Microsoft
Software Engineer @ Azure Cosmosdb
University of Maryland Jan 2011 - Dec 2015
Graduate Research Assistant
Microsoft May 2013 - Aug 2013
Research Intern
Minekey Sep 2009 - Jun 2010
Technical Manager
Minekey Jun 2007 - Sep 2009
Senior Software Engineer
Education:
University of Maryland 2010 - 2015
Doctorates, Doctor of Philosophy, Computer Science, Philosophy
Indian Institute of Technology, Kharagpur 2002 - 2007
Ramakrishna Mission Residential College 2000 - 2002
Ranaghat Pal Chowdhury Higher Secondary School 1994 - 2000
Skills:
Algorithms Machine Learning Distributed Systems C Data Structures C++ Python Java Computer Science Hadoop Linux Azure Documentdb Azure Documentdb Databse Latex Natural Language Processing Programming Big Data Data Mining Mapreduce Rest Pattern Recognition Software Engineering Perl Scalability Text Mining Parallel Computing Databases Information Retrieval C# Artificial Intelligence High Performance Computing Azure Documentdb Database
Languages:
English Hindi Bengali
Certifications:
Machine Learning Introduction To Data Science Mining Massive Datasets Statistical Inference
Vtl Electronics Ltd. Jun 2017 - 2018
Technical Manager
Thales Jul 2016 - Sep 2016
Train Tester
Vtl Electronics Ltd. Mar 16, 2015 - Jan 4, 2016
Dy Manager-Technical
Stesalit Limited-Electronic Systems Division Nov 2012 - Jan 2015
Embeded Design and Development Engineer
Stone India Limited Apr 2011 - Oct 2012
Junior Manager-Technical
Education:
Karnataka State Open University 2012 - 2015
Bachelors, Bachelor of Technology, Engineering, Electronics
Birla Institute of Technology, Mesra 2005 - 2008
Nandanpur High School(H.s) 2002 - 2004
Nandanpur High School
Sarberia High School
Birla Institute of Tecnology, Kolkata
Skills:
Embedded Software Electrical Engineering Testing Electronics Embedded Systems Engineering C Matlab Product Development Pcb Design Embedded C Lean Manufacturing Project Engineering Circuit Design Manufacturing Firmware Hardware Architecture I2C Spi
- Redmond WA, US Momin Mahmoud AL-GHOSIEN - Sammamish WA, US Samer BOSHRA - Woodinville WA, US Brandon CHONG - Kirkland WA, US Madhan GAJENDRAN - Bengaluru, IN Mikhail Mikhailovich KOLTACHEV - Redmond WA, US Orestis KOSTAKIS - Bellevue WA, US Aravind Ramachandran KRISHNA - Kirkland WA, US Liang LI - Sammamish WA, US Jayanta MONDAL - Seattle WA, US Balachandar PERUMALSWAMY - Kirkland WA, US Karan Vishwanath POPALI - Bellevue WA, US Adrian Ilcu PREDESCU - Sammamish WA, US Vivek RAVINDRAN - Redmond WA, US Ankur Savailal SHAH - Redmond WA, US Pankaj SHARMA - Kirkland WA, US Dharma SHUKLA - Bellevue WA, US Ashwini SINGH - Bellevue WA, US Vinod SRIDHARAN - Redmond WA, US Hari Sudan SUNDAR - Redmond WA, US Krishnan SUNDARAM - Bellevue WA, US Shireesh Kumar THOTA - Redmond WA, US Oliver Drew Leonard TOWERS - Seattle WA, US Siddhesh Dilip VETHE - Redmond WA, US
International Classification:
G06F 17/30
Abstract:
Databases are often provided according to various organizational models (e.g., document-oriented storage, key/value stores, and relational database), and are accessed through various access models (e.g., SQL, XPath, and schemaless queries). As data is shared across sources and applications, the dependency of a data service upon a particular organizational and/or access models may become confining. Instead, data services may store data in a base representation format, such as an atom-record-sequence model. New data received in a native item format may be converted into the base representation format for storage, and converted into a requested format to fulfill data requests. Queries may be translated from a native query format into a base query format that is applicable to the base representation format of the data set, e.g., via translation into an query intermediate language (such as JavaScript) and compilation into opcodes that are executed by a virtual machine within the database engine.
Partitioning Advisor For Online Transaction Processing Workloads
- Redmond WA, US Jayanta Mondal - Hyattsville MD, US
International Classification:
G06F 17/30
Abstract:
A partitioning advisor for online transaction processing (OLTP) workloads generates a workload dependency graph based on a schema defining a structure of a relational database and a workload associated with an OLTP application that accesses the relational database. Based on the workload dependency graph, the partitioning advisor generates one or more partitioning strategy recommendations for sharding the relational database. The partitioning advisor may also render a visualization based on the workload dependency graph, enabling a user to see the impact each recommended partitioning strategy is predicted to have.