Rahul Yadav

Senior Data Scientist at Oracle

Academic Background: I graduated with a master's degree in Computer Science and Engineering, specializing in Artificial Intelligence from The State University of New York at Buffalo. My academic journey had been diverse, covering a wide range of subjects, including NLP, Computational Linguistics, Pattern Recognition, Machine Learning, and Algorithms Design. My Bachelor's in Electronics Engineering from the esteemed Indian Institute of Technology (BHU) Varanasi, served as the foundation that inspired me to delve into the captivating fields of Artificial Intelligence and Machine Learning.

Practical Experience: Complementing my academic foundation, I've engaged in real-world projects that have further enriched my knowledge. Notably, my master's project involved the development of an innovative open-domain retrieval-augmented generation chatbot. Additionally, as a Research Assistant in our network lab under the guidance of Professor Hongxin Hu, I contributed to the creation of a cutting-edge Network Intrusion Detection System driven by deep learning.

Technical Skills: My skill set is versatile and robust, encompassing proficiency in various programming languages such as Python, Java, C++, PLSQL, and SQL. I also possess expertise in a plethora of frameworks and tools, including PyTorch, TensorFlow, Keras, Langchain, LlamaIndex, VectorDBs(ChromaDB, Faiss, elasticsearch), MLFlow, PySpark, Pandas, Flask, MongoDB, and SpringBoot. Furthermore, I have hands-on experience working with cloud platforms like Oracle, Azure, Google Cloud Platform (GCP), Amazon Web Services (AWS), Atlas, OpenAI.

Professional Roles: My journey in the realm of advanced NLP, generative AI, Large Language Models (LLMs), and machine learning has been shaped by a series of impactful roles:

Senior Data Scientist at Oracle: I lead the development of Generative AI initiatives and spearhead innovative use cases in statistical data science and machine learning
Senior Data Scientist at Hilabs: Here, I led development of innovative end-to-end system for contract document processing, integrating advanced AI technologies for entity extraction and indexing. Successfully implemented scalable solutions, ensuring security and privacy, with proven effectiveness through client validation and integration.
Machine Learning Engineer Intern at Apexanalytix: Here, I played a pivotal role in the development of a Knowledge-Specific Conversational Agent, refining my skills in crafting high-performance conversational systems powered by Large Language Models (LLMs).
Senior Application Engineer(ML) at Oracle: In this role, I engineered an in-memory multivariate anomaly prediction service with a focus on model explainability, delving into traditional machine learning, data analysis, and database optimization.
Project Engineer(ML) at Wipro Limited: Here, I implemented chatbot services that significantly reduced workloads and enhanced the user experience of the employee helpline system.

Summary: Experienced AI professional with a passion for leveraging advanced technologies like NLP, Machine Learning, and Large Language Models (LLMs) to solve real-world problems. Strong academic background complemented by over 5 years of industry experience, leading projects and implementing scalable AI solutions. Eager to continue contributing to groundbreaking advancements in AI and software development.

I am highly enthusiastic about exploring opportunities in this space. If you have relevant openings or are interested in having a conversation on these topics. Feel free to check out my Resume and drop me an e-mail. I'd be happy to connect for a quick call to discuss!

~ Email | Resume | Github | LinkedIn ~

Aug '24	Joined Oracle as a full time Senior Data Scientist
Feb '24	Joined Hilabs as a full time Senior Data Scientist
Jan '24	Joined Apexanalytix as a full time Data Scientist
Jan '24	Graduated with my Masters in CS (with a specialization in AI) from UB
Aug '23	Fall Internship : Got internship extension at Apexanalytix as Machine Learning Engineer Intern (Part Time)
Aug '23	Started my final semester(Fall 2023) at UB.
May '23	Summer Internship : Joined Apexanalytix as Machine Learning Engineer Intern
Feb '23	Started working as a Research Assistant in UB networking lab under guidance of Professor Hongxin Hu
Jan '23	Started my second semester(Spring 2023) at UB.
Aug '22	Started my graduate studies at State University of New York at Buffalo(UB) - Fall 2022 Semester.
Sept '20	Joined Oracle (OFSS) as full time Senior Application Engineer (ML)
June '20	Completed my 2 years working at Wipro Limited.
June '18	Joined Wipro Limited as full time Project Engineer (ML)
May '18	Completed my Bachelor of Engineering(BE/Btech) in Electronics Engineering from IIT BHU (Varanasi).

State University of New York at Buffalo

Master of Science | Computer Science and Engineering
Specialization: Artifical Intelligence
Aug '22 - Dec '23'
Coursework:
• CSE-635: Natural Language Processing and Text Mining
• CSE-567: Computational Linguistics
• CSE-574: Intro Machine Learning
• CSE-555: Intro Pattern Recognition
• CSE-587: Data Intensive Computing
• CSE-531: Algorithms Analysis and Design
• CSE-529: Algorithms for Modern Computing Systems
• CSE-589: Modern Networking Concepts
• MGO-665: Technological Entrepreneurship
• CSE-705: Seminar on Recent Advances in Deep Learning & Reinforcement Learning

Indian Institute of Technology (Banaras Hindu University), Varanasi

Bachelor of Technology | Electronics Engineering
July '14 - May '18

	Senior Data Scientist \| Oracle Malvern, PA, US Aug '24 - Present ProSchedAI: Predictive Scheduling and Delay Mitigation • Led a project at Oracle to address construction clients' concerns about schedule delays in their projects and activities, which were affecting operational efficiency and profitability • Developed a machine learning solution to predict both the occurrence and duration of delays, while providing explanations and mitigation strategies using large language models (LLMs) • Designed and implemented a multi-task learning model using classification and regression techniques, alongside LLM-generated reports to explain delay reasons and offer actionable insights • Achieved 92% accuracy in delay prediction, with an average error of ±2.5 days for delay estimates. Clients reduced project delays by 15% after implementing the recommendations provided
	Senior Data Scientist \| Hilabs Bethesda, MD, USA Feb '24 - May '24 GenAI Driven Contract Analyzer: Streamlining Claim Verification • Led development of scalable end-to-end system for processing contract documents of insurance providers, emphasizing entity extraction to facilitate pricing configuration in claim processing applications • Engineered custom document processing pipeline with LayoutLM extracting entities with images, tables, and text elements • Developed Langchain service for indexing extracted elements and managed metadata within OpenSearch to optimize retrieval • Implemented a robust infrastructure leveraging self-hosted fine-tuned Mistral AI LLM and asynchronous processing via SQS to efficiently handle requests, while prioritizing the utmost security and privacy of sensitive legal contracts • Successful POCs followed by integration for 3 clients to validate solution effectiveness and potential for widespread adoption
	Data Scientist/ML Engineer Intern \| Apexanalytix Greensboro, NC, USA May '23 - Feb '24 Generative Knowledge Specific Chatbot • Developed advanced Retrieval Augmented Generation(RAG) Chatbot with LLM for intelligent knowledge access across teams • Engineered efficient retrieval pipeline with Parent Child document indexing using LangChain and Chroma VectorDB • Designed agile RAG pipeline, integrating MMR scoring for chunk retrieval and Azure OpenAI LLM for response generation • Implemented user feedback collection to monitor chatbot performance and gather data for iterative refinement and fine-tuning • Achieved 89% approval in human evaluations and integrated technology into 12 internal and 18 external client applications
	Senior Application Engineer(ML) \| Oracle Bengaluru, India Sept '20 - Aug '22 Preemptive Anomaly Prediction in Corporate Billing • Designed and implemented in-memory multivariate anomaly prediction system for corporate billing, addressing complex monthly billing challenges • Utilized Oracle in-database ML for Semi-Supervised classification with both local and global model explainability • Optimized service with indexing and parallel processing, automating 1.2M bills and 5M segments in 20 minutes, achieving an impressive 92% anomaly class precision • Integrated system with both Cloud and On-premise services of Oracle Revenue Management and Billing (ORMB), filed innovation patent at USPTO
	Project Engineer(ML) \| Wipro Limited Bengaluru, India June '18 - Sept '20 Chatbot Services for Employee Helpline Portal’s Ticketing System • Developed Employee Helpline Portal Chatbot with effective retrieval of historical ticket resolutions for enhanced user support • Implemented query intent classification and BERT-powered semantic search to deliver precise responses • Successfully integrated the chatbot into the portal and achieved the target human agent intervention reduction of 70% • Achieved 79% accuracy score in evaluation and decreased wait times from 18 to 4 minutes, improving overall user experience

	Topic-Based Empathic Chatbot (BabbleGo) Collaborators: Kajol and Ankit Shaw PyTorch, Hugging Face, Python, OpenAI, GenerativeAI, Reterival, Haystack, Elasticsearch, Jupyter Notebook \| Feb. 2023 - May. 2023 [code][report][slides] • Implemented open-domain chatbot with retrieval-based and casual conversation capabilities. • Developed logical and rule-based dialog manager for effective query redirection based on user interaction • Integrated retrieval and generative models, improving conversational accuracy by 35% while maintaining resource efficiency.
	Network-based Intrusion Detection System (NIDS) Research Assistant, Supervisor: Dr. Hongxin Hu Collaborators: Feng Wei PyTorch, TensorFlow, Data Analysis, Deep Learning, Python, Flask, Jupyter Notebook \| Feb. 2023 - May. 2023 [code][Results][Data Analysis] • Implemented an Intrusion Detection System using deep neural detectors to efficiently identify and respond to potential security threats in the network, mitigating the risk of data loss and downtime. • Conducted comprehensive analysis on 12 different network attack datasets and evaluated the performance of 4 deep neural detectors. Reported insights into the limitations and effectiveness of methods.
	LLM-Powered SQL DB agent LangChain, OpenAI, LLM, Python, SQL, APIs, SQLAlchemy \| May. 2023 - Aug. 2023 [code][Slides] • Designed, and implemented an LLM-powered SQL Database Agent, enabling intuitive natural language interactions with SQL databases.. • Seamlessly integrated LangChain to extract comprehensive table descriptions and contextual information directly from SQL databases. This context was then leveraged to enhance the generative capabilities of the OpenAI model. • Introduced a robust query execution layer within the agent, proficiently managing SQL query execution and proficiently handling database validation errors to ensure accurate and reliable query results.
	Intelligent Search for Offers Streamlit, Sentence Transformers, Rerankers, Python, Pandas \| May. 2023 - Aug. 2023 [Live Demo][Code] • Independently designed and developed the Offers Search Engine, implementing hybrid semantic and exact search techniques for improved offer retrieval. • Spearheaded the integration of search and reranking pipelines, resulting in a more 40% more accurate search and 70% reduction in retrieval times for extensive datasets. • Successfully deployed the service with a user-friendly interface using Streamlit, enhancing user engagement and ensuring a seamless experience for offer searches.
	Retrieval Augmented Generation Chatbot LLM, ChromaDB, NLP, Sentence Transformers, Huggingface, Python, LangChain, OpenAI \| July. 2023 - Dec. 2023 • Spearheaded the design and implementation of a Retrieval Augmented Generation (RAG) chatbot system, seamlessly integrating Large Language Models (LLMs) with retrieval mechanisms to enhance information quality and context relevance. • Optimized retrieval pipeline with Langchain and integrated OpenAI and LAMMA generative models, elevating user trust and experience through coherent responses from diverse sources of generation. • developed a user-friendly solution with a Streamlit-based UI interface, ensuring an intuitive and accessible user experience. Significantly, I enhanced the system's performance to provide seamless access to a vast database of 30 documents, with response times consistently under 5 seconds.
	Text Chat Application C++, Networking, Linux, Socket Programming, Protocols, ChatApplication, Server, Client \| Feb. 2023 - May. 2023 [code][Report] • Developed a client-server chat application following the conventional client-server model, providing the capability for numerous clients to log in, establish their identity, and communicate with each other via the central server. • Utilized socket programming to establish robust connections between the server and clients, ensuring reliable message transmission. Implemented a buffering system to manage message storage and retrieval, particularly for clients who were offline at the time of message receipt. • Conducted comprehensive testing of the application in a live production environment to assess its accuracy and efficiency, guaranteeing that it met the performance requirements and functioned reliably under real-world conditions

Graduate Research Assistant | CactiLab - UB
Jan '22 - Mar '22 | Jan '23 - Mar '23

Working as a graduate research assistant for SUNY research foundation(RF) under the supervision of Dr. Hongxin Hu, at the CactiLab - University at Buffalo. I collaborated with Feng Wei and utilized PyTorch, TensorFlow, Python, Flask, and Jupyter Notebook to develop a Network-based Intrusion Detection System (NIDS). This system effectively harnessed the power of deep learning to proactively identify and respond to potential security threats within network environments, thereby reducing the risk of data loss and network downtime. In addition to the implementation of the NIDS, I conducted in-depth analyses on 12 distinct network attack datasets, evaluating the performance of four different deep neural detectors. My research yielded valuable insights into the strengths and limitations of these methods, contributing to our understanding of effective network security measures.

Paper Title Virtual Conversation with Real-Time Prediction of Body Moments/Gestures	Conference Name ICMLIP 2019
Authors Gopichand Agnihotram, Rajesh Kumar, Pandurang Naik, Rahul Yadav	Link springerLink

Patent Title Method And System For Multimodal Analysis Based Emotion Recognition	Agency USPTO
Inventors Rahul Yadav, Gopichand Agnihotram	Applicant Wipro Limited	Link 16/795840
Patent Title Anomaly detection for bill generation	Agency USPTO
Inventors Rahul Yadav, Nidhi Rajput, Veresh Jain, Krishnapriya, Amit, Saroj, Aashima	Applicant Oracle Financial Services Software Limited	Link US17/710745
Patent Title Technology system for assisting financial institutions in debt collection	Agency USPTO
Inventors Rahul Yadav, Veresh Jain, Shubham Negi, Rahul Kiran Gaddam, Phanindra Choda	Applicant Oracle Financial Services Software Limited	Link US17/659017

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