جستجوی استاد راهنمای دانشگاهی

Alan Mantoothمشاهده پروفایل

استاد

• Analog and Mixed-Signal IC Design
• Power Electronics
• Semiconductor Device Modeling
• +۳ مورد دیگر

Alan Mantooth is a Distinguished Professor holding the Twenty-First Century Research Leadership Chair in Engineering within the Department of Electrical Engineering at the University of Arkansas, Fayetteville. He serves as Director of the National Center for Reliable Electric Power Transmission (NCREPT), Executive Director for GRAPES (NSF I/UCRC) and SEEDS (DoE Center), and Deputy Director of the NSF Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS). His educational background includes: B.S. in Electrical Engineering, University of Arkansas M.S. in Electrical Engineering, University of Arkansas Ph.D. in Electrical Engineering, Georgia Institute of Technology Dr. Mantooth's research centers on analog/mixed-signal IC design, power electronics CAD, and semiconductor device modeling with emphasis on harsh-environment applications. His pioneering work in silicon carbide (SiC) and gallium nitride (GaN) power systems has enabled high-temperature operation for electric vehicles and renewable energy infrastructure, significantly advancing reliability in extreme conditions. His 2025 publications reveal strong trends toward AI-driven power electronics (e.g., SolarFormer++ for PV profiling), wide-bandgap device modeling (β-Ga2O3, SiC), and innovative packaging solutions. Key themes include reliability engineering for extreme environments, multi-physics optimization, and explainable AI for safety-critical power systems. Major scientific recognition includes: IEEE Fellow (2009) for power electronic device modeling Three R&D 100 Awards (2009, 2014, 2016) for SiC power modules IEEE Power Electronics Society Technical Achievement Award (2019) Multiple university teaching/research awards including SEC Faculty Achievement Award (2015) As an exceptional mentor (UA Outstanding Mentor 2006-2008), he co-founded Lynguent and Ozark Integrated Circuits. His centers NCREPT, GRAPES, and SEEDS have secured major funding from NSF, DoE, and industry partners, supporting over 350 refereed publications and numerous patents. Current research focuses on AI-enhanced power electronics, recyclable packaging, and next-generation wide-bandgap device characterization. He leads the NCREPT test facility and multi-institutional teams developing grid-connected power electronic systems, secure energy delivery architectures, and thermal management solutions for high-power-density applications, with direct impact on electric transportation and renewable energy integration.

Hala Lamdouarمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Hala Lamdouar is a Research Fellow at the University of Oxford , specifically affiliated with the Institute of Biomedical Engineering (IBME). She works under the supervision of Professor Alison Noble and completed her DPhil (PhD) at Oxford's Visual Geometry Group (VGG), advised by Professor Andrew Zisserman and Professor Weidi Xie . Her academic journey began with an Engineering degree in signal and image processing from ENSEIRB-MATMECA , Bordeaux, France, followed by a Master's in applied mathematics focusing on machine learning and computer vision at Ecole Normale Superieure , Paris, where she also worked on autonomous driving perception solutions for Valeo . Education Engineering degree, ENSEIRB-MATMECA, Bordeaux MSc in Applied Mathematics (MVA), Ecole Normale Superieure DPhil (PhD), Visual Geometry Group, University of Oxford Lamdouar's research centers on Video Understanding through single and multi-modal learning, particularly in clinical settings and challenging datasets. Key contributions include motion segmentation techniques for detecting camouflaged objects, the creation of the MoCA dataset (Moving Camouflaged Animals), and scalable synthetic data pipelines for motion-based object segmentation. Her work combines ConvNets, Transformers, and optical flow analysis to address partial occlusion and motion absence in videos. Notable achievements include the Best Paper Award at the CVPR Workshop on Robust Video Scene Understanding (2021). She has published in top conferences like BMVC (2021), ICCV (2021), and ACCV (2020), with applications in biomedical imaging, autonomous systems, and unsupervised learning. Additional affiliations include the Centre for Doctoral Training in Autonomous Intelligent Machines & Systems (AIMS).

• Mechatronics
• Signal Processing
• Control Systems
• +۷ مورد دیگر

Jonas Sjöberg is a Full Professor of Mechatronics at Chalmers University of Technology, where he leads the Mechatronic research group in the College of Engineering. His research spans multiple aspects of mechatronic systems with a strong focus on automotive applications. Sjöberg holds leadership roles in numerous research projects related to autonomous vehicles, vehicle control systems, and transportation safety. His research interests encompass a broad spectrum of mechatronics applications, with particular emphasis on model-based methods, signal processing, control systems, system identification, and optimization for design and product development of mechatronic systems. Sjöberg's work bridges theoretical control engineering with practical automotive applications, especially in the domains of Automotive Active Safety and Hybrid Electric Vehicles. Analysis of Sjöberg's recent publications reveals a strong research trajectory focused on autonomous vehicle technologies, with particular attention to vehicle dynamics control, intersection safety, road surface condition estimation, and optimization of vehicle maneuvers. His work demonstrates a consistent approach of applying advanced control theory to solve real-world transportation challenges, with increasing emphasis on machine learning techniques integrated with traditional control systems. Sjöberg actively supervises research and education at both undergraduate and graduate levels while leading multiple research projects funded by VINNOVA, the European Commission, and other organizations. His research group collaborates extensively with both academic institutions and industry partners in the automotive sector. His laboratory work focuses on mechatronic systems development, particularly for automotive applications including autonomous bicycles, bus docking systems, and vehicle control algorithms. The research group maintains strong connections with the automotive industry, particularly in Sweden's robust vehicle technology ecosystem.

• Generative Programming
• Model-Based Development
• Software System Families
• +۷ مورد دیگر

Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.

Chunming Qiaoمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Optical Networks
• Wireless Networks
• +۶ مورد دیگر

Dr. Chunming Qiao is a SUNY Distinguished Professor and Chair of the Department of Computer Science and Engineering at the University at Buffalo (SUNY) , leading the Lab for Advanced Network Design, Evaluation and Research (LANDR) since 1993. His work spans cyber-physical systems , optical networks , and Internet of Things (IoT) , with a focus on safety, reliability, and protocol design. Education: PhD in Computer Science from the University of Pittsburgh (1993) BS in Computer Science and Engineering from the University of Science and Technology of China (1985) Dr. Qiao’s research interests combine theoretical and applied network design, including autonomous vehicles , quantum computing , and cloud services . He pioneered optical burst switching (OBS) and iCAR systems for wireless convergence, cited in BusinessWeek and Wireless Europe . His recent publications emphasize quantum networking , federated learning , and autonomous driving security , with projects on entanglement routing , edge inference optimization , and LiDAR adversarial attacks . Articles span IEEE and ACM venues , and include best paper awards . Scientific Awards: TC-CSR Distinguished Technical Achievement Award (2015) SUNY Chancellor's Award for Excellence (2013) IEEE Fellow (2009) UB Exceptional Scholar-Sustained Achievement Award (2005) Dr. Qiao has secured over two dozen NSF grants and collaborations with Google , Cisco , and NEC Labs . His 7 US patents and consulting experience highlight his industry impact, while his editorial roles and conference leadership underscore academic influence. He actively contributes to multi-disciplinary research through the New York State Center of Excellence in Bioinformatics and Life Sciences and CEDAR , advancing high-performance computing and document analysis .

• Robot Vision
• Robot Navigation
• Computer Vision
• +۷ مورد دیگر

Helder Araujo is a Professor at the Department of Electrical and Computer Engineering, University of Coimbra. His research focuses on Robot Vision , Computer Vision , and Medical Imaging , with applications in Capsule Endoscopy , Autonomous Driving , and Sensor Fusion . Recent publications highlight advancements in 3D Object Detection using transformer networks (e.g., RetSeg3D , DDet3D ), Medical Robotics (e.g., Self-supervised monocular pose estimation ), and Federated Learning (e.g., FAIR-FATE ). Key subfields include Transformer Models , Deep Learning , and Autonomous Navigation . His projects span Medical Imaging (e.g., Multi-Cam Capsule Endoscopy ), Autonomous Robotics (e.g., Learning to Navigate Endoscopic Capsule Robots ), and Edge Computing (e.g., Benchmarking CNN Inference on Low-Power Devices ). Grants include funding from Fundação para a Ciência e a Tecnologia (e.g., PTDC/EEI-ROB/1155/2020) and the European Commission (e.g., AdvanCed Hardware/Software Components for Embedded Vision). He has served as an invited researcher at Université Blaise Pascal (2012–2014). Peer review activities cover journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and Autonomous Robots . His work bridges Medical Imaging , Autonomous Systems , and AI Fairness .

Kristie Youngمشاهده پروفایل

پژوهشگر ارشد

• Human Factors
• Distracted Driving
• Human Machine Interface (HMI) Design
• +۹ مورد دیگر

Kristie Young is a Senior Research Fellow at the Monash University Accident Research Centre (MUARC), within the Faculty of Medicine, Nursing and Health Sciences at Monash University. She is actively involved in high-impact research on human factors in road safety, with a focus on distracted driving, driver behavior, and vehicle automation. She leads and contributes to numerous national and international research projects and is accepting PhD students. Education: Bachelor of Applied Science (Psychology) (Honours), Deakin University PhD in Human Factors, Monash University Kristie's research centers on human factors in road transport, particularly driver distraction, adaptation to in-vehicle technologies, Human Machine Interface (HMI) design, and automated vehicles. Her work integrates naturalistic driving studies, driving simulations, and surveys to understand real-world driver behavior. She investigates how drivers self-regulate their engagement with non-driving tasks and how technology can be designed to enhance safety. Her research has significant implications for road safety policy, vehicle design, and public education campaigns, especially targeting young and high-risk drivers. Her recent publications reveal a strong trend in using naturalistic driving data to analyze driver distraction, self-regulation, and the role of executive function. There is also growing emphasis on automated vehicles, HMI design, and the application of AI for distraction detection. Her work spans cognitive psychology, transportation engineering, public health, and policy development. Scientific Awards: ARC Discovery Early Career Researcher Award (2016) Best Paper by an Early Career Researcher (2013) Best Research Paper 2020 Monash Injury Research Institute Award for Outstanding Research by an Early Career Researcher (2011) MUARC 2024 Seeding Grant Kristie has secured competitive research grants, including an ARC DECRA grant, and leads major projects such as the implementation of the National Driver Distraction Roadmap and research on automated vehicles for high-risk drivers. She advises on policy through submissions to government inquiries and collaborates extensively with national and international institutions. Her research is supported by organizations such as the Australian Research Council, Transport for NSW, and the Road Safety Trust (UK). Kristie is a key member of the Human Factors and Sustainable Safety team at MUARC, contributing to one of Australia’s leading road safety research centers. Her work involves interdisciplinary collaboration with psychologists, engineers, and public health experts, and she utilizes advanced facilities such as the Integrated Facility for Recording Driver and Road User Behaviour.

Lim Jit Yanمشاهده پروفایل

مدرس

• Few-Shot Learning
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Lim Jit Yan is a Lecturer at the School of Information Technology, Monash University Malaysia. He holds a PhD in Information Technology (2023) from Multimedia University, specializing in self-supervised metric-based meta-learning for few-shot image classification, and a B.IT (Hons) in Artificial Intelligence (2019) from the same institution. His research focuses on few-shot learning, computer vision, and deep learning, with contributions to medical image analysis (e.g., Covid-19 detection), transfer learning applications, and generative models. He has published extensively since 2021, with notable work on self-supervised feature fusion and prototypical networks for few-shot learning. Research interests include few-shot learning techniques, neural architecture design for image classification, and real-world applications like healthcare diagnostics and autonomous systems. Recent work emphasizes self-supervised learning and transformer-based models to address data scarcity challenges in AI. No formal scientific awards are listed. Advising details are not provided in the text, though his publications suggest collaborative research with colleagues like Lim K.M. and Lee C.P. His research spans theoretical advancements and applied projects, including work on pill image recognition, traffic sign detection, and brain tumor classification.

• Embedded Systems
• Internet of Things
• Wireless Sensor Networks
• +۵ مورد دیگر

João Luís Marques Pereira Monteiro is a Full Professor at the Department of Industrial Electronics, School of Engineering, University of Minho, Portugal. He has been with the university since 1980, progressing from Assistant Trainee to his current position. He has held significant leadership roles including Pro-rector (2005-2009), Director of the Algoritmi Center (1998-2006, 2010-2013), and Dean of the School of Engineering (2013-2019). He currently coordinates the Embedded Systems Research Group (ESRG) and is a Senior Researcher at the Algoritmi Research Center. His educational background includes: Bachelor's degree in Electrical Engineering and Computers from the University of Porto (1980) PhD in Informatics and Systems Engineering - specialization in Computer Engineering - from the University of Minho (1991) Habilitation (Dr habil) from the University of Minho (2003) Professor Monteiro's research spans multiple domains in engineering and computer science, with a strong focus on practical applications. His work in embedded systems has led to innovations in real-time processing, sensor networks, and hardware-software co-design. In medical applications, he has developed textile-based sensors for vital sign monitoring. His recent work focuses on computer vision for autonomous vehicles, particularly 3D object detection from point clouds. He also contributes to educational initiatives in data science and AI, particularly in developing countries. His recent publications show a clear progression toward applications requiring real-time processing on resource-constrained devices. There's a strong emphasis on autonomous systems, particularly self-driving vehicles, with multiple papers on 3D object detection from point clouds. His work bridges embedded systems, computer vision, and wireless communications, often focusing on practical implementations for real-world applications like emergency responder localization and medical monitoring. Professor Monteiro has supervised more than a dozen doctoral students, many of whom have gone on to become faculty members at national and international universities. His research has been supported by various funding agencies including FCT (Portuguese), ADI (Portuguese), and FP7 (European funding), as well as industry partners. He leads the Embedded Systems Research Group (ESRG), which is part of the Algoritmi Research Center. The ESRG focuses on developing embedded solutions for various applications including autonomous vehicles, medical devices, and industrial monitoring systems. The group works on both hardware and software aspects of embedded systems, with particular expertise in real-time processing and resource-constrained environments.

Song Young-minمشاهده پروفایل

استاد

• Flexible optoelectronic devices
• Nanophotonics
• Biomimetic cameras
• +۵ مورد دیگر

Professor Song Young-min is joining the Department of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST) as a Professor, with his appointment beginning July 1, 2025. His research focuses on developing innovative bio-inspired optical systems for robotics, with particular expertise in biomimetic cameras and neuromorphic vision systems. Professor Song's research interests span flexible optoelectronic devices and nanophotonics, with specific applications in biomimetic cameras for intelligent robots , optoneuromorphic devices and systems , nanophotonics-based reflective displays , and infrared-controlled radiative cooling devices . His work bridges electrical engineering, materials science, and biological inspiration to create energy-efficient vision systems that reduce computational demands. His recent publications demonstrate significant advancements in bio-inspired vision technology, particularly in feline-inspired vertical pupil systems that improve object tracking stability and cuttlefish-inspired W-shaped pupil designs for uneven lighting conditions. These innovations show how hardware improvements can substantially reduce energy consumption in robotic vision systems. Professor Song has established significant research collaborations with institutions including MIT (working with Frédo Durand on the Artificial Compound Eyes with Artificial Intelligence project), EPFL, and Northwestern University. His work has been published in high-impact journals including Nature, Science Robotics, and Science Advances. His laboratory focuses on developing next-generation vision systems that integrate biological inspiration with cutting-edge optical engineering, with applications ranging from surveillance robots to autonomous vehicles. Current projects include improving wide-angle imaging capabilities and developing more efficient optic flow processing systems for drone navigation.

Junfeng Zhaoمشاهده پروفایل

استادیار

• Connected and Automated Vehicles
• CAV System Integration and Safety Assessment
• Motion Planning and Controls
• +۵ مورد دیگر

Dr. Junfeng Zhao is an Assistant Professor at Arizona State University's Polytechnic School within the Ira A. Fulton Schools of Engineering. He serves as the Principal Investigator of the Battery Electric & Intelligent Vehicle (BELIV) Lab and holds graduate faculty appointments in both the Robotics & Autonomous Systems (RAS) and Clean Energy Systems (CES) programs. Dr. Zhao's educational background includes: Ph.D. in Mechanical Engineering from The Ohio State University (2015), recipient of OSU Presidential Fellowship M.S. in Mechanical Engineering from University of British Columbia (2009) B.S. in Electrical Engineering from Tsinghua University (2007) Dr. Zhao's research focuses on connected and automated vehicles (CAV), with expertise spanning system integration, safety assessment, motion planning and controls, cooperative perception, AI/ML applications in automotive systems, and electrified propulsion control. His BELIV Lab develops advanced systems for battery electric and intelligent vehicles, targeting safer, cleaner, and more energy-efficient transportation solutions. With industry experience at General Motors R&D and Cummins Technical Center, his work bridges theoretical research with practical automotive applications. Analysis of Dr. Zhao's recent publications reveals a strong emphasis on cooperative perception frameworks, safety assessment methodologies for automated driving systems, integration of satellite positioning for vehicle localization, and novel testing approaches using digital twin and augmented reality technologies. His research consistently addresses critical challenges in autonomous vehicle deployment. Professional recognitions include: OSU Presidential Fellowship during doctoral studies 15 patents in automotive and vehicle systems Dr. Zhao actively mentors 9 current graduate students across PhD and Master's programs, with research areas spanning autonomous driving, battery management systems, and intelligent transportation. His former students have secured positions at major companies including Caterpillar Inc., BYD North America, and TSMC. The BELIV Lab has established itself as an Autoware Center of Excellence, received funding from industry partners like Cox, and has been featured in media coverage including The State Press and documentaries on self-driving technology. BELIV Lab maintains active industry collaborations and participates in outreach activities such as the 10th Annual Hands-on STEM Fair, demonstrating Dr. Zhao's commitment to inspiring future engineers and advancing autonomous vehicle technology through both academic research and practical implementation.

Srinivasa G. Narasimhanمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Computational Imaging
• +۵ مورد دیگر

Srinivasa G. Narasimhan is the U.A. and Helen Whitaker Professor of Robotics at Carnegie Mellon University's Robotics Institute within the School of Computer Science. He directs the Illumination and Imaging Laboratory (ILIM) and leads the Computational Imaging group, focusing on the physics of computer vision and graphics. His research develops novel imaging technologies for applications in robotics, transportation, medical imaging, and environmental sensing. Research interests span computational imaging, light transport modeling, and active perception systems. Key areas include: Physics-based vision for atmospheric and material interactions Novel camera designs and programmable lighting systems Robust perception for autonomous vehicles and medical diagnostics Non-line-of-sight imaging and computational scatterography Publications demonstrate strong emphasis on 3D reconstruction, computational optics, and vision systems for intelligent transportation. Recent works leverage self-supervised learning for dynamic scene understanding and develop novel sensors for medical and automotive applications. Awards and honors include: Best Paper awards at CVPR (2019, 2022), IV (2021), and ICCP (2020) Marr Prize Honorable Mention (ICCV 2013) Multiple demo awards at CVPR/ICCP Current advising includes 5 PhD students and 1 master's student. Major grants include NSF EXPEDITIONS (Computational Photo-Scatterography), DARPA REVEAL, and industry support from Ford, GM, Adobe, and Zillow. Manages multiple labs developing technologies like adaptive headlights, thermal imaging systems, and MHz-rate light steering devices.

• Computer Vision
• Machine Learning
• Video Analysis
• +۵ مورد دیگر

Prof. Laura Leal-Taixé is an Associate Professor at the Technical University of Munich (TUM) leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Chair, promoted from a 2017 Tenure Track Assistant Professorship. Her work focuses on advancing computer vision and machine learning, particularly in video analysis, multi-object tracking, and autonomous systems. She received a Sofja Kovalevskaja Award (2017) for her project socialMaps, which integrates dynamic social data into traffic modeling. Education: B.Sc./M.Sc. in Telecommunications Engineering, Technical University of Catalonia (UPC), Barcelona Ph.D. in Information Processing, Leibniz University Hannover (2014) Postdoc at ETH Zurich (2014–2016), and Senior Researcher at TUM’s Computer Vision Group (2016–2019) Research Interests: Multi-object tracking and segmentation in videos Motion analysis and semantic segmentation for autonomous driving Deep learning for video understanding Social dynamics modeling in urban environments Awards & Grants: €1.65M Sofja Kovalevskaja Award (Humboldt Foundation, 2017) DAAD Australia-German Joint Research Scheme (2017) Multiple travel grants from CVPR and Women in Computer Vision Labs & Collaborations: Dynamic Vision and Learning Group at TUM Collaborations with ETH Zurich, Northeastern University, and NVIDIA

Kwame Porter Robinsonمشاهده پروفایل

استادیار

• Community-Based Economies
• Digital Sustainability
• Generative Justice
• +۵ مورد دیگر

Kwame Porter Robinson serves as Assistant Professor of Technology, Information Systems and Analytics at Wayne State University, where he develops community-based economic systems through co-designed AI and automation. His work bridges academic research with practical applications in digital sustainability and generative justice, informed by prior experience as a Department of Defense data scientist and NGO consultant. His educational background spans technical and creative disciplines: Ph.D. in Information, University of Michigan (2024) M.S. in Computer Science, University of Maryland (2012) B.S. in Electrical Engineering (honors), New Mexico State University (2008) B.F.A. in Graphic Design (magna cum laude), Boston University (2002) Research focuses on decolonial computing and community economies , examining how human-AI collaboration can create unalienated labor systems in artisanal contexts. His methodology emphasizes bottom-up technology co-design with communities, particularly evident in Detroit-based artisan studies and blockchain implementations for economic empowerment. Publications since 2019 reveal accelerating focus on computational reparations and generative justice, with recent work exploring fractal scaling in AI governance and worker-owned AI ecosystems. Key themes include decolonial transitions, circular value flow, and environmental sustainability in crafting economies. Scientific recognition includes: Rackham Merit Fellowship (2019-2024) with multiple research grants AWS Cloud Credits for Research (2019) Best Student Talk award for ethical AI (2023) National Intelligence Meritorious Unit Citation (2012) Teaching includes Business Information Systems and Blockchain & CyberSecurity courses, reflecting his industry-academia synthesis. While specific grant details beyond Rackham awards aren't provided, his DoD background and AWS credits indicate strong industry partnerships. Collaborative work with scholars like Ron Eglash demonstrates interdisciplinary team engagement in justice-oriented computing initiatives.

Yan Xiaمشاهده پروفایل

پژوهشگر

• 3D Vision
• Localization and Navigation
• Robotics
• +۲ مورد دیگر

Yan Xia is a Senior Researcher at the Computer Vision Group of the Technical University of Munich (TUM) and a Research Scientist at the Munich Center for Machine Learning. She collaborates with Prof. Daniel Cremers and previously completed her PhD at TUM under supervision of Prof. Uwe Stilla and Prof. Daniel Cremers, with a visiting period at the Visual Geometry Group of the University of Oxford under Dr. João Henriques.