I am a senior ML scientist at Diadia Health.
Before this, I was a postdoc in the Machine Learning & Analytics Group at Lawrence Berkeley National Laboratory, doing scientific ML (e.g., physics-informed neural networks) and interpretability (e.g., developing new ways to visualize loss landscapes and characterize learning).
I did my PhD in Biophysics at Stanford, where I developed new computational methods (using ML and topological data analysis) to study the brain’s dynamical organization during ongoing cognition.
T. Xie*, J. Chen*, Y. Yang*, C. Geniesse*, G. Shi, A. Chaudhari, J.K. Cava, M.W. Mahoney, T. Perciano, G.H. Weber, R. Maciejewski. LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics. IEEE Computer Graphics and Applications, 2024. [pdf] (*equal contribution)
C. Geniesse*, S. Chowdhury*, M. Saggar. NeuMapper: A Scalable Computational Framework for Multiscale Exploration of the Brain’s Dynamical Organization. Network Neuroscience, 2022. [code] (*equal contribution)
H. Xie, R. Beaty, S. Jahanikia, C. Geniesse, N. Sonalkar, M. Saggar. Spontaneous and Deliberate Modes of Creativity: Multitask Eigen-Connectivity Analysis Captures Latent Cognitive Modes During Creative Thinking. NeuroImage, 2021.
N. Sonalkar, S. Jahanikia, H. Xie, C. Geniesse, R. Ayub, R. Beaty, M. Saggar. Mining the Role of Design Reflection and Associated Brain Dynamics in Creativity. Design Thinking Research. Understanding Innovation. Springer, Cham, 2020.
C. Geniesse, O. Sporns, G. Petri, M. Saggar. Generating Dynamical Neuroimaging Spatiotemporal Representations (DyNeuSR) Using Topological Data Analysis. Network Neuroscience, 2019. [code] [demos]
NeuMapper. A scalable Mapper algorithm for neuroimaging data analysis. The Matlab implementation was designed specifically for working with complex, high-dimensional neuroimaging data and produces a shape graph representation that can be annotated with meta-information and further examined using network science tools.
Reciprocal Isomap. A reciprocal variant of Isomap for robust non-linear dimensionality reduction in Python. The ReciprocalIsomap transformer was inspired by scikit-learn’s implementation of Isomap, but the reciprocal variant enforces shared connectivity in the underlying k-nearest neighbors graph (i.e., two points are only considered neighbors if each is a neighbor of the other).
Landmark Cover. A Python implementation of NeuMapper’s landmark-based cover. The LandmarkCover transformer was designed for use with KeplerMapper, but rather than dividing an extrinsic space (e.g., low-dimensional projection) into overlapping hypercubes, the landmark-based approach directly partitions data points into overlapping subsets based on their intrinsic distances from pre-selected landmark points.
DyNeuSR. A Python visualization library for topological representations of neuroimaging data. The package combines visual web components with a high-level Python interface for interacting with, manipulating, and visualizing topological graph representations of functional brain activity.