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Click here or scroll down to respond to this candidateCandidate's Name
Phone: PHONE NUMBER AVAILABLE Address: NStreet Address , 5035S East End Ave, Chicago Email: EMAIL AVAILABLE EDUCATIONUniversity of Chicago Chicago, ILMaster of Science in Computer Science Sep. 2022 - Mar. 2024 University of California, Los Angeles Los Angeles, CA Bachelor of Science in Mathematics/Economics Sep. 2018 - May. 2022 Coursework : Distributed System, Data Structures and Algorithms, Machine Learning, Optimization, Web Development, Databases, Linear Algebra, Discrete Mathematics, Introduction to Programming for Internet. WORK EXPERIENCEMRS.AI Shanghai, ChinaSoftware Engineering Intern Jul. 2023- Sep. 2023Designed a microservice architecture for customer service platform using Tornado, enabling dynamic service registration and subscription, which reduced response times for high-volume customer inquiries by 10%.Developed a customer routing system that determined service allocation based on customer interaction patterns, improving first-contact resolution rates by 13%.Built an AI agent for flight booking utilizing LangChain by prompt engineering and integrated a RAG framework, significantly reducing manual effort and booking errors.Created an ensembled model to extract specific data for personalized recommendations, enhancing the AI agents effectiveness in cold start scenarios and improving customer satisfaction scores by 3.5. Schlumberger Beijing Geoscience Center (Fortune 500) Beijing, China Software Engineering Intern, the Well Integrity Group Jun. 2021 - Sep. 2021Applied Fourier transformation and wavelet denoising, as baseline models for denoising detective waves.Conducted and tested multiple deep learning models, with the 2D CNN Autoencoder reducing noise by 35% on simulated wave datasets and improving signal clarity.Generated 864M data points using Gaussian pulses to simulate oil wells and evaluated the denoised waves by comparing them to noise-free signals, confirming a significant increase in signal-to-noise ratio. Morgan Stanley Securities Shanghai, ChinaAnalyst Intern, Macroeconomics Research Sep. 2020 - Nov. 2020Collected and analyzed industry time series data from the Wind Database, conducting exploratory data analysis to identify key economic trends and patterns.Engineered features by forecasting inflation rates with an ARIMA model and incorporating additional economic indicators like CPI, unemployment rate, and GDP growth, improving model input relevance.Predicted the federal funds rate six months in advance using a Naive Bayesian model, achieving a mean absolute error of 0.02%, with predictions closely mirroring actual rates. PROJECTSAmerican Express Credit Default PredictionPreprocessed a large dataset of anonymized customer transactions, utilizing imputation for missing values and scaling to normalize features, ensuring data consistency across 1.6 M records.Engineered features by calculating key metrics to get customer behavior, enhancing model input quality.Developed and fine-tuned a gradient boosting model using XGBoost, optimized through hyper-parameter tuning, achieving an AUC of 0.79 in predicting customer defaults.Implemented a cost-sensitive evaluation metric to account for the financial impact of false positives and false negatives, improving model performance in real-world application scenarios. Comparison of Dimension Reduction Models on Chinese Stock Return Performance AnalysisCollected time series data from the top Chinese companies, using Z-score analysis and correlation filtering to exclude companies with irrelevant data, ultimately selecting 22 key features after addressing NaN values.Applied dimensionality reduction techniques, including PCA, t-SNE, and SVD, to optimize feature selection for stock return classification, with PCA outperforming other methods in preserving variance.Tuned a neural network model using cross-validation, achieving over 65% accuracy in predicting stock performance for most months in 2019, demonstrating the model's potential for portfolio construction. SKILLS Python, C++/C, R, PyTorch, TensorFlow, Spark, LangChain, CNN, Auto-encoder |
