From developing new surgical tools to advancing autonomous systems for extreme environments, the 2025-26 Robotics Institute Fellows are driving innovative, interdisciplinary robotics research across the University of Toronto.
The Robotics Institute Fellowship brings together graduate students and postdoctoral researchers from engineering and computer science who are addressing some of humanity’s most pressing challenges with robotics-focused solutions. Designed to support cutting‑edge work and strengthen cross‑disciplinary collaboration, the program recognizes top research-stream trainees pursuing research aligned with the institute’s vision for trustworthy, impactful robotics.
Meet the inaugural Robotics Institute Fellows and learn more about how their research is shaping the future of robotics:
Chengnan Shentu
Chengnan (Jimmy) Shentu is a PhD student in the Department of Computer Science. His research addresses fundamental challenges in the manipulation and control of continuum robots through interdisciplinary collaborations across computer science and engineering. By developing an open-source, customizable research platform, implementing real-time simulation tools, and leveraging visual imitation learning for robot manipulation, Shentu aims to advance continuum robots from lab prototypes to real-world deployment in minimally invasive surgery, industrial inspection, and operations in hard-to-reach environments. As a graduate of the Robotics Leadership Program, Shentu is passionate about supporting the next generation of roboticists through mentorship, outreach and education.
Connor Holmes
Connor Holmes is a PhD candidate at the University of Toronto Institute for Aerospace Studies (UTIAS). His research focuses on creating smarter, more reliable ways for robots to understand where they are and what’s around them by bridging engineering, computer science and optimization theory. By developing algorithms that help robots avoid critical mistakes and make better decisions, his work supports the predictable and adaptable use of autonomous systems in high-stakes environments like factories, construction sites, and remote and planetary environments. Holmes is also committed to helping shape Canada’s national identity in robotics and AI through participation in the Canadian Robotics Council. As a graduate of the Robotics Leadership Program, he is also passionate about supporting youth outreach and engagement in STEM through service leadership.
Daniil Lisus
Daniil Lisus is a PhD candidate at the University of Toronto Institute for Aerospace Studies (UTIAS). His research tackles one of the biggest challenges in autonomous driving: reliable localization in challenging driving conditions such as driving in darkness, snow, fog or rain. By combining physics‑based radar modelling with machine‑learning techniques from computer vision and robotics, Lisus aims to build mapping and perception systems that enable autonomous vehicles to operate safely and consistently in all environments at all times. Beyond research, Lisus is passionate about STEM outreach, and has led multiple workshops for students and teachers as a graduate of the Robotics Leadership Program.
Ella Walsh
Ella Walsh is a PhD student co‑supervised by Professor Eric Diller (MIE) and Professor Jessica Burgner‑Kahrs (MCS). Walsh’s research focuses on improving mechanical design of continuum robots for practical applications such as minimally invasive surgery. She is developing magnetic mechanisms that enable two flexible surgical continuum robots to temporarily join together inside the body. By configuring the robots in this way, this technique could increase the force and stability of the robots, making it easier for surgeons to complete procedures without compromising on safe navigation through hard-to-reach spaces. As an NSERC CREATE Healthcare Robotics Fellow, Walsh leads lectures and workshops on continuum and soft robots for high school students. She is also the co‑founder of OrthoFlexion, a med‑tech start‑up creating wearable sensors to help children with orthopedic conditions maintain brace adherence.
Mingfeng Yuan
Mingfeng Yuan is a postdoctoral researcher at the University of Toronto Institute for Aerospace Studies (UTIAS) at the Toronto Robotics and AI Laboratory (TRAIL), supervised by Professor Steven Waslander. His research focuses on building trustworthy autonomous systems that can interact effectively in complex real-world and high-stakes environments. His long-term goal is to build a general-purpose “brain” for cognitive robots that can generalize across different robotic embodiments. By combining robotics, computer vision, large foundation models and reinforcement learning, Yuan develops embodied AI agents that use multimodal long-term memory and 4D scene understanding to navigate, manipulate and reason safely alongside people. His work aims to enable robots to operate reliably in challenging outdoor and industrial settings, such as warehouses, mines and forests, while remaining socially aware and aligned with human goals. Yuan has led award-winning robotics teams in international competitions and served as a technical lead on Canadian Space Agency aerospace missions.
Spencer Teetaert
Spencer Teetaert is a PhD student co‑supervised by Professor Tim Barfoot (UTIAS) and Professor Jessica Burgner‑Kahrs (MCS). Inspired by state estimation techniques traditionally used for mobile robots, his research works towards accurately tracking the shape and movement of continuum robots in real time. Teetaert is also collaborating with researchers at the University of Oxford to integrate compact lidar sensors for continuum robots, enabling advanced capabilities such as localization and mapping. His work has led to the first continuous‑time stochastic estimator for these systems. Teetaert is passionate about increasing access to robotics research. He is committed to the open-source movement, making all of his research papers and code publicly available. In 2022, he developed an open sim‑to‑real pipeline for quadrotors that enabled remote international participation at IROS 2022 and has since been incorporated into graduate robotics courses at the University of Toronto and the Technical University of Munich.
Stephen Yang
Stephen Yang is an MASc student in the Department of Mechanical & Industrial Engineering (MIE). Yang’s research focuses on integrating robotics and magnetic actuation to advance tools for minimally invasive surgery. By replacing traditional gears with magnetically driven motion, his work aims to create safer, less invasive surgical instruments that expand the applications of robotic microsurgery. By collaborating with clinicians and researchers across engineering disciplines, Yang aims to create a translational pathway from the lab to clinical practice. Yang is also a Healthcare Robotics (HeRo) NSERC CREATE trainee, and a recipient of the NSERC CGS-M Award, and the Barbara and Frank Milligan Graduate Fellowship.
Sujith Santharuban
Sujith Santharuban is a PhD student in the Department of Mechanical & Industrial Engineering (MIE), working with Professor Goldie Nejat. Drawing from robotics, machine learning, cognitive science and biomedical engineering, Santharuban’s research focuses on developing multimodal perception systems that help robots better understand human intentions. His work aims to maintain trust and collaboration with human partners, as well as create safer and more adaptive systems for environments such as homes and hospitals. Santharuban is also a recipient of the QEII-GSST Scholarship.
Tianran Liu
Tianran Liu is a PhD student at the University of Toronto Institute for Aerospace Studies (UTIAS). His research resides at the intersection of computer vision, generative modelling and autonomous robotics. By leveraging advanced generative models, his work aims to extract universal physical priors from large-scale sensor data, facilitating robust planning and generalization across diverse, unstructured real-world environments. He also focuses on developing scalable, high-fidelity world models as neural simulators, enabling long-horizon forecasting and closed-loop evaluation to significantly enhance the planning and safety of autonomous agents. Liu is also the recipient of Vector Scholarship in AI, 2025 Didi Graduate Scholarship, and Master fellowship at University of Ottawa.
Wenda Zhao
Wenda Zhao is a postdoctoral fellow at the University of Toronto Institute for Aerospace Studies (UTIAS). His research focuses on developing lidar-based autonomy systems that can operate reliably in extreme environments where human and GPS access is limited or unavailable, including lunar and Martian surfaces, underground mines and disaster zones. Zhao also explores using visual-language foundation models to reason about physical interactions between robots and their environments, enabling autonomous systems to make sense of their surroundings and adjust how they move and navigate accordingly. As a member of U of T’s Autonomous Drone Racing team, Zhao designs AI-enabled visual localization and perception systems for high-speed flight.