# Saksham Sharma > Senior Software Engineer at Microsoft | UCSD (MS), IIT-Guwahati (B.Tech.) Location: San Francisco Bay Area, United States Profile: https://flows.cv/saksham Currently I am working as a Senior Software Engineer at Microsoft (Azure Networking team). Previously, I worked as a AI Software Engineer at GE Healthcare. I graduated with a Master's degree in Computer Science (AI & ML) from University of California San Diego. I did my undergrad in ECE from Indian Institute of Technology (IIT)-Guwahati. Prior to joining UCSD, I worked as a Software Engineer at Samsung R&D for 2 years. Broadly my interest lies in Distributed Systems, Artificial Intelligence and Machine Learning. ## Work Experience ### Senior Software Engineer @ Microsoft Jan 2022 – Present | Redmond, Washington, United States • Responsible for designing and implementing the new features for Virtual Networks (VNETs). ### Senior Software Engineer, AI @ GE Healthcare Jan 2021 – Jan 2022 | San Francisco Bay Area • Designed and developed Self-Services that enabled data scientists to quickly deploy deep learning models into model inferencing service for both cloud and on-premise environments. • Built REST APIs and python based Command Line Interface through which users can interact with Self-Services. ### Software Engineer, AI @ GE Healthcare Jan 2018 – Jan 2021 | San Francisco Bay Area • Developed a software platform for data scientists to train and test deep learning models using Amazon SageMaker. • Designed and implemented a feature which allows users to evaluate their deep learning models. • Implemented a docker container for pre-processing of the medical images and creating corresponding TensorFlow records. • Developed training and testing capabilities for deep learning models for on-premise environment using Kubernetes. • Added support for mask and ROI annotations which enabled training for segmentation deep learning models. • Enhanced UI to improve user workflow on the platform using TypeScript and Angular 7 framework. • Technologies: Python, AWS, Amazon Sagemaker, TensorFlow, Kubernetes, Helm, Docker, Angular 7, TypeScript ### Graduate Teaching Assistant @ University of California, San Diego - Jacobs School of Engineering Jan 2017 – Jan 2018 | La Jolla, California 1) CSE 150: Introduction to Artificial Intelligence (Jan 2018 - Mar 2018) I was a Teaching Assistant for the course "Introduction to Artificial Intelligence: Search and Reasoning (CSE 150)" taught by Prof. Sicun Gao. ==================================================================== 2) CSE 258: Web Mining and Recommender System (Sep 2017 - Dec 2017) I was a Teaching Assistant for the course "Web Mining and Recommender System (CSE 258)" taught by Prof. Julian McAuley. My responsibilities were as follows: a) Holding 3 office hours every week. b) Grading assignments and homework. c) Answering questions on Piazza. ==================================================================== 3) CSE 150: Introduction to Artificial Intelligence (Jun 2017 - Aug 2017) I was a Teaching Assistant for the course "Introduction to Artificial Intelligence: Probabilistic Reasoning and Decision Making (CSE 150)" taught by Prof. Lawrence Saul. My responsibilities were as follows: a) Holding a discussion session every week. b) Holding 2 office hours every week. c) Correction of assignments and exams. ### Senior Software Engineer @ Samsung Electronics Jan 2016 – Jan 2016 | Bengaluru Area, India Background: Brain tissue can be classified into 3 classes called gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Due to finite imaging resolution, the intensity level at a voxel may be due to a mixture of tissue classes. Such voxels are called 'partial volume (PV)' voxels and the phenomenon is known as 'partial volume effect (PVE)'. Contributions: • Worked on PV estimation in 3D T1 weighted MR images of brain for better tissue quantification. • After investigating state-of-the-art techniques, refined the criteria for identification of voxels with PVE. • The results matched the ground truth with an accuracy of 92-98%. Technologies: C/C++, Insight Segmentation and Registration Toolkit ### Software Engineer @ Samsung Electronics Jan 2014 – Jan 2016 Brain tissue classification: • Worked on 3 class (gray matter, white matter and cerebrospinal fluid) classification of 3D T1 weighted MR images of brain. • The task was accomplished using the concepts of Markov Random Fields, k-means and Expectation-Maximization algorithm. Brain MR image segmentation: • Segmented several brain structures like Hippocampus, Amygdala, Caudate and Brain stem in 3D T1 weighted MR images of brain. • The task involved several steps like skull stripping, rigid, affine and non-rigid registration. • DICE similarity coefficients were computed to compare the results with state-of-the-art techniques. Technologies: C/C++, Insight Segmentation and Registration Toolkit ### Student Trainee @ Samsung Electronics Jan 2013 – Jan 2013 Project Title: Adaptive Streaming and Scalable Transcoding • Worked on adaptive streaming and scalable transcoding for Internet Protocol camera. • Developed 'Scalable Transcoder' module which produced three outputs of different resolutions using Pyramidal-Decomposition. For resizing the image, I implemented bilinear, bicubic, lanczos, bspline and Gaussian blur algorithm using C++. • Also developed 'Bandwidth Adaptation Logic' module which checked the available bandwidth at regular intervals. Based on that, it decided whether to stream low or high resolution video. HTML 5 and JavaScript were used for developing this module. ### Research Intern @ Image and Communication Lab, Hanyang University, ERICA campus Jan 2012 – Jan 2012 Project Title: Edge Directed Image Interpolation • Developed an edge directed, high performance and low complexity algorithm for obtaining high resolution image from low resolution image. • Explored fundamentals of image processing such as nearest neighbor, bilinear, bicubic interpolation, Canny, Sobel and Prewitt edge detection. MATLAB was used extensively for programming purposes. • The proposed algorithm produced results which were comparable to state-of-the-art techniques such as New Edge-Directed Interpolation and Soft-Decision Adaptive Interpolation with 10-15% improvement in (run-time) performance. ## Education ### Master of Science (Artificial Intelligence) in Computer Science UC San Diego Jan 2016 – Jan 2018 ### Bachelor of Technology (B.Tech.) in Electronics and Communication Engineering Indian Institute of Technology, Guwahati Jan 2010 – Jan 2014 ## Contact & Social - LinkedIn: https://linkedin.com/in/saksham-sharma-iitg --- Source: https://flows.cv/saksham JSON Resume: https://flows.cv/saksham/resume.json Last updated: 2026-03-22