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Hello, I'm Adith
Ph.D. Student | NLP & NLU · Machine Learning · Scalable AI Systems
I am a Ph.D. researcher specializing in Natural Language Understanding (NLU), with a focus on bias, fairness, and explainability in large language models. My work sits at the intersection of statistical NLP, model interpretability, and applied machine learning, aiming to build AI systems that are not only high-performing but also transparent and reliable in real-world settings.
I hold an M.Sc. in Computer Science from New York University , where I conducted applied ML research in scientific simulation and time-series modeling and served as a Teaching Assistant for graduate and undergraduate Machine Learning courses. Alongside research, I bring strong industry experience: as a Cloud ML Engineer Intern at Rheem Manufacturing, I designed cloud-scale ML pipelines ingesting 100M+ events per day, built predictive models for IoT-driven energy optimization, and optimized production inference systems to reduce latency by 30%. Previously, at Unique World Robotics, I developed precision-agriculture ML models achieving 97% accuracy and deployed real-time, cost-efficient ML APIs on AWS.
My background bridges ML research and production engineering, with interests in NLU, LLM evaluation, responsible AI, and scalable ML systems, and a goal of translating rigorous research into impactful, deployable AI solutions.
Education
Purdue University
PhD in NLP
Specializing in NLP/NLU, with research centered on bias, fairness, and explainability in large language models. My work focuses on rigorous evaluation, interpretability, and responsible adaptation of LLMs, with the goal of improving their reliability and trustworthiness in real-world decision-making settings.
New York University
Masters in Computer Science
Completed advanced graduate coursework in Machine Learning, Deep Learning, Computer Vision, Operating Systems, and Java Programming, complemented by a Marketing elective at NYU Stern that strengthened my understanding of the intersection between technology, product strategy, and business decision-making. In parallel with my studies, I gained hands-on industry experience as a Machine Learning Engineer Intern, where I worked on deploying production APIs, optimizing cloud infrastructure, and building scalable, data-driven ML solutions.
National Institute Of Technology,Warangal
Bachelor Of Technology-Computer Science
Studied at the National Institute of Technology Warangal—one of India’s highly competitive engineering institutions—where I completed rigorous coursework in Cloud Computing, Data Science, Data Mining, Distributed Systems, and Database Management Systems (DBMS). My bachelor’s thesis centered on developing a review-based group recommender system using deep learning techniques, aiming to enhance user recommendations through advanced AI methodologies.
Professional Highlights
Machine Learning Engineer
Rheem Manufacturing May 2024-May 2025
Worked on production machine learning and data systems for large-scale IoT and energy optimization platforms. Designed and operated Elasticsearch-based data pipelines backed by AWS (S3, EC2), ingesting and indexing 100M+ events per day to support real-time analytics and ML workflows. Built data and ML APIs consumed by downstream services, and developed a retrieval-augmented generation (RAG) system integrating LLMs with internal data sources to accelerate engineering insights and debugging. Instrumented pipelines and services using Datadog to monitor data quality, latency, and system health, and optimized inference and processing workflows to reduce end-to-end latency by ~30%.
Industry Researcher
Sep 2024-May 2025
I contribute to the OpenFAST software development on the computer science and machine learning side, focusing on enhancing simulation capabilities for offshore wind energy systems. My work involves developing and integrating ML-based surrogate and regression models alongside physics-based simulations to improve the efficiency and fidelity of wind turbine performance analysis in dynamic ocean environments. By combining data-driven modeling, evaluation, and large-scale simulation codebases, I help accelerate renewable energy research and enable more scalable, computationally efficient simulation workflows.
Machine Learning Engineer
Unique World Robotics May 2021-Aug 2023
Developed and deployed machine learning models for precision agriculture, using IoT sensor data (DHT11) to predict optimal crop patterns and irrigation strategies. Built deep learning models with PyTorch and TensorFlow, achieving 97% predictive accuracy across varying environmental conditions. Deployed the end-to-end solution on AWS (EC2, S3), reducing infrastructure costs by ~30%, and designed RESTful APIs to streamline IoT data ingestion and cloud integration, improving system scalability and adaptability in production environments.
Academic Experience
Research Assistant
Aug 2025- Present
Conducting research in Natural Language Processing (NLP) and Natural Language Understanding (NLU), with a focus on bias, fairness, and explainability in large language models. My work investigates evaluation methodologies, interpretability techniques, and responsible adaptation of LLMs, aiming to improve their reliability, transparency, and robustness in real-world applications. This research emphasizes rigorous experimentation, analysis, and model evaluation, bridging theoretical insights with applied NLP systems.
Course Assistant(ML)
Sep 2024-May 2025
As a course assistant for both graduate (CS-GY 6923) and undergraduate (CS-UY 4563) Machine Learning courses, I assist in preparing course materials, grading assignments, and providing support to students during office hours. I collaborate with professors to ensure students grasp key concepts in machine learning, deep learning, and algorithmic problem-solving. Additionally, I help facilitate discussions, explain complex topics, and guide students through hands-on coding exercises and projects.
Undergraduate Research Assistant
Aug 2022-Aug 2023
As a research assistant, I developed a review-based group recommender system using deep learning techniques, including CNN and attention mechanisms. My research aimed to improve recommendation accuracy by leveraging user reviews, focusing on group decision-making dynamics. This work involved extensive experimentation with neural networks, data preprocessing, and model optimization, contributing to advancements in personalized recommendation systems.
If you're interested in collaborating on cutting-edge technology, or if you just want to say hello, feel free to reach out!