Research

I participated in a research project titled "Modelling Variability of Intensive Care Unit Demand: A Comparative Cost Analysis and Performance Evaluation of Fixed versus Variable Beds Arrangements with a Simulation Approach." The study addressed the sustainability challenges posed by limited ICU bed availability and high operational costs. Our goal was to compare the effectiveness of fixed and variable bed allocation strategies using simulation modeling.

I contributed to the development of an integrated simulation framework that incorporated a predictive model for ICU length of stay (LOS), accounting for day-to-day uncertainties in patient demand. The project aimed to inform healthcare policy by evaluating cost-efficiency and performance across different ICU management strategies. We placed third in the competition with this model and were invited to present our work at the ESICM LIVES 2023 conference in Milan, Italy.

As part of a team-based research project, I contributed to a comparative study on theoretical Quantum Random Access Memory (QRAM) architectures. We conducted an extensive literature review and technical analysis of four prominent QRAM models: the Bucket-Brigade Model, Quantum Optical Fanout Model, Topological Model, and Super-Tree Model.

Our work involved dissecting the underlying mechanisms of each architecture—such as how they route quantum data, scale with qubit count, manage coherence, and handle error correction—and systematically evaluating their feasibility, scalability, and resource requirements. We compiled our findings into a formal research paper and delivered a presentation to an academic panel, earning second place in the competition.

Key Focus Areas

• Comparative analysis of QRAM architecture models
• Evaluation of scalability and resource requirements
• Coherence management and error correction strategies
• Feasibility assessment for practical implementation

This research project involved developing a neural network-based exoplanet classification system designed to automatically analyze NASA TESS mission data and categorize celestial objects as Confirmed Planets (CP), Planet Candidates (PC), or False Positives (FP). The system addresses the challenge of processing the massive backlog of TESS observations that would take astronomers days to review manually.

Using a deep neural network trained on over 5,000 observations with 13 key astrophysical parameters, we achieved an accuracy of approximately 78% in classification. The system features a two-tier approach: first checking NASA's database using fuzzy matching (2% error margin) for known planets, then deploying our AI model for new discoveries. Users interact through an intuitive web interface where they can input individual observations or upload batch CSV files, receiving instant predictions with confidence scores, temperature analysis, and habitability assessments.

This is significant because TESS generates thousands of observations daily, creating a bottleneck in exoplanet discovery. Our automated system helps researchers quickly identify promising candidates, prioritize follow-up observations, and potentially accelerate the discovery of habitable worlds beyond our solar system.

This research project presents an educational SAP-1 processor implementation on a BASYS3 FPGA board that communicates with an Arduino Mega through SPI to provide real-time visualization of processor operations. The system runs a basic instruction set (LDA, ADD, SUB, OUT, SNG, HLT) using two separate finite state machines—one for handling user inputs and another for executing instructions.

Students interact directly with the processor using switches to select programs and input operand values, and buttons to advance through execution states. After each instruction completes, the processor sends its complete internal state as 40-bit SPI packets to the Arduino, which then displays this information on five seven-segment displays, LEDs, and through audio feedback.

This setup allows students to actually see what's happening inside a processor as it runs, making computer architecture concepts much easier to understand without needing any external debugging tools. The project was deemed challenging for an undergraduate research project and was completed with full functionality, earning recognition at IEEE TUAC 2025.