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
Qualcomm
Developing Small Language Models (SLMs).
Student Researcher
Q-Lab UCSD
Architecting a model-agnostic system that translates natural-language prompts into physically-realistic, fully populated 3D scenes in Unreal Engine, integrating LLM-based reasoning, retrieval, and geometry-aware generation into a unified framework. Adviced by Prof. Lianhui Qin.
Student Researcher
UC San Diego Shiley Eye Institute
Working on developing multimodal deep learning models to enhance glaucoma diagnosis and progression prediction by integrating longitudinal and cross-sectional ocular data—including imaging, clinical, and temporal signals. Designing architectures that leverage temporal analysis to identify patients at risk of rapid progression or requiring surgical intervention, improving early detection accuracy and supporting clinical decision-making. This research tests the hypothesis that multimodal longitudinal models outperform traditional cross-sectional approaches in predicting glaucoma outcomes.
Education
M.S Computer Science and Engineering
University of California, San Diego (UCSD)
CGPA: 3.92/4
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: 7.00/10 Project CGPA: 9.0/10
Publications
SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
arXiv
View PaperFrom Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages
arXiv
View PaperArchitectural 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 leading development of AI-powered features—including course summaries, peer-driven insights, and coding assistance—by leveraging advanced language models such as LLaMa3-70B. Integrated the vLLM inference engine to improve the efficiency and performance of LLM queries, reducing latency and enabling scalable AI-generated responses across the system. Developed and deployed multiple AI-driven endpoints using FastAPI, with version control through Git and agile management under Scrum methodology. Ensured robustness and reliability of application components by writing unit tests and performing debugging with pytest, ultimately 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
