My name is Ross B. Alexander and I’m a first-year graduate student pursuing a Master of Science in Aeronautics & Astronautics at Stanford. (M.S. 2021; Ph.D. 2024)
From 2015 to 2019, I pursued a Bachelor of Science in Aerospace Engineering at Texas A&M University. In May 2019, I graduated with my Bachelor of Science in Aerospace Engineering.
In early 2019, I was selected as a Stanford Graduate Fellow in Science & Engineering! I am very grateful to have been nominated and selected for support as a doctoral fellow through the SGF program.
From the Office of the Vice Provost for Graduate Education:
Each year, SGF awards approximately 100 fellowships providing stipends and tuition support to outstanding students pursuing doctoral degrees in science and engineering. Since the first fellowships were awarded in 1997, over 1600 Stanford Graduate Fellows have received their PhDs from Stanford.
The program was initiated by Gerhard Casper, then President of Stanford University, and is designed to support the University’s commitment to attracting the very best graduate students while reducing its dependence on federal funding for PhD training.
Students must be nominated for the SGF by their department. Most nominees are students who are newly admitted to an eligible science or engineering department. Other nominees are promising students who have already completed a year or more of graduate study at Stanford or elsewhere, and have demonstrated excellence in doctoral level research and study.
Fellows are selected each year by the Graduate Fellowships Faculty Advisory Committee, made up of faculty from many eligible departments.
My future graduate research is focused on statistical machine learning, reinforcement learning, decision theory, autonomous driving, and human-centered autonomous systems.
In March 2020, I joined the Stanford Intelligent Systems Laboratory (SISL). I’m currently working on sponsored research with Ford Motor Company on planning under uncertainty with sensor occlusions in urban driving scenarios. The principal research focus is on improving pedestrian collision avoidance in occluded scenarios by fusing intelligent infrastructure data into planning algorithms. The approach utilizes partially observable Markov decision processes (POMDPs) to enable autonomous driving that is responsive and robust to uncertainty in the environment and sensors.
Stanford Profiles: @ross-alexander