Prajol Shrestha

Pattern Recognition Lab (LME)

FAU Erlangen-Nürnberg

I am a doctoral researcher at the Pattern Recognition Lab of Friedrich-Alexander-Universität Erlangen-Nürnberg, where my research sits at the intersection of AI and robotics. I develop learning-based methods for swarm behavior, motion planning, and control — combining machine learning, model predictive control, sensor fusion, deep learning, computer vision, and deep reinforcement learning to make groups of robots perceive, plan, and act intelligently.

Before starting my Ph.D. in February 2025, I completed my M.Sc. in Information and Communication Technology at FAU (2020–2024), writing my master’s thesis at the Pattern Recognition Lab on automated analysis of retinal photoreceptors in multimodal adaptive optics imaging. I hold a B.E. from the Institute of Engineering, Tribhuvan University (Kathmandu Engineering College, 2014–2019).

The loop below summarizes how the pieces of my research fit together: robots sense the world, estimate its state, plan, and act — while learning improves the loop end to end.

Beyond research, I write notes on the ideas behind AI, mathematics, and computing — and occasionally on philosophy and living well. I believe most “hard” concepts in machine learning become common sense when viewed from the right angle.

news

Aug 10, 2026	Our brainbot paper is published in Physical Review Research 8, 033164 — open access. 🎉
May 21, 2026	Updated preprint (v2) on arXiv: “Physics-informed digital twin and onboard control of a brainbot for intelligent active matter”.
Mar 09, 2025	Our paper “Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging” was presented at BVM 2025. 🎉
Feb 01, 2025	I joined the Pattern Recognition Lab at FAU Erlangen-Nürnberg as a doctoral researcher, working on applied machine learning for swarm robotics. 🤖

selected publications

PRR
Physics-informed digital twin and onboard control of a brainbot for intelligent active matter

Isa Mammadli, Prajol Shrestha, Jayant Pande, and 6 more authors

Physical Review Research, 2026

My contribution: design and onboard implementation of the model predictive control (MPC) for autonomous trajectory tracking.

Abs DOI arXiv Bib HTML

Establishing adaptive particles that sense their state, anticipate their evolution, and compute control inputs onboard has been a major challenge in non-equilibrium physics. We address this challenge by realizing an autonomous brainbot, building on a recently developed programmable bristlebot. First, we construct a physics-informed digital twin of the device, based on a kinematic model that reproduces measured trajectory statistics and generates long, statistically faithful synthetic trajectories. The kinematics forms the foundation for implementing onboard model predictive control (MPC), enabling autonomous trajectory tracking, demonstrated by accurate execution of a non-trivial target path. This provides a proof of principle for a brainbot that senses its state, predicts its evolution, and computes control inputs onboard, unlike conventional active particles with fixed motility, thereby transforming the brainbot into an agentic physical entity. By integrating physical modeling, data-driven parameter identification, and control into a unified framework, our approach provides a scalable platform for machine-learning-enabled multi-agent studies and lays the groundwork for intelligent, adaptive active matter.
@article{mammadli2026brainbot, title = {Physics-informed digital twin and onboard control of a brainbot for intelligent active matter}, author = {Mammadli, Isa and Shrestha, Prajol and Pande, Jayant and Novkoski, Filip and Mohapatra, Siddhant and Noirhomme, Martial and Maier, Andreas and Vandewalle, Nicolas and Smith, Ana-Sun{\v c}ana}, journal = {Physical Review Research}, volume = {8}, number = {3}, pages = {033164}, year = {2026}, publisher = {American Physical Society}, doi = {10.1103/rd1t-h2v6}, note = {My contribution: design and onboard implementation of the model predictive control (MPC) for autonomous trajectory tracking.} }
BVM
Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging

Prajol Shrestha, Mikhail Kulyabin, Aline Sindel, and 4 more authors

In Bildverarbeitung für die Medizin 2025 (BVM), 2025

Abs DOI arXiv Bib HTML

Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors for improved accuracy. Precise segmentation is crucial for understanding each cone cell’s shape, area, and distribution. It helps to estimate the surrounding areas occupied by rods, which allows the calculation of the density of cone photoreceptors in the area of interest. In turn, density is critical for evaluating overall retinal health and functionality. We explored two U-Net-based segmentation models: StarDist for confocal and Cellpose for calculated modalities. Analyzing cone cells in images from two modalities and achieving consistent results demonstrates the study’s reliability and potential for clinical application.
@inproceedings{shrestha2025cones, title = {Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging}, author = {Shrestha, Prajol and Kulyabin, Mikhail and Sindel, Aline and Pedersen, Hilde R. and Gilson, Stuart J. and Baraas, Rigmor C. and Maier, Andreas K.}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2025 (BVM)}, series = {Informatik aktuell}, pages = {254--259}, publisher = {Springer Vieweg, Wiesbaden}, year = {2025}, doi = {10.1007/978-3-658-47422-5_55}, }