anguy.app

About

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hi there.

i’m andrew. thank you for stopping by, check out my resume and work!

i’m always looking to learn from others in any field. i find personal joy in the passions and interests of people i meet.

here i aim to document my learning, experiences, and work. if we’re going to be replaced by ai it might as well be accurate.

please get in touch!

Interests

Rapid Chess Soccer Hiking Game / Decision Theory Tech Sector Coverage Kung Fu Movies

University Purdue University

Studying IE · Stats Minor

Diet Coke or Coke Zero Coke Zero

Current Favorites Ip Man 3 (2015)

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$INTC

Resume

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Education & Academic Honors

Purdue University GPA 3.89 / 4.00

B.S. Industrial Engineering · Minor in Statistics

West Lafayette, IN · Est. Graduation May 2028

Purdue Presidential Scholar Simplot Foundation Scholar Dr. James Barany Endowed Scholar Redinger Scholar

Semiconductors & Microelectronics Semiconductor Fabrication Data Science

Tool	Stack
Python	pandas Â· scikit-learn Â· NumPy
SQL	Snowflake Â· MSSQL Â· Azure
Tableau	dashboards Â· reporting
Excel	parametric modeling
Claude Code	agentic tooling

Operations Research Database Languages Query Logic Statistical Modeling Data Visualization

Professional Experience

Open a card — each line draws itself →

Built a tool-removal framework joining 13 disparate datastores (7000 nodes) targeting 14,000 ft² of under-utilized fab space.
Designed ETL scenario modeling logic aggregating 1M+ fab-planning datapoints across 36-month horizons.
Published a Tableau and Web GUI reporting ecosystem adopted by IE Strategy and Budget teams for FY27 R&D planning.
Replaced 2 repetitive manual processes with auditable data architectures, saving 30 hrs/month.

Developed a predictive model framework to evaluate opponent tendencies and inform match preparation.
Translated Big Ten benchmark data across 200+ athletes into 19 player-specific improvement plans.
Standardized twice-weekly scouting, recruiting, and performance reports for coaching and training staff.

Built drone automation utilities in C and Python, producing 56 FPV video missions with 100% on-time client delivery.
Designed custom drone frames in SolidWorks, scaling the fleet to 4 aircraft across multi-rotor and fixed-wing platforms.

Served a Korean-speaking customer base as the sole weekend cashier and clerk.

Extracurricular Involvement

Directing member recruitment and engagement for Purdue’s semiconductor student organization.

Competed on a 6-member team as a 2000+ ELO player against top US universities for team Purdue.

Projects

Engineered a KPI framework scoring 200+ recruiting emails per week with rule-based logic.
Led a 5-member team to build the ETL pipeline and export the top 15% of recruits, saving 40 hrs/month of manual review.
Visualized model performance in Tableau for Purdue Athletics stakeholders; green-lit for continued implementation.

Built 6 parametric models projecting 40-year DCFs, applying NPV/IRR benchmarks to classify recommendations.

Chess

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Live from the Lichess API — the bars extruded off the board out there are these numbers.

Ratings

Format	Rating	Games
fetching lichess/an_d22…

Record

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Repertoire

Opening	Side
Goring Gambit	White
Scotch Game	White
Italian Game	White
Caro-Kann	Black
Modern Defense	Black

Competitive

CCL Team Captain 2000+ ELO 6-member team Aug 2024 – May 2026

lichess.org/@/an_d22 →

Markets

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The columns running across the floor outside are a procedural animation, not live quotes. No price is shown against them anywhere on this page.

Designs and manufactures DRAM and NAND flash memory. One of three remaining DRAM makers at scale, alongside Samsung and SK Hynix.
Memory is sold as a commodity, so pricing moves with the balance of bit supply against bit demand rather than with individual design wins.
Supplies HBM — high-bandwidth memory stacked beside AI accelerators — which is allocated under long-term agreements rather than sold spot.

Designs and manufactures x86 processors for PCs and servers, and sells discrete accelerators and networking silicon.
Intel Foundry manufactures for external customers as well as for Intel’s own product lines, reported as a separate operating segment.
One of the few firms that both designs and fabricates at the leading edge, which makes it capital-intensive in a way fabless designers are not.

Builds etch and deposition equipment used to cut and build the structures on a wafer, plus wafer-cleaning systems.
Etch demand scales with 3D NAND layer counts — each additional layer adds process steps to the same wafer.
Revenue tracks industry wafer fab equipment spending, which is set by memory and logic capex budgets a year or more ahead.

The largest semiconductor equipment maker by revenue, and the one with the broadest product range.
Sells deposition, etch, ion implantation, chemical-mechanical planarisation and inspection systems, plus display manufacturing equipment.
Serves both leading-edge and trailing-edge nodes, and runs a services business tied to the installed base rather than to new tool orders.

Supplies inspection and metrology systems that find defects and measure features between the steps of a fabrication flow.
Holds the majority share of the process-control segment, well ahead of its nearest competitors.
Inspection intensity rises as feature sizes shrink: smaller structures and more process steps mean more points at which a wafer has to be measured.

The sole supplier of extreme ultraviolet lithography systems. No leading-edge logic or memory is patterned without one.
High‑NA EUV is the next generation, raising numerical aperture to print smaller features in a single exposure.
Unit volumes are low and unit prices run into the hundreds of millions, so a handful of shipments moves a quarter. Sales are also subject to export controls.

Designs GPUs and data-centre accelerators. Fabless — it designs but does not manufacture, outsourcing fabrication to foundries.
Sells systems and networking alongside the silicon, and the CUDA software stack that most of the training ecosystem is written against.
Accelerator demand propagates upstream: an order here becomes HBM allocation, then wafer starts, then equipment bookings, with a lag at each step.

Company descriptions only. Nothing here is investment advice, a recommendation, or a statement of any position held.

Pitch Analytics

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Purdue D1 Soccer Predictive Analytics — opponent tendencies, player tracking, and zone-level performance. Click a zone to read it.

low med high shading = relative zone weight

Zone weights are placeholders shaped like the real model output — swap in the live numbers and both this field and the 3D surface outside re-render from them.

Scope

Predictive framework for opponent tendencies and match prep.
Big Ten benchmark data across 200+ athletes.
19 player-specific improvement plans delivered.
Twice-weekly standardized reports to coaching and training staff.

Context

Big Ten NCAA D1 4-4-2 base shape 200+ athletes Public historical data

Contact

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Everyone has something to teach that I’d like to learn. Please reach me at:

Email [email protected] LinkedIn Connect with me GitHub View my code Lichess an_d22

End of Curve

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