Resume

1. 610 Minor Avenue, Suhas Pillai Phone no: (585)2019974
San Jose,California ­ 95125 Email: suhas.callin@gmail.com
Personal Website: https://suhaspillai.github.io GitHub: https://github.com/suhaspillai
EMPLOYMENT
Machine Learning Engineer TeraDeep April 2017 ­ Present
• Developing deep learning algorithms for segmentation and detection of objects in satellite imagery.
• Designing and developing algorithms to reduce memory footprint of big and deep neural network
models.
• Designing and developing deep learning algorithms for Field Programmable Gate Arrays (FPGAs).
Teaching Assistant Rochester Institute of Technology September – December 2016
• Teaching Assistant for Master's Machine Intelligence Course.
Summer Internship Kodak Alaris June – September 2016
• Developed system for classifying, segmenting and recognizing handwritten characters from
unstructured scanned documents using Deep Learning algorithms.
• Developed Offline handwriting recognition system from scratch for scanned documents.
System Test Engineer Infosys Limited, India October 2011 ­ July 2014
• Responsible for testing of SAP CRM, SAP SD and SAP MM applications.
• Executed end to end testing right from creating test cases to logging defects following Software
Testing Life Cycle.
• Conducted SAP CRM training sessions, along with creating automation scripts for automation testing
PUBLICATIONS
• R. Oruganti, S. Sah, Suhas Pillai, and R. Ptucha, “Image Description through Fusion Based
Recurrent Multi­Modal Learning”, Proceedings of International Conference on Image Processing,
Phoenix, AZ, 2016.
• S. Kulhare, S. Sah, Suhas Pillai and R. Ptucha, “Key Frame Extraction for Salient Activity
Recognition,” Proceedings of International Conference on Pattern Recognition, Cancun, Mexico,
2016.
• Presented poster on Dysarthric Speech Recognition at 2016 Western New York Image and Signal
Processing Workshop.
EDUCATION
Rochester, NY Rochester Institute of Technology August 2014 – May 2017
• Master of Science in Computer Science. CGPA: 3.33/4.0.
• Graduate Course Coursework: Data Structures and Algorithms, Intelligent Systems, Machine Learning
and Neural Networks, Parallel Computing, Spoken Language Processing, Topics in Mobile and
Pervasive Systems, and Pattern Recognition.
Mumbai, India University of Mumbai August 2007 – August 2011
• Bachelor of Engineering in Information Technology, August 2011, First Class.
RESEARCH EXPERIENCE :
Thesis : Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural
Networks. http://scholarworks.rit.edu/theses/9407/
• Understanding speech patterns and developing speech recognition system for people with speech
impairment using concepts from speaker adaptation and speaker verification methods.
• Developing Offline handwriting recognition system and applying key concepts to dysarthric speech
recognition.
TECHNICAL EXPERIENCE
• Developed Offline Handwriting Recognition system using Multidimensional Multidirectional

2. Recurrent Neural Network in python from scratch.
• Developed Math Symbol Recognition system using combination of Random Forests for
classification, Minimum spanning tree for segmentation and Line Of Sight algorithm for
parsing.
• Developed Spectral Convolutional Neural Networks that generalizes Convolutional Neural
Networks using graph Fourier Transform. Spectral domain provides significant speedup in
convolutions, along with faster convergence due to powerful representation in spectral domain.
• Parallelizing Convolutional Neural Networks for deep learning of images on GPU using CUDA and
across multiple cores using pj2 library.
• Designing and developing sorting and map reduce applications in a distributed and memory
constrained environment using Raspberry Pi.
• SmartBlind Internet of Things application using Raspberry Pi to view current temperature and light
intensity from sensors, using an android client.
• Longest Common Subsequence: Developed quadratic time linear space algorithm for finding longest
common subsequence for DNA matching / String matching, using dynamic programming.
• MeetCI: Developed a computational intelligence software design automation framework by
integrating popular machine learning libraries.
ACHIEVEMENTS
• Runner up at 2015 RIT Deep Learning Challenge.
• Received BRAVO Award for superior performance at Infosys.
• Completed an online course on Machine Learning, offered by Stanford University on Coursera and
Standford's CS231n: Convolutional Neural Networks for Visual Recognition
• Represented Badminton competition at state level and national level.
LANGUAGES AND TECHNOLOGIES
• Python, Cython, Java, C++, Lua, CUDA, Multiprocessing, XML, Matlab, Octave
• Microsoft Visual Studio 2010, Eclipse, Praat, Festival
• Machine and Deep Learning libraries : Torch, PyTorch, TensorFlow, Caffe, cuDNN, Scikit Learn,
pyBrain, Fann, MatConvNet
• Speech Recognition Toolkit : Kaldi