CV - Shubham Pateria
Author:
Shubham
Last Updated:
há 9 anos
License:
Creative Commons CC BY 4.0
Abstract:
Shubham Pateria's CV
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Discover why 18 million people worldwide trust Overleaf with their work.
Shubham Pateria's CV
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Discover why 18 million people worldwide trust Overleaf with their work.
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\name{Shubham Pateria}
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\address{shubham.nitd@gmail.com\\Contact: 9663856201\\https://www.linkedin.com/in/shubham-pateria-64217435}
\address{Samsung R\&D, Phoenix Building\\Baghmane Tech Park\\Doddanekundi, Bangalore - 56}
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\section{EDUCATION}
\textbf{National Institute of Technology}, Durgapur, WB, India\\
{\sl Bachelor of Technology}, Electronics and Communication Engineering, May 2013\hfill \newline
GPA: 8.66/10
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\section{PROJECTS}
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\textbf{Multiple Mixer Based Iterative Composition for Power Reduction in Display Processing Pipeline}:
Worked on developing a new process architecture for display frame composition using display processor equipped with multiple pipes and mixer. Frame composition is performed in iterations over multiple mixers to handle higher display load (number of frames) and reduce dependency on GPU. \newline
Guide: Krishna Kishore Jha, Chief Engineer, Samsung R\&D, Bangalore\newline
\(http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7411205\)
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\textbf{Aspect Based Sentiment Analysis in SemEval-2016 (independent activity)}:
Participated in the SemEval - 2016 Workshop. For this participation, we developed a supervised classification model to predict aspect-specific sentiment polarity within a consumer review. The model used weighted SVM classifier trained over following features: target n-grams, lexicon scores, negation terms, neutral terms, aspect category specific keywords and precedent-polarity sequence. The model was developed using Python with NLTK 3.0, scikit-learn packages and external lexicon resources (MPQA Subjectivity).\newline
The paper describing the system is under review by NAACL SemEval committee.
\par
\textbf{Power Efficient, Bandwidth Optimized and Fault Tolerant Sensor Management for IOT in Smart Home}:
We worked on an extension of data correlation and prediction models used for spatio-temporal pattern analysis in IoT or sensor networks and applied these for analyzing relation between various atmospheric factors like temperature, humidity, precipitation etc. \newline
\(http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7154732\)
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\textbf{Studies on Power Control in Cognitive Radio Systems Using Spectrum-sensing Side Information (Undergraduate Project)}:
Transmission power for a Cognitive Radio (CR) can be obtained as a function of path-loss (\(\eta\)). In our study, average Signal-to-Noise Ratio (SNR) (\(\gamma\)) sensed over a given time and bandwidth (TW) window was taken with a predetermined energy threshold (\(\lambda\)) to estimate a probability of detection (Pd) as function of (\(\gamma, \lambda, T,W\)). Assuming a deterministic channel fading, the path-loss (\(\eta\)) could then be modeled against the Pd values. Transmission power curve is then obtained from (\(\eta\))-Pd relation.\newline
Guide: Dr. Sumit Kundu, Dept. of ECE, National Institute of Technology, Durgapur\newline
Period: Oct, 2012 to March, 2013
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\textbf{Localization in a 3-D Map Using Reverse Projection of Sensor Data}:
An RGBD sensor equipped robot was used for depth estimation by ray-tracing along 8 directions (at 45-degree angles) per plane (x,y,z). Each ray was transformed into vector with depth and angle - v(d,\(\theta\)). Then, in a simulated map, boundary cells were first sampled by density. Sampled cells were used as virtual transmitter for sensor rays along 24 vectors obtained. The degree/intensity of superposition of reverse-projected vectors was used for approximate localization. The method was developed using Microsoft Kinect RGBD camera and TurtleBot using ROS. \newline
Guide: Dr. K. Madhav Krishna, Robotics Research Center, International Institute of Information Technology, Hyderabad\newline
Period: May, 2012 to July, 2012
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\section{SKILLS}
\textbf{Languages}: C, C++, Python, C\#, Java, Bash.
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\textbf{Web Development}: HTML, CSS (elementary)
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\textbf{Applications}: MATLAB, Octave, Vi/Vim, Eclipse, Visual Studio.
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\textbf{Operating Systems}:
Windows, Linux, Android, ROS (elementary)
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\section{EXPERIENCE}
\employer{Sapient Global Markets}
\location{Bangalore}
\dates{Oct 2013 - Feb 2014}
\title{\textbf{Trainee - Technology}}
\begin{position}
Underwent training, during employment, in C\# and Microsoft .Net technologies with Mock Project to design a virtual trading platform for handling commodities-trading portfolios.
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\employer{Samsung R\&D}
\location{Bangalore}
\dates{June 2014 - present}
\title{\textbf{Senior Software Engineer}}
\begin{position}
Kernel development and driver development for KGSL (GPU driver) and MDP (Display driver). Bootloader development for MDP
Development for Android graphics framework (pipeline, event synchronization, hardware abstraction etc.)
Research focused on mobile computing, processing architecture, image processing in graphics/display pipeline.
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\employer{IIIT, Hyderabad}
\location{Hyderabad}
\dates{May 2012 - July 2012}
\title{\textbf{Research Intern}}
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My responsibilities included collection of Kinect sensor data with TurtleBot and mapping using Octomap, developing simulation model for robot localization in 3-D map using ROS packages and studying the validity of reverse-projection method.
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\employer{IIIT, Pune}
\location{Pune}
\dates{May 2011 - July 2011}
\title{\textbf{Trainee}}
\begin{position}
Training program on Embedded Systems in Robotics
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\section{INTERESTS}
Machine Learning, Computer Vision, Kernel architecture and driver development, C Programming
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\section{RELEVANT COURSES}
Machine Learning, Andrew Ng, Coursera\newline
Probabilistic Graphical Models, Daphne Koller, Coursera\newline
Algorithms: Design and Analysis, Tim Roughgarden, Coursera\newline
Practical Machine Learning, Jeff Leek, Coursera\newline
Statistical Inference, Brian Caffo, Coursera\newline
Introduction to Computational Thinking and Data Science, MITx: 6.00.2x, John V. Guttag, edX
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