Karan Hora is a Masters student in Computer Science at SUNY Buffalo with a GPA of 3.8/4.0. He has experience in machine learning, data analysis, software engineering and internships applying machine learning to problems in healthcare, finance, education and transportation. His skills include Python, C++, Java, Scala, Spark, SQL, and machine learning libraries. He is currently interning at Karuna Labs developing an adaptive VR application for healthcare using Python, C#, Unity and SQLite.

1. K A R A N H O R A
Buffalo NY | +1 (716) 717-0990 | karanhor@buffalo.edu | github.com/h2k804 | linkedin.com/in/karan-hora
EDUCATION
Masters, Computer Science (specialization in Machine Learning) Aug ’17 - present
State University of New York at Buffalo, GPA – 3.8/4.0
SKILLS
§ Python, Intermediate: C++, Java, Scala
§ Hadoop, Spark, SQL, NumPy, SciPy, Pandas, R
§ Machine Learning – Scikit-learn, TensorFlow
§ Computer Vision – MATLAB, OpenCV
EXPERIENCE
Machine Learning Intern | Karuna Labs, San Francisco June ’18 - present
Adaptive VR healthcare application – Python, Scikit-learn, C#, Unity, SQLite
- Working on a virtual reality application for helping patients with chronic pain to regain movement by performing exercises.
- Prepared specialized training regimes using supervised learning algorithms using movement data captured through multiple
sensors, and historical patient data.
- Enhanced VR games to be adaptive to patient data and performance in real-time.
- Implemented local storage using SQLite.
Data Analyst | Mu Sigma Inc, Bangalore, India Nov ‘16 - May ‘17
Credit Card Transactions Analysis Dashboard – Spark, Scala, Hadoop, AngularJS
- Developed an interactive dashboard application to analyze millions of credit card transactions using statistical and machine
learning models for leading payment card network.
- Built data pipeline to gather, combine and maintain data from credit unions and daily updated transactional data.
- Applied regression and classification machine learning algorithms in Spark.
Software Engineer in Test | PayU Payments Pvt Ltd, Gurgaon, India Jul ‘16 - Oct ‘16
Credit Risk Analysis – Python
- Developed regression model to identify potential customers with minimal credit risk using their transactional data.
- Gathered, combined and maintained data of customers with around 200 features.
Intern | Dept. of Information Technology, Govt. Of Delhi, India Sept ‘16 - Oct ‘16
Adaptive Learning Game Development – Java, Android
- Created an app to help children learn new subjects (math, languages) and skills (handwriting) by analyzing their performance and
iteratively taking them through the various levels in that skill.
Intern | Interglobe Technologies, Gurgaon, India Jul ‘15 - Sept ‘15
Flight Price Fluctuation Prediction – R, Python
- Developed a regression model for predicting the fluctuation in the price of an airline ticket using data gathered from airline APIs.
PUBLICATIONS
“Classifying Exoplanets as Potentially Habitable Using Machine Learning,” Springer, Advances in Intelligent Systems and Computing,
ICT Based Innovations, vol. 653 [link]
RECENT PROJECTS
Unsupervised Image Segmentation of Brain tumor scans using Generative Adversarial Networks – Tensorflow, MATLAB
Segmentation of Brain MRI to detect tumors using Generative Adversarial Networks. Implementation of DualGAN architecture
proposed by Yi et. al. (2017). Achieved DICE score of 0.64.
Music Genre Verification using Bayesian Networks, SVMs, Decision Trees and CNNs – Tensorflow
To identify whether two music samples belong to the same genre. Implemented in three ways: Bayesian Network implemented
in Python, SVMs and random forest using Scikit-learn and a Siamese deep neural network using Tensorflow. Accuracy 84%.
Scene Classification using Bag-of-Words using K-means Clustering and Spatial Pyramid Matching – MATLAB
Classifying an image into one of eight trained scenes using the BoW approach. Used SIFT feature transforms and K-Means
clustering to segment the images into K Visual Word. Spatial Pyramid Matching was used to classify test images. Accuracy 55.67%.
Image Segmentation of MRI short axis Cardiac Scans using Convolutional Neural Networks on GPUs – Python, Theano
Developed an automated model using deep learning for semantic segmentation of the left ventricle in cardiac MRI scans,
calculating ejection fraction and classifying a patient as potentially at risk or not.
CERTIFICATIONS
Massachusetts Institute of Technology, “15.071x Analytics Edge”, R and Machine Learning (edx.org)
Caltech, “Learning from Data”, Machine Learning (edx.org)