Internship · AI Governance & Cybersecurity
GovTech Singapore
Supported public-sector AI governance and cybersecurity work through research, technical memos, and playbook development.
UCLA Mechanical Engineering · Motorsport · Data Analytics
Kristofer Tsai
Engineering better decisions through data.
I build analytical workflows for race telemetry, vehicle performance, sports analytics, and user behaviour. The goal is simple: turn complex datasets into clear, actionable insight.
About Me
I am a UCLA Mechanical Engineering student from Singapore, class of 2028, interested in applying data analysis, machine learning, and mechanical design to motorsport and automotive engineering. After completing two years of National Service with the Singapore Army, I moved to the U.S. to study at UCLA and have developed projects spanning motorsport, data analysis, and sports analytics.
Outside of engineering, I enjoy playing golf and tennis, following motorsport and basketball, and organising canyon drives along Angeles Crest Highway and Mulholland Highway with Bruin Auto Club.
| School | UCLA |
|---|---|
| Degree | B.S. Mechanical Engineering |
| GPA | 3.8 |
| Hometown | Singapore |
| Campus involvement |
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| Key coursework |
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Experience
Selected experience in motorsport telemetry, AI governance, operations, machine learning, and applied analytics.
National Service · Logistics & Operations
Singapore Armed Forces
Served in logistics and equipment support roles during Singapore National Service, coordinating supply, equipment readiness, and operational support.
Internship · Data Auditing & Market Research
Singapore Mint
Supported data digitisation, auditing, and circulation research.
Analyst · Vehicle Dynamics & Telemetry
Bruin Formula Racing
Working with UCLA's Formula SAE team on vehicle dynamics and telemetry analysis, with the work documented below as a series of case studies.
Research Project
rideOS
Predicting NBA Game Outcomes
Comparison of machine-learning models using historical NBA data.
Student Club
DataRes
DataRizz: Dating App Analysis
Consumer analytics project examining reviews, sentiment, and profile behaviour.
Student Club
Bruin Sports Analytics
Distance in Modern Golf
Published sports-analytics article on PGA Tour driving distance, official earnings, and strategic value.
Personal Project · Race Strategy & Data Analysis
Formula 1 Race Strategy Analysis
Independent analysis of Formula 1 race strategy using FastF1 and OpenF1 data across multiple race case studies.
Bruin Formula Racing
UCLA Formula SAE · Vehicle Dynamics & Telemetry Analysis
I am working with Bruin Formula Racing on vehicle dynamics and telemetry analysis, focusing on turning early-stage Formula Student sensor logs into usable engineering insight. The work spans data cleaning, channel validation, motor-torque response, thermal response, and endurance-style analysis.
2026 testing
347 channels
Inverter / BMS / IMU / thermal
Telemetry Validation
01
From Raw Logs to Usable Telemetry
Data cleaning, channel validation, and analysis limitations
Audited a 347-channel telemetry log to identify usable inverter, BMS, coolant, thermal, and IMU signals, excluding invalid GPS, wheel-speed, VCU, tyre, brake, and damper data from performance conclusions.
Usable
Caution
Excluded
Torque Analysis
02
Motor Torque Response & Operating Behaviour
Torque tracking, RPM usage, and operating-region analysis
Analyses how accurately the inverter delivered requested torque and where the motor operated during dynamic running. The study compares torque command and feedback, identifies common RPM and torque ranges, and quantifies the system’s operating behaviour.
Thermal Analysis
03
Thermal Response During Dynamic Running
Battery, inverter, coolant, and thermal-load behaviour
Tracks how validated battery, inverter, coolant, and power-module temperatures changed during a 93.6-second dynamic-running segment. The analysis compares short-term temperature rise, cooling behaviour, and the relationship between motor demand and inverter heating.
Independent Analysis · Public Data
F1 Strategy Analysis
Historical Formula 1 strategy studies built from publicly available data. Each case reconstructs a decision, tests its trade-offs, and separates what the evidence supports from what it cannot prove.
Personal research
Not affiliated with Formula 1 or any Formula 1 team
F1 Strategy Analysis
Case 01 · Singapore 2023
Singapore 2023: Stop for Fresh Mediums or Stay on Hards?
A data-led reconstruction of Mercedes’ late VSC stop, the fresh-medium break-even calculation, and the Sainz–Norris DRS defence that decided the race.
F1 Strategy Analysis
Case 02 · Brazil 2024
Brazil 2024: How Did Verstappen Turn P17 into Victory?
A data-led reconstruction of Verstappen’s wet-weather recovery, the Lap 28 stay-out decision, the red-flag benefit, and the pace that converted track position into victory.
F1 Strategy Analysis
Case 03 · France 2021
France 2021: When Did Verstappen’s Second Stop Become the Winning Strategy?
A data-led reconstruction of Red Bull’s Lap 32 second stop, the tyre offset that powered Verstappen’s recovery, Mercedes’ response window, and whether staying on the hard tyre remained viable.
Student Clubs & Research
Full project write-ups for the rideOS, DataRes, and Bruin Sports Analytics work referenced above.
Research Project
rideOS
Predicting NBA Game Outcomes
Sports machine-learning research project using NBA game data, engineered statistical features, and WEKA classifiers to test outcome prediction.
Highlight: Processed 1,200+ games and compared models with F-measure across J48, Naïve Bayes, logistic regression, and KNN-based methods.
PythonWEKAMachine LearningAPI Data
Student Club
DataRes
DataRizz: Dating App Analysis
Collaborative consumer analytics project using sentiment analysis and data visualisation to understand dating-app reviews, profile behaviour, and engagement patterns.
Highlight: Applied VADER sentiment scoring and visual analysis to compare Tinder, Hinge, and Bumble user experience trends.
PythonVADER NLPpandasVisualisation
Student Club
Bruin Sports Analytics
Distance in Modern Golf
Published sports-analytics article examining whether PGA Tour driving distance is associated with official earnings and what distance means strategically.
Highlight: Regression found only a weak relationship with official earnings; distance creates opportunities but does not explain overall tournament success.
PythonPGA DataRegressionSeaborn
Skills
Data Analysis
Motorsport & Engineering
Programming
Tools
Outside Engineering
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Golf
Approximate handicap: 10 (unofficial)
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Tennis
Approximate UTR: 5 (unofficial)
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Track Days
Spa-Francorchamps · Nürburgring Nordschleife · Buttonwillow
Contact
The best way to reach me is by email or LinkedIn.