Kushal Banda

I'm

About

Dedicated Deep Learning professional with extensive expertise in AI and Machine Learning. Proven ability to leverage advanced algorithms for actionable insights from vast datasets. Committed to driving innovation in the ever-evolving AI landscape and delivering transformative business outcomes.

City: Hyderabad, Telangana

Email: kushalbanda265@gmail.com

Interests

Machine Learning

Deep Learning

NLP

Generative AI

Visualization

Data Analysis and Statistics

Validation

Development Processes and Tools

Education

B.Tech in Aritfical Intelligence

Relevant Coursework

Python
Machine Learning
Deep Learning Neural Networks
Database Management
IOT
Reinforcement Learning

CBSE

Relevant Coursework

Mathematics
Science
Social Studies
English

Certifications

Machine Learning

Tensorflow

Gen AI with LLMs

Experience

Amplelogic

May 2024 - Present

Gen AI Developer

Developed and implemented generative AI models, including open-source large language models, to enhance predictive analytics in pharmaceutical manufacturing.
Engineered advanced AI-driven systems for real-time anomaly detection and automated control processes, increasing operational efficiency by over 25%.
Utilized generative AI to simulate and predict drug interactions, reducing potential adverse effects and improving patient safety.
Led cross-functional teams to integrate AI technologies with existing healthcare systems, ensuring compliance with medical data regulations.
Pioneered the use of generative models for generating synthetic biological data, aiding in faster and more ethical medical research.
Directed the application of LLMs for automating the extraction and analysis of medical texts, significantly improving knowledge discovery processes.
Created machine learning models to analyze genomic data, enhancing the accuracy of personalized treatment plans through predictive genomics.

Codebugged AI

April 2023 - April 2024

Data Scientist

Spearheaded the development of an AI-driven system to detect and classify seizures and various brain activities in critically ill patients.
Experience in managing and interpreting tabular data formats, especially in medical records and patient demographic information.
Analyzed electroencephalography patterns and Spectrogram signal processing.
Extracting relevant features from EEG signals using spectral analysis techniques like Fast Fourier Transform (FFT) or wavelet transforms.
Engineered a deep learning model to enhance electroencephalography pattern classification, resulting in a 30% faster diagnosis time and 20% improvement in accuracy.
Used Kullback-Leibler Divergence statistical measure to compare the probability distributions of predicted and actual EEG data, refining the model’s accuracy.
Commitment to ethical research practices and strict adherence to data privacy laws, particularly in handling sensitive health information.