ZHOU, ZHENG

Education

Hong Kong University of Science and Technology (HKUST) Expected 9/2026 - 7/2027

Master of Science in Financial Mathematics (Incoming)

• Anticipated Coursework: Quantitative Modeling of Derivatives Securities, Quantitative Methods for Fixed-income Instruments, Mathematical Market Microstructure, etc.

National University of Singapore (NUS) 8/2023 - 6/2025

Master of Computing

Zhejiang University (ZJU) 9/2018 - 7/2023

Bachelor of Engineering in Software Engineering

• Overall GPA: 3.84/4
• Honors, Awards & Scholarships: Outstanding Undergraduate Thesis, Excellent Engineer Scholarship, Zhejiang University Scholarship - Third Prize, Academic Excellence Award

Grand Resources Group 12/2025 - 4/2026

Commodity Researcher Intern, Precious Metals Team Shanghai, China

• Developed three automated trading programs in Python/Go on QuantDo's InfiniTrader platform for precious metals cross-market arbitrage. The programs evolved from requiring manual entry/exit point input for one-time spread execution to fully automated trading with dynamic entry/exit calculation and multiple position scaling methods (pyramiding, average, one-time), utilizing FAK orders on the Shanghai Gold Exchange to avoid counting toward the order cancellation limit.

Shanghai Artificial Intelligence Laboratory 5/2025 - 12/2025

Algorithm Intern, Embodied AI Center Shanghai, China

• Designed and implemented the complete online evaluation system for the Challenge of Multimodal Robot Learning in InternUtopia and Real World (IROS 2025), hosted on EvalAI: built a test server that provisions container instances from participant submissions, executes remote evaluations, and synchronizes results back to the platform, automating the entire judging workflow.
• Developed the real-robot deployment pipeline for the onsite finals, creating a seamless interface layer that translates between standardized observation/action formats and physical robot I/O, allowing participant teams to deploy their Docker images directly on hardware without additional integration work.

ZhuoShi Fund 5/2024 - 4/2025

Quantitative Trader Intern, Options Team Beijing, China

• Obtained a full-time return offer within 2 months.
• Traded commodity options as a quantitative trader, making data-driven decisions based on risk management requirements, volatility surface analysis, position Greeks, and underlying price movements.
• Developed and maintained automation scripts for dynamic trading parameter configuration, improving operational efficiency.
• Independently designed and implemented a real-time margin optimization algorithm that efficiently identifies near-optimal combinations based on exchange portfolio rules and minimizes position restructuring needs; reduced margin requirements by over 50% while maintaining latency constraints.

Microsoft 7/2022 - 10/2022

Software Engineer Intern, Microsoft 365 Suzhou, China

• Transformed action item handling and email outreach by designing and implementing a flexible schema for various action item types across teams, along with a customizable email sender designed for this new schema, significantly reducing the time required for integrating new action item types.
• Used TypeScript to implement a generic and customizable dropdown component.

ByteDance 1/2022 - 4/2022

C++ Development Intern, Lynx Arch and Optimization Hangzhou, China

• Implemented performance test cases with Google Benchmark and developed front-end mini-programs to measure the performance of the high-performance cross-platform framework.

Akuna Capital 7/2021 - 9/2021

Development Intern, FPGA Software Team Shanghai, China

• Designed and implemented a solution to auto-generate customized configuration files for the trading engines, based on the specific hardware specifications of the machines.

Optimal Control for Dynamic Cosserat Rods (Undergraduate Thesis) 6/2022 - 6/2023

State Key Laboratory of Computer-aided Design & Computer Graphics, Zhejiang University

• Used reinforcement learning to perform deformable object manipulation on Cosserat rods by modeling the control problem and designing the reward function. The learned control scheme works extremely well in several scenarios.
• Built the entire experimental framework from scratch: developed Python-based learning system using Stable Baselines, implemented real-time visualization using libigl, and designed ZeroMQ-based communication between Python and C++ components (visualization and the provided rods simulator).