Sean Marcus Pereira

Long Beach, CA Email: adjpcx@r.postjobfree.com LinkedIn https://www.linkedin.com/in/sean-marcus-pereira GitHub: https://github.com/pereirasean Mobile: 858-***-**** EDUCATION

California State University Long Beach, U.S.A. Aug 2019 – May 2021 Master’s in Computer Science GPA (3.3/4.0)

Courses: Advanced Programming Language, Advanced Analysis of Algorithms, Advanced Software Engineering, Topics in Distributed Computing, Fault Tolerance in Computing Systems, Pattern Recognition, Data Visualization, Introduction to Data Structures and Algorithms, Introduction to Operating Systems. Sardar Patel Institute of Technology, India Jul 2015 – May 2019 Bachelor's in Electronics Engineering GPA (7.97/10) TECHNICAL SKILLS

Languages: Python, C, Java, C, C++, C#, SQL.

Databases: MongoDB, PostgreSQL, MySQL, NoSQL.

Web Stack: HTML5, CSS3, JavaScript, React.js, Node.js, D3.js, Bootstrap, Flask, Django, Express, AJAX, jQuery, JSON, Angular.

Web Services: RESTful, XML, SOAP, Spring MVC, Hibernate.

Web Servers: Digital Ocean, Heroku, Cloudflare.

Other Technologies: Tableau, Git, SoapUI, Postman, Web Scraping, Machine Learning, Babel, Linux, Docker. WORK EXPERIENCE

Teaching and Research Assistant - California State University Long Beach Aug 2020 - Current

Instructing and assisting students with Python, MySQL, and Tableau technologies, include helping and reviewing lesson plans and conducting doubt sessions for students over Zoom calls.

Conducting and grading quizzes, assignments, and assisting Professor with Research work on numerous projects, involves coordination with Graduate Assistants of different Universities.

Technology: Python, Selenium, Beautiful Soup, Twitch API, NLP, Machine Learning Libraries (sci-kit-learn, matplotlib, seaborn)

Project Intern- Tech Mahindra Ltd, Mumbai India June 2018 - July 2018

Drafted seven use cases to research on Application of Artificial Intelligence in Business Operational Systems (B/OSS) of Communication Providers.

Delivered presentation of seven-use cases to senior stakeholders within the company; one use case was shortlisted.

Developed technical architecture for "Call Drop & Network Fault Prediction and Management" was selected for Tech Mahindra's Telecom customer development.

Industrial Intern- Honeywell Automation India Ltd, Pune India June 2017 - July 2017

Evaluated code for Process History Database (P.H.D) that records data from a Historian in a tabulated format using Excel Companion and worked on a research project on Industrial Internet of Things (IIoT) preparation a PowerPoint Presentation and presented to Stakeholders.

PROJECTS

Multi-Layer Perceptron from scratch Nov 2020 - Dec 2020

Developed a Multi-Layer Perceptron (M.L.P.) back-propagation network style of artificial neural network classifier. A Single M.L.P. was constructed with one hidden layer and one multi-class output layer. ANN/MLP libraries were not utilized for the project, i.e., code was written from scratch. The accuracy obtained was 75%.

Technology: Python.

Interactive visualization from eye gaze dataset using D3.js Nov 2020 – Dec 2020

Built, designed, and engineered an interactive visualization using the given Dataset that captures eye gaze recorded during a human-computer interaction session. JavaScript D3 library was utilized. Project aims to provide interactive visualization support to users in examining whether a particular trend/pattern is present.

Technology: JavaScript, HTML, CSS, D3.js, Java.

Web Scraping Dow Jones Website for George Mason University Oct 2020- Dec 2020

Spearheaded and improved a Web scraping code for George Mason University in coordination with the university's research assistant.

Extracted and converted information of 10000 companies from Dow Jones website to a CSV file, further programmed sentimental analysis using N.L.P from the data obtained.

Technology: Python (NumPy, pandas, sklearn, Selenium, Beautiful Soup). Building Decision Tree from scratch and Ensemble the Tree Oct 2020- Nov 2020

Created and shaped an automated Ensemble Decision Tree Builder and wrote a binary pattern recognition Decision Tree Ensemble using Builder using KRK training dataset.

Built Two Decision Trees for the Ensemble, first with an initial selection of vectors and second with the same number but of boosted feature vectors.

Streamlined an ensemble, structured as a weighted vote of two Decision Trees based on both the model's accuracy resulting in a boosted accuracy of 88%.

Technology: Python.

Interactive Word Cloud Generator Sept 2020- Oct 2020

Implemented an interactive tag cloud to visualize text of 300 words. The input was a free text extracted from a Wikipedia page.

Developed a placement algorithm using Python, with the team's help, to place the word on the canvas in various orientations (0 or 90) and colors.

Programmed Interactive Features, clicking on the word, directs to the Wikipedia page, and numerous animations were integrated on canvas using JavaScript, HTML, and CSS.

Connected Python API with JavaScript Frontend using Flask.

Technology: JavaScript, HTML, CSS, Python (Flask, PIL). Sudden Cardiac Death (S.C.D.) Prediction using E.C.G. Machine Aug 2018 – Dec 2019

Engineered a Sudden Cardiac Death prediction model using Python, where a database was formulated based on a combination of E.C.G. Data generated from an E.C.G. Machine and an online Medical database (PhysioNet).

Managed testing and analysis of various Machine Learning algorithms on Dataset, Classification, and Regression Tree Model (CART), which resulted in the highest accuracy of 83% compared to the other models.

Signal Processing was done on signal extracted from the E.C.G machine, using LabView and MATLAB.

Technology: Python, LabView, MATLAB.

Railway Crowd Management System for Suburban Railway using Image Processing Jan 2018 -- May 2018

Co-led a team to develop a passenger safety APP where Crowd details of Railway Compartments and Foot Over Bridge (F.O.B.) was displayed.

Collected data with cameras installed at various Compartments, F.O.B., and Booking counters, data was processed and analyzed using Image Processing algorithms and tools. An accuracy of 90% was obtained of the number of passengers detected in each frame by filtering each snapshot's noise.

Technology: Python, Embedded C, Java.

PUBLICATIONS

Prediction of Sudden Cardiac Death using Classification and Regression Tree Model with Coalesced based E.C.G. and Clinical Data: https://ieeexplore.ieee.org/document/8723979

A.I. Use Cases in Operational Support System and Business Support System: https://ieeexplore.ieee.org/document/8723979

C-Indicator: Crowd Management System form Suburban Railway using Image Processing: https://www.ripublication.com/Volume/awmcv11n1spl.htm

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