Christon Nadar

• Applied machine learning topics

• Dual role as student research assistant and master thesis student. • Backend software development of audio analyzing toolkit based on python. • Experienced in python based GUI frameworks: PyQt, Tkinter, python-VLC, etc. • Research focuses on DNN based multitask learning methods for joint rhythm and harmony tasks. • Implementation of the attention model for multitask learning.

• Focus: Applied deep learning in Music Information Retrieval (MIR). • Research carried out in the area of genre-based musical beat tracking and tempo estimation using TCN and RNN approach (a comparative study). • Implemented two unsupervised loss function for musical beat modeling. • Improved the state of the art algorithm on existing in-house musical chord detection algorithms based on the CNN approach and published a conference paper.

• Re implemented Facebook AI Detectron project and gave a lead for further research and development • Documented literature review on state of the art in semantic segmentation and localization of object in 3D space.

• Supported creative promotional and branding strategies and ideas which was implemented in various platforms. • Developed and implemented promotional video strategy for YSEALI Oceans, a U.S State Department initiative for ASEAN Nations. • Received five-month VISA extension to support marketing department in Washington, D.C.

• Nominated one among all the Indian applicant - IAESTE Cobe offer • Created and directed a documentary on Hip-Hop culture in India, a U.S. Embassy funded grant. • Oversaw all elements of documentary production, including scripting, conducting interviews, creating custom graphics and animation and composing background music. • Supported event planning and implementation for showcase event in Washington, D.C. in May 2016.

Applied deep learning and audio signal processing.

Audio Engineering and game development.