Developed a novel approach using synthetic plant images from generative models for rare species identification, achieving a 29% improvement in zero-shot learning and an additional 31% improvement in few-shot learning over pre-trained weights, setting a new state-of-the-art in rare flora classification.

Led research on LLMs as autonomous researchers, utilizing a 1.2 million DBLP papers dataset. My DPO-optimized models outperformed supervised fine-tuned models by 27% in the novel creativity index. They achieved a 42% improvement in automated user satisfaction scores, with 89% expert validation of generated research ideas.

Engineered an enhanced intent classification system using contrastive learning on LLM-generated paraphrased data, achieving a 4.78% performance improvement over previous models and reaching 91.783% accuracy on an ambiguous version of the CLINC150 dataset, outperforming GPT-4 and Claude Opus by 3.1%.

Led a data-intensive project utilizing machine learning on satellite imagery to estimate Maternal and Child Health indicators, securing 1st place in a competitive Kaggle-style challenge among more than 30 teams and received a Letter of Recommendation for exceptional performance.

Executed comprehensive data preprocessing, cleaning, and feature engineering with statistical evidence and understanding of the effect of each parameter in the geographically tagged datasets, reducing features from 10,000 to 70 compelling features.

Optimized machine learning models using a boosting on error ensemble approach, significantly improving RMSE from 35.45 to 10.76.

Engineered a tailored recommendation system to match student interns with appropriate companies, utilizing data visualization tools like Seaborn and Matplotlib to derive business insights, resulting in a 20% increase in successful intern placements.

Conducted a thorough comparison of Recommendation Systems as a Service provided by various cloud platforms, achieving a 15% higher accuracy with the custom model versus leading cloud alternatives with language models and skill extractions.

Designed and deployed the recommendation model using Python on an AWS EC2 instance, with a Flask API for real-time predictions, backed by a robust data pipeline using PyMongo and automated re-training with Cron jobs, leading to a 30% reduction in recommendation latency and a 99.9% uptime.

Architected and deployed a robust Deep Learning model using advanced RNN, GRU, and LSTM networks for a state-of-the-art Digital Twin of Cryogenic Energy Storage Systems, achieving system prediction accuracy of 95% and reducing predictive maintenance costs by 25%.

Innovated a machine learning solution with few-shot learning, allowing plant operators to manage operations without prior domain knowledge, resulting in a 40% decrease in operational errors and a 30% improvement in decision-making efficiency.

Developed and launched an interactive web application to demonstrate the model's predictive capabilities, leading to a 50% increase in user engagement and providing a potential roadmap for scaling up to other energy systems.

Pioneered a task-agnostic model employing the Reptile meta-learning algorithm on the Abstraction and Reasoning Corpus, achieving a notable 78% accuracy across training and test datasets with a CNN architecture without using any domain-specific language.

Led a crowdsourcing initiative on Appen to annotate datasets, enabling the application of natural language rules to develop a robust NLP model that mirrors the agnostic capabilities of the original system.

The innovative approach and model versatility have set the stage for groundbreaking advancements in NLP model generalization, potentially reducing the need for task-specific training data by up to 53% by maintaining the model's efficiency, reaching efficiency near DSL.