Hey, I'm Roya! πŸ‘‹πŸ»

MS CS at Dartmouth College 🌲

Passionate about computational science, ML/AI, and all things research.

Roya

From Panthers to Pines

A tasteful collection of achievements and honors I've received at Adelphi University and Dartmouth College throughout my time there 🐾🌲

Adelphi University

Adelphi University

Bachelor of Science in Computer Science (GPA: 4.0)

May 2025 πŸŽ“, Summa Cum Laude

Details

  • β€’ Honors College
  • β€’ Software Engineering Track
  • β€’ Minor in Statistics and Data Analytics
  • β€’ Dean's List (All Semesters)
  • β€’ Deans Circle Inductee

Achievements

  • β€’ Honors College Summer Research Fellowship Recipient πŸ“š
  • β€’ Served as a 4x teaching assistant in introductory programming courses, statistics, algorithms, and numerical calculus! 🏫
  • β€’ Honors College Mentor 2x πŸ‘©πŸ»β€πŸ’»
  • β€’ Co-Founder and VP of the W-STEM club! πŸ‘©πŸ»β€βš•οΈ
  • β€’ Publicity Chair for NSLS, the largest honor society on campus! πŸŽ‰
  • β€’ Presented my thesis at the Northeast Regional Honors Conference in March 2025! ✈️
  • β€’ Equestrian Team Competition Rider 🐎
Dartmouth College

Dartmouth College

Master of Science in Computer Science

Expected June 2027

Details

  • β€’ Joining the Hassanpour Lab in Winter 2026 to do research on real-time DL on ultrasound scans! 🩻

Achievements

  • β€’ Guarini Graduate Merit Scholarship (75%)

My Experience

Take a walk down memory lane with me to see how far I've come! πŸ“

Coding Instructor

Coding Minds Academy | Jun. 2022 - Oct. 2022

Mobile Frontend Developer Intern

Pure Match | Dec. 2022 - Apr. 2023

Undergraduate TA (Math/CS Department)

Adelphi University | Jan. 2023 - May 2025

Software Engineer Intern

North Atlantic Industries | Jun. 2023 - Dec. 2024

Undergraduate Student Researcher

Adelphi University | Mar. 2024 - May 2025

Student Researcher

New York Proton Center | Mar. 2024 - Present

Graduate Student Researcher

Hassanpour Lab (Dartmouth College) | Jan. 2026 - Present

Courses That Made Me Think (Really Hard)

A curated list of the courses that shaped my passion for machine learning, AI, and computational research -- from theory to application!

CSC 344Algorithms and ComplexitiesAdelphi University
A
MTH 225Statistics and Data AnalyticsAdelphi University
A
CSC 263Database Management SystemsAdelphi University
A
MTH 253Linear AlgebraAdelphi University
A
CSC 302Artificial IntelligenceAdelphi University
A
CSC 335Machine LearningAdelphi University
A
BIO 585Computational BiologyAdelphi University
A+
COSC 274Machine Learning and Statistical AnalysisDartmouth College
IP
COSC 267Human Computer InteractionDartmouth College
IP

Tools of the Trade

I've gotten pretty good at some tools throughout my career. Here's my favorites!

What I'm Digging Into

A showcase of my research projects that highlight my contributions to the field.

Optimizing Dual Energy CT Models for Accurate Proton Stopping Power Estimation in Pediatric Cancer Therapy

Optimizing Dual Energy CT Models for Accurate Proton Stopping Power Estimation in Pediatric Cancer Therapy

My senior thesis focuses on improving the accuracy of proton stopping power estimation using dual energy CT models, particularly in pediatric cancer therapy. This research is conducted in cooperation with the New York Proton Center. Through this work, I have created the first DECT analysis tool that is currently being used by the New York Proton Center in their labs. My research advisor is Dr. Sixia Chen from the Computer Science department at Adelphi University. Additionally, I was awarded the prestigious Honors College Summer Research Fellowship to pursue these studies.

Undergraduate Thesis (Adelphi University)

πŸ“„ Read Full Thesis
Quantitative Benchmarking of SECT and DECT Proton Models in Noisy Imaging Conditions

Quantitative Benchmarking of SECT and DECT Proton Models in Noisy Imaging Conditions

This project investigates the comparative robustness of single-energy CT (SECT) and dual-energy CT (DECT) based proton stopping power estimation models under varying levels of acquisition noise. Pediatric CT phantom datasets are used to benchmark model performance using quantitative metrics, assessing the stability and accuracy of predicted stopping power ratios. The study builds on Schneider’s stoichiometric calibration (1995) and applies it across multiple noise conditions to evaluate clinical reliability. This project is currently under active development and intellectual property protection in collaboration with the New York Proton Center. Principal Investigator: Dr. Han (New York Proton Center)

New York Proton Center