WIMIN

Plenary Talks

2023 Speakers:

Melody Chan, Brown University.

Melody Chan majored in computer science and mathematics at Yale University, winning a Goldwater Scholarship as well as the Alice T. Schafer Prize for undergraduate mathematics research. She studied for the Math Tripos at the University of Cambridge, received a master’s degree at Princeton under the direction of graph theorist Paul Seymour, and received her PhD from Berkeley under the supervision of Bernd Sturmfels for work in tropical geometry at the intersection of graph theory and algebraic geometry. She joined the faculty at Brown University after a postdoctoral fellowship at Harvard. She has received a Sloan Research Fellowship, the AWM—Microsoft Research Prize in Algebra and Number Theory, and is a Fellow of the American Mathematical Society, which cites her “mentorship and mathematical exposition” as well as her contributions to research. In addition, she is an accomplished violinist who studied at Juilliard with Itzhak Perlman and Dorothy DeLay and has performed at the Lincoln Center for the Performing Arts.

Points, lines, and planes: finite geometries

Abstract:

Suppose you have n distinct points in the plane, not all on a line.  Consider the lines in the plane determined by these points, i.e., the lines passing through at least two of the points.  What is the smallest possible number of such lines?
 
We’ll answer this question and use it as an entry point to the study of finite geometries.  In particular, I will define matroids and give a short survey of this active area of research in combinatorics, including mentioning some spectacular recent developments in the field.
 

Karamatou Yacoubou Djima, Wellesley College.

Karamatou Yacoubou Djima grew up in Benin, Ivory Coast, and Togo, before moving to the US to study mathematics and engineering at CUNY Staten Island. She first conducted research during her undergraduate years at CUNY, with a project in applied dynamical systems that was directed by Andrew Poje and a project on supersolids in physics directed by Anatoly Kuklov. Her master’s degree and PhD were both supervised by Wojciech Czaja at the University of Maryland in College Park. After receiving her PhD, Karamatou was a postdoctoral fellow at Swarthmore College before joining the faculty at Amherst College and then moving to Wellesley College. Her research interests lie between applied harmonic analysis and machine learning, with an overarching goal of representing data via simple building blocks that both reduce the computational cost of algorithms while preserving relevant information detected from hidden structures in the data. She has applied these techniques in projects ranging from early detection of autism spectrum disorder and macular degeneration to motion detection in Pixar animations.

Classifying medical images using tools from mathematical data science

Abstract:

Mathematical data science has permeated all areas of modern medicine, from processing medical images to diagnosing medical conditions. In this talk, I will discuss how I use applied mathematics for my ongoing interest in detecting autism using placental images. In recent studies, differences in the morphology of the placental surface network have been associated with developmental disorders. This suggests that the placental surface network could potentially serve as a biomarker for the early diagnosis and treatment of autism. In this talk, I will describe the automated extraction of vascular networks using applied harmonic analysis techniques, which express data into simple, efficient building blocks. Then, I will survey other tools that I am exploring to tackle this problem, including deep learning and network theory.