SHM GARANGANAO ALMEDA
Hello friend! I'm Shm!
(pronounced "Shim", rhymes with Jim, pronouns They/Them/He/Him)
Currently, a PhD Student Researcher in
the EECS Program at the University of California, Berkeley.
Previously, an Undergraduate Researcher at
The College of New Jersey, where I
received my B.S. in Computer Science with a minor in Fine Art.
My research interests in Human Computer Interaction, Education, and Equity stem from a desire to improve accessibility and fairness for digital artists and creatives like myself.
My interest in digital art stems from watching a lot of anime.
With Janaki Vivrekar and Nate Weinman
With John Canny
Accessible Sign Language Recognition with the Leap Motion Controller
Mentor: Dr. Andrea Salgian, TCNJ
Communities that use visual languages to communicate are underrepresented by translation and language learning tools. This project utilizes the LeapMotion Python and C++ API to use measurements generated by the LeapMotion infrared hand-tracking controller in a simple decision tree. This algorithm to translate fingerspelling is the first step towards a complete ASL recognition program.
Research funded by Phi Kappa Phi Research Award Fall 2018
Poster Awarded 1st Place At: SIGCSE 2019 ACM Student Research Competition
Published in: SIGCSE '19 Proceedings of the 50th ACM Technical Symposium on Computer Science Education
Algorithms for Protein Variant Libary Design
Mentor: Dr. Dimitris Papamichail, TCNJ
When ordering oligonucleotides for a protein variant library, there are many factors to take into account. Our research team developed an algorithm to determine the breakpoints and overlaps for oligonucleotides that minimizes the total cost of synthesis. We attempt to make this work accessible and useful to the greater scientific community by developing a web tool that implements our algorithm and an online database with results generated by our programs.
Research funded by TCNJ MUSE Summer 2018 and by CRA-W CREU 2018-2019
Poster Presented At: Grace Hopper Celebration 2019
bPigment: a Parametric Color-Mixing Model
Mentors: Jose Echevarria & Stephen DiVerdi, Adobe Research Labs
Implemented a novel parametric color-mixing model that emulates the behavior of physical pigments and produces unique effects that transcend existing color-blending technology.
Independently developed a graphical painting application bPigment using Python and Tkinter to demonstrate the new color-mixing technology.
Preparing publication for Eurographics 2019