Prospective students

I am hiring motivated undergraduate, masters, and Ph.D. students with background in robotics, computer vision, machine learning, algorithm design, software systems, and hardware design.

  • If you are already at ASU and want to work with me, please send me an email at jnaneshwar.das@asu.edu with your CV and a few lines on any projects you have worked on, related to the areas listed above. 
  • 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 CPS Virtual Organization’s Active Resources initiative, the testbed includes standardized UAV hardware and an end-to-end simulation stack built upon open source technologies. The testbed facilitated a pilot student UAV challenge held at the TIMPA airfield in Arizona on October 3-4, 2016, where four student teams from Vanderbilt University, University of Arizona, UCLA, and UPenn demonstrated with varying degrees of autonomy, the deployment and retrieval of a mosquito trap. This task was motivated by Microsoft Research’s Project Premonition, which also funded the hardware for the participating teams. 

In May 2018, we held the second CPS challenge, this time open to teams worldwide. The goal of this second challenge was to use a quadrotor aircraft with downward facing camera, and possibly other sensors, to scan an area for a lost aircraft (light mock model), and recover it safely back to base. The student team from Embry-Riddle Aeronaurical University was the winner of the 2018 challenge

 

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.