Java程序辅导
C C++ Java Python Processing编程在线培训 程序编写 软件开发 视频讲解
C C++ Java Python Processing编程在线培训 程序编写 软件开发 视频讲解
Xiaohe Luo xiaohel@princeton.edu | (858)284-8652 | Sherrerd Hall, Charlton St. EDUCATION Princeton University Princeton, NJ Ph.D., Operations Research & Financial Engineering Expected Graduation: Jan 2023 • Overall GPA: 3.81/4.00 • Core Courses: Statistical Foundations of Data Science, Reinforcement Learning, Machine Learning & Pattern Recognition, Statistical Theory, Probability Theory, Linear & Nonlinear Optimization, Convex & Conic Optimization • Research Interests: Stochastic Optimization, Reinforcement Learning, Machine Learning University of California, San Diego La Jolla, CA Bachelor of Science, Joint Mathematics-Economics Sept 2017 • Overall GPA: 3.93/4.00 • Honors & Awards: Magna Cum Laude, Award for Excellence in Joint Mathematics – Economics (one per year) INDUSTRY EXPERIENCE King Street Capital Management, L.P New York, NY Data Science Intern June 2020 to Aug 2020 • Revenue Prediction via Neural Networks - Conducted feature engineering for revenue prediction of “time-lagged” industries such as hotels, cruise lines, and traveling services which may recover rapidly after the pandemic via time-series credit card data. - Successfully identified the key features and achieved a 20% increase in the estimated profit of our portfolio by leveraging Long Short-Term Memory (LSTM) and Recurrent Neural Network with Attention. • Performance-based Unsupervised Learning - Classified hundreds of industries into different categories based on the level of impact of Covid-19 measures on the YoY revenues across 2 years for market analysis. - Designed the proper distance metric which highlights the differences in the change of YoY revenue trend before and after lockdown for better implementation of clustering algorithms. - Applied clustering methods such as K-means and Agglomerative Hierarchical clustering to identify underlying factors, resulted from Covid-19 measures, that directly associate with businesses’ performance. SELECTED RESEARCH PROJECTS Stochastic One-dimensional Search at CASTLE Lab | Python Princeton, NJ Ph.D. Research Oct 2019 to Present • Designed a one-dimensional stochastic search algorithm (SBES), based on entropy reduction, for effectively finding the optimum of a black-boxed unimodal function under high noise and expensive computational cost. The algorithm is proven to be robust by both theoretical guarantees and empirical results. • Built a simulator with visualization tools that implements the SBES algorithm in various applications such as tuning hyperparameters of neural networks, optimizing step-sizes of stochastic gradient algorithms and maximizing offline revenue with noisy feedback. Random Matrices | Matlab & Mathematica La Jolla, CA Collaborative Undergraduate Research Jul 2017 to Sept 2017 • Researched random symmetric band matrices, specifically matrices with independent diagonals but correlated entries along each band; Proved asymptotic theorems using techniques in combinatorics and real analysis. • Developed applications in Matlab and Mathematica that perform numerical simulations and graphing to approximate the limiting spectral distribution of the eigenvalues of large random band matrices. SKILLS & OTHER LEADERSHIP EXPERIENCE Actuarial Exams: Passed Exam P/Probability Theory Programming Languages: Proficient in Python, R, Matlab, and Stata; Functional in Java and JavaScript Languages: Fluent in Chinese and Cantonese; Conversational in Japanese Leadership Experience: Teaching assistant of Convex & Conic Optimization, Princeton University 2019-2021 • Instructed classes of 50 students on convex and conic optimization theory; designed precept materials and led weekly precepts that solidify students’ understanding of abstract optimization concepts and algorithms.