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| import streamlit as st | |
| import base64 | |
| # Function to display resume contents | |
| def display_resume(): | |
| st.title("Ruthvik Kilaru") | |
| if 'section' not in st.session_state: | |
| st.session_state.section = 'welcome' | |
| if st.sidebar.button("Profile Summary"): | |
| st.session_state.section = 'profile_summary' | |
| if st.sidebar.button("Functional Skills"): | |
| st.session_state.section = 'functional_skills' | |
| if st.sidebar.button("Technical Skills"): | |
| st.session_state.section = 'technical_skills' | |
| if st.sidebar.button("Work Experience"): | |
| st.session_state.section = 'work_experience' | |
| if st.sidebar.button("Education"): | |
| st.session_state.section = 'education' | |
| if st.sidebar.button("Research and Publications"): | |
| st.session_state.section = 'research_publications' | |
| if st.sidebar.button("Certifications"): | |
| st.session_state.section = 'certifications' | |
| if st.session_state.section == 'welcome': | |
| st.header("Welcome") | |
| st.write("Click on the buttons in the sidebar to navigate through the resume sections.") | |
| elif st.session_state.section == 'profile_summary': | |
| st.header("Profile Summary") | |
| st.write(""" | |
| - Detail-oriented professional with over 4 years of extensive experience in analyzing complex datasets to drive informed decision-making. | |
| - Proficient in ETL processes, data warehousing, big data technologies, Hadoop, Spark, and Kafka, Python, R, SQL, machine learning frameworks such as TensorFlow and scikit-learn, statistical analysis, data visualization, and business intelligence tools. | |
| - Experienced in developing and deploying data-driven solutions to solve complex business problems. | |
| - Skilled in data preprocessing, feature engineering, and model optimization, with a focus on achieving high accuracy and interpretability. | |
| - Experienced in natural language processing, computer vision, and time series analysis. | |
| - Expert in transforming raw data into actionable insights, enabling businesses to optimize strategies and improve operational efficiency. | |
| - Adept at communicating findings to stakeholders through clear, concise reports and dashboards. Passionate about leveraging data to solve problems and support strategic initiatives. | |
| """) | |
| elif st.session_state.section == 'functional_skills': | |
| st.header("Functional Skills") | |
| st.write(""" | |
| - Data Analysis | |
| - Data Cleaning | |
| - Feature Engineering | |
| - Data Visualization | |
| - Data Mining | |
| - Data Preprocessing | |
| - SQL | |
| - Business Intelligence (BI) | |
| - Data Integration | |
| - Statistical Analysis | |
| - Data Reporting | |
| - Predictive Modeling | |
| - Data Preprocessing | |
| - Machine Learning | |
| - Model Evaluation | |
| """) | |
| elif st.session_state.section == 'technical_skills': | |
| st.header("Technical Skills") | |
| st.write(""" | |
| - Programming Languages: Python, R, C, C++, HTML, CSS | |
| - Big Data & Machine Learning: PowerBI, Spark, Kafka, VectorDB, SQL(t-SQL, p-SQL) | |
| - Data Science and Miscellaneous Technologies: A/B testing, Jira, Shell scripting, ETL, Data science pipelines based on CI/CD, PowerBI(PQ, DAX, Slicers), Snowflake, NLP, GANs, LLMs, APIs(REST), Excel(Power Pivot, VBA, Macros), AWS(EC2, Kinesis streams, S3 buckets, Redshift), Google Analytics(BigQuery) & Ads, Langchain, Github, Docker & Kubernetes | |
| """) | |
| elif st.session_state.section == 'work_experience': | |
| st.header("Work Experience") | |
| st.subheader("Illinois Institute of Technology, Chicago, IL | Research & Teaching Assistant | Jan 2023 – Till Date") | |
| st.write(""" | |
| - Directing joint research on how student behavior affects academic performance. | |
| - Utilizing advanced methods for person detection, emotion recognition, and posture tracking through algorithms such as Yolo, HaarCascade, PoseNet, Openpose, and AlphaPose. | |
| - Prototyping an Educational Advising Chatbot using RAG architecture with Langchain on AWS, showcasing integration of advanced AI frameworks and cloud technologies. | |
| - Organizing and preparing data for RAG-based chatbot by employing web scraping tools like BeautifulSoup and Unstructured.io, effectively gathering and structuring web data. | |
| - Improving chatbot’s performance by refining and prompt-tuning it with synthetic data generated through LLMs and CI workflows, applying Evol Instruct and Contrastive Learning methods to enhance metrics. | |
| - Conducting instructor-led recitations, grading assignments, and addressing over 250 student queries through 30+ interactive sessions, increasing engagement. | |
| - Developing a Python grading algorithm with Professor Yong Zheng, automating processes and saving 40 TA hours. | |
| """) | |
| st.subheader("Genesis Solutions, Hyderabad, India | Data Scientist | Jan 2021 – May 2022") | |
| st.write(""" | |
| - Leveraged advanced regression techniques, including polynomial regression and ridge regression, to achieve a remarkably low Root Mean Squared Error (RMSE) of 5.27 in Stock Price Prediction Analysis. | |
| - Demonstrated proficiency in accurately forecasting stock prices, as evidenced by model's performance. | |
| - Achieved an R-squared value of 0.87, indicating model's robustness by explaining 87% of variability in stock prices. | |
| - Implemented a sophisticated text detection system using OpenCV for image processing and PyTesseract for Optical Character Recognition (OCR). | |
| - Attained impressive Precision (0.92), Recall (0.89), and F1-score (0.90) in text region identification within images. | |
| - Optimized algorithm for efficiency, enabling it to process images at a speed of 15 frames per second, making it suitable for real-time applications. | |
| """) | |
| st.subheader("Vedanta Resources Pvt Ltd, Orissa, India | Data Engineer | Jul 2019 – Nov 2021") | |
| st.write(""" | |
| - Worked on real-time data in production department to make necessary technical improvements in that area. | |
| - Developed and deployed Kafka and Spark-based data pipeline(Debezium connectors, Apache hudi, STARGAZE, Hive for metadata), enhancing data throughput by 15% and reducing waste by 10% via real-time analytics of 1+ million daily data points. | |
| - Enhanced SQL database for supply chain efficiency, halving query times and cutting inventory costs by 5% through improved Forecasting. | |
| """) | |
| elif st.session_state.section == 'education': | |
| st.header("Education") | |
| st.write(""" | |
| - Masters in Information Technology (Data Science) from Illinois Institute of Technology, College of Computing – 2024 | |
| - Bachelor of Technology from National Institute of Technology, Jamshedpur, India – 2019 | |
| """) | |
| elif st.session_state.section == 'research_publications': | |
| st.header("Research and Publications") | |
| st.write(""" | |
| - International Conference on Recent Trends in Computer Science and Technology (ICRTCST) – IEEE 2021 | |
| - Prediction of Maize Leaf Disease Using ML Models | |
| - Implemented Support Vector Machine (SVM) techniques that demonstrated superior accuracy in detecting maize diseases, achieving a high accuracy rate of 95.6%. This highlights ability to effectively apply machine learning algorithms to solve real-world agricultural problems. | |
| - Integrating Discrete Wavelet Transform (DWT) and YOLOv5 architecture for feature extraction and disease classification, my research contributed to a predictive model that increased precision of crop yield forecasts due to early disease detection, reflected in a sensitivity improvement of up to 92.3%. | |
| """) | |
| elif st.session_state.section == 'certifications': | |
| st.header("Certifications") | |
| st.write(""" | |
| - Building Transformer-Based Natural Language Processing Applications from NVIDIA – Feb 2024 | |
| - Generative AI with Diffusion Models from NVIDIA – Feb 2024 | |
| - Building LLM-Powered Applications from W&B – Jan 2024 | |
| - GCP - Google Cloud Professional Data Engineer from Udemy – Jan 2024 | |
| - Large Language Models: Application through Production from Databricks – Dec 2023 | |
| - Deep Learning- PadhAI from One Fourth Labs – Sep 2021 | |
| - Machine Learning from Stanford Online – Aug 2021 | |
| - Foundations in Data Science- PadhAI from One Fourth Labs – May 2021 | |
| """) | |
| # Add custom CSS to set the background image | |
| st.markdown( | |
| f""" | |
| <style> | |
| .stApp {{ | |
| background-image: url("data:image/jpg;base64,{base64.b64encode(open("pic.jpg", "rb").read()).decode()}"); | |
| background-size: cover; | |
| }} | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| if __name__ == "__main__": | |
| display_resume() | |