San Francisco Bay Area
Semiconductor manufacturing does not lose yield because people stop trying. It loses yield because process variation builds quietly. Then output becomes less stable, troubleshooting takes longer, and engineers spend more time reacting than improving. When process variation is not controlled, we end up with lower yield and repeat defects, more downtime and process interruptions, and slower troubleshooting and longer recovery time which also results in more rework and wasted material as well as higher pressure on production teams to keep the line moving. This is why I’m serious about building a strong foundation in semiconductor process engineering. I’m most interested in work where the goal is clear: improve process stability, support manufacturing, and help teams solve the problems that keep output from running cleanly. Here’s what I bring to that work: 1. Data-driven process analysis: I use structured problem-solving, process modeling, and data analysis to understand variation, spot patterns, and support better engineering decisions. 2. Process documentation and systems thinking: I build and interpret process flow diagrams, P&ID-style logic, and technical documentation to support clear communication, stable execution, and safer troubleshooting. 3. Continuous improvement mindset: I like work that turns messy problems into repeatable solutions. I use root-cause thinking, careful validation, and simple documentation to help improvements stick. My project work includes process design, mass and energy balances, process control modeling, and technical reporting. Across those projects, I’ve learned how to turn complex systems into clear engineering logic, test assumptions, and communicate findings in a way that helps others act on them. I’m now looking to apply that discipline in a real semiconductor manufacturing environment where precision, consistency, and learning speed matter. If you hire for Process Engineer, Manufacturing Engineer, Process Technician, or other semiconductor manufacturing roles, I’d be glad to connect.
I worked in a team of 4 students on this academic process control lab, simulating an industrial temperature control loop using an Arduino-based heater/sensor system and data-driven modeling for controller analysis. I was responsible for running controlled experiments, collecting transient temperature data, and validating whether an ARIMAX model could predict controller temperature response under different power inputs. - Experimental Runs & Data Capture (Arduino/MATLAB): Executed 24 step-input runs (8 power levels, 12.5%–100% with 3 trials) and exported .mat datasets to build a clean modeling baseline. - System Identification (ARIMAX): Fit ARIMAX models per power step and validated prediction accuracy, improving fit from ~62.5% at 12.5% power to ~91.1% at 100% power. - Model Validation: Compared modeled vs. measured step responses, analyzed residual spikes, and proposed a single long run with 12.5% step increments to improve fit and reduce noise impact.
Project context: Circular-economy refining concept that re-processes used motor oil into base lubricating oils, gasoline-range products, and asphalt using filtration, vapor recovery, hydrotreatment, and distillation. I was responsible for engineering the end-to-end process and economic design of a 500,000 bbl/year used oil re-refinery to maximize product recovery while minimizing emissions, waste, and utility intensity. - Re-refinery Plant Design: Engineered a 500,000 bbl/yr used motor oil re-refinery concept to recover base lube oils, gasoline-range product, and asphalt with focus on safety and sustainability. - Unit Ops Integration: Integrated coarse filtration, vapor recovery, hydrotreatment, and distillation into a complete process train, aligning separation logic with product specs and operability needs. - Mass/Energy + Utilities: Built mass and energy balances plus utility specs (electric heat, hydrogen feed, cooling water) to validate conditions and guide equipment sizing and operating targets. - PFD/P&ID + Controls Development: Created PFDs and a P&ID with control strategy for pressure, temperature, level, and flow to support stable operations and reduce process and safety risk. - Tech + Economic Deliverables: Delivered 5 memos and a final design report including $38.58M CAPEX and $41.98M OPEX estimates, recommending optimization and diversification to improve NPV.
Worked in a team of 4 students on this academic process engineering project modeling a patent-based ammonium nitrate prill production process for industrial supply chains (mining/construction applications). I was responsible for translating the patent description (U.S. Patent 2,568,901) into an end-to-end process model and quantifying the mass and energy requirements so we could validate its feasibility and utility. - Process Modeling in Excel: Modeled a patent-based ammonium nitrate prill production process, building a flow diagram and material/energy balances across the heater, reactor, and evaporator. - Mass Balance & Yield: Calculated 88% nitric acid conversion and 11,198 kg/yr ammonium nitrate output via extent-of-reaction and component balances; quantified excess ammonia needs. - Energy Balance & Utilities: Estimated duties (heater 507.6 W; reactor −711.8 W), sizing cooling water at 0.959 kg/h to hold 180°C; presented PFDs to faculty highlighting assumptions.
Decathlon operates high-volume warehouses to receive inventory, fulfill orders, and move product to stores. I was responsible for accurate inventory intake and fast, error-free order fulfillment, solving the core problem of product shipment delays and stock inaccuracies in a high-volume warehouse environment. - Maintaining Inventory Accuracy: Entered inbound SKUs into internal systems to keep counts accurate, reduce stock errors, and support reliable picking, replenishment, and fulfillment flow. - Supporting Order Fulfillment: Successfully boxed, labeled, and palletized outbound orders to meet Decathlon standards, reducing rework risk and keeping daily shipping targets on track. - Safe Material Handling: Operated platform lift and coordinated with 2 teammates to move goods safely and efficiently, preventing incidents and improving daily throughput.
Cazadero Music Camp is a youth music and camp program focused on structured activities, student safety, and skill-building in groups. I was responsible for leading and supervising 20–30 middle school students daily, addressing the core challenge of keeping programs safe, on schedule, and productive despite conflicts and shifting priorities. - Team Leadership: Successfully led groups of 20–30 students through scheduled activities, keeping sessions on track and ensuring program objectives were met consistently. - Operations and Safety Management: Coordinated daily logistics, enforced safety routines, and maintained a structured environment to reduce disruptions and prevent incidents. - Conflict Resolution: Managed student conflicts in real time using clear communication and de-escalation, restoring focus and enabling smooth group execution.