I am a senior computer vision engineer at Bear Flag Robotics. Here I have the privilege of working with an incredible team of roboticists and field operations specialists to build autonomous tractors that help solve the labor shortage problem in farming.
Prior to BFR, I was a machine learning engineer at Revelio Labs. Going further back, I was a postdoctoral scholar at Stanford University from 2019-2021, working with Manish Saggar at the Brain Dynamics Lab.
Prior to Stanford, I completed my PhD in the Department of Mathematics at The Ohio State University in May 2019, under the supervision of Facundo Mémoli. I completed my undergraduate degree at Tufts University (B.S. Engineering Science).
My research is in applied topology and geometry, and in developing foundational applications of such methods in the empirical sciences (such as neuroimaging).
You can reach me via email at samir chowdhury01 at gmail dot com.
Activity:
- (June 2023) New paper: Temporal Mapper: Transition networks in simulated and real neural dynamics. With Mengsen Zhang and Manish Saggar. Network Neuroscience.
- (November 2022) New paper: Distances and isomorphism between networks: stability and convergence of network invariants. With Facundo Mémoli. Journal of Applied and Computational Topology.
- (January 2022) New paper: NeuMapper: A scalable computational framework for multiscale exploration of the brain’s dynamical organization. With Caleb Geniesse and Manish Saggar. Network Neuroscience.
- (January 2022) New paper: Path homologies of motifs and temporal network representations With Steve Huntsman and Matvey Yutin. Applied Network Science.
- (December 2021) The University of Oklahoma Topology and Data Science Seminar.
- (December 2021) New preprint: Hypergraph Co-Optimal Transport: Metric and Categorical Properties. With Tom Needham, Ethan Semrad, Bei Wang, and Youjia Zhou.
- (September 2021) Talk at the Florida State University Machine Learning Seminar.
- (August 2021) Joint Statistical Meetings - talk at the session on Geometric and Topological Information in Data Analysis.
- (August 2021) I served as a TA for the MIT Summer Geometry Institute.
- (July 2021) Co-organized the Second Graduate Student Conference: Geometry and Topology meet Data Analysis and Machine Learning (GTDAML21).
- (July 2021) Talk at the Stanford Computational Imaging journal club.
- (June 2021) HSE University ATA Seminar.
- (April 2021) Paper: Quantized Gromov-Wasserstein. With David Miller and Tom Needham. ECML 2021.
- (December 2020) UC Davis MADDD Seminar
- (September 2020) EPFL Applied Topology Seminar
- (August 2020) ATMCS-AATRN talk is now available here
- (July 2020) My talk at the MBI workshop on OT and TDA is now available
- (June 2020) Oral presentation at DiffCVML, a CVPR workshop
- (June 2020) Poster at OHBM 2020 (joint w/ Caleb Geniesse and Manish Saggar)
Past activity highlights:
- (June 2019) I was a coorganizer for GTDAML2019 (official title: First Midwest Graduate Student Conference: Geometry and Topology meet Data Analysis and Machine Learning)
- (2018-2019) Katie Ritchey and I coorganized TAGGS, a seminar for graduate students and postdocs in topology and geometry (both theory and applied)
- (Summer 2017) I was a TA for the Summer@ICERM 2017 REU on Topological Data Analysis
- (January 2017) I was a TA at the 3rd School on Topological Data Analysis in Mexico for a course on network data analysis