Prospective students

  • If you are already at ASU and want to work with me in the areas of robotics, computer vision, machine learning, and algorithm design, please send me an email at with your CV and a few lines on any projects you have worked on, related to the areas listed. 
  • If you are planning to apply to a program at ASU, please follow standard application procedures. Please be aware that acceptance decisions are made by a committee.

Environmental monitoring

Our methodology for sampling, modeling, and prediction considers both in-situ and ex-situ labeling of samples. Cheap but potentially more noisy remotely sensed data can guide collection of water, leaf, soil, or air samples for ex-situ analysis to improve prediction accuracy. Observations can be assimilated to update probabilistic predictive models, so as to allocate resources such as UAVs. Bayesian optimization in the contextual bandits setting is a powerful method to close the loop on such decision problems. Deep reinforcement learning is a possible technique for learning policies to optimize robotic sampling in uncertain and unstructured environments.

Precision agriculture

We are developing smart robotic systems to improve efficiency and yield of farm operations. Our goal is to provide specialty crop growers with a data-driven deployment strategy that makes synergistic use of a networked robotic system working interactively with a human scout.

Semantic object mapping

With applications in geology, land-cover dynamics studies, and disaster response, we are exploring methods to organize and analyze large aerial image datasets, in order to search for and map semantic objects (damaged infrastructure, rocks, trees).  

Cyber-physical systems (CPS) challenge

We are developing a cloud-enabled testbed for UAV education and research. Supported by the NSF Cyber-physical Systems (CPS) program, the testbed includes standardized UAV hardware and an end-to-end simulation stack built upon open source technologies. The testbed facilitated UAV competitions held at the TIMPA airfield in Tucson Arizona, in 2016, 2018, and 2019. In these events, teams from Vanderbilt University, University of Arizona, UCLA, University of Pennsylvania, Embry-Riddle Aeronautical University, Halmstad University(Sweden), and Arizona State University demonstrated with varying degrees of autonomy, the deployment and retrieval of sensor probes and other objects. The first competition in 2016 motivated by Microsoft Research’s Project Premonition, which also funded the hardware for the participating teams. 

In May 2020, we will hold the final chapter of this series, SoilScope. The goal is to autonomous deploy and recover a soil sampling probe with onboard instrumentation to measure soil moisture. 


Robotics and AI for the arts

Projection mapping, interactive art installations, drone shows -- these are examples of robotics, computer vision, and AI seamlessly blending together to inspire the public, and promote STEM education in a fun and engaging setting.

We foresee the technology developed for STEM research translating into audio-visual art projects, driven by students with diverse interests and backgrounds.