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Progressive Learner | AI Enthusiast
Big Data, IoT and Machine Learning Enthusiast, Learning and progressing each day. Started pursuing PhD in Artificial Intelligence from Fall 2019. Previously have worked on innovative research projects like: 1. Highway asset maintenance using computer vision 2. Trajectory analysis of pedestrians to manage static and dynamic occlusions in the scene 3. Action detection and recognition for surveillance applications in real-time 4. Mixed precision training and inference of triplet loss network for pedestrian Re-identification on the edge. 5. Scalable LSTM modules for multi-people trajectory analysis for surveillance systems (Duke-MTMC dataset). 6. MigraineCloud : An IoT framework for capturing triggers and predicting Migraine attacks. Please see my CV for more information and connect! Github: https://github.com/shreymohan Personal Blog : https://novelette07.wordpress.com/
University of North Carolina at Charlotte
Doctor of Philosophy - PhD, Computer Engineering
January 1, 2019 – Present
University of North Carolina at Charlotte
Master's degree, Computer Engineering
January 1, 2016 – January 1, 2018
Collge of Technology, Pantnagar
Bachelor of Engineering - BE, Electrical, Electronic and Communications Engineering Technology/Technician
January 1, 2011 – January 1, 2015
iVizz
Senior ML Engineer
April 1, 2022 – March 1, 2023
Remote
VECROS
AI Engineer
August 1, 2021 – September 1, 2021
Delhi, India
University of North Carolina at Charlotte
Research Assistant
September 1, 2018 – December 1, 2020
Charlotte Metro
Bloom Energy
Machine Learning Engineer
June 1, 2017 – August 1, 2017
United States
Image Recognition using Logistic Regression
September 1, 2017 – Present
In this project the Logistic Regression algorithm was implemented in python which was then used to classify images of cars and non-car images.
Parallelizing the training of Restricted Boltzman Machine Algorithm
March 1, 2017 – April 1, 2017
This project was an attempt to parallelize the training of RBM algorithm to reduce the training time and efficiently use the algorithm for predictions.
Emotion Detection
January 1, 2017 – February 1, 2017
Two Algorithms, Naive Bayes and Restricted Boltzman Machine were used to classify live twitter feed into 13 discrete emotions.
Smart Home
November 1, 2016 – December 1, 2017
Smart Home is an IoT framework which automates the controls of home appliances using an android mobile application.
Cultural Fit Analysis
The candidate's project diversity spans machine learning, IoT, and parallel computing, indicating a broad interest in technical challenges. The roles held (Senior ML Engineer, AI Engineer, Research Assistant) align well with a data-intensive, research-driven environment. However, the target role is 'Big Data Engineer', and while there's ML/AI experience, explicit big data technologies (e.g., Spark, Hadoop, Kafka) are not mentioned in the resume, which might indicate a gap in direct cultural fit for a pure Big Data Engineering role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving approach and an ability to work on complex research-oriented tasks. The experience in formulating constraints and analyzing requirements suggests good analytical and operational fit. However, direct evidence of collaboration or stress handling is not explicitly provided in the resume.