I Love to spend my time finding more about the things I know and a sometime about things
I don't.
If you like to create and kill 'BUGS', Party Hard, Dream Big or a noob Philospher like
me, you will feel right at home with me.
I am more of a laid-back ambitious coder who is worried about not putting my skills
to proper use.
My interests include Automation, Machine Learning, Mathematics, Analytics. I have more
than 4 years of developing and programming experience,
with two plus years of professional experience in Data Science domain.
I like to share what I know and would like to learn more about what I dont.
If you feel I can help you or better if you can help me in any way please ping me.
*Self Recognized
Building pipelines used by ML backend for recruitment Chatbot, used by different staffing agencies. Advancing legacy platform towards a microservice based architecture while being more ML friendly for quick cycles of modeling and development in general.
Learning To Rank: Modelling Ranking systems to score based on LambdaRank and LambdaMart using behavour specific and clickstream data. Data Pipelines: Creating reusable and robbust data and process pipelines to be used as kubeflow spark components
Dynamic Pricing statistical model to Predict and affect event ticket pricing based on live demand and previous trends. Recommendation System for event space having highly volatile items. Worked with Named Entity Recognition using NLTK and RNN to extract event details from event banner and posters.
Worked in Machine Learning and Data Science with Medical Data to provide Cognitive and Data-Driven Intelligence in the field of healthcare and medical screening autonation. Microscopic Video analysis and modeling for particle detection and cell tracking using Deep-CNN to report and classify disease with Human level accuracy. ELT, EDA, and visualization of various medical data forfor R&D to recognize, analyse and solve domain-specific or hardware-specific challenges. Implementation of multiple research works in AI/ML for applied and business use-cases with modification to adapt data, product and hardware constraints.
Worked in Computer Vision developing state-of-art object recognition model for the ARM-architecture Trained and tested model for low processing CPU's to replicate the bench-marked results and achieved almost 10 percent more efficiency Implemented Darknet's YOLO for ARM (aarch_64_v8.2), debugged the cross platform application Got working experience with remote GDB in cross compilation on X15 and Raspberry Pi Used Arm-Compute Library, Intel-MKL library for accelerating the computing efficiency on the ARM-architecture, Intel
Contributed and monitored the open source project AWS-ELK-BILLING,
and was able to get over 100 stars in github in less than 2 months.
Used Docker
architecture to construct the entire stack for the project.
Worked on
elsaticsearch to increase the product search accuracy and also implemented Machine
Learning to make email-recommending system.
Parsed and Scrapped data for Analysis
and Machine Learning from various e-commerce websites like flipkart, amazon,
snapdeal.
Got familiar with test driven development and also applied continuous
intigration using TravisCI which improved the development time and accuracy
Supported fellow juniors with C/C++ programming language under the direction and
guidance of teachers, using lectures and assignments.
Tutored over 50 students in
a large group setting to help them master assignments and reinforce concepts.
Participated actively in these websites for Coding Competitions and other programming
events to gain experience and sharpen my skills.
Managed official pages for both
the organisations for publicity, development and co-ordinations among other
Campus-Ambassadors.