Seri Park is a product & ux designer pursuing an MFA in Design and Technology at Parsons, NYC. She creates intuitive, immersive experiences that merge ux, storytelling, and interactive systems.

Graduating May 2025 – Open to Full-Time.


UX PRODUCT DESIGN
UI/UX–Web AppTeam
2024
Tremaine CollectionGraphic, UI/UX DesignTeam
2020
FIT Collabo: TVPoint AppProject Planning, UI/UX DesignTeam
2023
FIT Collabo: HomeBBar App UI/UX, Product DesignPersonal
2024
EmoQUI/UX, Branding–Web AppPersonal
2020
Young&Beautiful UI/UX–Web AppPersonal
2022
Recyle Me Product DesignPersonal
2024
New Pain Communication ToolProduct Design *COMING SOON*
Personal
2024
CommUnityUI/UX, Data VizPersonal
2024
Self As SystemUI/UX DesignTeam
2024
Future Card’s Travel InitiativeUI/UX,App (with Unity)Experimental
2022
The Journal of SensoryUX ResearchPersonal
2022
Delivery Service
GRAPHIC DESIGN

Book DesignExperimental
2021
Perfect Symmetry

Poster DesignPersonal
2020
Counter NarrativeMini Book DesignPersonal
2022
Designing K-Pop’s InfluenceBrandingPersonal
2022
Old Town LunenburgBrandingPersonal
2022
Branding Korea’s Coastal IdentityInfographics–Data VizPersonal
2020

Correlation Between Covid-19 and Public Transportation in Saint Louis City

Postcard DesignPersonal
2020
Saint Louis Postcards
ARCHIVES(Personal)

Design Fiction (with Unity)Experimental
2022
Pain Measurement MachineInteractive InstallationExperimental
2022
Vision of the Future3D Motion (with OpenAI)Experimental
2022
Future HumanBook DesignPersonal
2022
MetamorphosisPackaging DesignPersonal
2020
Insouciant HoneyComic–IllustrationPersonal
2018
If Only You Had Heard Me Then...IllustrationPersonal
2018
Illustrations Archives3D DesignPersonal
2018
Mormoloyce Phyllodes

CONTACT

Open to workEmailparks755@newschool.edu






Standardizing Sensation: A Machine for Measuring Pain


Self Initiated
How accurate are my senses? Do others feel the same emotions when exposed to the same stimuli? Sensation is measurable, but perception is deeply personal.

In medicine, the Numeric Pain Rating Scale (NPRS) quantifies pain, yet without standardized stimuli, its accuracy is uncertain. This project envisions a machine that delivers controlled, quantifiable stimuli for NPRS tests, ensuring consistent measurement across patients.

To simulate thousands of trials efficiently, a 3D physics engine and ML-Agents in Unity were used. Machine learning refines accuracy by adjusting force, measuring impact, and reinforcing learning through repeated tests. Over time, the machine optimizes the expected stimulation, creating a standardized experience.

While factors like temperature and texture were excluded in this simulation, future iterations could integrate robotic arms and machine learning to refine the accuracy of sensory measurement. This project explores the potential for a universally controlled pain assessment system, bridging the gap between subjective sensation and objective data.