Developing deep learning predictive models to generate alpha for futures high frequency trading and equities statistical arbitrage as part of Jump Core Strategies

Utilized recurrent neural networks in PyTorch to perform supervised learning of error diagnosis from syndrome measurements for continuous quantum error correction with applications in quantum annealingPublished manuscript "Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits" in New Journal of PhysicsImplemented a policy-gradient-based reinforcement learning algorithm for robust optimization of the variational parameters of the quantum approximate optimization algorithm on IBM Q quantum devices using Qiskit

Developed hybrid quantum-classical deep learning models with TensorFlow Core, Probability, and Quantum for perception, prediction, and control of quantum dynamicsAuthored manuscript "Provably Efficient Variational Generative Modeling of Quantum Many-Body Systems via Quantum-Probabilistic Information Geometry"

Developed a benchmark for value alignment and safe exploration of reinforcement learning agents in text adventure games and applying pretrained language models with ethical understanding in PyTorch to induce moral behaviorPublished manuscript "What Would Jiminy Cricket Do? Towards Agents That Behave Morally" in NeurIPS 2021 Datasets and Benchmarks TrackPublished manuscript "Moral Scenarios for Reinforcement Learning Agents" in ICLR 2021 Workshop on Security and Safety in Machine Learning Systems

Developed asynchronous gossip algorithms using MPI in C++ for distributed stochastic optimization of large-scale regression models as part of the Advanced Scientific Computing team

Developed active ring-down methods for the dispersive readout resonator using quantum simulation and computational optimization in Python to achieve faster surface code error correction cycles on Google's quantum processors

Implemented quantum machine learning algorithms, including the quantum variational classifier and quantum support vector machine, on IBM Q quantum devices with Qiskit and scikit-learn to develop real-world artificial intelligence solutions for IBM's Global Chief Data Office

Developed a high-performance parallel-in-time ordinary differential equation solver of the Lindblad master equation using PETSc in C/C++ for efficient and scalable quantum optimal control of LLNL's quantum system

Simulated and analyzed beyond the Standard Model physics with the PYTHIA event generator and ROOT data analysis framework in C++ to search for the undiscovered W' and Z' bosons in collaboration with the ATLAS experiment at CERN

Developed and simulated using Monte Carlo methods in Mathematica a novel experiment to determine the direction of the gravitational acceleration of antihydrogen in affiliation with the ALPHA experiment at CERNPublished manuscript "Concentric Cone Antihydrogen Gravity Experiment" as first author in AIP Conference Proceedings of the 25th International Conference on the Application of Accelerators in Research and Industry