I am a Postdoctoral Fellow at Rice University under the supervision of Nai-Hui Chia. I earned a PhD in Computer Science at UT Austin while advised by Scott Aaronson. Prior to that, I received a Bachelor of Science in Engineering from Cornell University in both Computer Science and Engineering Physics.
My focus is on the application of learning theory to quantum problems. That is, given some unknown quantum system, try and learn it, under varying definitions of the word “learn”. During my PhD, I focused on leveraging properties of the stabilizer formalism to tackle topics such as tomography, PAC/SQ/Agnostic learning, property testing, and pseudorandomness. I am additionally broadly interested in quantum information, quantum complexity theory, and theoretical computer science.
Publications [Author Order is Alphabetical unless specified by an asterisk (*)]
Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates II: Single-Copy Measurements
Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates
Improved Stabilizer Estimation via Bell Difference Sampling
Clifford Circuits can be Properly PAC Learned if and only if RP=NP
Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom
On the Hardness of PAC-learning stabilizer States with Noise
* Investigating quantum approximate optimization algorithms under bang-bang protocols
* Simulation of qubit quantum circuits via Pauli propagation