Tony Alarcon
Software Engineer, Safety-Aware Drone Ecosystem (SADE) at University of Notre Dame
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolTony Alarcon is a software engineer and Ph.D. candidate specializing in AI-enabled autonomy for safety-critical UAV systems, with eight years of experience building deployable autonomy stacks and cloud-backed services. He blends research in reinforcement learning, large language models, coverage path planning, and uncertainty-aware decision-making with hands-on engineering—shipping containerized FastAPI services on AWS for NASA’s Safety-Aware Drone Ecosystem and mission-planning systems for multi-UAV search. His publications span top robotics and cyber-physical systems venues and reflect practical contributions in safe separation, UAV health analytics, digital twins, and multi-agent RL. Tony pairs academic rigor (Ph.D., MS Notre Dame; BS Astrophysics UCLA) with applied skills in Django, PostgreSQL, Docker, and geospatial ML, having fine-tuned DeepLabV3 models for terrain-aware planning. He also mentors and teaches, earning a campus teaching award, and has a knack for turning formal safety requirements into scalable, auditable system interfaces for real-world missions.
8 years of coding experience
Certificate, Data Science, Certificate, Data Science at UCLA ExtensionCertificate, Computer Science, Certificate, Computer Science at Santa Monica CollegeDoctor of Philosophy - PhD, Computer Science and Engineering, Doctor of Philosophy - PhD, Computer Science and Engineering at University of Notre DameUniversity of California, Los Angeles
Spanish
Github Skills (5)
prediction4
ieee3
vulnerability2
replication2
machine-learning2
Github contributions (5)
TonyAlarcon/TonyAlarcon_1.github.io
Apr 2020 - Jun 2022
Contributions:350 pushes, 1 branch in 2 years 2 months
tpchambers/Vulnerability-Prediction
May 2022 - May 2022
Replication and Analysis of Bilgin's et. al., IEEE published work "Vulnerability Prediction From Source Code Using Machine Learning"
Contributions:21 commits in 1 day
predictionmachine-learningvulnerabilityreplicationieee
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