Upcoming conferences and talks

• Minisymposium on Learning from Data with Information Theory and Causal Inference, SIAM Annual Meeting, Jul 8 - 12
• Banff BIRS workshop on Analysis of Complex Data: Tensors, Networks and Dynamic Systems, Banff, AB, May 12 - 17
• UCLA Statistics & Data Science Department Seminar, Los Angeles, CA, May 23
• Tensors: Algebra, Geometry, and Applications Workshop, Fort Collins, CO, May 29 - Jun 7
• Workshop for Women in Algebraic Statistics, Oxford University, Jul 8 - 18, 2024

Organization of programs, workshops, and minisymposia

• Algebraic Statistics and Our Changing World, IMSI Long Program, Fall 2023 -- coorganizing with Mathias Drton, Elizabeth Gross, Lek-Heng Lim, Sonja Petrovic, Jose Rodriguez, Bernd Sturmfels, and Piotr Zwiernik
• Algebraic Statistics, BIRS Oaxaca, Mexico, May 2023 - coorganizing with Carlos Amendola, Anthea Monod, and Bernd Sturmfels
• Tensor Methods and Emerging Applications to the Physical and Data Sciences, IPAM Semester Program, Spring 2021 - coorganizing with Thomas Barthel, Victor Batista, Fernando Branado, Gero Friesecke, Lek-Heng Lim, Jianfeng Lu, and Ming Yuan
• Theory and methods for tensor decomposition, in the SIAM AG Meeting (Bern, Switzerland), July 9-13, 2019 - coorganized with Tammy Kolda
• Graphical models, in the SIAM AG Meeting (Bern, Switzerland), July 9-13, 2019
• Algebraic Methods in Statistics, in the Joint Statistical Meetings (Vancouver) July 2018 - coorganized with Jose Rodriguez
• Theoretical Challenges in Tensor Decomposition, in the SIAM Annual Meeting (Portland) July 9-13, 2018 - coorganized with Anna Seigal
• Algebraic Statistics, in AMS Spring Eastern Sectional Meeting (Northeastern) Apr 2018 - coorganized with Kaie Kubjas
• Tensors: from Algebra to Applications, in SIAM Conference on Applied Algebra and Geometry (Georgia Tech) Aug 2017 - coorganized with Anna Seigal

Mentorship and outreach

• Undergraduate students at UBC:
  • 2023: Joshua Boyd and Young Lin
  • 2022: Christian Campbell. In joint work with Sharvaj Kubal and Christian Campbell, we studied Log-concave Density Estimation with Orthogonal Independent Components
  • 2022: Niko Nikov. In joint work with Mathias Drton, Marina Garrote-Lopez, and Niko Nikov, we have studied Learning Linear Non-Gaussian Cyclic Causal Models, which will be submitted to a journal soon.
  • 2021: Alex Dong. We studied the set of tensors of nonnegative rank at most 3.
  • 2020: Tommi Muller and Karim Halaseh. We studied Orthogonal Decomposition of Tensor Trains which is now published in Linear and Multilinear Algebra.
  • 2020: Yiheng Liu and Huanqing Wang. We studied Learning Linear Non-Gaussian Graphical Models with Multidirected Edges which is now published in the Journal of Causal Inference.

• In the spring of 2019 I mentored Ali Zartash through the Undergraduate Research Opportunity (UROP) program at MIT. Our work is now written up in an article titled Kernel Density Estimation for Totally Positive Random Vectors.
• In the fall of 2017 I mentored two undergraduate students via the Undergraduate Research Opportunity (UROP) program at MIT. We worked on a research project titled "Decomposing Tensors into Equiangular Tight Frames".
• In the year 2018 I mentored and worked on two research problems with two high-school students, Melinda Sun and Haneul Shin, via the PRIMES program. These projects studied different aspects of piecewise linear supermodular functions and their associated subdivisions. Some of our work can be found in Bimonotone subdivisions of point configurations in the plane.
• I gave lectures in the Berkeley Math Circle each year from 2012 to 2016.

Seminars

• In the time of COVID-19, I co-organized the Algebraic Statistics Online Seminar together with Carlos Amendola, Mathias Drton, Elizabeth Gross, Sonja Petrovic, and Piotr Zwiernik.
• In 2017-2018 I founded the seminar on applied algebra and geometry at MIT. In the fall of 2018 the seminar took the form of an "Applied Algebra Day" with 11 expert speakers from the field.
• In 2015-2016 I coorganized the seminar on applied algebra at the Berkeley math department together with David Dynerman.

Past invited talks

• Learning Linear Non-Gaussian Causal Models via Algebraic Constraints, AAAI Bridge on Continual Causality, Vancouver, BC, Feb 20 - 27, 2024
• Learning Linear Non-Gaussian Causal Models via Algebraic Constraints, Andre-Aisenstadt Prize Lecture, CRM, Montreal, QC, Dec 6, 2023
• Learning Linear Non-Gaussian Causal Models via Algebraic Constraints, Mathematics of Machine Learning - Symposium at 2023 CMS Winter Meeting, Montreal, QC, Dec 4, 2023
• Learning Linear Non-Gaussian Causal Models via Algebraic Constraints, Joint Statistics Meetings, Toronto, Aug 5-10, 2023
• Learning Linear Non-Gaussian Causal Models via Algebraic Constraints, When Causal Inference meets Statistical Analysis, Paris, France [hybrid], Apr 17-21, 2023
• Robust Eigenvectors of Symmetric Tensors, Joint Mathematics Meetings, Boston, Jan 2023
• Linear Non-Gaussian Causal Models, Joint Mathematics Meetings, Boston, Jan 2023
• Structured Log-Concave Density Estimation, Joint Mathematics Meetings, Boston, Jan 2023
• Structured Log-Concave Density Estimation, Algebraic Statistics, Oberwolfach, Germany, Dec 4 - 10, 2022
• Log-concave Graphical Models, Applied Combinatorics, Algebra, Topology, and Statistics Seminar, KTH Royal Institute of Technology [hybrid talk], Sep 27, 2022
• Log-concave Graphical Models, Combinatorial, Computational, and Applied Algebraic Geometry, Seattle, Jun 27 - Jul 1, 2022
• Orthogonal and Incoherent Tensor Decompositions, CAIMS (Canadian Applied and Industrial Mathematical Society) Annual Meeting Prize Talk, Kelowna, BC, Jun 13 - 16, 2022
• Log-Concave Graphical Models, Algebraic Statistics 2022, University of Hawaii, May 16-20, 2022
• Incoherent tensor decomposition, Mathematics Colloquium, University of Idaho [online], Nov 4 2021
• Log-concave graphical models, Algebra, Combinatorics, and Geometry Seminar, San Francisco State University [online], Nov 17 2021
• Hidden Variables in Linear Causal Models, AMS Fall Western Sectional Meeting, University of New Mexico [online], Oct 23-24 2021
• Log-concave Graphical Models, SIAM Conference on Applied Algebraic Geometry, Texas A&M, Aug 16-21, 2021
• Orthogonal and Incoherent Tensor Decompositions, International Conference on Large-Scale Scientific Computations, June 7-11, 2021, Sozopol, Bulgaria / [online]
• Orthogonal and Incoherent Tensor Decomposition, SIAM Conference on Applied Linear Algebra, May 17-21, 2021 [online]
• Orthogonal Tensor Decomposition, First Annual Meeting of Young Bulgarian Mathematicians, Bulgarian Academy of Sciences, May 19-20, 2021 [online]
• Learning Totally Positive Densities, Online Workshop "High-Dimensional Covariance Matrices, Networks and Concentration Inequalities", May 20-24, 2021 [online]
• Orthogonal and Incoherent Tensor Decomposition, Codes and Expansions Online Seminar, May 25, 2021 [online]
• Hidden Variables in Non-Gaussian Linear Causal Models, Workshop on Mathematical Foundations and Algorithms for Tensor Computations, IPAM, May 3-7, 2021 [online]
• Density Estimation under Total Positivity and Conditional Independence, UBC/PIMS Colloquium lecture for receiving the Mathematical Sciences Young Faculty Award, April 21, 2021 [online]
• Hidden Variables in Linear Causal Models, Number Theory and Algebraic Geometry Seminar, Simon Fraser University, April 15, 2021 [online]
• Estimating Totally Positive Densities, SIAM Conference on Computational Science and Engineering, March 1-5, 2021 [online]
• Hidden Variables in Linear Causal Models, Algebra in Statistics and Computation Seminar, UW Madison, Feb 11, 2021 [online]
• Orthogonal Decomposition of Tensor Trains, Texas A&M Working Geometry Seminar, Feb 10, 2021 [online]
• Orthogonal Decomposition of Tensor Trains, Nonlinear Algebra Seminar Online, Nov 3, 2020 [online]
• Hidden Variables in Linear Causal Models, Institute of Applied Math Colloquium, UBC, Nov 2, 2020 [online]
• Orthogonal tensor decomposition, Pure Mathematics Colloquium, St Andrews University, Oct 9, 2020 [online]
• Duality of graphical models and tensor networks, Joint Statistical Meetings, Aug 6, 2020 [online]
• Superresolution Imaging and Total Positivity, Algebraic Statistics 2020 Virtual Conference, June 26, 2020 [online]
• Learning Densities under Total Positivity, Boise State Mathematics Colloquium, March 3, 2020
• Density Estimation under Total Positivity, MIFODS workshop on Learning with Complex Structure, MIT, Jan 27-29, 2020
• Density Estimation under Total Positivity, Aalto Mathematics Colloquium, Helsinki, Finland, Oct 2019
• Nested Covariance Determinants in Gaussian Graphical Models, Graphical Models Workshop, TU Munich, Oct 2019
• Nested Covariance Determinants in Gaussian Graphical Models, SIAM Pacific Northwest Sectional Meeting, Seattle, Oct 2019
• Orthogonal Tensor Decomposition, Algebraic Geometry and Number Theory Seminar, Simon Fraser University, Oct 3, 2019
• Duality of Graphical Models and Tensor Networks, AI and Tensor Factorizations for Physical, Chemical, and Biological Systems Conference, Santa Fe, NM, Sep 2019
• Orthogonal Tensor Decomposition, SIAM AG Conference 2019, Early Career Prize Lecture, Bern, Switzerland, July 2019
• Nested Covariance Determinants in Gaussian Graphical Models, SIAM AG Conference 2019, Bern, Switzerland, July 2019
• Maximum Likelihood Estimation under Total Positivity, Northeastern Pick My Brain Seminar, Mar 20, 2019
• Statistical Estimation under Algebraic Constraints, UW Madison EE Seminar, Feb 28, 2019
• Statistical Estimation under Algebraic Constraints, UNC Chapel Hill STOR Seminar, Feb 15, 2019
• Algebraic Structure in Hidden Variable Models, Duke Statistics Seminar, Feb 13, 2019
• Statistical Estimation under Algebraic Constraints, Stanford Statistics Seminar, Jan 29, 2019
• Orthogonal Tensor Decomposition, UBC Math Colloquium, Jan 18, 2019
• Maximum Likelihood Estimation under Total Positivity, UBC Math of Information Seminar, Jan 17, 2019
• Statistical Estimation under Algebraic Constraints, UC Irvine Mathematics, Jan 10, 2019
• Statistical Estimation under Algebraic Constraints, Frontiers in CMS, Caltech, Jan 7, 2019
• Maximum Likelihood Estimation under Total Positivity, Stochastics Seminar, University of Utah, Dec 7, 2018
• Orthogonal Tensor Decomposition, Mathematics Colloquium, University of Utah, Dec 6, 2018
• Maximum Likelihood Estimation under Total Positivity, Workshop in Operations Research and Data Science, Fuqua School of Business, Duke University, Dec 1, 2018
• Orthogonal Tensor Decomposition, Applied Math Seminar, Duke University, Nov 30, 2018
• Maximum Likelihood Estimation under Total Positivity, Applied Math Seminar, CU Boulder, Nov 27, 2018
• Maximum Likelihood Estimation under Total Positivity, Workshop on Nonlinear Algebra in Applications at ICERM, Nov 12-16, 2018
• Graphical Models from the Perspective of Algebra and Geometry, Nonlinear Algebra Bootcamp at ICERM Sep 4-12, 2018
• Maximum Likelihood Estimation under Total Positivity, SIAM Annual Meeting, Portland OR, July 9-13, 2018
• Maximum Likelihood Estimation under Total Positivity, Discrete Math Seminar, UMass Amherst, Feb 22, 2018
• Maximum Likelihood Estimation under Total Positivity, Johns Hopkins University, Feb 15, 2018
• Maximum Likelihood Estimation under Total Positivity, Applied Math Seminar at Duke, Jan 31, 2018
• Maximum Likelihood Estimation under Total Positivity, Computational and Applied Math Colloquium at UChicago, Jan 25, 2018
• Maximum Likelihood Estimation under Total Positivity, Microsoft Research, Redmond WA, Nov 2017
• Maximum Likelihood Estimation under Total Positivity, CMO Oaxaca, Beyond Convexity, Nov 2017
• Decomposing Tensors into Frames, SIAM-AG, GeorgiaTech, Aug 2017
• Orthogonal Tensor Decomposition, NSF CBMS Conference on Tensors, Auburn AL, Aug 2017
• Geometry of Log-Concave Density Estimation, Oberwolfach MFO Algebraic Statistics Meeting, Oberwolfach Germany, Apr 2017
• Geometry of Log-Concave Density Estimation, Joint Math Meetings, Atlanta GA, Jan 2017
• Superresolution without Separation, MIT LIDS Seminar, Sep 2016
• The Geometry of Positive Semidefinite Rank, AMS Spring Western Sectional Meeting, Salt Lake City UT, Apr 2016
• Orthogonal Tensor Decomposition, ETH Zürich, Nov 2015
• Superresolution without Separation, SIAM AG 2015, Daejeon Sout Korea, Aug 2015
• Orthogonal Tensor Decomposition, SIAM AG 2015, Daejeon South Korea, Aug 2015
• The Geometry of Positive Semidefinite Rank, SIAM AG 2015, Daejeon South Korea, Aug 2015
• The Geometry of Positive Semidefinite Rank, GOAL workshop, Berkeley CA, May 2015
• Super-Resolution Imaging and Tchebychev Systems, Seminar in Computational Algebraic Geometry, Berkeley CA, Mar 2015
• Orthogonal Tensor Decomposition, Simons Workshop on Tensors in Computer Science and Geometry, Berkeley CA, Nov 2014
• Orthogonal Tensor Decomposition, Computational Algebraic Geometry Seminar, Berkeley CA, Oct 2014
• Robust Toric Ideals, AMS Western Fall Sectional Meeting, San Francisco CA, Oct 2014
• Orthogonal Tensor Decomposition, AMS Western Fall Sectional Meeting, San Francisco CA, Oct 2014
• Orthogonal Tensor Decomposition, AMS Meeting Eau-Claire, Eau-Claire WI, Sep 2014
• Orthogonally Decomposable Tensors, ICML Workshop on the Method of Moments and Spectral Learning, Beijing, Jun 2014
• Orthogonally Decomposable Tensors, Workshop on Optimization and Algebraic Geometry, Daejeon South Korea, Jun 2014
• Fixed Points of the EM Algorithm and Nonnegative Rank Boundaries, Computer Science Seminar, UW, Seattle WA, May 2014
• Fixed Points of the EM Algorithm and Nonnegative Rank Boundaries, Conference on Applications of Real Algebraic Geometry, Helsinki Finland, Mar 2014
• A Tropical Proof of the Brill-Noether Theorem, Joint Mathematical Meetings, Boston MA, Jan 2012
• How to win in Bidding Hex. Stanford Undergraduate Math Organization speaker series, Stanford CA, May 2011