Simplex source: Wikipedia

Topological data analysis (TDA) is a subfield of algebraic topology whose application to areas of study outside mathematics is becoming more and more common. In computer science, specifically in the fields of machine learning and networks, TDA has been used to both take the topology of the input data into account in order to inform models, as well as to incorporate topological information directly into the design of models. TDA helps us extract topological features from the data and use this information to better understand and learn from the data, and along with statistical tools and approaches, we can discuss the significance of such features. My research will focus on answering important structural questions about how artificial neural networks function through the study of topological features and their statistical significance.

Publications

  1. Ferracina, F., Krishnamoorthy, B., Halappanavar, M., Hu, S. and Sathuvalli, V., 2025. Predictive analytics of selections of russet potatoes. Crop Science, 65(1), p.e21432. (doi)

  2. Nakamura, A., Ferracina, F., Sakata, N., Noguchi, T. and Ando, H., 2025. Reducing Total Trip Time and Vehicle Emission through Park-and-Ride–methods and case-study. Journal of Cleaner Production, p.144860. (doi)

  3. Ferracina, F., Beeler, P., Halappanavar, M., Krishnamoorthy, B., Minutoli, M. and Fierce, L., 2024. Learning to Simulate Aerosol Dynamics with Graph Neural Networks. ACS ES &T Air, 2(8), pp.1426-1438. (doi)

  4. Ferracina F., Lu A.K.A., Du X.N., Chen N., Ojovan M.I., Suito H., Louzguine-Luzgin D. V., 2026 Viscosity Rise of Supercritical Liquid Copper Above the Frenkel Line and Related Structural Features: A Molecular Dynamics and Topological Data Analysis Study. Vol. 38. (doi)

Repositories

  1. Project Burbank: Classification of Russet Varieties

  2. GLAD: Graph Learning of Aerosol Dynamics and a tutorial on using GLAD

Talks

  1. Topological Data Analysis and Machine Learning: Mathematical Foundations and Industrial Applications. 2nd International Conference on Recent Advances in Engineering and Sciences-2026 (ICRAES-2K26). April, 2026. Bharati Vidyapeeth’s College of Engineering, Lavale, Pune, India.

  2. Enhancing explainability of causal discovery AI - from the G-RIPS Sendai 2024 Fujitsu project. Approaching the World through the Lens of Causality Symposium at Tohoku University. February, 2026. Sendai, Japan.ß

  3. Applications of Persistent Homology to Problems in Industry. KISTEC Learning and Training Program in Mathematical Literacy. January, 2026. Kanagawa, Japan.

  4. Persistent Homology Analysis of Urban Transit Networks: Multi-Scale Topological Characterization of Bus Route Patterns. Urban OR Winter Seminar 2025 at Keio University. December, 2025. Yokohama, Japan.

  5. Topological Phantom Generation for Small-Dataset Medical Image Classification: A Persistent Homology Framework. Poster presentation in the HeKKSaGOn workshop at The University of Osaka. October, 2025. Osaka, Japan.

  6. Under Covers with the Mapper: Transforming High Dimensional Data into Actionable Insights . Mitsubishi Electric Corporation. July, 2025. Amagasaki, Japan.

  7. Learning to Simulate Aerosol Microphysics with Graph Neural Networks. Tohoku University MCCS Departmental Seminar. December, 2024. Sendai, Japan.

  8. Simulating Aerosol Chemistry with Graph Neural Networks presented at the 8th Cascade RAIN Meeting

  9. Collection of Articles Presentation presented at WSU Vancouver.

Past Presentations on Existing Literature

  1. Using Topological Data Analysis for Insights into Biological and Artificial Neural Networks
  2. Topological Data Analysis (TDA) and Bayesian Classification of Brain States
  3. Modeling Stochastic Processes in Ecology
  4. Pirotta et al: State-space modelling of the flight behaviour of a soaring bird provides new insights to migratory strategies
  5. The Effects of Being Caught: How Fisheries Affect Oceanic Whitetip Sharks
  6. The (Markov) Hidden Connection: Quantifying Neuronal Spikes and Forest Fires (with Jacob Pennington)
  7. Cui et al.: Paxos Made Transparent: presenting CRANE, an SMR system to replicate general server programs