Tanush Paradeshi

Hello I’m Tanush, a sophomore at Carnegie Mellon University studying computer science and statistical machine learning. I enjoy building things across web and mobile development, machine learning, and data science, with projects spanning computer vision, bioinformatics, and computational modeling of physical systems. I’m particularly interested in math, probability, and algorithms, and I’ve also spent time doing competitive math and programming.


Education

Carnegie Mellon University

B.S. Computer Science + Statistics/Machine Learning (edit as needed)

Relevant Coursework

Skills

Proficient Programming Languages

Python, C/C++, Java, JavaScript, HTML/CSS, SQL, R, MATLAB

Packages / Software

React Native, TensorFlow, Keras, PyTorch, scikit-learn, Matplotlib, Pandas, NumPy, Seaborn, jsPsych, Gmsh, PyRadiomics, SimNIBS, Ionic, Firebase, Flask, Angular, Django, MongoDB

Projects

CellCounter: Cell Count Estimation in Spatial Transcriptomics

Computer vision pipeline for inferring cell counts from Visium spatial transcriptomics histology images (StarDist + OpenCV).

Cone Beam CT Scan Radiomics ML Pipeline

Radiomics + ML pipeline on Cone Beam CT scans to predict medical outcomes (feature selection + classical ML).

Transcranial Magnetic Brain Stimulation Optimization

Large-scale finite-element modeling and unsupervised learning for optimizing deep transcranial magnetic stimulation.

Machine Learning–Based Diagnosis of Retinitis Pigmentosa

ML–based diagnosis of retinitis pigmentosa from retinal images, deployed as an end-to-end mobile React application..

UCI Online Learning and Decision Neuroscience Experiments

Three Javascript-based web experiments analyzing human estimation, feedback learning, and crowd behavior.

Procedurally Generated 3D Maze Game

3D maze escape game with raycasting, procedural generation, and adversarial agents.

AnkiLite: Time-Aware Spaced Repetition Learning System

Full-stack Anki-inspired flashcard system with configurable scheduling, time-aware grading, and both spaced-repetition and cram review modes.