Embedded Files
Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998. He is an IEEE Fellow. He was the director of the GRASP laboratory from 2008 to 2013, Associate Dean for Graduate Education from 2012-2016, and Faculty Director of Online Learning from 2013- 2017. He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens, 1986, and his PhD in Computer Science from the University of Karlsruhe, 1992, under the supervision of Hans-Hellmut Nagel. He received the Best Conference Paper Award at ICRA 2017. He co-chaired ECCV 2010 and 3DPVT 2006. He received the 2026 Provost's Award for Distinguished PhD Mentoring.His most cited works have been on event-based vision, equivariant learning, 3D human pose, and hand-eye calibration.
Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998. He is an IEEE Fellow. He was the director of the GRASP laboratory from 2008 to 2013, Associate Dean for Graduate Education from 2012-2016, and Faculty Director of Online Learning from 2013- 2017. He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens, 1986, and his PhD in Computer Science from the University of Karlsruhe, 1992, under the supervision of Hans-Hellmut Nagel. He received the Best Conference Paper Award at ICRA 2017. He co-chaired ECCV 2010 and 3DPVT 2006. He received the 2026 Provost's Award for Distinguished PhD Mentoring.His most cited works have been on event-based vision, equivariant learning, 3D human pose, and hand-eye calibration.
Somayeh Hussaini graduated from QUT in 2020 with a Bachelor of Engineering (Honours) in Mechatronics, achieving First Class Honours. Her Honours project, which focused on deep learning temporal features for vision-based aircraft detection, was published at ACRA 2020 and earned her the IEEE Best Final Year Thesis Award across all fields of Electrical Engineering and Information Technology. In 2021, Somayeh began her PhD, researching biologically-inspired spiking neural networks for scalable visual place recognition under the supervision of Dr. Tobias Fischer and Professor Michael Milford. She has published her research in the IEEE Robotics and Automation Lett ers (RAL) journal and the International Conference on Robotics and Automation (ICRA), presenting her work at ICRA in 2022 and 2023. Her research interests span robotics, computer vision, and neuromorphic computing.
Somayeh Hussaini graduated from QUT in 2020 with a Bachelor of Engineering (Honours) in Mechatronics, achieving First Class Honours. Her Honours project, which focused on deep learning temporal features for vision-based aircraft detection, was published at ACRA 2020 and earned her the IEEE Best Final Year Thesis Award across all fields of Electrical Engineering and Information Technology. In 2021, Somayeh began her PhD, researching biologically-inspired spiking neural networks for scalable visual place recognition under the supervision of Dr. Tobias Fischer and Professor Michael Milford. She has published her research in the IEEE Robotics and Automation Lett ers (RAL) journal and the International Conference on Robotics and Automation (ICRA), presenting her work at ICRA in 2022 and 2023. Her research interests span robotics, computer vision, and neuromorphic computing.
Roberto Pellerito is a PhD student in Computer Science at the Robotics and Perception Group, University of Zurich and ETH Zürich supervised by Professor Davide Scaramuzza. He's also co-founder of Ememe where he works on generative animations. He graduated cum laude from a B.Sc in Control Engineering at Politecnico di Milano in Italy in 2021, and co-founding Automation Engineering Association @ PoliMi. He earned an M.Sc. in Robotics, Systems and Control from ETH Zurich in Switzerland in 2023, working at the Robotics Systems Lab under Prof. Marco Hutter and at Robotics and Perception group under Professor Davide Scaramuzza. He worked for Disney Research in Zurich in 2022 developing algorithms for human pose estimation and animations.
Roberto Pellerito is a PhD student in Computer Science at the Robotics and Perception Group, University of Zurich and ETH Zürich supervised by Professor Davide Scaramuzza. He's also co-founder of Ememe where he works on generative animations. He graduated cum laude from a B.Sc in Control Engineering at Politecnico di Milano in Italy in 2021, and co-founding Automation Engineering Association @ PoliMi. He earned an M.Sc. in Robotics, Systems and Control from ETH Zurich in Switzerland in 2023, working at the Robotics Systems Lab under Prof. Marco Hutter and at Robotics and Perception group under Professor Davide Scaramuzza. He worked for Disney Research in Zurich in 2022 developing algorithms for human pose estimation and animations.
Shintaro Shiba received his Ph.D. in Engineering, focusing on event-based motion estimation, under the supervision of Prof. Guillermo Gallego (TU Berlin, Germany) and Prof. Yoshimitsu Aoki (Keio University, Japan). Event cameras represent the intersection of his diverse backgrounds in neuroscience, machine learning, and autonomous driving. His academic journey began with Bachelor’s and Master’s degrees in neuroscience. He was interested in studying animal brains before entering the AI field, inspired by their remarkable efficiency, robustness, and accuracy in perceiving and recognizing the world. Before pursuing his doctoral studies, he worked as a machine learning software engineer specializing in production-scale MLOps, including ML and data pipelines, for autonomous driving at Woven by Toyota. Through this experience, he gained practical expertise in translating machine learning research into reliable products that address real-world challenges.
Shintaro Shiba received his Ph.D. in Engineering, focusing on event-based motion estimation, under the supervision of Prof. Guillermo Gallego (TU Berlin, Germany) and Prof. Yoshimitsu Aoki (Keio University, Japan). Event cameras represent the intersection of his diverse backgrounds in neuroscience, machine learning, and autonomous driving. His academic journey began with Bachelor’s and Master’s degrees in neuroscience. He was interested in studying animal brains before entering the AI field, inspired by their remarkable efficiency, robustness, and accuracy in perceiving and recognizing the world. Before pursuing his doctoral studies, he worked as a machine learning software engineer specializing in production-scale MLOps, including ML and data pipelines, for autonomous driving at Woven by Toyota. Through this experience, he gained practical expertise in translating machine learning research into reliable products that address real-world challenges.
Daniel Gehrig obtained his Ph.D. in 2023 from the University of Zurich (UZH) in Switzerland, while working on computer vision and robotics research with event cameras at the Robotics and Perception Group (RPG). For his work, his Ph.D. was awarded with highest distinction, and the UZH annual award. Before that he completed his master’s studies in 2018 in in Mechanical Engineering at ETH Zurich, where he achieved with the highest possible score, and was thus awarded the Willi Studer Prize. During his studies, he came into contact with event-based vision while doing his master’s thesis on event- and frame-based feature tracking at the Robotics and Perception Group for which he was awarded the ETH Medal for the best master’s thesis of the year. His work has been featured prominently in IEEE Spectrum and on popular channels like Two Minute Papers. Daniel Gehrig’s research interests lie at the intersection of robotics, computer vision, and machine learning for event-based vision.
Daniel Gehrig obtained his Ph.D. in 2023 from the University of Zurich (UZH) in Switzerland, while working on computer vision and robotics research with event cameras at the Robotics and Perception Group (RPG). For his work, his Ph.D. was awarded with highest distinction, and the UZH annual award. Before that he completed his master’s studies in 2018 in in Mechanical Engineering at ETH Zurich, where he achieved with the highest possible score, and was thus awarded the Willi Studer Prize. During his studies, he came into contact with event-based vision while doing his master’s thesis on event- and frame-based feature tracking at the Robotics and Perception Group for which he was awarded the ETH Medal for the best master’s thesis of the year. His work has been featured prominently in IEEE Spectrum and on popular channels like Two Minute Papers. Daniel Gehrig’s research interests lie at the intersection of robotics, computer vision, and machine learning for event-based vision.
Shuang Guo is a Ph.D. student at the Robotic Interactive Perception Lab at the Technical University of Berlin, working with Prof. Guillermo Gallego. Previously, he obtained his B.Eng. and M.Eng. degrees from Harbin Institute of Technology in 2019 and 2021, respectively. His research interests lie in computer vision and robotics. His current work focuses on event-based motion estimation, bundle adjustment, SLAM, panoramic imaging, and image reconstruction. His research objective is to develop algorithms that unlock the full potential of event cameras, including their high dynamic range (HDR) and high temporal resolution, and to advance the maturity of event cameras and their related applications.
Shuang Guo is a Ph.D. student at the Robotic Interactive Perception Lab at the Technical University of Berlin, working with Prof. Guillermo Gallego. Previously, he obtained his B.Eng. and M.Eng. degrees from Harbin Institute of Technology in 2019 and 2021, respectively. His research interests lie in computer vision and robotics. His current work focuses on event-based motion estimation, bundle adjustment, SLAM, panoramic imaging, and image reconstruction. His research objective is to develop algorithms that unlock the full potential of event cameras, including their high dynamic range (HDR) and high temporal resolution, and to advance the maturity of event cameras and their related applications.
Page updated
Google Sites
Report abuse