Price Drop Prediction System
Predicts product price drops across 1,200 products using 8 years of monthly pricing data. Improved alert precision by 3× and increased AUC from 0.43 to 0.70 over fixed threshold baselines.
Hi, I'm Bhoomika, a Data Science undergraduate at San José State University with a strong foundation in machine learning, large language models, and applied AI. Some of my recent work focuses on LLM safety evaluation, reinforcement learning-based emotion prediction, and machine learning for health and biological data. I'm especially excited about making AI systems safer, more interpretable, and useful in practice. I enjoy turning complex research ideas into systems that create real-world impact.
Research Interests
Advisor: Prof. Erik Cambria
Developing an agentic reinforcement learning framework to anticipate speaker emotions before the next utterance using prior conversation history. Focusing on speaker-state memory, cross-speaker affect signals, and reward design for more context-aware dialogue systems.
Advisor: Prof. Satish Kumar Thittamaranahalli
Building a FastMap-based framework for edge-orientation optimization under asymmetric routing demands. Modeling street and warehouse routing as a combinatorial optimization problem with directed arcs and capacity constraints.
Advisor: Prof. Leilani Gilpin
Developing systems that integrate large language models with symbolic structures to enable structured, verifiable reasoning. Focusing on architectures that combine neural language understanding with formal logic for robust inference.
Advisor: Pranoy Kovuri (AI Engineer at Apple)
Building a policy-driven framework to evaluate LLM guardrails against runtime-defined moderation rules. Designing structured test suites across attack types and analyzing failure modes such as false positives, partial leaks, policy conflicts, and degradation under constraint.
Advisor: Dr. William Andreopoulos
Building ML pipelines for low-cost biosensors to classify RNA sequences for virus detection in water samples. Achieving 95% accuracy using a Mixture-of-Experts architecture with reproducible feature extraction.


Advisor: Prof. Jelena Gligorijevic
Building a time-aware transformer model to predict post-treatment blood pressure control from longitudinal EHR data. The project focuses on modeling patient history across diagnoses, medications, labs, and vitals to support therapy response prediction.
Advisor: Nazanin Sabri (PhD at UCSD)
Analyzing large-scale social media data using topic modeling and sentiment analysis to map community structures and value systems across online subcultures.
Advisor: Dr. Teng Moh
Investigating how chain-of-thought reasoning affects instruction-following accuracy in LLMs, with a focus on identifying which constraint types are most vulnerable.
Break Through Tech
Selected for a competitive, industry-aligned AI/ML fellowship focused on applied machine learning, technical mentorship, and project-based collaboration.
Software and Computer Engineering Society · SJSU
Building self-update and restart features for SCE's CI/CD system to improve deployment reliability across internal tools.
SiMa.ai × SJSU
Developing YOLO-based edge vision pipeline for real-time occupancy estimation across video streams.
EPA ESA × SJSU
Built an AI agent to validate and normalize 200+ county-level soil datasets. Automated compliance checks using rule-based and LLM-assisted evaluation, reducing manual effort by 70%.
San José State University
Developed ML pipeline for low-cost biosensors, classifying 500+ RNA sequences with 95% accuracy using Mixture-of-Experts in PyTorch.
Paragon Policy Fellowship
Analyzed large public datasets using SQL to identify trends in AI governance and healthcare. Translated quantitative findings into actionable policy briefs.
AI4ALL
Built an accessibility tool simplifying complex text using FLAN-T5, reducing text length by 60–80% while preserving meaning.
Predicts product price drops across 1,200 products using 8 years of monthly pricing data. Improved alert precision by 3× and increased AUC from 0.43 to 0.70 over fixed threshold baselines.
Streaming pipeline analyzing prompt–response events via Kafka. Handles 1K+ events/min with <200ms latency, flagging 15–20% of outputs for intervention.
Text simplification tool using FLAN-T5 for readers at varying levels. Reduced text length by 60–80% while preserving meaning across 100+ passages.
Mood tracking app using NLP and sentiment analysis to detect emotional patterns and surface data-driven insights.
Responsible Computing Club · SJSU
Selected for a global initiative promoting ethical technology and responsible AI practices on campus.
Applied Engineering Organization · SJSU
Organized and led technical workshops on AI, GenAI, Python, and Java for peers and club members.
AI/ML Club · SJSU
Managing and collaborating on AI projects across student teams at SJSU.
Always on the lookout for opportunities to learn more and grow.