Alan Papalia

I am joining the University of Michigan as an Assistant Professor in the NAME department in January 2026. If you're interested in working with me, please see the information below.

A bit about me

I am a postdoctoral researcher at Northeastern University, working with Hanu Singh and Michael Everett. 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?

I am not admitting students for the upcoming academic year (2025-2026). If you are interested in working with me starting in Fall of 2026, feel free to reach out to me now and we may be able to discuss potential fit.

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 people of all backgrounds and identities. I believe the most important characteristics for success are intrinsic motivation and a drive to work on important and challenging problems. 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 very incomplete) list of topics that I am excited about includes:

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.

Select publications

An Overview of the Burer-Monteiro Method for Certifiable Robot Perception

Alan Papalia, Yulun Tian, David M. Rosen, Jonathan P. How, and John J. Leonard

arXiv preprint arXiv:2410.00117, 2024

arXiv Bib

@article{papalia2024overview,
  title = {An Overview of the Burer-Monteiro Method for Certifiable Robot Perception},
  author = {Papalia, Alan and Tian, Yulun and Rosen, David M. and How, Jonathan P. and Leonard, John J.},
  journal = {arXiv preprint arXiv:2410.00117},
  year = {2024},
  archiveprefix = {arXiv},
}

Certifiably Correct Range-Aided SLAM

Alan Papalia, Andrew Fishberg, Brendan W. O’Neill, Jonathan P. How, David M. Rosen, and John J. Leonard

IEEE Transactions on Robotics, 2024

arXiv Bib

@article{papalia24cora,
  author = {Papalia, Alan and Fishberg, Andrew and O'Neill, Brendan W. and How, Jonathan P. and Rosen, David M. and Leonard, John J.},
  journal = {IEEE Transactions on Robotics},
  title = {Certifiably Correct Range-Aided SLAM},
  year = {2024},
  volume = {40},
  number = {},
  pages = {4265-4283},
  keywords = {Simultaneous localization and mapping;Robots;Optimization;Certification;Sensors;Manifolds;Extraterrestrial measurements;Certifiable perception;range-aided simultaneous localization and mapping (RA-SLAM);Riemannian Staircase;semidefinite programming;ultrawideband (UWB)},
  archiveprefix = {arXiv},
}

SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

Alan Papalia, Joseph Morales, Kevin J. Doherty, David M. Rosen, and John J. Leonard

In IEEE Intl. Conf. on Robotics and Automation (ICRA), 2023

arXiv Bib

@inproceedings{papalia23icra,
  title = {SCORE: A Second-Order Conic Initialization for Range-Aided SLAM},
  author = {Papalia, Alan and Morales, Joseph and Doherty, Kevin J. and Rosen, David M. and Leonard, John J.},
  booktitle = {IEEE Intl. Conf. on Robotics and Automation (ICRA)},
  archiveprefix = {arXiv},
  year = {2023},
}