Python Aws

Arpan Roy *****.*****@*****.***

480-***-**** linkedin.com/in/arpanroy93

SUMMARY

MS CS graduate with and a strong background in software development, scripting, cloud and academic research in Data Mining and Machine Learning. Actively seeking full-time positions in software development, machine learning and cloud computing.

Education

Arizona State University Tempe, AZ

Master of Science in Computer Science, 3.53/4 May 2020

RV College of Engineering Bangalore, India

Bachelor of Engineering in Computer Science and Engineering; Aug. 2011 { July. 2015 Programming Skills

Languages and Technologies: Python, C,SQL,Reactjs, Tensor ow, Flask, D3 (Javascript),Selenium, AWS, Raspberry Pi,Python(major libraries I’ve leveraged Boto3, Scikit learn, pandas, numpy, keras, TensorFlow) Operating Systems: Linux, Windows

Concepts :Machine Learning, Cloud Computing, Databases, Edge Computing, IoT, Autoscaling Certifications

AWS Certi ed Solutions Architect: Associate : Nov 27th 2020 - Nov 27th 2023

The React BootCamp by Bob Ziroll: Lifetime

Professional Experience

Epi nder Tempe, Arizona

Development intern May 2020 - Present

Core responsibility: Working on automating the task of EpiFinders human annotators, who have to pore over hundreds of pages of medical texts and label each sentence/paragraph with an annotation, so that if an epilepsy diagnosis had to be made they could refer back to their annotations and isolate the symptoms that go with each type of epilepsy. (technologies/languages:Python)

Noble Research Institute Ardmore, Oklahoma

Scienti c Computing Intern June 2019 - August 2019

Core responsibility: Developed web-based scienti c tools for data analysis and visualization. Key challenges involve uploading large les upto 55GB through a web browser by le chunking and uploading chunks in parallel, asynchronously. Other tasks involved writing event listeners to trigger bioinfomatic pipelines that identify complete and Truncated T-DNA Insertions on a linux server. This enabled researchers who are not familiar with the linux command line to drag and drop large les instead of focusing on how to transfer their data to the server.(technologies/languages: Javascript, Express, NodeJS, Ajax)

Seagate Technology Bangalore, India

Software Developer October 2015 - June 2018

Core responsibility: Worked as part of storage controller development team on Dothill/RealStor (SAN) storage arrays. Have extensively analyzed, provided code xes, action plans (quick x) for customer DU/DL cases across most of the software modules of storage array stack (RAID, Snapshot, Thin provisioning, and Tiering). Also involved in multiple generation of stable/patch software releases to storage array OEM customers.(Technologies/languages: C/C++)

Personal Projects

Automate sending connections to technical recruiters/ managers on Linkedin: LinkedIn is a social network speci cally designed for career professionals to connect.The project automates sending connection requests to LinkedIn pro les, particularly of hiring managers and recruiters.As a job seeker, I am currently using the program to broaden my reach and maximize my chances of getting hired.(Languages/technologies: Python,Selenium)

(Github Link:https: //github.com/Arpan-Roy-1993/AutomateConnection Requests)

Academic Projects

Real time object detection with Raspberry Pi and AWS cloud: Built an elastic and responsive application that utilizes cloud resources and IoT devices to achieve real time object detection. This autoscaling implementation was done from scratch. The videos are recorded on a Raspberry Pi that receives an unpredictable workload. Based on demand, the workload is processed by either the PI or the cloud. The application was developed using AWS based cloud and Raspberry Pi based IoT. The project leverages several concepts related to edge computing as well as cloud computing. (Technologies/Languages: Python, AWS) (Fall 2020)

Image,Video data analysis, recommendation and classi cation: Developed a recommendation based system for images by collaborative ltering and implementing machine learning concepts such as dimensionality reduction, latent semantic analysis, page ranking, clustering and multi-dimensional index structures targeted towards multimedia databases from scratch. Contrived several optimization techniques to tackle challenges associated with query processing, storage and retrieval of multimedia and Web data. (Technologies/Languages: Python) (Fall 2018)

NoSQL Database Implementation based on existing relational model: Developed an XML database that stores semi- structured data in the form of an interval tree. Several crucial functionalities such as Select, GroupBy, Sort and non-trivial join operations (nested-loop and sort merge) were re-written. Was able to leverage query optimization strategies to e ciently retrieve data. (using left deep query plans and indexing strategies)

(Technologies/Languages: Java,Relational database core functionalities) (Spring 2019)

Clinical report segmentation using named-entity recognition: Clinical narratives, such as radiology and pathology reports, are stored in electronic form. However, they are also commonly entered as free text. Developed a robust and scalable medical report segmentation system requiring minimum user input that extracts information from free-text clinical narratives. The system (composed of BERT and other classiers) automatically segment medical reports into semantic sections. An accuracy of 98 percent was obtained.(Technologies/Languages: Python, Tensor ow) (Spring 2019)

Textual Entailment using Tensor ow: Developed a computer program that recognizes a directional connection between text sentences and classies the relationship (Positive, Negative or Neutral entailment) using a combination of simple neural networks followed by an LSTM.The program has been tested on a question-answering system that uses textual entailment to verify answers from stored information. An accuracy of of 55 percent was obtained

(Technologies/Languages: Python, Tensor ow) (Spring 2019)

A Multi-Stage System for Visually Exploring Events in Social Streams : Social stream analysis is a complex task and is crucial in several applications. The sheer volume and diversity of tweets poses signi cant challenges that can overwhelm any single algorithm attempting to make meaning. To overcome these challenges to facilitate e cient data exploration, I have implemented a framework to handle complex social streams. It consists of two parts: 1) Data pre-processing by segregating tweets on the basis of time intervals (hour,day, month) and recognizing important subevents.2) An e cient way to classify and cluster tweets of subevents and display them with aesthetic visualizations.(Technologies/Languages: Javascript(D3), Python)(Spring 2020)

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