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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

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Posts

portfolio

publications

Cost-Effectiveness of Liquid Biopsy for Colorectal Cancer Screening in Patients Who Are Unscreened

Published in JAMA Network Open, 2023

I really enjoyed getting to work with such a great team on this paper, all of whom brought tremendous time and talent to the task. This was also a great opportunity to build up the cost-effectiveness and probabilistic sensitivity analysis side of my modelling skills. Getting to play a part in this sort of research where there is a real opportunity to shift healthcare policy and impact lives was deeply rewarding too.

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Optical and atomic decoherence in quantum nondemolition measurement induced atomic ensemble entanglement

Published in AVS Quantum Science, 2023

This project served as my introduction to the Lindblad equation and decoherence more generally, which are often under addressed in a standard undergraduate physics curriculum. I learned a lot and it was a great project for sharpening my Mathematica skills. I am very grateful to my co-authors and my then advisor for this opportunity.

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Quantifying the potential benefits of early detection for pancreatic cancer through a counterfactual simulation modeling analysis

Published in Nature Scientific Reports, 2023

Counterfactuals and causality, along with programming and methodologic similarities, are two features that overlap between my physics research and background and my statistical modelling work at Columbia. This project was a really cool idea, as counterfactual simulation should really be more widely adopting when thinking of anything related to policy.

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Association Among Individual Race, Hospital Racial Composition, and Access to Minimally Invasive Hysterectomy for Patients With Uterine Leiomyomas

Published in Obstetrics & Gynecology, 2024

This was one of my first non-CISNET related projects in my position at Columbia University. I definitely underestimated how many attempts it would take for me to accurately create the target cohort and I am very grateful to the patience and support of my co-authors for this task. It was also my first “big-data” challenge, where even though the computations needed, and code used was simple, I had to be much more mindful of using resources efficiently. I learned a lot on this project, and it was rewarding to be a part of social determinants of health research with the goal of addressing systemic biases. I really hope the importance of studies such as this are made known to the broader public, particularly in the current challenging funding environment.

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Excess morbidity and mortality associated with underuse of estrogen replacement therapy in premenopausal women who undergo surgical menopause

Published in American Journal of Obstetrics and Gynecology, 2024

I really enjoyed working with my co-authors on this project as it was a great opportunity to employee Bayesian methods and was very policy change focused. It is always an interesting experience getting to build of an existing model, it feels like reading someone elses code so closely is a window into their mind. It definitely makes me reflect on earlier code I have written and wish my apologies to anyone who has had to inherit it! Learning is thankfully a lifelong pursuit and this project definitely helped improve my coding.

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Noise-Aware Detectable Byzantine Agreement for Consensus-based Distributed Quantum Computing

Published in IEEE QCNC, 2025

This project was made possible thanks to the Quantum Open Source Foundation and their mentorship programme. I’m tremendously grateful to my mentor Dr. Chen for all of the support as well as dreamining up the original project idea. This was my first foray into distributed quantum computing and cryptography which has ignited my research interest ever since.

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talks

Imputation of SEER Data using Artificial Neural Networks

Published:

This project was presented as a part of the CISNET Junior Investigator series. The goal of this work was to use neural networks to remedy known missing data issues in a series of SEER databases. The Multilayer Perceptron model was an excellent candidate for imputation over this dataset due to the high dimensionality of underlying data. Our results were later integrated into certain CISNET models.

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CISNET Gastric Cancer Modeling

Published:

This presentation encompassed the now complete, first generation gastric cancer microsimulation model. The goal of this talk was to demonstrate how well targets were being met and explain the decision-making that guided the model to its final structure.

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Age-Period-Cohort Methods in Cancer Epidemiology

Published:

This talk covers a new methodology we developed to assist model decision making through visualization and information-theoretic metrics. Many microsimulation models take into account multiple time based variables and choosing which approach best fits the underlying data was largely a process of guesswork. We aimed to put this procedure on more rigorous footing to better inform other CISNET groups.

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Causal Discovery in Alzheimer’s Disease Research

Published:

This project was inspired by recent graphical transformer machine learning based methods for causal discovery. The challenge for causal discovery benchmarking is the need for a known underlying causal structure to compare with. My colleague Dr. Jiheum Park found an excellent dataset on Alzheimer’s progresson which has a known gold standard causal structure. This was a great way to test a novel AI based methodology.

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Quantum Zero-Knowledge Protocols

Published:

This project was the final assesment of my graduate level cryptography course with Dr. Rosario Gennaro. This course was instrumental in shaping my future research direction. It was my first theoretical computer science course and it couldn’t have happened at a better time for such an exciting emerging crossover between physics and cryptography.

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Complex Network Analysis of Global Conflicts

Published:

This project involved deploying complex network analyis metrics to a live dataset of global conflict events to explore the dynamics of formation and counterformation in conflict zones. I partitioned the data into friends/enemy networks based on allegiances at particular conflict events and explored scaling, betweeness, clustering and distance metrics to better understand how conflict propagates.

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teaching

General Physics I

Undergraduate lab, NYU, Physics, 2020

I was the Lab Teaching Instructor covering experiments on the following topics:

  1. Measurements and Uncertainty
  2. Motion Sensing
  3. Particles in Equilibrium
  4. Kinematics
  5. Newton’s 2nd Law
  6. Work - Energy Relations
  7. Ballistic Pendulums
  8. Torque and Angular Momentum

General Physics II

Undergraduate lab, NYU, Physics, 2021

I was the Lab Teaching Instructor covering experiments on the following topics:

  1. Electrostatics
  2. Electric Field Mapping
  3. Voltage, Current, and Resistance
  4. Electron Charge to Mass Ratio
  5. Current Balances
  6. Electromagnetic Induction
  7. Diffraction and Interference
  8. Oscilloscopes and EKG Readings