جست‌وجوی استاد و پژوهشگر

Sylvain Lefebvreمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +۴ مورد دیگر

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

Ramin Hasaniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Robotics
• Neural Networks
• +۳ مورد دیگر

Ramin Hasani is a researcher at TU Wien's Cyber-Physical Systems department. He holds a Dr.techn. (Doctor of Engineering) and specializes in machine learning applications for robotics, control systems, and biologically-inspired neural networks. His work focuses on developing interpretable neural architectures like Liquid Time-Constant Networks and Neural Circuit Policies, emphasizing safety and stability in autonomous systems. Hasani's research bridges neural network theory with practical robotics challenges, including autonomous racing, medical data analysis, and adversarial robustness. Key research areas include continuous-time neural networks, formal verification of neural ODEs, and bio-inspired control mechanisms derived from biological neural circuits (e.g., Caenorhabditis elegans). He collaborates extensively with institutions like MIT and ETH Zurich, contributing to projects in health-monitoring systems and end-to-end robot learning frameworks. His publications consistently address real-world challenges such as sepsis prediction via reinforcement learning and robust CNN architectures for image classification. Recent work highlights include developing stable recurrent networks through Gershgorin loss functions and advancing zero-shot transfer learning for autonomous systems. Hasani's interdisciplinary approach integrates principles from neuroscience, control theory, and machine learning to create auditable, high-performance AI solutions for cyber-physical environments.

Katharina Hoedtمشاهده پروفایل

پژوهشگر

• Adversarial Examples & Robustness
• Interpretable Machine Learning
• Neural Networks
• +۳ مورد دیگر

Katharina Hoedt is a University Researcher and Assistant at the Institute of Computational Perception at Johannes Kepler University Linz (JKU), with a Vienna-based research presence. She holds a PhD in Computer Science from JKU (2020) and has been involved in research roles since 2016, including at the Austrian Research Institute for Artificial Intelligence (OFAI). Her work focuses on adversarial machine learning, model interpretability, and neural network robustness, particularly within music information retrieval domains. Education: PhD in Computer Science (2019–2020), DI (Diploma) in Computer Science (2013–2016), Bachelor of Science in Informatics (JKU Linz). She has taught courses on Machine Learning and Pattern Classification, and Artificial Intelligence at JKU. Research Interests: Adversarial examples and robustness, interpretable machine learning, neural network inner workings, and applications in music classification. Her publications explore adversarial attacks, explanation validity, and model defense strategies in audio and music contexts. Labs/Teams: Active member of the Institute of Computational Perception, collaborating on interdisciplinary projects combining AI with musicology and signal processing.

Pascal Schöttleمشاهده پروفایل

استاد

• IT Security
• Machine Learning
• Adversarial Examples
• +۶ مورد دیگر

Pascal Schöttle is a Professor for IT Security and Machine Learning at the Department of Digital Business & Software Engineering at MCI - The Entrepreneurial University in Innsbruck. He previously held roles including Associate Professor (2020-2022), University Lecturer (2018-2020), and Post-Doc positions at the University of Innsbruck and University of Münster. His academic journey includes a PhD in Computer Science from the University of Münster (2014), and prior studies in IT Security and Mathematical Engineering. Research focuses on adversarial machine learning, IoT security, blockchain applications, and game-theoretic approaches to cybersecurity. Notable projects include leading the Josef Ressel Center for Security Analysis of IoT Devices (2023-present) and managing the SMiLE project on encrypted machine learning (2021-2023). Awards include the IEEE Best Student Paper (2012) and CAST IT Security Runner-Up (2008). Education: PhD (Computer Science, 2014), MSc (IT Security, 2011), BSc (Mathematical Engineering, 2008) Teaching: Courses include IT Security, Machine Learning, Algorithms & Data Structures, and Blockchain Grants: FFG (2021-2023), FWF (2019-2023), University of Innsbruck (2017-2019) Labs/Teams: Research Center CDG, Josef Ressel IoT Security Group

Robert Legensteinمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuromorphic Computing
• Spiking Neural Networks
• +۳ مورد دیگر

Dr. Robert Legenstein is a Full Professor and Institute Head at the Institute of Machine Learning and Neural Computation , Graz University of Technology. He serves as Speaker of the Graz Center for Machine Learning and Action Editor for Transactions on Machine Learning Research (TMLR) . His research bridges computational neuroscience and machine learning, focusing on neuromorphic computing systems that mimic biological neural networks. Research Leadership: Leads EU-funded projects like Adaptive Optical Dendrites (FET-Open) , SYNCH (FET-Proactive) , and Stochastic Assemblies in SNNs (FWF) . Scientific Contributions: Develops learning algorithms for spiking neural networks (SNNs), with applications to memristive architectures, neuroprosthetics, and energy-efficient AI systems. Key Publications: 15+ recent works on topics including dendritic computing, hardware-aware training, and context-dependent neural processing. Teaching Roles: Offers courses like Deep Learning , Principles of Brain Computation , and Data Structures & Algorithms . Contact: robert.legenstein@tugraz.at | +43 316 873 5824 | Inffeldgasse 16b/I, 8010 Graz, Austria.

• Audio Signal Processing
• Music Information Retrieval
• Deep Learning
• +۳ مورد دیگر

Alexander Schindler is an External Lecturer at the Department of Information Systems Engineering at Technische Universität Wien. His research focuses on audio signal processing, music information retrieval, and deep learning applications in multimodal analysis. He coordinates the approacH project (2010–2013) funded by the European Commission, exploring audio-visual search engines. His academic background includes a Dipl.-Ing. in Technical Engineering and a Dr.techn. from TU Wien. Key research areas include acoustic scene classification, deepfake detection, and music video analysis. He has supervised students on topics like bird song identification and machine outage prediction. Notable publications span from unsupervised cross-modal learning (2020) to multi-modal MIR frameworks (2019). He holds a Bakk.techn. and advanced technical qualifications. Recent work includes advancements in deepfake audio detection (2025) and audio-visual surveillance systems (2024). His projects bridge theoretical research with practical applications in forensic analysis and industrial predictive maintenance. He contributes to international conferences like ACM SAC and DCASE, focusing on neural network architectures for audio analysis.