Plan before committing.
I spend time defining the problem, identifying constraints, and checking assumptions before treating a concept as a solution.
About
I care about what survives contact with evidence.
I’m Jan Kazimierczak, a second-year Engineering Science student at the University of Toronto, expecting to graduate in May 2029. I work across physical systems and software, with an emphasis on careful planning, technical analysis, observable behavior, and direct communication about what did and did not work.
Background
Why engineering became the direction.
My interest in engineering began around age 13 with the first Falcon Heavy launch. Watching both boosters land with precision made engineering feel tangible: careful reasoning could produce something difficult, coordinated, and real.
The work documented here is smaller in scale, but it reflects the same attraction to systems where analysis, design choices, physical constraints, software behavior, and testing have to agree.
Design principles
Three commitments that recur across the work.
Raise concerns clearly.
When a method appears unsafe, weak, or insufficiently supported, I believe the concern should be voiced and investigated.
Compromise without hiding trade-offs.
Team decisions benefit from different perspectives, but the reasons, uncertainties, and sacrifices behind a decision should remain visible.
Technical toolkit
Capabilities tied to project evidence.
No proficiency bars or detached logo wall: each capability below points to a documented use in the portfolio.
01
Programming and analysis
Python supported rapid bridge-design iteration and train-position simulation. MATLAB processed and visualized proxy-test data for BikePack Buddy.
PythonMATLABStructural analysis
View structural-analysis evidence
02
CAD and mechanism design
The screw-sorting project documents CAD concept development and the selection of feeder, head-classification, and length-sorting subsystems.
CADMechanism designConcept selection
View mechanism evidence
03
Machine vision
A Raspberry Pi camera system and CNN classifier were tested for screw-head classification, with the reported accuracy and shortfall kept explicit.
Raspberry PiCNN classificationImage augmentation
View classification evidence
04
Prototyping and validation
Across the case studies, design decisions were examined through calculations, low-fidelity prototypes, subsystem prototypes, fabrication, and physical tests.
PrototypingProxy testingFabrication
Compare validation methods
05
Experimental data systems
The grid-fin study connects load-cell instrumentation and Arduino acquisition to a Python-to-CSV workflow, R visualizations, ANOVA, and Tukey HSD follow-up.
ArduinoPythonR
View aerodynamics evidence
Education
University of Toronto
Bachelor of Applied Science in Engineering Science · Expected May 2029
Portfolio scope
Course-based engineering design, structural analysis, physical prototyping, and documented technical reflection.
Next
Start with the projects—or start a conversation.
The project pages contain the technical depth; email and professional profiles are collected on the contact page.