Dhara Tamhane Phone: 408-***-**** Email: ac4dig@r.postjobfree.com
Santa Clara, CA
LinkedIn Github
Education
MS in Software Engineering Santa Clara University
Santa Clara, CA September 2015 – June 2017
BS in Computer Science Ganpat University
Gujarat, India August 2004 – May 2008
Work Experience
Software Developer (Contract) A1 Software group (Selly Automotive)
San Francisco October 2017 - January 2018
Design and develop components of business access layer and data access layer and integrate these layers to store or retrieve specific information
Based on requirements, develop applications built on top of entity framework 7.1
Importing historic data of companies, clients and prospects in new CRM
Communicating with customer support representatives in order to debug and resolve bugs in system
Documenting and performing unit testing of developed features
Tools & Technologies: Asp .Net C#, Entity framework, SQL server, HTML5, CSS3, JavaScript, SoapUI, Microsoft Azure
Summer Intern Gujarat State Petroleum Corporation Gandhinagar, India June 2010 – July 2010
•Data-driven statistical assessment of structured and unstructured data of Key performance indicators.
•Suggested recommendation in order to Increase efficacy and effectiveness of existing performance appraisal system.
•Tools & Technologies: MS access, VB.Net, MS Excel
Software Engineer Intern Bhaskaracharya Institute for Space Application and Geo-informatics
Gandhinagar, India Jan 2008 – June 2008
●Developed web application based on geographical information system.
●Implemented features for map based application to ease process of analyzing images received from satellite.
●Implemented functionalities such as map query Builder, multilayer, panning, unique color which made process of understanding complex data easier.
●Tools & Technologies: C#, HTML, JavaScript, CSS, XML, GML, Shape files
Skills
Programming Languages: Java, C#, HTML5, CSS3, JavaScript
Tools & Framework: MATLAB, Ionic, .Net, Google App engine/ Google cloud, Microsoft Azure
Database: MySQL, SQL server
Others: XML, JIRA, Agile/Scrum
Projects
Graduate Capstone Project - Github
●Developed a cross platform mobile app with a goal to provide information about various sports/gym facilities like wait times (real time/future), directions within campus etc.
●Worked on all phases of software development life cycle.
●Tools & Technologies: Java, Ionic Framework, HTML5, CSS3, Angular JavaScript, Karma, Jasmine, Jira, Maven, Google App Engine, Google Data Store
Campus Smart Cafe - Github
●Developed Java system application (GUI) that provides user a secure platform for ordering food from campus café or vending machine according to his/her set preference. Incorporated map and graph features to ensure user friendly experience.
●Tools & Technologies: Java, Swing, MySQL
Model For Organizing Threads - Github
Developed distinct thread models in Java applicable in given scenario.
Implemented multi-threading, inter process communication and increased time efficiency.
Tools & Technologies: Java, Operating system
Prediction Model for Housing Price - Github
●Developed prediction model for house price, training dataset through Gradient Descent.
●Scaled the projection for different learning rate and timeframe required for the convergence for each learning rate.
●Tools & Technologies: Machine Learning, MATLAB, Supervised Learning
Dataset Classification (Supervised Learning)- - Github
Classified test data of Iris flower (Anderson’s Iris dataset) introduced by Ronald Fisher in 1936 using Minimum Risk Bayes Decision Theoretic classifier.
Developed model from training data and classified test data accurately (99.83%) from the data.
Tools & Technologies: Machine Learning, MATLAB, Supervised Learning
Dataset Classification (Unsupervised Learning)- Github
Classified test data of Iris flower (Anderson’s Iris dataset) introduced by Ronald Fisher in 1936 using K-means algorithm.
Divide given data in K number of groups and verify accuracy of applied algorithm
Tools & Technologies: Machine Learning, MATLAB, Unsupervised Learning
Directed Research – Deep Learning
Trust Aware Recommendation System
Researched about how to generate recommendations according to users’ preferences, trust and associated groups using Modified matrix factorization algorithm with multilayer architecture by incorporating deep auto-encoder (artificial neural network). Recommends according preference, social trust and associated community.