About Jeremy Castagno


I am an engineer, computer scientist, and roboticist. I worked three years as a control systems engineer for Valero Energy specializing in safety systems. I later decided to pursue a PhD in robotics at the University of Michigan where I currently focus on urgent landing of Unmanned Aircraft Systems (UAS). I propose utilizing offline data sources such as city maps, satellite images, and airborne LiDAR point clouds to create risk-evaluated emergency landing site databases, including building rooftops. Real-time controlled touchdown is aided by my algorithm Polylidar3D that identifies flat surfaces from dense point clouds and transforms them to polygonal representations.

My area of expertise is in machine learning, simulation/modelling, and developing robust decision-making strategies. My future research goals are to create algorithms and data structures for robots which utilize the geometry of their environments for resource efficient mapping, collaboration, and visualization. I plan to investigate methods of fusing both deep learning and deterministic explainable algorithms to ensure robust and well understood decisions. I enjoy programming in C++, Python, and using the Unreal Engine for simulations. Full CV or Shortened Resume.


PhD, Robotics, University of Michigan (Expected 2021)
MSc, Robotics, University of Michigan (2018)
BSc, Chemical Engineering, Brigham Young University (2013)
  Minors: Computer Science and Mathematics

Work Highlights

Research Assistant, A2SYS Lab at UMICH (May 2016 - Present)
- Presented methods to incorporate rooftops as emergency landing sites for drones
- Conducted multi-city analysis predicting roof shapes using ML with sensor fusion
- Created state of the art polygon extraction library for both 2D and 3D data
Independent Contractor, Department of Defense, (2019-2020)
- Reviewed predictive maintenance methods for DOD assets and interviewed personnel
- Presented whitepaper proposal for video/audio information extraction using CV/NLP
Research Intern, NASA Langley, (2018-2020)
- Conducted experiments for landing site selection of drones with onboard LiDAR
- Designed software architecture for online sensing with emergency landing directives
Process Control Engineer, Valero Energy, (2013-2015)
- Conducted simulation, hardware-in-the-loop, and field testing of safety systems
- Led advanced control system upgrade with an estimated savings of 2 million/year
Research Assistant, PRISM Lab at BYU (2012-2013)
- Investigated optimal real-time parameter estimation for towed cable systems
- Programmed MATLAB and C++ interfaces for benchmark laboratory systems

Project Highlights

Roof Shape Classification

Multimodal data fusion

Satellite images and LiDAR data are use as input to a neural network to predict roof shape. A diverse annotated roof image dataset for small to large urban cities is created and used to evaluate our model.

Urgent Landing

UAS Rooftop Landing

Offline construction of a landing site database which is risk evaluated for people and property. Flat roofs are identified from LiDAR and image data and then processed to extract obstacle-free surface(s) for landing. We use a multi-goal planner which chooses a Pareto Optimal landing site.

Gas-fired Heaters

Safety Systems

Safety Interlock Systems (SIS) play a vital role in ensuring the operability and safety of process units. I lead the SIS design and verification through factory acceptance tests (FAT) and site acceptance tests (SAT) of 4 heaters.

Gasoline Blending

Optimizing Product Quality

Lead control system engineer to help design and implement a major overhaul of the gasoline blending system. Complete upgrade of the basic control system (BCS) and the installation and programming of an advanced process control using online analyzers.

Media and Awards

Amazon re:MARS

Invited Speaker

Presented work on urgent landing site identification for small UAS in urban cities. Demonstrated AI/ML predicting roof shapes and Polylidar3D extracting landing sites of flat rooftops.

Swarm and Search AI

1st Prize Winner

National DOD sponsored competition for creating intelligent algorithms for controlling a group of drones in tracking and estimating a wildfire boundary. We took first prize ($27,000) and won the innovation award.

Into the Dataverse

1st Prize Winner

Joint Artificial Intelligence Center and NSIN sponsored a 3-day competition. We presented methods for recording maintenance logs with an AI-enabling user interface. Awarded $15,000 contract.

Awarded by First Sea Lord

HMS Queen Elizabeth

Received Royal treatment as guests on board the HMS Queen Elizabeth on Nov. 20 2019. Recognized as winners of the “Swarm and Search AI Challenge: 2019 Fire Hack.” Presented award by First Sea Lord of the Royal Navy.

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