Developed a quantum-inspired AI framework, from conceptualization to deployment in a production environment, for training of Large Language Models 2x faster.

Led research initiatives focused on leveraging Quantum Machine Learning (QML) and Quantum Optimization techniques to address complex challenges in particle physics and beyond.• Developed a hybrid Quantum Generative Adversarial Network (QGAN) architecture with a quantum generator and classical discriminator utilizing PennyLane and PyTorch capable of learning random distributions. • Designed a hybrid Quantum Support Vector Machine (QSVM) classifier with a quantum feature map using Qiskit and trained using scikit-learn for quark-gluon jet classification for LHC data.• Devised an anomaly detection algorithm for high energy physics datasets leveraging state-of-the-art Quantum Autoencoder architectures.• Developed an algorithm formulating particle track reconstruction as a Quadratic Unconstrained Binary Optimization (QUBO) problem, solving it using quantum annealing in D-Wave Ocean, and using Variational Quantum Eigensolver (VQE) in Qiskit.• Applied ideas from quantum information science to discover viable pathways in resolving the black hole information paradox, deriving a novel bound on entanglement entropy for information extraction.

• Conducted computational research in particle physics and cosmology, utilizing advanced statistical analysis techniques to build models, validate models against large datasets, and developing efficient solutions for scientific computing challenges. • Collaborated on 8 projects with international teams, managing 5 projects by coordinating workflow, identifying team members' expertise, setting goals, and contributing to 8 published research papers.• Presented research findings in seminars and conferences in international avenues, demonstrating effective communication skills and ability to convey complex scientific concepts to diverse audiences.• Led three projects investigating potential sources of stochastic gravitational waves detected by pulsar timing arrays, employing Bayesian inference via MCMC methods to analyze data and construct predictive models.• Developed two public code packages in Python for fast calculation of stochastic gravitational waves from cosmic strings, and for investigating cosmological impacts of dark matter particles in the early universe, leveraging parallelization across high performance computing clusters.• Developed an optimized algorithm for calculating high order statistical correlation functions of inflationary perturbations, leveraging parallelized computation techniques, reducing the computation time by 90%.• Developed an autoencoder-based anomaly detection algorithm for high energy physics data utilizing PyTorch.• Designed an efficient deep neural network classifier for particle identification in the MiniBooNE experiment using TensorFlow, improving over the state-of-the-art decision tree classifier.

Conducted dissertation research on utilizing symmetry groups to understand the data from neutrino experiments, and building predictive models. • Developed a flavor symmetry and grand unification model for particle physics applying Group Theory techniques, capable of explaining neutrino mixing angles observed in experiments.• Designed two models to explain the discrepancy in the observed data of muon g-2 experiment at Fermilab, identifying new physics from novel particles.• Developed a fast algorithm for investigating signatures of particle production in the early Universe using high performance computing (HPC).

• Conducted research on Data-driven Cosmology, Computational Particle Physics, and theoretical Quantum Information Science supported by Department of Energy (DOE) and National Science Foundation (NSF).• Presented research finding in international conferences, contributed to published research papers.• Taught discussion and laboratory sessions for undergraduate Physics courses, communicating complex ideas to a diverse group of students. • Won Wayne Bomstad II Memorial Award for Teaching Assistant (lab) and Graduate Student Teaching Award for 2019-2020.

• Conducted in-depth analysis of diverse financial datasets, including price-volume time series, fundamentals, analyst recommendations, and news data, across various asset classes including equities, ETFs, forex, and futures markets, to identify patterns and develop algorithmic trading strategies.• Applied a wide range of quantitative techniques, including technical analysis, statistical arbitrage, sentiment analysis, and machine learning algorithms to uncover actionable insights and generate alpha for various financial assets and sectors, including commodities, metals, and energy.• Contributed to the firm's investment decision-making process by collaborating with cross-functional international teams to optimize portfolio performance and mitigate risk in dynamic market conditions.

• Taught laboratory classes on Programming Languages, Electrical Circuits, Electrical Machines and Electronics.• Managed a student pool of 160 on average.

• Designed an in-system programmer for PIC trainer kit using microcontroller and other embedded system based architectures.• Prepared documentation and provided training to other staff members.