Projects

Skyworks Solutions, Inc.

Co-op, Digital Supply Chain Team  |  06/2026 – 12/2026 (Expected)

• Architected a modular agentic workflow in Python using sqlglot, pandas, and LLMs to parse enterprise SQL definitions, automatically mapping SAP Material Master fields to supply chain bot logic to provide full data lineage visibility.
• Leveraged AI-assisted development (Cursor) to combine deterministic SQL parsing with LLM-based analysis, building node-based orchestration logic for mapping master-data fields to supply chain bot logic.
• Implemented a human-in-the-loop review layer, persisting verified mappings as golden references to accelerate root-cause analysis and bug isolation across automated bots.

• Refactored rolling-horizon linear programming (LP) optimization pipelines across SAW and BAW technologies by embedding custom Python modules into KNIME workflows.
• Reduced optimization runtime by 89% (7.5 min → 50 sec) and streamlined workflow design to decrease node count by 70% while preserving identical planning logic.

• Formulated a mixed-integer linear programming (MILP) model using Python (Pyomo/PuLP) and HiGHS to disaggregate weekly production plans into lot-level daily schedules.
• Modeled tool capacity, kit constraints, program constraints, part-tool mapping, material supply constraints, and whole-lot integrity to ensure feasible, executable daily schedules.
• Built a high-performance pipeline to solve within 15 seconds, cutting RF semiconductor de-commit rate below 1%.

Lehigh University, Department of Industrial and Systems Engineering

Ph.D. Candidate  |  08/2022 – Present

• Proposed a novel bilevel optimization formulation for server-side defense in one-shot collaborative machine learning, learning client-level aggregation weights to mitigate biased proxy data from unreliable clients while enforcing fairness constraints.
• Developed a server-side defense framework using Python, PyTorch, and MySQL, achieving improved fairness with minimal accuracy loss even when unreliable clients make up the majority of the system.

• Formulated a novel bilevel optimization model for distributed learning, integrating data distillation and unfairness mitigation to generate representative synthetic datasets, ensuring fairness, robustness, and privacy in server-side training.
• Implemented a one-shot machine learning framework with synthetic data generation and differential privacy to reduce client-server communication and enable scalable collaborative learning using Python, PyTorch, and MySQL.

Advanced Micro Devices, Inc. (AMD)

System Design Engineer, AsiaOps Planning (TW Team)  |  04/2021 – 07/2022

AU Optronics Corp. (AUO)

Senior Software Engineer, Production Planning Information Team  |  09/2017 – 04/2021

• Reformulated the material allocation model into a lightweight optimization model for critical-material pegging, enabling sales teams to confirm order feasibility in near real time during the COVID-driven surge in panel demand.
• Developed the solution end-to-end, including model formulation, SQL-based data preparation, and a C# optimization engine; partnered with an external contractor on the VSTO front end.
• Reduced solve time to minutes for near-real-time buyer decisions, cutting buyer workload by 95% and doubling order success rate.

• Developed a smaller-scale scheduling model based on the LCM DPS framework, incorporating multi-stage process linking across two to three sequential production stages.
• Led and mentored a 3-member engineering team on MIP modeling fundamentals.
• Replaced rule-based scheduling with the optimization model, cutting operational runtime by 90%.

• Independently formulated AUO's first in-house MIP project, optimizing 8-week WPS material allocation across products to minimize de-commitment.
• Modeled production priority, BU/BG allocation priority, cross-grade sequencing, and BOM constraints, including substitute materials and unit limits.
• Built the pipeline end-to-end: C#/ASP.NET optimization engine, automated SQL Server nightly job, and a VSTO dashboard for review and adjustment.
• Generated production-ready allocation plans each morning based on the prior day's submissions, balancing a ~2-hour optimization runtime with operational requirements.

• Pioneered AUO's first MIP-based production scheduling project, translating 4-month MPS inputs into site/line/shift/jig-level production decisions.
• Modeled part-line, part-jig, and cell-part mappings, grade priorities, shift and cell supply constraints, and jig/line capacities to minimize throughput de-commitment.
• Led model formulation and validation, partnering with an external solver vendor and using GMPL/GLPK prototypes to verify formulations and diagnose solution issues.
• Integrated a heuristic layer with the MIP model, reducing solve time while maintaining near-optimal solution quality for large-scale binary-variable instances.