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Matias Valdenegro-ToroView profile

Assistant Professor

• Machine Learning
• Robotics
• Computer Vision
• +5 more

Matias Valdenegro-Toro serves as Assistant Professor for Machine Learning at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen. His research focuses on enabling robots to achieve human-like perception, with expertise spanning Deep Neural Networks, Reinforcement Learning, and underwater robotic vision systems. Previously, he held positions as a Researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen and as a Marie Curie Fellow at Heriot-Watt University's Ocean Systems Lab. His academic credentials include a PhD in Electrical Engineering from Heriot-Watt University (2019), a Master of Science in Autonomous Systems from Hochschule Bonn-Rhein-Sieg (2014), and an Engineering Degree from Universidad Tecnologica Metropolitana (2009), preceded by four years of professional industry experience. Valdenegro-Toro's research integrates Machine Learning and Robotics to solve real-world perception challenges, particularly in underwater environments where his PhD work developed neural network systems for detecting submerged marine debris. His commitment to environmental protection drives his focus on ocean conservation through robotic technology, while his technical contributions emphasize Uncertainty Quantification in neural networks and robust Robot Vision solutions. His scientific recognition is highlighted by the prestigious Marie Curie Fellowship , awarded through the European Commission-funded Robocademy Initial Training Network (FP7-PEOPLE-2013-ITN). As an educator, he supervises Master's students in Hochschule Bonn-Rhein-Sieg's Autonomous Systems program and has taught at the University of Bremen. He actively contributes to the academic community through invited tutorials on computer vision paper writing at ICCV/CVPR and mentoring workshops for LatinX researchers at AAAI/AISTATS, reflecting his dedication to inclusive AI education. His collaborative research spans institutions including DFKI's Robotics Innovation Center and Heriot-Watt University's Ocean Systems Lab, focusing on practical deployment of perception systems for autonomous underwater vehicles in marine conservation applications.

• Combinatorial Optimization
• Polynomially Solvable Special Cases
• Assignment Problems
• +6 more

Eranda Dragoti-Cela is an Associate Professor at the Department of Optimization and Discrete Mathematics, Graz University of Technology. She specializes in combinatorial optimization, focusing on polynomially solvable special cases of NP-hard problems like the Quadratic Assignment Problem (QAP), portfolio optimization, and algorithmic graph theory. Education: M.Sc. in Applied Mathematics, University of Tirana (1992) Ph.D. in Technical Sciences, Graz University of Technology (1995) Habilitation in Applied Mathematics (2001) Her research spans combinatorial optimization (QAP, assignment problems, location problems), portfolio optimization (risk-return models, ordinal information integration), and graph theory (edge intersection graphs, network flows). Recent work includes linearization of quadratic shortest path problems and data arrangement on trees. She supervises bachelor's and master's theses in discrete mathematics and teaches courses like Combinatorial Optimization 1 and Risk Theory and Management , emphasizing algorithmic rigor and practical applications in finance and engineering.

Chen XihuiView profile

Researcher

• Media Computing
• Cybersecurity
• Federated Learning
• +2 more

Chen Xihui, Ph.D., is a Researcher at the Institute of Creative\Media/Technologies within the Department of Media and Digital Technologies at the University of Applied Sciences St. Pölten. His work focuses on media computing and digital technologies, with specialized expertise in cybersecurity frameworks. Primary research domains include: Federated learning systems and their security vulnerabilities Robustness of recommendation algorithms against adversarial attacks Privacy-preserving machine learning architectures Competitive data poisoning mitigation strategies Recent scholarly work demonstrates concentrated focus on security challenges in federated recommendation systems, particularly examining attack vectors and defensive mechanisms in distributed learning environments.

Sakeena MuntahaView profile

Researcher

• Machine Learning
• Computer Vision
• Image Processing
• +3 more

Sakeena Muntaha serves as a Junior Researcher at the University of Applied Sciences St. Pölten, affiliated with the Institute of Creative\Media/Technologies and the Department of Media and Digital Technologies since 2017. Currently on leave, she contributes to the institution's research mission through interdisciplinary projects spanning computer vision and applied machine learning. Her academic foundation includes a Master's degree in Computer Engineering from the National University of Sciences and Technology (NUST), Pakistan (2016) and a Bachelor's degree in Computer System Engineering from the NFC Institute of Engineering and Technology (NFCIET), Pakistan (2012). These qualifications underpin her technical expertise in visual computing systems. Dr. Muntaha's research program centers on machine learning and computer vision with dual application tracks: medical diagnostics (skin lesion segmentation, dermoscopy analysis) and environmental/urban systems (building footprint extraction, flood monitoring, real estate analysis). Her methodological approach integrates deep learning architectures with classical image processing techniques like level sets and Gabor filters, demonstrating versatility across domains from cultural heritage preservation to cybersecurity. Recent work shows increasing focus on robustness evaluation and real-world deployment challenges in vision systems. Analysis of her 15 most recent publications reveals strong thematic continuity in computer vision applications, with growing sophistication in handling real-world data constraints. Early work focused on medical imaging and malware detection, while recent publications emphasize urban infrastructure analysis and environmental monitoring, reflecting strategic alignment with societal challenges. The consistent use of deep learning frameworks across diverse domains highlights her technical agility. As an active member of the Media Computing Research Group, she contributes to projects including IMREA (Intelligent Multimodal Real Estate Assessment), Scribe ID AI (cultural heritage analysis), and ImmBild (location assessment via computer vision). Her collaborative research involves partnerships with institutions across Austria and Pakistan, though specific grant details and advising activities are not documented in available sources.

Alexander BucheltView profile

Researcher

• Artificial Intelligence
• Data Science
• Natural Language Processing
• +3 more

Alexander Buchelt serves as a Researcher in the Department of Computer Science and Security at University of Applied Sciences St. Pölten, contributing to the Data Science and Artificial Intelligence (BA) program. His industry background includes data analytics roles at Hutchison Drei Austria (2014-2018) and MindTake Research GmbH (2019-2020), followed by academic experience as a Student Assistant at FH St. Pölten (2020-2022). His research spans artificial intelligence with emphasis on natural language processing for German content moderation, fractal-based neural network architectures, and AI policy frameworks. Current work demonstrates strong focus on real-world implementation including drone applications in forest ecology, multilingual social media analysis, and regulatory compliance for emerging AI systems. Technical approaches integrate machine learning with domain-specific challenges across environmental science, media analysis, and telecommunications. Publication trends since 2022 reveal increasing emphasis on ethical AI deployment and practical system robustness, with 2024-2025 works addressing content moderation safeguards, neural network innovation, and environmental monitoring solutions. His research consistently bridges theoretical AI development with tangible societal applications, particularly in European regulatory contexts and German-language environments. As part of the Department of Computer Science and Security, Buchelt contributes to Austria's applied research ecosystem focused on practical technology solutions. His work aligns with the university's mission in developing industry-relevant AI applications while addressing emerging challenges in security, ethics, and cross-domain implementation.