News

• 2025

  • I accepted an offer from Capital Fund Management (CFM) in Paris, France as a Quantitative Researcher, beginning September 2025.

• 2024

  • Our paper Principal Component Analysis in Space Forms was accepted for publication as a regular paper in the IEEE Transactions on Signal Processing.

  • I gave a talk on Universal Representation of Permutation-Invariant Functions on Vectors and Tensors at the 35th International Conference on Algorithmic Learning Theory (ALT), San Diego.

  • I presented a poster on Universal Representation of Permutation-Invariant Functions on Vectors and Tensors at the Information Theory and Applications (ITA) Workshop, San Diego.

  • Our paper An Effective Neural Model for Circuit Netlist Representation was accepted at the 27th International Conference on Artificial Intelligence and Statistics (AISTATS).

  • Our paper Learning Ultrametric Trees for Optimal Transport Regression was accepted at the 38th Annual AAAI Conference on Artificial Intelligence.

  • Our paper Universal Representation of Permutation-Invariant Functions on Vectors and Tensors was accepted at the 35th International Conference on Algorithmic Learning Theory (ALT).

  • Our paper Optimal Tree Metric Matching Enables Phylogenomic Branch Length Reconciliation was accepted at the Research in Computational Molecular Biology Conference (RECOMB).

  • I taught DSC 40B, Theoretical Foundations of Data Science II, in the winter quarter.

• 2023

  • I presented a poster on Sum-Decomposable Models for Identifiable Set Functions at the TILOS Retreat and Industry Day, La Jolla, San Diego.

  • Our paper Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces was accepted for publication in Knowledge and Information Systems.

  • Our paper Principal Component Analysis in Space Forms became available on arXiv.

• 2022

  • I presented posters on Principal Component Analysis in Hyperbolic Spaces at:

    • TILOS Retreat and Industry Day, La Jolla, San Diego

    • ENCORE Fall Retreat, La Jolla, San Diego

  • I taught DSC 10, Principles of Data Science, in the fall quarter.

  • Our paper Learning Hyperbolic Embedding for Phylogenetic Tree Placement and Updates was accepted for publication in Biology.

  • Our paper HyperAid: Denoising in Hyperbolic Spaces for Tree-Fitting and Hierarchical Clustering was accepted for publication in the Proceedings of the International Conference on Knowledge Discovery & Data Mining (SIGKDD).

  • I received the Halıcıoğlu Data Science Institute (HDSI) Fellowship Award to work with Profs. Yusu Wang and Siavash Mirarab at UCSD.

  • Our paper Phylogenetic Placement Problem: A Hyperbolic Embedding Approach was accepted for publication in the 19th Annual Satellite Conference of RECOMB on Comparative Genomics (RECOMB-CG).

  • I began a postdoctoral position with Prof. Siavash Mirarab at University of California San Diego (UCSD).

• 2021

  • On November 2, I successfully defended my doctoral dissertation titled: Machine Learning in Space Forms: Embeddings, Classification, and Similarity Comparisons. My doctoral committee consisted of Ivan Dokmanic and Olgica Milenkovic (advisors), Bruce Hajek, and Maxim Raginsky.

  • Our paper Highly Scalable and Provably Accurate Classification in Poincare Balls was accepted for publication at the 21st IEEE International Conference on Data Mining (ICDM) [arXiv].

  • I gave a talk on Hyperbolic Distance Geometry Problems [abstract, presentation] at the Mini-Symposium on Sensor Network Localization and Dynamical Distance Geometry at The Fields Institute for Research in Mathematical Sciences.

  • Our paper On Procrustes Analysis in Hyperbolic Space was accepted for publication in the IEEE Signal Processing Letters.

• 2020

  • I gave an oral and poster presentation of Hyperbolic Distance Matrices at KDD 2020 (virtual event).

  • I received the KDD 2020 registration award.