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Social Media Python

Chicago, IL
May 20, 2020

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MONISHAA ARULALAN +1-773-***-**** Summary:

Emerging Computer Science Graduate Student specializing in Java, competitive Algorithms and Python. Seeking full-time software Academic engineering Qualifications: roles starting from May 2020.

• Illinois Institute of Technology, Chicago, IL Aug’18 – May’20 MS Computer Science

Relevant Coursework: Machine Learning, Data Mining, Algorithms, Networks, Advance Database organization, Big Data Technologies.

• College of Engineering, Guindy, Chennai, India Aug’14 – July’18 B.Tech Information Technology

Relevant Coursework: Operating Systems, Algorithms, Computer Architecture, Database Management Systems. Technical Skills:

• Programming skills : Java, Python, C / C++.

• Database : MySQL, MongoDB, NoSQL.

• Cloud technology : AWS

• Web Technologies : HTML5, CSS.

• Operating system : MacOS, Windows, Linux.

Professional experience:

Graduate Research Assistant, Illinois Institute of Technology Aug’19 – Feb’20

• Project Objective: Big Data Analysis with Machine Learning (ML) and Error-Embedded Deep Neural Networks Techniques for Enhancing Glucose Regulations and AP Algorithms. Funded by JDRF.

• Generated a code for cleaning data from the Tidepool datasets which contained more that 7,00,000 data points and identify trends in individual records using time series plots.

• Coded using Python.

Software Developer Intern, Qatar Computing Research Institute May’17 – July’17

• Developed a platform “QEvents” - Worked on the backend which includes collecting events from events and news websites and filtering the data to identify the date, time and location. The platform provides information to the user on events happening around them.

• Coded using Python (2.7.0). Used packages: Beautifulsoup, Feedparser, datefinder, Stanford NER. API used: Google Geocoding API. Database: MongoDB.

• Presented at ARC (Annual Research Conference) at Doha on March 2018. Projects:

Web Application : Online Shopping Portal

• Designed e-commerce application to buy groceries.

• Front-End : JSP, HTML, CSS, JS. Server-side: Servlet (Java). Server: Tomcat 8.5. Link State Routing Simulation using Dijkstra's Algorithm Aug’19 – Oct’19

• Implemented the Link-State Routing Protocol. Performed functions such calculating shortest path between two nodes using Djikstra’s Algorithm, generating routing table, modifying the network topology by adding and deleting new nodes. Face • Recognition Coded Using Java. Jan’19 – Mar ’19

• Classified if a specify person is present or not. Use of core Machine Learning techniques such as Convolution Neural Networks

(CNN), Support Vector Machine (SVM), Principal Component Analysis (PCA), K-nearest neighbors (Knn) and Transfer Learning for face recognition. Measuring precision, recall, F-1 score and accuracy and also analyzing different classifiers graphically. Performed as a part of Machine Learning Course.

Intention • Tech and Stack: Intensity Python, Detection Scikit-Learn, of Keras feelings and TensorFlow. on social media Aug’18 – Dec ’18

• Worked on sarcasm detection module. Performed feature extraction on tweets (n-grams, sentiment, topic (LDA), Parts of speech, capitalization) and used SVM classifier to classify the tweets as sarcastic or not. Obtained an accuracy of 76 percent. Evaluation • Coded system using Python using (2.Topic 7.0). Packages Modelling used: Gensim, SentiWordNet, sklearn. Jan’18 – Mar ’18

• Worked on extracting the topic from the given essay i.e. Topic modelling. Preprocessing the text by tokenization, stop words removal and stemming. Constructed a document term matrix and applied LDA (Latent Dirichlet Allocation) model to finally extract the topic. Vehicle • Coded Authorization using Python System (2.7.0). Packages used: Gensim(doc2bow), nltk (PorterStemmer, word_tokenize, stopwords)Aug . ’17 – Nov ’17

• Worked on extracting the number plate details from a given image which includes preprocessing the image to readable format and compare that with pre-existing data to extract the vehicle ID as a string. Deployed to production.

• Coded using MATLAB. Database used: SQLite using JDBC.

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