Miguel Saavedra-Ruiz
Scholar

Miguel Saavedra-Ruiz

Google Scholar ID: tK9Tln0AAAAJ
PhD student at Université de Montréal / Mila
RoboticsMachine LearningMobile RobotsDeep Learning
Citations & Impact
All-time
Citations
56
 
H-index
5
 
i10-index
2
 
Publications
10
 
Co-authors
9
list available
Resume (English only)
Academic Achievements
  • - Paper “Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments” accepted for publication at IROS 2025
  • - Paper “The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups” accepted for publication in RA-L
  • - Paper “One-4-All: Neural Potential Fields for Embodied Navigation” accepted at IROS 2023
  • - Awarded a doctoral scholarship (B2X) from Fonds de recherche du Québec - Nature et technologies for 2023-2027
  • - Received Excellence Scholarship and Writing Scholarship from DIRO
  • - Served as a volunteer at CS-CAN conference and Montreal Robotics Summer School (2023)
  • - Attended Upper Bound 2023 and received a talent bursary of 1,500 CAD from Amii
  • - Participated in ETH Robotics Summer School 2022
  • - Paper “Monocular visual autonomous landing system for quadcopter drones using software in the loop” accepted in Aerospace & Electronics Systems Magazine IEEE
Research Experience
  • - Continuing academic journey at Université de Montréal and Mila as a PhD student, supervised by Liam Paull
  • - Involved in multiple research projects such as “Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers”
Education
  • - PhD: Université de Montréal, starting Fall 2023, supervised by Liam Paull
  • - M.Sc.: Université de Montréal, 2023
  • - Postgraduate Diploma in Artificial Intelligence, 2021
  • - BEng in Mechatronics Engineering, Universidad Autónoma de Occidente (UAO), 2019
Background
  • PhD student at Université de Montréal and Mila, under the supervision of Liam Paull. Affiliated with the Robotics and Embodied AI Lab (REAL), his primary research interests lie in the intersection between dynamic scene understanding and world modeling for embodied AI.