Chaitanyananda B S

• Focus - Recommender Systems • Ownership of Collaborative Filtering and non-personalized recommenders such as Popularity and Trending • Sound knowledge of various algorithms and frameworks such as Matrix Factorization, LightFM, Bayesian Personalized Ranking, Approximate Nearest Neighbour search, etc. • Involved in the design and development of model training pipelines, and services from the ground up • Contributed towards an increase in customer engagement by enhancing content discovery and user coverage

• Responsible for end-to-end development and integration of ML features into a mobile application. • Interaction with the team regarding the domain knowledge and UI aspects to understand the core requirements. • Sound knowledge on data acquisition, data preparation, data analysis and data visualization. • Training, evaluation, and deployment of image classification and object detection Deep Learning models (on-device and cloud) with Transfer Learning approach to mobile apps using TensorFlow and Flutter.

• Involved in prototype design, programming of various sensor modules, and their integration to Raspberry Pi. The system records numerous vital environmental parameters and other vital information from the beehive and sends it to the remote database. • Creation of scheduled jobs and integration of real-time database Firebase Cloud Firestore using Python programming language. • Tools | Frameworks | Platforms | Languages: Python, Linux commands, Raspbian Linux OS, Firebase NoSQL Database

• Functional and performance testing in the pharmacy healthcare domain encompassing client-server architecture and web application. • Involved in data retrieval, analysis, and interpretation of critical validation areas such as batch jobs and interfaces. • Tools | Frameworks | Platforms | Languages: SQL, JIRA, HP-ALM, HP Load Runner

• "Deep Learning based image classification of bee species and detection of pollen on honeybees with automated identification of pollen source and pollen load color using a mobile application" • First-of-its-kind mobile application feature for beekeepers and gardeners that automatically detects various organic entities of a bee habitat from a single image captured using a smartphone. • Image processing for dominant color extraction of pollen & REST API based plant prediction.

• NLP project on the topic "Tweet annotation for migration relevance" - trained a text classification model to classify the tweets as human migration relevant or not. • Incorporated state-of-the-art feature engineering techniques: Word2Vec and tf-idf • Demonstrated the performance of various linear and ensemble classifiers based on different metrics.

Presented master seminar on the topic "AdaBoost technique for people detection" that aims to detect and track people from 2D laser sensor data.

• Focussed on subjects related to Machine Learning such as Advanced Data Science, Visual Analytics, Text Mining, Intelligent Systems, Deep Learning, Autonomous Learning, Sensor Data Fusion, Multimedia Databases. • Practical knowledge of various ML algorithms pertaining to classification, regression, and clustering on text and image data.

• Focussed on subjects such as Computer programming, Digital Signal Processing, Embedded Systems Design, Operating Systems, Image Processing. • Bachelor thesis on "GPS and GSM based automated accident alert and ambulance rescue system using ARM 7". Involved in the design and development of the system consisting of sensors, GPS and GSM modules, RF Communication, and IR Communication technologies. • Programming in C and C++