Dhanvi Bharadwaj Portfolio

About Me

Hello! I'm Dhanvi Bharadwaj - an undergraduate student at the University of Wisconsin-Madison, studying Physics and Data Science, focusing on Quantum Computing and Machine Learning. Ever since I was introduced to Quantum Computing a few years ago, I've been enamored by its immense potential to reshape the boundaries of computation and predictive modeling. I'm enthusiastic about exploring the vast applications of Quantum Algorithms in areas such as Machine Learning and Cryptography. I'm also fascinated by the transformative potential of Machine Learning, with its ability to extract meaningful insights from vast amounts of data and improve decision-making processes.

I also enjoy working with people passionate about technology and thrive in innovative workspaces. My experience in agile growth-oriented environments has allowed me to apply my technical and analytical skills to contribute to important projects. I'm currently seeking opportunities that stretch my interests in Software Development and Machine Learning applications. I would love to discuss innovative roles and projects, so send me an e-mail with any questions!

Professional Experience

Software Engineer Intern - qBraid (February 2024 - Present)

Quantum Machine Learning Intern - Oak Ridge National Laboratory (May 2023 - August 2023)

• Developed and implemented Quantum Machine Learning models using Pennylane and Qiskit frameworks, focusing on 2-6 qubits to assess predictive capabilities on highly multivariate and non-linear datasets.
• Optimized fidelity and cross-entropy loss methods for our models, to reduce loss function values by an average of 20%.
• Utilized state-of-the-art techniques such as data re-uploading to solve limitations posed by the no-cloning theorem and enhance the performance of our quantum models by 30%.
• Implemented and refined quantum circuit architectures such as SU(4) and IsingZZ coupling for multiclass classification tasks on imbalanced datasets.

Undergraduate Research Assistant - Thevamaran Lab (October 2020 - Present)

• Utilized Python to implement computational techniques to correct strain-overshoot in viscoelastic relaxation experiments, to help improve the accuracy of dynamic moduli calculations
• Collaborated with researchers to execute data analysis using SciPy that identified an opportunity to reduce noise from resonance effect
• Investigated the viscoelastic properties of vertically aligned carbon nanotubes (VACNT) across broad frequency and amplitude ranges using dynamic mechanical analyzer.
• Regularly presented research findings, insightful trends, and visualizations using Matplotlib/Power BI at weekly meetings, to a diverse audience of researchers.

Quantum Software Lead - Wisconsin Quantum Computing Club (August 2023 - Present)

• Leading a team of 5 students to organize the inaugural IBM Qiskit Fall Fest at UW-Madison consisting of Workshops, Tutorials, and a Hackathon
• Developing and leading interactive workshops for 80+ students in Quantum Simulations and Error-Correction
• Teaching introductory topics related to quantum computing and linear algebra
• Organizing career talks for students with leading experts in quantum computing from industry and academia

Peer Mentor Tutor - Physics Learning Center (September 2022 - Present)

• Facilitated dynamic and engaging collaborative learning sessions for over 30 students in university physics I & II, fostering a supportive and interactive environment conducive to effective learning.
• Fostered a sense of camaraderie and teamwork among students by organizing group problem-solving activities, encouraging active participation and collaborative problem-solving skills.
• Actively contributed to the continuous improvement of the Physics Learning Center by collaborating with fellow tutors and instructors to develop new educational resources and refine teaching methodologies.

Recent Projects

NBA MVP Prediction Model

• Developed and implemented machine learning models using Python to accurately predict 84% of all NBA MVPs, including the 2021-22 season award winner.
• Utilized powerful regression frameworks such as Random Forest, LightGBM, and XGBoost to compare the accuracy, average mean absolute error (MAE) and the coefficient of determination (R²) between the models .
• Incorporated mutual information based feature selection to improve accuracy of the model by 25%.

Quantum Random Password Generator

•Developed and implemented a quantum random number generator using Qiskit, harnessing the power of quantum computing to generate truly random and unpredictable numbers.
•Employed classical post-processing techniques to map the quantum-generated numbers to a desired range, ensuring compatibility with conventional applications and systems.
•Deployed website with Flask to strengthen 50+ users' passwords for increased protection against data breaches.

Wisconsin Land Tract Analysis

• Utilized Python to implement supervised machine learning techniques for predicting population in Wisconsin counties based on land features.
• Implemented a color-encoded map of different land features with raster data for each county in Wisconsin.
• Incorporated rasterized data, shapefiles and GeoDataFrames to construct detailed plots.

Awards & Recognition

• Recipient of the prestigious university-wide Hilldale Undergraduate Research Fellowship in recognition of my research project to support work at the Thevamaran Lab for the 2023-2024 academic year.

• Recognized for expanding functionality to the widely-used torchquantum library during the open-source quantum computing hackathon hosted by Unitary Fund in 2023.

• Maintained a consistent record of academic excellence by earning a place on the University of Wisconsin-Madison Dean's List for all semesters of undergraduate education.