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Posts
Supervising Masters/Undergrad Students
Published:
I am happy to supervise UCLA masters and undergraduate students who have interests in methodology and applications pertaining to the topics aligned with my research interest. Please send me an email and we can set up time to chat; please include a copy of your CV and transcripts in the email.
Recruiting Postdocs & Ph.D. students
Published:
I am recruiting postdocs and Ph.D. students to join my group. Please email me if interested.
- For postdoc positions, please include a copy of your CV and research statement.
- For Ph.D. students, please include a copy of your CV and transcripts.
publications
DAdam: A Consensus-Based Distributed Adaptive Gradient Method for Online Optimization
IEEE Transactions on Signal Processing, 2022
a consensus-based distributed adaptive moment estimation method (DAdam) for online optimization over a decentralized network
Bayesian Spiked Laplacian Graphs
Journal of Machine Learning Research, 2023
community detection and network modeling via a probablistic network model
Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
Journal of the American Statistical Association, 2023
detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models
Estimation of Gaussian Directed Acyclic Graphs using Partial Ordering Information with Applications to DREAM3 Networks and Dairy Cattle Data
Annals of Applied Statistics, 2023
an efficient algorithm for DAG estimation in the presence of partial ordering
Inference on the Change Point under a High Dimensional Covariance Shift
Journal of Machine Learning Research, 2023
an algorithm for change point detection in the presence of covariate shift
A Bayesian Framework for Sparse Estimation in High-dimensional Mixed Frequency Vector Autoregressive Models
Statistica Sinica, 2023
vector autoregressive model for high-dimensional mixed frequency data under the sparsity assumption
The Bayesian Nested Lasso for Mixed Frequency Regression Models
Annals of Applied Statistics, 2023
joint estimation of mixed frequency data via a Bayesian approach
Low Tree-rank Bayesian Vector Autoregression Models
Journal of Machine Learning Research, 2023
estimating a vector autoregressive (VAR) model assuming that Granger causal graph has a tree structure
A Multi-Task Encoder-Dual-Decoder Framework for Mixed Frequency Data Prediction
International Journal of Forecasting, 2023
DNN method for mixed-frequency data prediction
A General Modeling Framework for Network Autoregressive Processes
Technometrics, 2023
a general framework for modeling network autoregressive processes
Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees
Journal of Computational and Graphical Statistics, 2024
an ADMM algorithm for high dimensional structural VAR estimation in the presence of partial ordering
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
Transactions on Machine Learning Research (TMLR), 2024
DNN method for estimating the Granger causality of multiple dynamical systems via a VAE-based formulation
A Functional Coefficients Network Autoregressive Model
Statistica Sinica, 2024
modeling for multivariate time series where node values depend on its own past and its neighbors
Axiomatic Effect Propagation in Structural Causal Models
Journal of Machine Learning Research, 2024
flow-based decomposition of effect under a causal DAG
High Dimensional Logistic Regression Under Network Dependence
Journal of Machine Learning Research, 2024
Ising model with peer-effect from the network and the effect from covariates
A High-dimensional Approach to Measure Connectivity in the Financial Sector
Annals of Applied Statistics, 2024
explore connectivity patterns amongst US financial institutions based on debiased Lasso
Joint Learning of Linear Time-Invariant Dynamical Systems
Automatica, 2024
system identification of multiple related time-invariant linear dynamical systems
Bayesian Methodology for Adaptive Sparsity and Shrinkage in Regression
TBD, 2024
a data-adaptive method that performs model selection, covering the spectrum where the model collection spans from a sparse to a dense one
Deep Learning-based Approaches for State Space Models: A Selective Review
TBD, 2024
a review of deep-learning based SSMs for sequence modeling
A Generalized Bayesian Approach for High-dimensional Robust Regression with Serially Correlated Errors and Predictors
TBD, 2025
generalized Bayesian method for high-dimensional robust regression
A Penalty-based Method for Communication-Efficient Decentralized Bilevel Programming
Automatica, 2025
a distributed algorithm for bilevel-optimization
Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data
TBD, 2025
neural network-based method for detecting change points for multivariate time series
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
Transactions on Machine Learning Research (TMLR), 2025
estimate covariate-dependent graphical models using DNN with PAC guarantees
talks
teaching
STATS 102B: Intro to Computing and Optimization
Undergraduate course, UCLA, 2025
This course is offered in Spring Quarter 2025, Spring Quarter 2024, Spring Quarter 2023
STATS 202B: Matrix Algebra and Optimization
Graduate course, UCLA, 2025
This course introduces students to algorithms and their theoretical underpinnings extensively used in modern machine learning. It covers
- first order methods, including gradient descent and its variants;
- conditions for optimality for convex;
- constrained optimization problems and basics of duality theory;
- challenges posed by non-convex problems.
STATS 417: Models in Finance
Graduate course, UCLA, 2025
This course is offered in Winter Quarter 2025, Winter Quarter 2024