I am an AI engineer and researcher driven by the challenge of building accessible, high-fidelity language technologies. By combining computational linguistics with production-ready machine learning, I design speech systems capable of handling the complexities of real-world audio, from heavily accented regional dialects to noisy acoustic environments.
My core expertise lies in acoustic modeling, fine-tuning generative TTS pipelines, and building multi-task architectures that maximize performance on limited data. Whether extracting phonetic boundaries for language-learning applications, aligning deep speech embeddings to study human cognition, or stitching together real-time ASR-to-TTS inference graphs, I focus on the intricate science of speech processing first—ensuring the models I build are theoretically sound before scaling them for deployment.
Research Assistant – Dialectal Speech Processing
Sep 2025 – May 2026Research Assistant – Cognitive Modeling & Acoustic Analysis
Mar 2025 – May 2026Research Assistant – Applied NLP & Conversational Agents
Mar 2025 – May 2026Software Engineer
Derived real-time probabilistic estimates of acoustic ambiguity as speech unfolds using DTW-aligned Wav2Vec2.0 XLS-R embeddings and Bayesian updating. Successfully simulated human looking behavior and reproduced classic cohort and rhyme eye-movement patterns across both clean and noisy real-world speech tokens.
Manuscript Under ReviewBuilt an end-to-end ASR-to-TTS conversational framework combining Deepgram Nova 3 and Google TTS via structured SSML orchestration. Fine-tuned LLMs on HPC clusters to evaluate 200 synthetic user profiles, advancing personalized Augmentative and Alternative Communication (AAC).
Read Paper →Engineered an end-to-end data acquisition and machine learning pipeline to synthesize a 4.1-hour Nattuvangam acoustic dataset, achieving 70% classification accuracy on complex temporal patterns.
Read Paper →Successfully defended master's thesis on architecting a unified multilingual SER model. Implemented an auxiliary ASR multi-task learning objective to insulate the model from in-domain overfitting without adding runtime inference overhead. Attained zero-shot cross-lingual generalization across English, Italian, and Farsi targets using highly constrained data limits.
End-to-end development of a streaming Puerto Rican dialect speech-to-speech simulator designed to help clinical nursing and pharmacy cohorts overcome regional linguistic boundaries. Features specialized TTS alignment and LLM conversational inversion.
Benchmarked Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) algorithms within the SumoRL macroscopic transit environment. Optimized intersection phase vectors to dynamically reduce emergency vehicle idle delays by 45%.
Constructed a robust RAG extraction and search platform indexing 50,000+ localized documents via a distributed SOLR topology on GCP instances. Coupled the ingestion system with zero-shot T5 architectures to generate context-aware summarizations.
MS in Computer Science & Engineering (Research Track) • GPA: 3.81/4.0
Courses: Reinforcement Learning, NLP, Information Retrieval, Computational Linguistics, Parallel & Distributed Processing
MS in Computer Application, AI & Data Science | GPA: 8.83/10.0
2020 – 2022Bachelor in Computer Application, Software Engineering | GPA: 7.93/10.0
2017 – 2020