Aparna Gupta Email and Phone Number

Execute COMSOL simulations to optimize taper lengths and LiNbO3 rib width for acoustoelectric waveguides.Perform high-precision taper fiber pulling and testing for advanced optical applications.Develop firmware for Data Acquisition systems and galvanometer scanners, enabling high-speed raster scanning.Conduct comprehensive cavity resonance testing and fine-tune cavity parameters to facilitate the Purcell effect in emitter-cavity experiments for diamond color centers.Setup and calibrate single-photon detection systems to locate silicon vacancies in diamond color centers.

Lead Developer, Quantum Network Simulation Models: Spearheaded the development and optimization of advanced quantum network simulation models at the NSF Engineering Research Center for Quantum Networks, leveraging extensive expertise in quantum mechanics and computational methods to enhance the accuracy and scalability of simulations.Quantum Algorithm Design and Implementation: Designed and implemented cutting-edge quantum algorithms tailored for networking protocols using Qiskit and other quantum computing frameworks, addressing key challenges such as error correction and entanglement distribution to improve the performance and security of quantum networks.Optimization and Validation of Quantum Models: Conducted rigorous testing and continuous optimization of quantum network simulation models, ensuring they accurately reflect real-world scenarios and provide valuable insights for the advancement of quantum networking research and applications.

Developed Logical Clifford Synthesis Algorithms: Utilized symplectic geometry principles to create efficient algorithms for synthesizing logical Clifford operations, crucial for fault-tolerant quantum computation. This involved rigorous mathematical formulation and implementation using advanced Python libraries, ensuring high precision and performance in quantum circuits.Conducted In-Depth Study of Error Correction Codes: Undertook a comprehensive analysis of various quantum error correction codes, including surface codes and color codes. This included theoretical study, practical implementation, and performance evaluation, resulting in a deep understanding of the mechanisms to protect quantum information from decoherence and operational errors.Simulated and Optimized Quantum Error Correction Protocols: Designed and ran extensive simulations to test and refine quantum error correction protocols. Leveraged object-oriented programming to structure the simulations, enabling detailed analysis and optimization of the codes' performance in real-world quantum computing environments, thereby contributing to more robust and reliable quantum systems.

Predicted 3D Protein Structures Using CVAR: Utilized Convolutional Variational Autoencoders (CVAR) to predict the three-dimensional structure of proteins based on their polypeptide chains, significantly enhancing the understanding of protein folding mechanisms.Developed Algorithm for Minimum Energy Conformation: Created a generalized algorithm to determine the minimum energy conformation of proteins, contributing to advancements in computational biology and the development of more efficient drug design processes.Leveraged Quantum Computing Techniques: Applied quantum computing principles and methodologies to solve complex problems in protein structure prediction, demonstrating the potential of quantum algorithms in computational chemistry and biology.

Graded Assignments and Provided Feedback: Efficiently graded homework assignments and exams, offering detailed and constructive feedback. Specific assignments included tasks on SLR estimation and diagnostics, MLR collinearity and variable selection, and GLM logistic and Poisson regression. Conducted Office Hours and Study Sessions: Held regular office hours and hosted study sessions to assist students with course material, answer questions, and provide additional support. Topics frequently addressed included MLR hypothesis testing, ANOVA/ANCOVA estimation and inference, and mixed models for correlated data. Organized and Led Exam Review Sessions: Organized and led comprehensive review sessions before midterms and final exams, helping students consolidate their knowledge and prepare effectively for their assessments. Reviewed key concepts such as penalized regression, weighted least squares, and linear mixed models. Facilitated R Programming Support: Regularly hosted R support sessions and trained students on how to use R for their coursework. Provided one-on-one help with homework and projects involving R, covering practical applications in simple linear models, diagnostics, and prediction in multiple linear regression.

Developed ML Job Skills Analysis Model: Created a machine learning model to analyze job skills using data extracted from sites like Indeed, LinkedIn, and Handshake. This project aimed to help students at Eller, the business school of the University of Arizona, develop relevant skill sets for the job market. Data Extraction and Cleaning: Scraped job data from multiple online sources using Python libraries such as BeautifulSoup and Scrapy. Cleaned and preprocessed the data using pandas and NumPy to ensure high-quality input for machine learning models.Implemented Classification Models: Utilized machine learning classification models, including decision trees, random forests, and support vector machines (SVM), to categorize different skills into specific job categories. This helped streamline the process of identifying key skills required for various career paths.Enhanced Student Career Development: Provided actionable insights and recommendations to students, enabling them to focus on developing the most sought-after skills in their desired fields, thereby enhancing their employability and career prospects.

Compiled Day-to-day University Expenses: Managed and compiled daily financial transactions for the university, ensuring compliance with Generally Accepted Accounting Principles (GAAP) and university policies.Reconciled P-Cards: Efficiently reconciled procurement card (P-Card) transactions, verifying accuracy and adherence to budgetary constraints, and resolving discrepancies in a timely manner.Managed Accounts: Oversaw various financial accounts, maintained accurate records, and ensured timely processing of financial transactions, contributing to the university’s financial integrity and operational efficiency.

Edited and Managed Recorded Course Content: Edited and prepared recorded course videos for future dates, ensuring high-quality content delivery and accessibility for students, thereby enhancing their learning experience and resource availability.Graded Assignments and Provided Feedback: Efficiently graded homework assignments, providing detailed and constructive feedback to help students understand and improve their academic performance.Conducted Office Hours and Study Sessions: Held regular office hours and hosted study sessions to assist students with course material, answer questions, and provide additional support, fostering a deeper understanding of the subject matter.Organized and Led Exam Review Sessions: Organized and led comprehensive review sessions before midterms and final exams, helping students to consolidate their knowledge and prepare effectively for their assessments.