Hemanjali Kadali

Results-driven Software Developer with a robust background in computer science and a keen interest in innovation. Proficient in software development, testing, debugging, and system maintenance with expertise in C#, HTML, JavaScript, Python, SQL, and Java. Experienced in IoT applications and machine learning solutions. Strong academic foundation in Computer Science, with practical experience in projects and internships.

• CISCO: Networking essentials
• Programming essentials in C++
• Programming essentials in Python
• Programming essentials in C
• AMCAT: Software Engineer - IT Services and IT Product
• ORACLE: Database programming with SQL
• COURSERA: Data Science math skills by Duke University
• Predict future product prices using Facebook prophet
• OXFORD UNIVERSITY PRESS: Oxford Achiever

• Participated in 36hrs Hackathon conducted by Vidya Jyothi Institute of Technology
• Achieved Credential Azure Data Scientist Associate by Skillsoft
• Achieved Overview of Infosys Springboard by Infosys
• Published a journal in INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING & MANAGEMENT
• Published a journal in INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN COMPUTER SCIENCE, ENGINEERING and INFORMATION TECHNOLOGY

SOLDIER HEALTH AND POSITION TRACKING SYSTEM :

• An IoT application using GPS and GSM.
• Smart sensors are attached to the body of soldiers to detect health conditions and send alerts to the base station through GSM module and location through GPS module.

HELMET DETECTION AND LICENSE PLATE RECOGNITION :

• A machine learning application using Python and YOLO Algorithm.
• A real-time application to detect moving objects using a KNN classifier to classify helmet and extract license plate.

GENET: AUTOMATIC CURRICULUM GENERATION FOR LEARNING ADAPTATION IN NETWORKING :

• "Genet" is fully implemented using Python and Bash, and it has been integrated with three existing Reinforcement Learning (RL) training codebases.
• "Genet," an advanced training framework that systematically introduces curriculum learning to reinforcement learning-based network adaptation algorithms.

SPOTIFY RECOMMENDATION SYSTEM :

• The project was implemented using Python, Jupyter Notebook, clustering techniques, machine learning models including Random Forest, SVM, K-Nearest Neighbours, Decision Trees, XGBoost, and Logistic Regression, along with ensemble learning for improved accuracy.
• The project is about creating a Spotify music recommendation system that uses machine learning to recommend songs based on their 'mood'.

Turtleback Zoo:

• The project was implemented using Python and Flask, initially transitioning from Java Spring Boot and Thymeleaf due to its flexibility and ease of development.
• The project involves developing a database application for Turtleback Zoo using Python, Flask, and OracleDB to manage data related to zoo operations, including animal care and revenue events.