About me
I am a second-year Ph.D. student in the Yale Graphics Group, performing computer graphics research under Dr. Theodore Kim. I recieved my B.S. in Computer Science from Yale in 2024. My research interests include geometry processing, fractal geometry and physical simulation.
Publications
Computer graphics algorithms for generating photorealistic imagery are
widely perceived to be universal. However, recent works
have suggested that 3D algorithms for depicting synthetic humans are far from
generic, and instead favor historically hegemonic
characteristics. We present the first systematic
review of human depiction in the top computer graphics conference and
the journal of record (SIGGRAPH and ACM Transactions on Graphics) that confirms
previous hypotheses.
Algorithms that claim to be generically rendering "human skin'' are in fact imagined and formulated for translucent, "high albedo" materials such as white skin. Algorithms claiming to apply generically to "human hair" are formulated for "rods", "wires" and "threads" which are analogous to straight hair.
Our analysis reveals conceptual binarization, where algorithms for white skin are treated as computational substrate for "all" skin, imposing a hierarchical assumption that all skin descends from the math and physics of white skin. Hair algorithms follow a similar historical pattern, with the first examples of computer-generated Type 4 hair only appearing after the murder of George Floyd in 2020.
We offer a new conceptual label, McDaniels Methods, for characterizing and critiquing computer graphics algorithms that reinforce racial hierarchy under a false cover of diversity. We also offer an inverse label, Durald Methods, for algorithms that were closely co-designed with the people being depicted. Our analysis points the way towards several neglected avenues for future research.
Algorithms that claim to be generically rendering "human skin'' are in fact imagined and formulated for translucent, "high albedo" materials such as white skin. Algorithms claiming to apply generically to "human hair" are formulated for "rods", "wires" and "threads" which are analogous to straight hair.
Our analysis reveals conceptual binarization, where algorithms for white skin are treated as computational substrate for "all" skin, imposing a hierarchical assumption that all skin descends from the math and physics of white skin. Hair algorithms follow a similar historical pattern, with the first examples of computer-generated Type 4 hair only appearing after the murder of George Floyd in 2020.
We offer a new conceptual label, McDaniels Methods, for characterizing and critiquing computer graphics algorithms that reinforce racial hierarchy under a false cover of diversity. We also offer an inverse label, Durald Methods, for algorithms that were closely co-designed with the people being depicted. Our analysis points the way towards several neglected avenues for future research.
We present a novel, directable method for introducing
fractal self-similarity into
arbitrary shapes. Our method allows a
user to directly specify the locations
of self-similarities in a Julia set and is
general enough to reproduce other
well-known fractals such as the Koch snowflake.
Ours is the first algorithm to enable this level of general artistic control while also maintaining the character of the original fractal shape. We introduce the notion of placing “portals” into the iteration space of a dynamical system, bridging the aesthetics of iterated maps with the fine-grained control of iterated function systems (IFS). Our method is effective in both 2D and 3D.
Ours is the first algorithm to enable this level of general artistic control while also maintaining the character of the original fractal shape. We introduce the notion of placing “portals” into the iteration space of a dynamical system, bridging the aesthetics of iterated maps with the fine-grained control of iterated function systems (IFS). Our method is effective in both 2D and 3D.
We present an efficient new method for computing Mandelbrot-like fractals (Julia sets) that approximate a user-defined shape.
Our algorithm is orders of magnitude faster than previous methods, as it entirely sidesteps the need for a time-consuming numerical optimization.
It is also more robust, succeeding on shapes where previous approaches failed.
The key to our approach is a versor-modulus analysis of iterated function systems that allows us to formulate a novel shape modulus function that directly controls the broad shape of a Julia set, while keeping fine-grained fractal details intact.
Our formulation contains flexible artistic controls that allow users to seamlessly add fractal detail to desired spatial regions, while transitioning back to the original shape in others. No previous approach allows these sort of Mandelbrot-like details to be "painted" onto meshes.
The key to our approach is a versor-modulus analysis of iterated function systems that allows us to formulate a novel shape modulus function that directly controls the broad shape of a Julia set, while keeping fine-grained fractal details intact.
Our formulation contains flexible artistic controls that allow users to seamlessly add fractal detail to desired spatial regions, while transitioning back to the original shape in others. No previous approach allows these sort of Mandelbrot-like details to be "painted" onto meshes.
Talks
Other Projects
I operate an OpenWebRX+
instance located in downtown New Haven consisting of a
RTL-SDR tuner connected to a 2 meter dipole
antenna. The interface supports client-side decoding of
AM/FM/SSB, DMR, APRS, Fax, SSTV, CW, and a large number
of other protocols. You can right-click the tuning
arrows to change the SDR's center frequency.
The SDR should be available 24/7, except for when I am making radio transmissions, during which I disconnect the SDR's antenna to prevent damage.
The SDR should be available 24/7, except for when I am making radio transmissions, during which I disconnect the SDR's antenna to prevent damage.