Shefali Sharma

Shefali Sharma

Cupertino, California, United States
4K followers 500+ connections

Über uns

Experienced Software Engineer with a demonstrated history of working in the healthcare…

Activity

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  • Apple Graphic

    Apfel

    Cupertino, California, United States

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    Seattle, Washington

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    Seattle, Washington

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    Greater Chicago Area

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    Bengaluru Area, India

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    Pune Area, India

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Licenses & Certifications

Volunteer Experience

  • Microsoft Graphic

    Microsoft Women In Tech Initiative

    Microsoft

    - 1 year 4 months

    Bildung

    Training and mentoring young women technology enthusiasts by organizing workshops on Microsoft Technologies, webinars and free classes. Teaching women to help them join, grow and succeed in the IT industry. Build local communities for girls to meet, network, and learn from each other by sharing ideas.

  • CDAC Graphic

    Organizer

    CDAC

    - Present 10 years 7 months

    Science and Technology

    Volunteerism service during the inaugural function of opening of C-DAC Centre at National Institute of Technology, Silchar, Asssam.

  • Volunteer

    Unique Kennel Society

    - 7 months

    Animal Welfare

    Volunteered as a helper and aided in medical and socialization camps. Actively involved in rescue and rehabilitation of stray dogs.

  • Silchar Medical College & Hospital Graphic

    Blood Donor

    Silchar Medical College & Hospital

    - Present 11 years 8 months

    Health

    Voluntarily donated one unit of blood to Silchar Medical College and Hospital Blood Bank at National Institute of Technology, Silchar

Courses

  • Analysis of Algorithms

    CSE531

  • C#.Net

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  • Compiler Design

    CS 305

  • Computer Graphics

    CS 302

  • Computer Networks

    CS 306

  • Computer Organization and Architecture

    CS 303

  • Computer Security

    CSE565

  • Data Communication

    EC 321

  • Data Structures

    CS 201

  • Data Structures And Algorithms

    -

  • Database Management Systems

    CS 307

  • Design and Analysis of Algorithms

    CS 304

  • Discrete Mathematics

    MA 203

  • Formal Languages and Automata Theory

    CS 205

  • Graph Theory

    CS 203

  • Introduction to Parallel and Distributed Processing

    CSE570

  • Introduction to Switching Theory

    CS 202

  • Microprocessors and Microcontrollers

    EC 303

  • Modern Networking Concepts

    CSE589

  • Numerical Methods and Computations

    MA 301

  • Operating Systems

    CS 308

  • Principle of Programming Languages

    CS 204

  • System Programming

    CS 301

Projects

  • Therapy App - Physioguide

    It’s an Android App developed as part of Philips Hackathon 2016. The aim of this project is to help people minimise their cost on Physiotherapy exercises by provided the simplistic UI to choose, configure and use physiotherapy related exercise videos. The frontend is built in Angular.js and Backend in Django-REST framework. The Backend REST API is hosted in Pivotal using Cloud Foundry. The Database (ElephantSQL: PostgreSQL as a Service) too deployed on Pivotal.

    Tools used: Pycharm…

    It’s an Android App developed as part of Philips Hackathon 2016. The aim of this project is to help people minimise their cost on Physiotherapy exercises by provided the simplistic UI to choose, configure and use physiotherapy related exercise videos. The frontend is built in Angular.js and Backend in Django-REST framework. The Backend REST API is hosted in Pivotal using Cloud Foundry. The Database (ElephantSQL: PostgreSQL as a Service) too deployed on Pivotal.

    Tools used: Pycharm, CF-CLI, Slack (Communication), Waffle (Scrum), GitHub
    Code: Python (Django-REST framework), Angular.js, Postgres
    Deploy: Pivotal, Cloud-Foundry, ElephantSQL
    Design: Twelve-Factor App Methodology

  • Cosine Similarity

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    Implementing cosine similarity and find the similarity values between input words and all words in the vector model. Also create CYK parse chart for a sample input.

    See project
  • Data Analytics Pipeline Using Apache Spark

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    Data collection, data cleaning, word count, extracting features, calculating probability, assign labels and then classifying using Multi-class classification.

    See project
  • Hadoop Word Co-occurrence and Data Visualization

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    Collecting and processing data from Twitter and NYTimes. Calculating Word Co-occurrence with maximum word-count. Visualizing results as WordCloud using D3.js.

    See project
  • Handwritten Digits Classification

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    Implement Neural Network (forward pass and back propagation) Incorporate regularization on the weights (λ) Use validation set to tune hyper-parameters for Neural Network (number of units in the hidden layer and λ). Run the deep neural network code we provided and compare the results with normal neural network. Run the convolutional neural network code and print out the results, for example the confusion matrix.

    See project
  • Logistic Regression(LR) and SVM

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    Implementing Logistic Regression to classify hand-written digit images into correct corresponding labels. Support Vector Machines to perform classification on our dataset.

    See project
  • Twitter Data Analysis and Extraction

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    Data collection and Exploratory Data Analysis on ’flu/influenza’. Compare results for different States with CDC results.

    See project
  • Connected Components Using Apache Spark

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    Implement efficient end-to-end Apache Spark program for finding connected components.

    See project
  • Distributed Memory Connected Components

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    Connect distributed memory components using MPI.

    See project
  • Exploiting vulnerabilities in a Web Application

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    • Exploited Injection flaws by performing several types of injection attacks and performed stored and reflected Cross Site Scripting attacks in WebGoat, an insecure Java web application.

  • Gaussian Kernel in NVIDIA CUDA

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    Implement efficient NVIDIA CUDA program for fast computing of Gaussian kernel density estimate. The Kernel Density Estimation is frequently used technique in machine learning and statistics. The goal is to estimate the probability density function of a random variable. Let vector X = ( x 1 , x 2 , … , x n ) X=(x1,x2,…,xn) be a sample drawn from some univariate distribution with an unknown density function f f. We can estimate f f, as: ^ f h ( x ) = 1 n ⋅ h n ∑ i = 1 K ( x − x i h )…

    Implement efficient NVIDIA CUDA program for fast computing of Gaussian kernel density estimate. The Kernel Density Estimation is frequently used technique in machine learning and statistics. The goal is to estimate the probability density function of a random variable. Let vector X = ( x 1 , x 2 , … , x n ) X=(x1,x2,…,xn) be a sample drawn from some univariate distribution with an unknown density function f f. We can estimate f f, as: ^ f h ( x ) = 1 n ⋅ h n ∑ i = 1 K ( x − x i h ) , fh^(x)=1n⋅h∑i=1nK(x−xih), where h h is bandwidth (think smoothing factor), and K K is a kernel function. In the specific case of Gaussian kernel density estimate, K K is defined as follows: K ( x ) = 1 √ 2 π exp ( − x 2 2 ) . K(x)=12πexp⁡(−x22). Given an input vector X = ( x 1 , x 2 , … , x n ) X=(x1,x2,…,xn), and some predefined bandwidth h h, we can estimate density over X X, as Y = ( y 1 , y 2 , … , y n ) Y=(y1,y2,…,yn), where y i = ^ f h ( x i ) yi=fh^(xi). Hence, your task is to compute Y Y given X X and h h. To solve the problem you may assume the following: Input vector X X and output vector Y Y are single precision. The size n n of X X (and Y Y) is such that it exceeds capacity of a single CUDA thread block, but is small enough to be processed by a single kernel invocation (e.g. with multiple blocks).

    See project
  • Parallel Prefix in OpenMP

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    • Developed generic parallel prefix program which can be used for multiple operators such as plus, multiplication.
    • Implement using C++ and Intel OpenMP.

  • Reliable Transport Protocols

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    • Programmed three transport layer protocols – Alternating Bit Protocol, GO Back N and Selective Repeat for reliable delivery at the transport layer of the Protocol Stack.
    • Analysed and compared the performance of each of the protocols at different window sizes, loss and corruption probabilities.

  • Text Chat Application

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    • Created a client-server architecture based text chat application involving up-to 4 clients concurrently using IO Multiplexing techniques.
    • C++ and the BSD sockets API were used to implement this project.

  • Porting and tuning of HMMER source code on CPU and accelerators (Nvidia CUDA architecture and Intel Xeon Phi)

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    • Remove/update deprecated code to allow porting of GPU-HMMER on NITS super-computing facility.
    • Proper knowledge of the flow of the application and profiling techniques helped identify hotspots.
    • Utilized optimization techniques on the time-consuming functions to improve performance.

    Tools used: Nvidia-NSight, Intel-Vtune, Gprof
    Code: OpenMP, MPI, CUDA
    Deploy: NITS Supercomputer, PARAM-Yuva

    Other creators
  • Building a Search Engine

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    PageRank Algorithm and for spidering web content

    See project

Languages

  • Englisch

    Native or bilingual proficiency

  • Hindi

    Native or bilingual proficiency

Organizations

  • E-VOLUTION

    Manager

    - Present

    A non-profit module to give an insight into the evolution of computers from the very beginning. It also enlightened people on the future of the computers that lay ahead. https://www.facebook.com/evolution.tecnoesis

  • Computer Science Society, NIT SILCHAR

    Executive Head

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    https://www.facebook.com/CSS.NITS

  • Computer Science Society, NIT SILCHAR

    Technical Member

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    https://www.facebook.com/CSS.NITS

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