Chandrashekar V

Software Engineer

London, England, United Kingdom13 yrs 8 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Experienced in developing multimedia infotainment systems.
Strong background in data mining and information retrieval.
Proficient in multiple programming languages and software development.

Stackforce AI infers this person is a Software Engineer with expertise in multimedia systems and data-driven applications.

Contact

Skills

Core Skills

Software DevelopmentAlgorithmsMultimedia SystemsData MiningInformation RetrievalStatistics

Other Skills

Data StructuresNatural ImagesCC++PythonMatlabPHPMySQLJavaScriptHTMLBasic javaJavaMachine LearningText MiningRecommender Systems

Experience

13 yrs 8 mos

Total Experience

3 yrs 5 mos

Average Tenure

8 yrs 2 mos

Current Experience

Meta

Software Engineer

Mar 2018 – Present · 8 yrs 2 mos · London, United Kingdom

Software DevelopmentData StructuresAlgorithms

Flipkart

Software Engineer

Mar 2015 – Jan 2018 · 2 yrs 10 mos · Bengaluru Area, India

Worked as part of Data Platform

Nvidia

Software Engineer

Sep 2013 – Feb 2015 · 1 yr 5 mos · Bengaluru Area, India

Working in Nvidia Multimedia team to design, implement and support high-
quality Multimedia Infotainment Systems for automotive customers.

Multimedia SystemsSoftware Development

International institute of information technology, india

Research Assistant

May 2012 – Aug 2013 · 1 yr 3 mos · Hyderabad Area, India

The work is about finding semantically associated itemsets in large tagset data like Flickr, Youtube, IMDB, Wikipedia with applications to Search Engines,Tag Recommendation systems, Topic Detection, Event Identification, User Profiling, Photo Clustering.
I am guided by Dr. Shailesh Kumar (Google Inc, India) and Prof. C V Jawahar (CVIT, IIIT-H, India).

Data MiningInformation Retrieval

Siemens corporate technology & research

Summer Intern

May 2011 – Jul 2011 · 2 mos · Bangalore, India

Developed an alternate approach to adaboost for selecting efficient features using
statistics of Natural Images instead of training data. Since the complete class of non
faces is subset of Natural Images, we used the properties of Natural Images to reduce
computation and remove bias.

StatisticsNatural Images

Education

International Institute of Information Technology Hyderabad (IIITH)

MS by Research — Computer Science

Jan 2012 – Jan 2013

International Institute of Information Technology Hyderabad (IIITH)

Bachelor of Technology (B.Tech.) — Computer Science

Jan 2008 – Jan 2012

DAV Public School, Kota

Jan 2006 – Jan 2008

Don Bosco School, New Delhi

Jan 2001 – Jan 2006

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