Hemanth Gandhi Muppalla

Motivated and detail-oriented aspiring data analyst with hands-on experience in Python programming, statistical analysis, and data manipulation. Demonstrates strong organization, effective communication, and decision-making skills, with a proven ability to collaborate in team settings and stay calm under pressure. Certified in Microsoft Azure AI, Generative AI, and Machine Learning, with additional credentials from Kaggle in Python and programming fundamentals. Eager to apply analytical thinking and AI knowledge to solve real-world problems and contribute to data-driven decision-making.

• Ongoing Intern at YBI Foundation
  Actively learning Python, data manipulation, and analysis while building hands-on projects with mentor support.
• Certified Azure AI Explorer
  Earned 14+ Microsoft Azure AI badges covering areas like Generative AI, Computer Vision, Responsible AI, and Language Services.
• Kaggle Learner
  Completed the “Intro to Programming” micro-course and participated in community discussions and hands-on challenges.
• Team Collaborator in Academic Projects
  Contributed to team-based assignments with a focus on problem-solving, task planning, and effective communication.
• Self-Learner and Tech Enthusiast
  Dedicated time to explore AI trends, take online certifications, and build knowledge in emerging technologies.

Microsoft Azure AI Fundamentals Series (Completed on 28th March 2024)
Earned verified badges in the following areas:

• Fundamentals of Responsible Generative AI
• Fundamentals of Azure OpenAI Service
• Fundamentals of Generative AI
• Fundamentals of Knowledge Mining and Azure AI Search
• Fundamentals of Azure AI Document Intelligence
• Fundamentals of Azure AI Speech
• Fundamentals of Conversational Language Understanding
• Fundamentals of Question Answering with the Language Service
• Fundamentals of Text Analysis with the Language Service
• Fundamentals of Optical Character Recognition (OCR)
• Fundamentals of Facial Recognition
• Fundamentals of Computer Vision
• Fundamentals of Azure AI Services
• Fundamentals of Machine Learning
• Fundamental AI Concepts

Intro to Programming – Kaggle
Completed foundational training in Python programming and data manipulation.

Filter Design and Analysis Methods – MathWorks Training Services
Successfully completed 100% of the self-paced course.
Hemanth Muppalla, 2025

Azure AI Champion – Microsoft (March 2024) successfully completed a comprehensive suite of 15+ Azure AI certifications in a single month, demonstrating a strong commitment to mastering AI concepts, including Generative AI, Computer Vision, and Natural Language Processing

Kaggle Programming Certification – Intro to Programming (2024) recognized for completing a foundational programming course and engaging in practical data analysis exercises on Kaggle’s interactive learning platform

MathWorks course completion – filter design and analysis methods (2025) earned a 100% completion certificate in self-paced professional training, showcasing knowledge of signal processing and digital filter design

Self-motivated learner independently pursued advanced online certifications across platforms like Microsoft, Kaggle, and MathWorks to stay ahead in AI and data science

AI-Powered Smart Traffic Management SystemRole: Project Lead | Duration: Jan 2025 – Apr 2025
Technologies: Python, TensorFlow, MobileNetV2, OpenCV, Matplotlib, Google Colab, MQTT (simulated)

Overview:
Developed a cost-effective AI + IoT simulation system to prioritize emergency vehicles by classifying them in real time and dynamically controlling traffic signals without physical hardware.

Key Contributions:

• Built a deep learning model using MobileNetV2 and transfer learning to classify ambulances, fire trucks, and police vehicles from image inputs.
• Simulated traffic light behavior based on AI outputs using MQTT-like logic within Google Colab, avoiding the need for IoT hardware.
• Designed system architecture, use case, class, and sequence diagrams to structure the solution effectively.
• Created visual feedback using emojis and image processing to mimic traffic light responses based on detection results.
• Tackled challenges like dataset limitations, platform constraints, and model misclassifications.

Impact:

• Demonstrated a hardware-free prototype that supports emergency traffic prioritization.
• Promoted smart city development and reduced carbon emissions by minimizing vehicle idling.
• Positioned for real-world scalability with plans for live camera feed support and IoT device integration.