Hi, I'mRonit Gandotra
Data Scientist & AI Enthusiast
Building and analyzing AI agents to create practical solutions for complex problems
About Me
AI Enthusiast & Data Scientist
I'm a data scientist with a particular interest in AI agents and systems. I enjoy building, testing, and analyzing AI agent capabilities to understand their strengths and limitations.
My focus is on developing practical AI solutions that can solve real-world problems effectively. I specialize in creating and breaking AI agents to improve their robustness and reliability.
AI Agent Development
Building task-oriented AI agents that can understand instructions and complete specific objectives.
Agent Testing & Analysis
Testing AI systems to identify weaknesses and improve their performance in different scenarios.
Data Science
Applying statistical methods and machine learning to extract insights from complex datasets.
Software Development
Building practical applications that utilize AI capabilities to solve specific problems.
Interests
AI Agents
Machine Learning
Data Analysis
Location
Faridabad, India
Education
B.Tech. Bioinformatics
My Skills
I've developed a diverse skill set through academic training and hands-on experience in multiple projects and internships.
AI Agents & Automation
Machine Learning & NLP
Data Science & Visualization
Backend & Cloud
Frontend & Mobile
DevOps & Tools
Work Experience
My professional journey includes internships and projects where I've applied my skills to solve real-world problems in data science and bioinformatics.
IOT Data Analyst Intern
Country Delight
- Developed an AI-driven chatbot using LangChain and CrewAI, enabling autonomous customer interactions for refunds, product inquiries, ticket generation, and support communication, reducing manual effort by 30%.
- Utilized NLP and Generative AI to analyze customer patterns, complaints, and pain points, providing actionable insights for improved customer experience and service optimization.
- Integrated AI with IoT ecosystems, automating real-time data processing, anomaly detection, and predictive insights to optimize operations and enhance decision-making.
AI Intern
RechargeZap
- Developed a full-stack automation application for bug reporting using FastAPI and React, enhancing accuracy and reducing manual effort by 40%.
- Built and optimized LangChain pipelines for efficient data parsing, cutting processing time by 30%.
- Integrated AI-based suggestions and metadata generation into the CMS platform, boosting SEO performance and organic search traffic by 25%.
- Created an AI-based customer feedback handling system using LangChain, improving response times and providing actionable insights.
Bioinformatics Development Intern
Genique
- Executed and optimized bioinformatics pipelines on patient RNA sequencing data to identify anomalies in the gut microbiome, leading to actionable insights for clinical applications.
- Automated multiple analytical processes, significantly enhancing the efficiency and reproducibility of gut microbiome analysis.
- Developed and implemented robust pipelines to detect mutations associated with potential cancer risks in patient DNA sequencing data, improving early detection strategies.
- Conducted quality control and validation of sequencing data to ensure accuracy and reliability of bioinformatics analysis.
Software Development Intern
Indian Council of Medical Research (ICMR)
- Developed "E3Pred", a standalone software for analyzing protein sequences from FASTA files, automating feature generation and using machine learning methods to predict potential E3 ligases.
- Utilized Python and machine learning libraries (Scikit learn) to enhance software capabilities.
ML Intern
Rawal Lab
- Performed meticulous data analysis on large-scale proteomic datasets, utilizing Python and R.
- Developed a deep learning model using Python and TensorFlow to predict potential vaccine candidates among 9,000 drug samples, increasing prediction efficiency by 20%.
Projects
Here are some of my recent projects that showcase my skills and interests in AI, data science, and bioinformatics.
CrewAI-Based Customer Service Chatbot
An AI-driven multi-agent chatbot to automate customer interactions, handling refunds, product inquiries, and ticket generation.
- Developed an AI-driven multi-agent chatbot to automate customer interactions, handling refunds, product inquiries, and ticket generation, reducing manual effort by 30%.
- Integrated real-time API interactions and sentiment analysis for contextual understanding and autonomous decision-making.
- Optimized response generation with token usage tracking and error handling, ensuring cost-efficient and high-quality customer support.
Technologies
E3Pred: E3 Ligase Prediction Tool
A standalone software tool for protein sequence analysis in FASTA format with machine learning models to predict potential E3 ligases.
- Created a standalone software tool for protein sequence analysis in FASTA format.
- Automated feature generation, selected top features, and deployed machine learning models to predict potential E3 ligases.
- Integrated UbiBrowser API for substrate interaction prediction.
Technologies
NLP based Sentiment Analysis
An NLP pipeline to analyze customer chat data, including preprocessing, sentiment analysis, and visualization for e-commerce applications.
- Developed an NLP pipeline to analyze customer chat data, including preprocessing, sentiment analysis, and visualization.
- Applied techniques relevant to e-commerce for improving customer satisfaction.
Technologies
Academic Publications
My research has been published in peer-reviewed journals, demonstrating my contributions to the fields of bioinformatics and machine learning.
Autism Biomarker Identification using an Integrative Systems Biology and Machine Learning Approach Highlighting TC.FEV.OM, acrA, and ABCB-BAC Genes in Gut Microbiome Analysis
Gandotra, R., et al.
- Conducted extensive analysis of gut microbiome datasets using an integrative computational approach.
- Applied multiple machine learning algorithms with Scikit-learn and Cytoscape to identify biomarkers related to Autism Spectrum Disorder (ASD).
Machine Learning-Based Approaches for Vaccine Target Identification: Implementation and Insights
Gandotra, R., et al.
- Performed comprehensive data analysis on large-scale datasets using PyTorch, TensorFlow, and Scikit-learn.
- Developed predictive models to identify potential vaccine targets, enhancing the efficiency of vaccine development processes.