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Graduate Student Researcher at Mila - Quebec Artificial Intelligence Institute
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
=============== WEBSITE: viv92.github.io ===============
Université de Montréal
Doctor of Philosophy - PhD (Dropout), Computer Science
September 1, 2021 – December 1, 2021
Oregon State University
Master of Science - MS, Computer Science
September 1, 2018 – July 1, 2021
National Institute of Technology Karnataka
Bachelor of Technology (BTech), Electrical and Electronics Engineering
August 1, 2011 – May 1, 2015
Mila - Quebec Artificial Intelligence Institute
Graduate Student Researcher
July 1, 2021 – Present
Montreal, Quebec, Canada
Oregon State University
Graduate Research Assistant
July 1, 2019 – June 1, 2021
Corvallis, Oregon Area
Oregon State University
Graduate Teaching Assistant
September 1, 2018 – June 1, 2019
Corvallis, Oregon Area
AitoeLabs
Machine Learning Engineer
May 1, 2017 – April 1, 2018
Mumbai Area, India
Texas Instruments
Software Design Engineer
July 1, 2015 – November 1, 2016
Bangalore
Texas Instruments
Summer Intern
May 1, 2014 – July 1, 2014
Bangalore
Domain Adaptation in Reinforcement Learning
June 1, 2019 – Present
This project extends the work on Combined Reinforcement Learning via Abstract Representations to include domain adaptation across different environments. This is achieved by modifying the architecture of the learning problem in order to enable learning of common abstractions over multiple environments. Implemented in PyTorch.
XNN: Explainable Neural Network to Disentangle Visual Concepts
June 1, 2019 – Present
XNN is a novel neural network architecture designed to yield saliency maps that can be disentangled to represent visual concepts. Hence the generated saliency maps can be used to attribute explanations to the Neural Net's classifications. The XNN architecture uses stacked Sparse Reconstruction Autoencoders (SRAE) with a novel "concept loss", a "pull away term" and Integrated-Gradient based visualization to generate explainable saliency maps. Implemented in PyTorch.
Exploring Deep Autoregressive Models for Density Estimation
January 1, 2019 – March 1, 2019
Probabilistic Graphical Models course project exploring Deep Autoregressive models for density estimation such as Neural Autoregressive Density Estimator (NADE) and Masked Autoregressive Density Estimation (MADE). Our hypothesis was that deep autoregressive models such as MADE generate richer samples than those generated by mixture models such as GMM. We verify the hypothesis by training a MADE model and a GMM on 3 different datasets (MNIST, Atari frames and Anime faces) and comparing the samples generated.
Reformulation and Analysis of Trust Region Policy Optimization (TRPO)
October 1, 2018 – December 1, 2018
Convex Optimization course project on Reformulation and Analysis of Trust Region Policy Optimization (TRPO) under Dr. Xiao Fu. This project re-derives the TRPO algorithm and implements it to optimize an industrial operation using a discrete event simulator.
Simulation Optimization using Reinforcement Learning on a discrete event simulator
September 1, 2018 – Present
This is an ongoing research project under Dr. Prasad Tadepalli, exploring various reinforcement learning methodologies along with transfer learning to optimize different industrial processes involved in construction planning domain. Currently the project is focused on optimizing an earthmoving operation formulated as a MDP with resource contraints.
Object Tracking and Surveillance using Computer Vision
November 1, 2017 – February 1, 2018
This project involves building computer vision applications for object tracking, object counting, object tampering, intrusion detection and surveillance by performing transfer learning over object localization networks such as Darknet YOLO, Faster-RCNN and MobileNet-SSD. Kalman filter based SOR Tracker was used for tracking. Developed using C++ and Caffe.
Video Summarization using Submodular Functions
July 1, 2017 – September 1, 2017
This is a toolkit developed to perform Visual Data Subset Selection and Visual Data Summarization Using Submodular Functions. The toolkit employs several summarization models that perform Feature Subset Selection based on Diversity Functions, Coverage Functions and Representation Functions. One of the following three summarization algorithms can be used: Budgeted Greedy, Stream Greedy and Coverage Greedy. Developed in C++.
TI-ADC Project-2014
January 1, 2014 – Present
This project being our entry for TI-ADC 2014 competition, involved implementing an automated 4-wheel to 2-wheel drive conversion for a four wheel electric drive vehicle using MSP430 and BJTs for transmission switching. Software tool used: CCS
Human Activity Recognition - A Literature Survey
January 1, 2014 – May 1, 2014
This project, under the guidance of Asst. Prof. H Girish Navada, is aimed at studying and extensively reviewing the current progress in the field of Video based Activity Recognition.
Hardware realization of PID controller for speed control of DC motor
November 1, 2013 – Present
This project, under the guidance of Asst. Prof. Ashwini Chaturvedi, involved hardware realization of PID controller using IC 741 op amps and Arduino UNO. Software tools used: MATLAB and PROCESSING
Human Controlled Bot
August 1, 2013 – November 1, 2013
This project, under the guidance of Asst. Prof. H Girish Navada, is aimed at controlling the motion of an AVR controlled wheeled bot through speech and Hand Gesture Recognition.
Account and inventory management internal website for Dept. of Electrical and Electronics, NITK.
September 1, 2012 – November 1, 2012
This project involved developing an account and file management internal website for Dept. of Electrical and Electronics, NITK. Account management implemented via MYSQL. File handling and file search engine implemented using PHP.
Cultural Fit Analysis
The candidate's background is heavily skewed towards academic research in Computer Science, Machine Learning, and Reinforcement Learning, with a strong emphasis on theoretical and experimental projects. While this demonstrates intellectual curiosity and deep technical skills, the target role of 'Data Analyst' typically requires a more direct focus on data manipulation, statistical analysis, business intelligence, and reporting tools (e.g., SQL, Tableau, Power BI, advanced Excel, A/B testing, data warehousing). The current project portfolio, while technically impressive, does not directly align with the typical responsibilities and toolsets of a Data Analyst role, suggesting a potential mismatch in cultural fit for a standard data analyst team unless the role has a strong research or advanced analytics component.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong research-oriented mindset and a capacity for independent problem-solving. The academic background suggests a structured approach to complex challenges. However, without specific psychometric test results or interview data, it is difficult to assess soft skills like teamwork, communication style, or stress handling in an operational context.