Ms. Lakshmi Durga Panguluri applies AI and ML across industries, optimizing models, enhancing NLP, and mentoring teams while contributing to AI research.
In a world driven by data, Ms. Lakshmi Durga Panguluri has made significant contributions to the fields of Artificial Intelligence (AI) and Machine Learning (ML). With a strong academic foundation, extensive technical expertise, and experience across multiple industries, she has developed AI-driven solutions and data science strategies. This article explores her professional journey, key projects, and the impact of her work in AI and data science.
Her academic background provided the necessary groundwork for her career. She earned her Master of Science in Management Sciences and Quantitative Methods from a U.S. university, receiving the Dean라이브 바카라 Excellence Scholarship and graduating with a 3.85 GPA. Her undergraduate degree in Mechanical Engineering, completed with distinction, helped her develop analytical and problem-solving skills that would later be applied to AI and data analytics.
Her professional experience includes working with advanced AI technologies, particularly in Generative AI and Large Language Models (LLMs). In her recent role at an AI research firm, she has implemented Retrieval-Augmented Generation (RAG) workflows to improve question-answering systems and fine-tuned LLMs such as Mistral-7B and Llama2.
By utilizing tools like Axolotl, ChromaDB, and LangChain, she has contributed to enhancing AI applications, optimizing their performance, and improving efficiency. Her expertise in prompt engineering and optimization techniques has supported the development of AI solutions for various use cases.
Her experience spans multiple industries, including financial services and healthcare. While working with a global consulting firm, she developed fraud detection models that identified fraudulent patterns using machine learning algorithms like Random Forest classification combined with contextual embedding techniques like Word2Vec.
In another role, she contributed to customer-facing AI solutions, such as automated email response systems powered by LSTM neural networks. A project she worked on received the Future Edge 50 Award, recognizing its contribution to improving customer experience using AI.
Her work in Natural Language Processing (NLP) includes sentiment analysis, entity recognition, and ESG (Environmental, Social, and Governance) classification. She has worked on AI applications in financial systems to enhance data-driven decision-making.
She has also contributed to model optimization through knowledge distillation, ONNX transformation, and frameworks like DeepSpeed. Her projects incorporate data-centric approaches to refining datasets and improving model accuracy, ensuring effective AI deployment. In addition to her technical work, she has taken on mentorship roles, guiding junior data scientists and streamlining workflows to enhance efficiency.
Her contributions to AI research include a U.S. patent for using machine learning to remediate network conditions, demonstrating her ability to apply AI in network optimization.
She has worked on various AI-driven projects, including developing RAG pipelines for improved information retrieval. By integrating tools like RAGAS and Colbert, she has contributed to enhancing model accuracy and grounding.
Her involvement in financial sentiment analysis includes leveraging frameworks like Huggingface BERT and FinBERT. She has worked on creating scalable AI solutions that support data science applications in enterprise environments.
Her expertise spans multiple platforms and tools, including AWS, Azure, Kubernetes, and Docker. Her ability to integrate these technologies into AI workflows has played a role in scaling models for production environments. She has also earned certifications in AWS Machine Learning, Generative AI, and Deep Learning.
She emphasizes the importance of collaboration and innovation in AI development. She believes AI can improve efficiency, address complex challenges, and support technological advancements across industries.
Her work highlights the evolving role of AI in business and research. From academic achievements to practical AI applications, she has contributed to projects that advance AI capabilities. As a data scientist and mentor, she continues to work on AI solutions that align with industry needs.
Her journey reflects the broader developments in AI and data science, illustrating how AI can be applied to solve real-world challenges and improve decision-making processes.