Computer Vision, Machine Learning, Deep Learning, Pattern Recognition

Sahaj Tushar Gandhi T 585-***-****

B *****.******@****.***.***

people.rit.edu/sxg2475

sahajgandhi

Education

**** - **** ****** ** ******* (M.S.), Rochester Institute of Technology, Rochester, NY. Computer Science (GPA: 3.67/4.0): Computer Vision, Deep Learning, Artificial Intelligence, Algorithms. 2013 - 2017 Bachelor of Technology (B.Tech), Veermata Jijabai Technological Institute, Mumbai, India. Information Technology: Cumulative Pointer Index - (8.09 / 10): Image Processing, Soft Computing, Artificial Intelligence. Research and Industrial Experience

Summer 2019 Waymo, Imaging Engineering Intern, Camera Team, Camera Systems Performance and Tradespace Analysis. 2018-19 School of Mathematical Sciences, Rochester Institute of Technology, Research Assistant to Dr. John Hamilton.

- Robustifying Deep Learning Models - Inventing structural priors for Deep Learning frameworks to accelerate global convergence for performing robust predictions.

2018-19 Machine Intelligence Lab (MIL), Rochester Institute of Technology, Graduate Student Researcher.

- Gesture Recognition for Chinese Sign Language(CSL) Interpretation: Designed a framework for performing pose inference by localization of anatomical (face, hand, and body) landmarks and depth estimation from videos using key-point estimation algorithms. Performed accurate feature extraction for robust interpretations of CSL videos to Mandarin and English texts. Summer 2016 ARCON Techsolutions Private Limited (India), Summer Research Intern.

- Secure Login Module based on Keystroke Dynamics: Proposed a novel approach to provide a robust form of access security by analyzing habitual typing rhythm patterns of a user, instead of expensive biometric scanners. Designed and Implemented a JavaScript tool to exhaustively capture any intruders’ network details during illegal SQL injections. Research Publications

April 2020 Event-Related Query Classification with Deep Neural Networks, Event-Related Query Classification with Deep Neural Networks. In Companion Proceedings of the Web Conference 2020. May 2019 Large Scale Sign Language Interpretation, 14TH IEEE International Conference on Automatic Face and Gesture Recognition, 2019 (FG-2019).

Research & Academic Projects

2019-20 Masters Thesis, Context Sensitive Image Denoising and Enhancement using U-Nets. Spring 2019 Instance Segmentation for Autonomous Driving. Implemented an efficient instance detection (cars, pedestrians, road lanes) framework for dynamic path selection in autonomous driving systems.

Spring 2019 Handwritten Math Recognition System.

Designed and developed a system for the classification and segmentation of handwritten mathematical equations using an attention-based deep CNN network on the CROHME 2013 dataset Spring 2019 OpenCV Master Header File for C++.

Developed a simplified program interface for faster development via the use of one include statement in C++. Fall 2018 Prediction of Personalities Types based on Emotion. Designed and Implemented a system which factored in Facial Emotions, Facial Features, and Galvanic Skin Response(GSR) values into Long Short Term Memory(LSTM) model in PyTorch to generate a score for each of the personality types. Fall 2018 Hierarchical Classification of Audio Signals. Constructed classification frameworks on the NSynth dataset in PyTorch using three structural models based on CNNs(spatial), LSTMs (uni-temporal) and Bi-LSTMs (bi-temporal) to classify audio notes into three music families and instrument classes. Spring 2018 Sudoku Construct Detector and Solver using Rule-Based Computer Vision. Formulated and Implemented a process to detect and solve any Sudoku puzzle and display it back on to the original image in MATLAB using Morphological operations, Connected Components and Hough Transform. Awards

2017 - 2019 Awarded the highest possible scholarship of 40% which is awarded to a select few Computer Science Graduate students Fall 2019 Nominated for Outstanding International Student Award at Rochester Institute of Technology Fall 2019 Invited Lecturer for the CSCI431 (Foundations of Computer Vision) course at Rochester Institute of Technology Software Development

Python, MATLAB, OpenCV, PyTorch, Tensorflow (and Keras), C++, C, Java, HTML, XML, R, SQL, Weka

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