of Computer Science . He holds a PhD in Computer Science (University of Melbourne, 2017) and a Civil Engineering degree in Computing (University of Chile, 2012). Research Focus : AI ethics, network analysis, natural language processing, and machine learning applications in social systems. Teaching : Leads undergraduate and postgraduate courses in Discrete Mathematics, Data Mining, and Computational Theory. Students : Advises multiple thesis and research projects, including works on AI-assisted urban planning, misinformation analysis, and algorithmic bias. Collaborations : Participates in the National Center for Artificial Intelligence (CENIA) as a co-investigator (2022-2027). Key Publications explore polarization detection, networked public spheres, and computational models for social systems. Current projects include unsupervised topic quantification and extreme multi-label classification in multilingual contexts.

David Becerra Alonsoمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +۵ مورد دیگر

David Becerra Alonso is a Full Professor in the Department of Quantitative Methods for Economics and Business at Loyola University Andalusia. With a strong interdisciplinary background spanning physics, computing, and bioinformatics, he bridges quantitative methods with practical applications across multiple domains including social sciences, healthcare, and education. His research expertise encompasses machine learning, artificial intelligence, natural language processing, and dynamical systems. Dr. Becerra Alonso applies these computational methods to address critical social issues, particularly in gender-based violence prevention through intimate partner femicide risk assessment, media bias detection, and educational technology development. His recent publications demonstrate a strong focus on applying AI to social challenges, with multiple 2024-2025 papers addressing media bias detection and intimate partner femicide prediction using advanced computational techniques. His work spans both theoretical machine learning contributions and practical applications in public health, journalism, and education. Principal Investigator for Sapiens project (2021-2023) on autonomous aquatic vehicle networks Researcher in Disorders of Consciousness (DoC) project (2018-2021) with European Commission Researcher in AIPE project (2024-2025) on AI for physical education of vulnerable adolescents Dr. Becerra Alonso supervises doctoral students working on computational approaches to social issues, including femicide analysis, sustainable development goal classification, and mathematics education in distance learning contexts. His leadership in the Quantitative Research Methods and Applications (MICA) research group supports interdisciplinary collaboration across quantitative methods and applied domains.