Siddhant H Mantri
AI/ML Enthusiast
siddhantmantri.com
About
I'm a tech enthusiast with a strong desire to learn and grow. I've self-taught various programming languages and technologies, and I'm always on the lookout for opportunities to advance my abilities. I'm an enthusiastic, people-oriented individual who enjoys expanding my network and collaborating with like-minded professionals. I thrive on the excitement of learning and look forward to contributing my passion for technology to any team or project.
Work Experience
ML Intern
Sudha Gopalakrishnan Brain Centre - IIT Madras
My work involves processing annotated brain sections, preparing datasets, and implementing deep learning models to accurately identify and delineate specific brain regions. This project aims to enhance the efficiency and accuracy of neuroimaging analysis, contributing to advancements in neuroscience research.
Research Intern
CFITL - Indian Institute of Technology Bombay
I am currently working on developing a multimodal chatbot—Shabdabot—intended primarily for educational use, with scope to expand into other domains. This chatbot leverages the Indo-Wordnet developed at the CFILT Lab to provide improved responses in multiple Indian languages. I am undertaking this work under the guidance of Prof. Pushpak Bhattacharyya and Prof. Malhar Kulkarni at the Computation for Indian Language Technology (CFILT) Lab.
ML Intern
Volkswagen Group Technology Solutions India
As part of Project AISHA 2.0, I contributed to multiple stages of developing an in-house, Retrieval-Augmented Generation (RAG)-based chatbot built from scratch. My key responsibilities included research on indexing and retrieval process which included comparison of various vector data stores to optimize the application. Additionally, I developed an enhancement service that significantly improved the chatbot's performance, reducing resource usage while accelerating response times. I also worked on Large Action Model development.
View LetterEducation
B.Tech Computer Science and Engineering (Cyber Security)
Mukesh Patel School of Technology Management and Engineering, NMIMS, Mumbai
CGPA: 3.86/4
B.S Data Science and Applications
Indian Institute of Technology Madras, Chennai
CGPA: 7.39/10 Project CGPA: 9.0/10
Publications
Architectural Framework for Automated Incident Response: Leveraging LLMs And Classifiers for Rapid Post-Attack Analysis and Reporting
ICTCS-2024 (Pub. Springer)
View PaperProjects
Automated Incident Response: Leveraging LLMs for Rapid Post-Attack Analysis and Reporting
Developed an AI-driven automated incident response framework integrating on-device Large Language Models (LLMs) and specialized classifiers to streamline post-attack analysis, reducing response times from days to hours. Architected a system for real-time log ingestion, multi-source data correlation, and comprehensive report generation, enabling rapid, accurate threat detection and response across networked environments.
Cybersecurity • Incident Response • Automation • Large Language Models (LLMs) • Machine Learning • Python • Flask • Wazuh • Suricata • SMTP • Log Analysis • Hugging Face • API Integration • Machine Learning • Log Analysis
View ProjectAI-Powered Learning Management System (LMS)
Contributed to the design and implementation of AI-driven features, including course summaries, peer-driven insights, and coding assistance, leveraging advanced language models such as Mistral8x7b and LLaMa3-70B. Integrated the vLLM inference engine to optimize performance, reducing latency and efficiently scaling AI-generated responses across the system. Implemented FastAPI to serve multiple endpoints for AI-powered features enhancing the overall learning experience for students.
Large Language Models • FastAPI • vLLM • Python • OpenAI
View ProjectRecipe for Rating: Predict Food Ratings using ML
Developed machine learning models to accurately predict food ratings based on recipe information and user reviews. Preprocessed and engineered features from a dataset containing multiple attributes. Evaluated multiple algorithms including logistic regression, boosting techniques like AdaBoost, and advanced algorithms like Multi Layer Perceptron. Achieved an accuracy of 77.166% by implementing a logistic regression model.
Python • Machine Learning Algorithms • Pandas • Numpy • Scikit-learn
View ProjectConnect
