جست‌وجوی استاد و پژوهشگر

Yusuf Yaşaمشاهده پروفایل

دانشیار

• Electrical Machines and Energy Conversion
• Power Electronics
• Hybrid and Electric Vehicles

Assoc. Prof. Dr. Yusuf Yaşa is an Associate Professor at Istanbul Technical University, Department of Electrical Engineering, specializing in Electrical Machines, Power Electronics, and Hybrid/Electric Vehicles. He holds a PhD from Yıldız Technical University and has served in academic and administrative roles at Bursa Technical University and Istanbul Technical University. PhD in Electrical Machines and Power Electronics, Yıldız Technical University (2006–2013) Current Vice Dean at Istanbul Technical University (2023–) Founding Partner of Yasa Motor Technologies (2018) and Nardan Power Conversion Systems Ltd. Co. (2023) His research focuses on noise mitigation in switched reluctance machines, battery cooling with graphene-enhanced phase change materials, and efficiency optimization in electric vehicle systems. He has led projects on DC fast-chargers and sensorless control of synchronous reluctance motors. His publications address energy conversion, battery management, and acoustic noise reduction. Recent research trends include advancements in electric vehicle modeling, state-of-charge estimation for Li-ion batteries, and thermal management solutions for battery systems. His work integrates simulation tools like ANSYS and machine learning for efficiency improvements. He has advised PhD and Master’s theses on topics such as battery charge rate estimation, graphene-doped PCM materials, and Kalman filter-based motor control. Collaborations span institutions like The University of Akron and companies in electric propulsion and robotics.

• DNA Nanotechnology
• Molecular Robotics
• Self-Assembling Nanostructures
• +۴ مورد دیگر

Dr. Shelley Wickham is an Associate Professor and ARC DECRA Fellow at the University of Sydney, holding joint appointments in the Schools of Chemistry and Physics. She serves as a Westpac Research Fellow and leads the DNA Nanotechnology Group at the Sydney Nano Institute. Dr. Wickham is also co-Champion of the Sydney Nano Institute Grand Challenge project in Molecular Nanorobotics for Health, co-lead of the School of Physics Grand Challenge on Nanoscale brain navigation for targeted drug delivery, and faculty mentor of the University of Sydney BIOMOD team. Bachelor of Science and Master of Science in Physics from University of Sydney PhD in Condensed Matter Physics from University of Oxford Postdoctoral Fellow at Harvard Medical School, Dana-Farber Cancer Institute, and Wyss Institute Dr. Wickham's research focuses on self-assembling nanotechnology and molecular robotics, particularly in the design and assembly of programmable nanostructures out of DNA. Her work spans applications in cell biology, materials science, and nanomedicine. Current research projects include design and synthesis of self-assembling DNA nanostructures, proto-cells made of DNA gels that move under flow, new plasma fabrication methods for biomolecule micropatterning, and DNA computation circuits for navigating the brain using machine learning. Her research aligns with the Faculty of Science Research Strengths in Molecules to Materials, Preventing and Treating Disease & Disorder, and Next Generation Materials. Analysis of Dr. Wickham's recent publications reveals a consistent focus on DNA nanotechnology with increasing sophistication in structural complexity and biological applications. Her work has evolved from fundamental DNA origami structures to increasingly complex multi-component systems with practical applications in nanomedicine and biomimetic engineering. Recent publications show strong interdisciplinary collaboration across chemistry, physics, biology, and engineering disciplines, with emphasis on real-world applications including drug delivery systems and biomolecular sensors. ARC DECRA Fellow Westpac Research Fellow BIOMOD World Champions (2019) Dr. Wickham actively mentors PhD students and postdoctoral researchers in her DNA nanotechnology group. She has secured significant research funding including ARC Discovery Projects, Westpac Scholarships, and NSW Health grants. Her current grants support projects such as '3D Bio-Nanomaterial Displays with Designer Architectures and Functions' and 'RNA aptamer sensing devices for rapid detection of blood clotting.' Dr. Wickham encourages applications from diverse backgrounds and maintains active collaborations with researchers at Harvard, Oxford, and other international institutions. Dr. Wickham leads the DNA Nanotechnology Group at the University of Sydney, which is part of the Sydney Nano Institute. Her lab focuses on building tools from DNA origami - including tweezers, spanners, wrenches and springs - to better understand biological processes at the nanoscale. The group has achieved notable success with the BIOMOD team winning world championships in 2019, and continues to develop innovative approaches to molecular robotics for healthcare applications.

• Artificial Intelligence
• Cognitive Science
• Human-Agent Interaction
• +۵ مورد دیگر

Prof. Dr.-Ing. Stefan Kopp is a faculty member at Bielefeld University's Faculty of Engineering and serves as Research Group Leader of the Cognitive Systems and Social Interaction Group . He also holds administrative roles as Vice Dean and Deputy CITEC Coordinator . His work focuses on Artificial Intelligence , Cognitive Systems , and Socio-Technical World research areas. Research Group Leader: Cognitive Systems and Social Interaction Group Vice Dean: Faculty of Engineering Deputy Coordinator: Center for Cognitive Interaction Technology (CITEC) Project Manager: TRR 318 "Constructing Explainability" subprojects His research explores human-agent interaction , multimodal conversational agents , and social AI through projects like 39-Inf-11 Human-Machine Interaction and 39-M-Inf-VKI Virtual Humans and Conversational Agents . Publications address topics including adaptive explanation generation , gesture synthesis , and social cognition in dynamic environments. Current research topics span cooperative AI , explainable decision-making , and sensorimotor grounding in artificial systems.

• Radiation Biophysics
• Cancer Radiotherapy
• Ionizing Radiation Effects
• +۴ مورد دیگر

David J. Brenner serves as Higgins Professor of Radiation Biophysics in Radiation Oncology and Environmental Health Sciences at Columbia University Medical Center. He directs both the century-old Center for Radiological Research and the Radiological Research Accelerator Facility (RARAF), leading interdisciplinary teams focused on radiation applications in medicine and safety. BA in Physics from Oxford University (1974) MSc in Radiation Physics from University of London (1976) MA in Physics Philosophy from Oxford University (1979) PhD in Physics from University of Surrey (1980) His research spans dual aspects of radiation: therapeutic applications in cancer treatment and risk assessment across diverse scenarios. Key initiatives include advancing carbon-ion therapy for pancreatic cancer, developing safe far-UVC light for pathogen elimination, and investigating low-dose radiation risks from medical imaging to nuclear terrorism. His team leverages RARAF's unique capabilities for mechanistic studies of radiation effects. Publications reveal dominant themes in radiation biophysics, with significant contributions to biodosimetry (RABiT platform), UV disinfection technology, and cancer risk modeling. Recent work emphasizes translational applications including medical countermeasures for radiation exposure and precision radiation oncology techniques. National Academy of Sciences Nuclear and Radiation Studies Board member National Council on Radiation Protection and Measurements member Radiation Research Society Failla Gold Medal recipient (2011) Oxford University Weldon Prize for mathematical biology (2015) Robert D. Moseley Award for Radiation Protection in Medicine Brenner leads multiple NIH-funded projects including biodosimetry development and UV disinfection research. His mentorship extends through directing Columbia's Radiological Research Accelerator Facility and training programs in radiological sciences. Current lab efforts focus on carbon-ion therapy mechanisms and 222-nm UV applications against drug-resistant pathogens, with active collaborations across oncology, microbiology, and physics disciplines.

Luis Duffaut Espinosaمشاهده پروفایل

استادیار

• Control Theory
• Estimation Theory
• Robotics
• +۷ مورد دیگر

Summary Luis A. Duffaut Espinosa is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. His research focuses on control theory, estimation, robotics, and nonlinear systems with applications in autonomy, quantum control, and environmental monitoring. He holds a Ph.D. in Electrical and Computer Engineering from Old Dominion University (2009) and has held academic positions at George Mason University and postdoctoral roles at Johns Hopkins University and the University of New South Wales. Education: Ph.D. in Electrical and Computer Engineering (2009), Old Dominion University M.S. in Mathematics (2005), Pontificia Universidad Católica del Perú B.S. in Physics (2003), Universidad Nacional de Ingeniería, Peru Research Interests: His work emphasizes data-driven control and estimation methodologies, including model-free approaches for power systems, environmental monitoring, and quantum control. Current projects include real-time data assimilation in harsh environments, resilient robotics in GPS-denied conditions, and SAR with small satellites. He co-directs the Autonomous and Intelligent Systems Research Laboratory (AIRLab) and is part of the CREATE center. Recognition: 2024 NSF CAREER Award for work on safety-aware data-driven control frameworks Teaching & Advising: He teaches courses in estimation theory, control systems, and signal processing. Advises a team of graduate and undergraduate students focusing on autonomy, robotics, and control systems. Notable students include Danial Waleed (Ph.D. 2024), Jacob Friz-Trillo (M.S. 2025), and Farnaz Boudaghi (Ph.D. candidate). Labs & Collaborations: AIRLab: Focuses on data-driven control for autonomy in robotics and engineered systems CREATE: Research on resilient energy and autonomous technologies

Andreas Malikopoulosمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Transportation Systems
• Control Theory
• +۴ مورد دیگر

Andreas Malikopoulos is a Professor at Cornell University's School of Civil & Environmental Engineering and Director of the Information and Decision Science Lab (IDS Lab). Previously, he held roles as the Terri Connor Kelly and John Kelly Career Development Professor at the University of Delaware (UD) and founding Director of UD's Sociotechnical Systems Center. He also served as the Alvin M. Weinberg Fellow at Oak Ridge National Laboratory (ORNL), Deputy Director of ORNL's Urban Dynamics Institute, and Senior Researcher at General Motors R&D. His research focuses on cyber-physical systems (CPS), stochastic control, and learning-driven approaches for optimizing energy efficiency and sustainable mobility in smart cities and transportation systems. Education: PhD (Mechanical Engineering, University of Michigan, 2008), M.S. (Mechanical Engineering, University of Michigan, 2004), Diploma (National Technical University of Athens, 2000). Research Interests: Analysis and control of CPS, stochastic scheduling, game theory, and mechanism design applied to emerging mobility systems (e.g., autonomous vehicles, electric vehicles). He emphasizes integrating learning and control for socially optimal solutions in transportation networks. Awards: IEEE ITS Young Researcher Award (2019), UD’s Outstanding Junior Faculty Award (2020), Alvin M. Weinberg Fellowship (2010), and recognition as a NAS Kavli Frontiers of Science Scholar (2012). He is an IEEE Senior Member, ASME Fellow, and serves on editorial boards of leading journals. Teaching: Focuses on optimal decision-making, control theory, and emerging mobility systems. Courses include stochastic optimal control and game theory at Cornell. Labs: Leads the IDS Lab, which develops scalable frameworks for CPS and smart city applications. Current projects include coordinated routing for mixed-traffic systems and AI-driven recommendations for autonomous vehicles.

Kaidi Yangمشاهده پروفایل

استادیار

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +۵ مورد دیگر

Kaidi Yang is an Assistant Professor at the National University of Singapore (NUS) in the Department of Civil and Environmental Engineering, specializing in Intelligent Transportation Systems and related fields. He holds a PhD from ETH Zurich (2019), an M.Sc. in Control Science and Engineering from Tsinghua University (2014), and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University (2011). His research focuses on advancing traffic control, connected/automated vehicles, shared mobility systems, and data privacy in transportation. He has contributed to developing algorithms for efficient traffic signal control, platooning coordination, and privacy-preserving data sharing in transportation networks. Education: Ph.D., Civil and Environmental Engineering (Transportation), ETH Zurich, 2019 M.Sc., Control Science and Engineering, Tsinghua University, 2014 B.Sc./B.Eng., Dual Degrees in Pure/Applied Mathematics and Automation, Tsinghua University, 2011 Yang has received prestigious awards including the Swiss National Science Foundation’s Postdoc Mobility Fellowship (2021–2022) and the IEEE ITS Conference Best Student Paper Award (2020). He serves as an Associate Editor for the IEEE Conference on Intelligent Transportation Systems (2024). His work bridges theoretical advancements in operations research, robotics, and machine learning with practical applications in urban mobility systems. Recent efforts emphasize integrating privacy-preserving techniques into traffic management and optimizing mixed-autonomy platoon control.

Marcelo M. Wanderleyمشاهده پروفایل

استاد

• Music Technology
• Computer Music
• Digital Musical Instruments (DMIs)
• +۵ مورد دیگر

Marcelo M. Wanderley is a Professor and Director of the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT) at McGill University's Schulich School of Music. His academic roles include Area Coordinator for Music Technology and membership on the Executive Committee. He holds a PhD in acoustics, signal processing, and computer science from Université Pierre et Marie Curie (Paris VI). Wanderley's research focuses on novel interfaces for music performance, digital musical instruments (DMIs), and human-computer interaction. He has authored influential works such as New Digital Musical Instruments: Control and Interaction Beyond the Keyboard (2006) and pioneered research in gestural control of music. His work integrates engineering, computer science, and musicology to create innovative performance tools, including the T-Stick and Karlax instruments. He has held visiting professorships at Université de Bretagne Sud, Universidade Federal de Minas Gerais (Brazil), and the University of Mons (Belgium), where he was awarded prestigious chairs. Wanderley's awards include the Inria International Chair (2016–2020) and the Distinguished Visitor Award from the University of Auckland. His research has been widely cited in the International Conference on New Interfaces for Musical Expression (NIME), and he actively contributes to editorial boards (e.g., Computer Music Journal ) and academic leadership roles. Wanderley's lab, the Input Devices and Music Interaction Lab (IDMIL), develops open-source frameworks like Puara and Probatio for DMI design and mapping. Key research areas include haptic feedback systems, motion capture of musical performances, and accessibility in music technology. He emphasizes interdisciplinary collaboration, bridging engineering, art, and cognitive science to advance musical expression and performance practices.

O. Burak Ozdoganlarمشاهده پروفایل

استاد

• Micro and Nano Manufacturing
• Mechanics of Material Removal
• Dynamics and Vibrations in Micro-scale Systems
• +۳ مورد دیگر

O. Burak Ozdoganlar is a Professor in the Departments of Mechanical Engineering and Biomedical Engineering at Carnegie Mellon University. His research focuses on multiscale (meso/micro/nano) manufacturing science, combining theoretical, numerical, and experimental analyses to advance three-dimensional device fabrication. He leads the Multiscale Manufacturing and Dynamics Laboratory (MMDL), with applications spanning medical, biomedical, energy, robotics, and aerospace fields. B.S., Istanbul Technical University, Turkey M.S., Ohio State University, Columbus Ph.D., University of Michigan, Ann Arbor Post-doc, University of Illinois at Urbana-Champaign Senior Member of Technical Staff, Sandia National Labs His work addresses mechanics of micro-scale material removal, dynamics of micro-scale structures, novel micro/nano-manufacturing techniques, and application-driven research. Key contributions include scalable fabrication of microneedle arrays, freeform 3D ice printing for vascular networks, and high-density soft-matter electronics. His research emphasizes predictability and precision in manufacturing processes. Recent publications highlight advancements in dissolvable microneedle arrays for transdermal delivery, freeform 3D printing of ice structures for biomimetic vascularization, and scalable methods for porous and soft-matter electronics. His work bridges fundamental mechanics with medical device innovation. Blackall Machine Tool and Gage Award, ASME, 2011 Russell V. Trader Career Faculty Fellow, CMU, 2009-2011 NSF CAREER award, 2006 Kuo K. Wang Outstanding Young Engineer, SME, 2007 Organizer, 'Manufacturing...The Future' symposium, NAE EU-American Frontiers Conference, 2011 Best paper award, NAMRI SME, 2007-2008 Struminger Teaching Fellow, CMU, 2007-2008 Ozdoganlar's Multiscale Manufacturing and Dynamics Laboratory (MMDL) develops cutting-edge manufacturing solutions for biomedical applications, including neural probes, cartilage implants, and biosensors. His research integrates mechanics, materials science, and process engineering to address challenges in device predictability and scalability.

Mahsa Ghasemiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Control Theory
• Active Perception
• +۳ مورد دیگر

Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.

• Global Navigation Satellite Systems (GNSS)
• Autonomous Systems
• Sensor Fusion
• +۴ مورد دیگر

Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.

Young-Jin Chaمشاهده پروفایل

استاد

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +۵ مورد دیگر

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

Fahim Khanمشاهده پروفایل

استادیار

• Computer Graphics
• Data Visualization
• Computer Vision
• +۵ مورد دیگر

Fahim Khan is an Assistant Professor in the Department of Computer Science and Software Engineering at California Polytechnic State University’s College of Engineering. He specializes in computer graphics, data visualization, computer vision, and machine learning, with a focus on applying these technologies to education, environmental monitoring, and public safety. His research emphasizes making complex data accessible through advanced tools, bridging the gap between raw data and actionable insights. He is deeply committed to inclusive education and fostering interdisciplinary collaboration. His work often integrates citizen science initiatives, empowering communities through mobile applications and machine learning. Notable projects include real-time rip current detection systems and platforms for high school students to engage in research. Khan advocates for equity in technology, designing inclusive learning environments and promoting diversity in STEM. He actively supports the university’s Learn by Doing philosophy, blending practical education with theoretical rigor. Professionally, he contributes to coastal observation networks and autonomous vehicle datasets while maintaining a balance through outdoor activities like exploring Pismo Beach. His research trends reflect a strong focus on mobile computing, environmental applications, and education technology, with recent efforts emphasizing citizen science and data-driven solutions. While no formal grants or advising records are detailed, his projects implicitly involve collaborative efforts. He is affiliated with labs focused on environmental monitoring and mobile technology development, though specific lab names are not mentioned.

Brian Surgenorمشاهده پروفایل

استاد

• Mechanical Engineering
• Machine Vision Systems
• Autonomous Vehicles
• +۳ مورد دیگر

Professor Brian Surgenor is a faculty member at Queen's University's Department of Mechanical and Materials Engineering, part of the Smith Engineering faculty. He holds a B.Sc. (1977), M.Eng. (AECL/Whiteshell), and Ph.D. (1983) in Mechanical Engineering from Queen's University. His research focuses on machine vision systems for automation, autonomous vehicle navigation, and mechatronic system design education. He has held key administrative roles including Department Head (1993-2002), Associate Dean (2008-2013), and Vice-Dean (2013-2016). His work emphasizes interdisciplinary innovation, such as the Mitchell Hall design project and contributions to Ingenuity Labs. Education: B.Sc. Mechanical Engineering, Queen's University (1977) M.Eng. Engineering Physics, McMaster University (AECL/Whiteshell) Ph.D. Mechanical Engineering, Queen's University (1983) Research interests include: - Pneumatic servosystems - Intelligent algorithms for machine vision - Off-road autonomous vehicle systems - Mechatronics education methodologies - Hybrid powertrain systems for vehicles His recent publications (2017–2024) explore autonomous systems, machine vision applications, and fuel cell hybrid technologies. Notable trends include advancements in UAV-based infrastructure inspection, terrain-adaptive autonomous driving, and low-cost machine vision solutions for small part sorting. His work bridges theoretical control systems with practical industrial automation challenges. He has contributed to laboratory design for CDIO curricula and pioneered mechatronics education through problem-based learning. His administrative leadership has shaped Queen's engineering graduate programs and research infrastructure. Currently involved in Ingenuity Labs, fostering cross-disciplinary innovation.

Melih Kandemirمشاهده پروفایل

دانشیار

• Machine Learning
• Reinforcement Learning
• Bayesian Inference
• +۳ مورد دیگر

Melih Kandemir is an Associate Professor at the Department of Mathematics and Computer Science, Southern Denmark University. He also serves as Research Group Leader at the Bosch Center for Artificial Intelligence (2018–2021) and held a previous role as Assistant Professor at Ozyegin University (2017–2018). His research focuses on machine learning, Bayesian methods, reinforcement learning, and uncertainty quantification. **Education**: PhD in Computer Science from Aalto University (2013), specializing in 'Learning Mental States from Biosignals'. **Research Interests**: Machine Learning, Bayesian Inference, Reinforcement Learning, Deep Neural Networks, Stochastic Processes. His work emphasizes theoretical foundations and practical applications in domains like medical imaging, control systems, and robotics. **Awards**: Two Best Paper Awards (2017). **Grants & Projects**: Includes the Carlsberg Young Researcher Fellowship (2022–2026), Novo Nordisk Foundation grants (2021–2024), and DFF-funded research on PAC-Bayesian reinforcement learning (2025–2027). **Labs/Teams**: Leads research on Bayesian deep learning and reinforcement learning within the Bosch Center for AI and SDU's interdisciplinary groups.