Nishant Bannikoppa

Data Scientist role at Indium Software, specializing in developing AI and ML solutions to drive business insights and automation.

Developed a Proof of Concept (POC) for a Finance Corp using the ChatGPT 4 Model on the iSAFE Automation Framework.

• Created a unique code generation project where users provide prompts via a Streamlit app, and the model returns the code in the iSAFE format as the response.
• Implemented storage of the code framework and understanding data in the ChromeDB vector database.

Spearheaded the development of a roboadvisor digital platform providing automated, algorithm-driven financial planning and investment services.

• Designed and implemented the platform architecture leveraging ChatGPT 4 as the GenAI Model.
• Integrated Faiss as the Vector Database for efficient data storage and retrieval.
• Developed a user-friendly web application using Flask to enable seamless interaction with the roboadvisor system.

Analyze neural data from sensors on mice sniffing gas to identify and classify the gas.

• Data Quality: Ensure high-quality data collection from lab mice sensors and preprocess the neural data to remove noise and artifacts.
• EDA: Perform EDA to identify patterns and trends in the neural data using statistical methods and visualization techniques.
• Algorithm Development: Develop machine learning algorithms, such as SVM or deep learning models, for accurate classification of neural data.
• Model Validation: Validate the algorithms using evaluation metrics and cross-validation techniques to ensure reliable performance.

Digitization of MRI Reports

• Automate MRI report generation in healthcare by developing pipelines in the Flywheel client.
• Leveraged FastAPI to streamline data transactions and telemetry management, improving data processing efficiency.
• Managed AWS platform for the project, ensuring optimal performance and scalability.
• Produced detailed documentation of project advancements and provided mentorship to new team members.
• Worked alongside team members and leaders to identify analytical requirements and collect information to meet customer and project demands.

• Developed ANPR (Automatic Number Plate Recognition System) for traffic management, utilizing Object Detection Models and OCR technologies.
• Collaborated with cross-functional teams to design and implement AI solutions to automate e-challans for public traffic violations.
• Leveraged deep learning frameworks such as Yolo V7, V8 and OCR models like keras-ocr, paddle-ocr to enhance system accuracy and performance.
• Played a key role in managing AWS platform and optimizing data pipelines for performance and scalability.

• Led the development of custom image detection solutions using TensorFlow and Keras, enhancing the capabilities of IRaaS (Image Recognition as a Service).
• Implemented computer vision techniques to detect human presence in robotic cells, improving safety and efficiency.
• Designed and implemented OCR solutions for water meter reading, leveraging CNN models and open-source technologies.
• Built a robust datalake solution using Apache Nifi, HDFS, Apache Kafka, and Apache Spark to ingest, store, and process large volumes of data.