Alan Papalia

I am actively recruiting PhD students for the upcoming year

I am joining the University of Michigan as an Assistant Professor in the NAME department. If you're interested in developing robots that can operate independently in unstructured and remote environments, please see the information below on working with me and then email me.

A bit about me

I am an incoming Assistant Professor to the University of Michigan. I am spending 2025 as a postdoctoral researcher at Northeastern University, working with Hanu Singh and Michael Everett on various problems in field robotics and robotic navigation. I received my PhD in 2024 from the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution Joint Program, where I worked with a tremendous group of people in the Marine Robotics Group under the supervision of John Leonard. During my graduate studies I was lucky to be chosen as a MathWorks Fellow, a Woods Hole Next Wave Fellow, and an Undersea Technology Innovation Scholar.

My research develops fundamental capabilities for field robotics, with a focus on building the tools necessary for autonomous ocean observation and maintaining the health of our planet. I believe that robotics has a critical role to play in understanding and protecting our world, particularly in the face of climate change. My work seeks to identify problems where robotics can make a meaningful difference and develop the algorithms and systems necessary to unlock this impact.

My PhD research focused on enabling long-term, low-cost underwater navigation (a serious limitation to widespread autonomous ocean observation). This direction led to the development of a state-of-the-art SLAM backend – built on tools from optimization, geometry, and graph theory – that is both faster than existing methods and provides rigorous performance guarantees. This paints a picture of future work I am interested in: solving fundamental robotics problems that are motivated by important societal challenges.

Want to work with me?

If you are interested in working with me, read the information below and then reach out with a short email outlining your background and interests. I am looking for students who are excited to advance the state of the art in robotics and to tackle difficult fundamental problems in localization, state estimation, and autonomous monitoring of the environment.

Excellence comes from a wide range of past experiences. There is no single path that I expect students to come from, and I strongly encourage applications from all people. I believe the most important characteristics for success are intrinsic motivation, a drive to work on important and challenging problems, and an ability to push through when things aren’t working. Ideally, you should have at least one core strength (e.g. mathematical problem solving, programming, robotics experience, deep learning research) and be excited to develop additional strengths over the course of your PhD. It is helpful to have prior programming experience and some technical project or work experience outside of the classroom (e.g. research, internships, work, projects).

A short (and incomplete) list of topics that I am excited about includes:

• Localization and navigation (SLAM) – specifically, I am interested in long-term, low-cost navigation. We want to build smarter robotic systems that intelligently fuse information from a combination of sensors in ways that are more robust and reliable than existing systems.
• Long-term autonomy in challenging environments – developing algorithms that allow robots to operate independently for long periods of time in difficult conditions.
• Field robotics – developing systems that can operate in challenging and remote environments, with a particular focus on underwater systems.
• Adaptive sampling and exploration – developing algorithms that allow robots to make intelligent decisions about where to go and what to measure in order to maximize the information gained (with a focus on environmental monitoring).
• Merging machine learning and model-based robotic systems – finding effective ways to combine data-driven and model-based approaches to leverage the strengths of both.
• Algorithms with provable performance guarantees – building systems that are robust to noise, uncertainty, outliers, and other practical challenges.
• Learned sensor models – developing algorithms that can learn the characteristics of sensors and use this information to improve the performance of robotic systems.

The common thread in all of these topics is that advances in these directions will lead to serious improvements in what robots can do in the wild, allowing for people to apply robotics to solve important problems in the world. My underlying motivation is developing robots that allow us to better understand the natural world, but the technologies we work on are fundamental tools in robotics and are relevant anytime we want to deploy robots in the real world.