Page 1 of 1

(585) 309-0119

srinathobla@mail.rit.edu SRINATH OBLA github.com/srinathos

linkedin.com/in/srinathos

EDUCATION

Rochester Institute of Technology MS in Computer Science October 2019

Focus: Machine learning, Pattern Recognition, Computer Vision, Computational Geometry, Parallel Computing GPA 3.85

University of Mumbai, India BE in Computer Engineering June 2016

Focus: Image processing, Big data analytics, Data warehousing, Software development life cycles and tools

LANGUAGES AND TECHNOLOGIES

• Languages: Java, Python (Proficient), MATLAB, C++ (Intermediate), JavaScript, SQL, HTML, XML, PHP, Bash, R (Familiar)

• ML/CV: PyTorch, Keras, TensorFlow, Caffe2, sk-learn, OpenCV, dlib, Pandas, Numpy, Point Cloud Library, MeshLab

• Frameworks and Tools: Parallel Java 2, Hadoop, Android, Git, Raspberry Pi, AWS, Django, Docker, CUDA, CSS

RESEARCH AND EXPERIENCE

Thesis – Python, PyTorch, Matlab Rochester Institute of Technology Ongoing

• Enabled deep neural networks to process encrypted data without decryption using Homomorphic encryption (HE).

• Discovered a novel approach using weighted polynomial approximations to construct activations functions for HE.

• Developed scripts to analyze hidden layer outputs in CNNs, train multiple networks and record network statistics.

• Proficient in transfer learning, data augmentation, hyperparameter tuning and hypotheses testing.

• Awarded a $2000 scholarship by TSO Logic, an AWS Company for contributing to the field.

Research Assistant – PyTorch, Matlab Rochester Institute of Technology Dec 2018

• Identified that modern activation functions have bounded derivatives that contribute to good performance.

• Developed multiple custom activation functions and tested against datasets like FMNIST and CIFAR-10.

• Achieved an accuracy of 92.33% on CIFAR-10 using custom activation functions with the identified property.

Teaching Assistant – OpenCV, C++, MATLAB Rochester Institute of Technology May 2018

• Provided teaching assistance on topics including Image/video processing, motion tracking, depth estimation.

• Graded exams and developed future homework and projects to be assigned in the course.

Intern – Java, Android Yu Televentures March 2015

• Performed reliability and performance tests on system, third-party applications and the Android OS pre-launch.

• Provided the developer community with documentation and developer access support.

PROJECTS

Differentiating between music and advertisements – Python, Pandas, sklearn May 2019

• Developed an MLP classifier to identify audio playing in radio streams as either music or advertisements.

• Achieved an accuracy of 93.25% and an F-score of 0.93 using features engineered from raw audio streams.

• Created a labelled dataset of 213,000 samples from music and advertisement radio audio.

Automatic blurring of faces in videos – dlib, OpenCV March 2019

• Discovered faces in videos using a Convolutional Neural Network and preserved privacy by blurring them.

Segmenting, Classifying and Parsing handwritten math equations – Python, sklearn May 2018

• Developed a model with an accuracy of 84.52% that segments a group of handwritten strokes into meaningful symbols

• Achieved a classification accuracy of 87.9% by training a random forest classifier on 101 symbol classes.

• Engineered 36 features (bounding-box, histogram) determining spatial relationships between symbols.

Monitoring speed and direction in vehicular traffic flow – OpenCV, C++, MATLAB Dec 2017

• Built a computer vision model to identify and track vehicles in a parking lot with pedestrian traffic.

• Estimated speed of vehicles over multiple frames and reported detected anomalies.

3D Object Reconstruction using depth sensors – C++, PCL, OpenNI, Meshlab Dec 2017

• Captured dense point clouds of the object from multiple angles using a depth sensor and a custom turntable.

• Created a 3D model by aligning clouds using iterative closest points (ICP) and stitching them.

Fingerprint detail extraction using Mobile phone cameras – MATLAB May 2017

• Processed fingerprints from RGB images using morphology and color space transformations.

• Extracted ridge and valley fingerprint features (minutiae) by processing skeletonized fingerprint image.