9 citations · 36 across the 22 of their papers we have counts for
24 papers
Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text
Sina Mansouri, Mohit Marvania, Abolfazl Safikhani
The widespread adoption of large language models (LLMs) has intensified the demand for principled methods to distinguish human from machine-generated text. Watermarking provides a…
Longitudinal Omics Data Analysis: A Review on Models, Algorithms, and Tools
Ali R. Taheriyoun, Allen Ross, Abolfazl Safikhani +2
Longitudinal omics data (LOD) analysis is essential for understanding the dynamics of biological processes and disease progression over time. This review explores various statistic…
Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models
Sepideh Mosaferi, Abolfazl Safikhani, Peiliang Bai
This paper addresses the problem of detecting change points in the spectral density of time series, motivated by EEG analysis of seizure patients. Seizures disrupt coherence and fu…
Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models
Yuhan Tian, Abolfazl Safikhani
Sequential (online) change-point detection involves continuously monitoring time-series data and triggering an alarm when shifts in the data distribution are detected. We propose a…
A Functional Coefficients Network Autoregressive Model
Hang Yin, Abolfazl Safikhani, George Michailidis
The paper introduces a flexible model for the analysis of multivariate nonlinear time series data. The proposed Functional Coefficients Network Autoregressive (FCNAR) model conside…
A Unified Framework for Fast Large-Scale Portfolio Optimization
Weichuan Deng, Pawel Polak, Abolfazl Safikhani +1
We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple obj…
Theoretical analysis of deep neural networks for temporally dependent observations
Mingliang Ma, Abolfazl Safikhani
Deep neural networks are powerful tools to model observations over time with non-linear patterns. Despite the widespread use of neural networks in such settings, most theoretical d…
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
Xiudi Li, Abolfazl Safikhani, Ali Shojaie
Regime shifts in high-dimensional time series arise naturally in many applications, from neuroimaging to finance. This problem has received considerable attention in low-dimensiona…
A unified framework for change point detection in high-dimensional linear models
Yue Bai, Abolfazl Safikhani
In recent years, change point detection for high dimensional data has become increasingly important in many scientific fields. Most literature develop a variety of separate methods…
Non-Stationary Time Series Model for Station Based Subway Ridership During Covid-19 Pandemic (Case Study: New York City)
Bahman Moghimi, Camille Kamga, Abolfazl Safikhani +2
The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City. Understanding the temporal pattern of…
Hybrid Modeling of Regional COVID-19 Transmission Dynamics in the U.S
Yue Bai, Abolfazl Safikhani, George Michailidis
The fast transmission rate of COVID-19 worldwide has made this virus the most important challenge of year 2020. Many mitigation policies have been imposed by the governments at dif…
A General Modeling Framework for Network Autoregressive Processes
Hang Yin, Abolfazl Safikhani, George Michailidis
The paper develops a general flexible framework for Network Autoregressive Processes (NAR), wherein the response of each node linearly depends on its past values, a prespecified li…
Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
Peiliang Bai, Abolfazl Safikhani, George Michailidis
We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structur…
A Fast Detection Method of Break Points in Effective Connectivity Networks
Peiliang Bai, Abolfazl Safikhani, George Michailidis
There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop f…
Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package
Peiliang Bai, Yue Bai, Abolfazl Safikhani +1
Vector Auto-Regressive (VAR) models capture lead-lag temporal dynamics of multivariate time series data. They have been widely used in macroeconomics, financial econometrics, neuro…
Regularized Estimation in High-Dimensional Vector Auto-Regressive Models using Spatio-Temporal Information
Zhenzhong Wang, Abolfazl Safikhani, Zhengyuan Zhu +1
A Vector Auto-Regressive (VAR) model is commonly used to model multivariate time series, and there are many penalized methods to handle high dimensionality. However in terms of spa…
Inference on the change point in high dimensional time series models via plug in least squares
Abhishek Kaul, Stergios B. Fotopoulos, Venkata K. Jandhyala +1
We study a plug in least squares estimator for the change point parameter where change is in the mean of a high dimensional random vector under subgaussian or subexponential distri…
Spectral Conditions for Equivalence of Gaussian Random Fields with Stationary Increments
Abolfazl Safikhani, Yimin Xiao
This paper studies the problem of equivalence of Gaussian measures induced by Gaussian random fields (GRFs) with stationary increments and proves a sufficient condition for the equ…
Uncertainty Estimation in Functional Linear Models
Tapabrata Maiti, Abolfazl Safikhani, Ping-Shou Zhong
Functional data analysis is proved to be useful in many scientific applications. The physical process is observed as curves and often there are several curves observed due to multi…
Predicting Short-Term Uber Demand Using Spatio-Temporal Modeling: A New York City Case Study
Sabiheh Sadat Faghih, Abolfazl Safikhani, Bahman Moghimi +1
The demand for e-hailing services is growing rapidly, especially in large cities. Uber is the first and popular e-hailing company in the United Stated and New York City. A comparis…
Short-Term Prediction of Signal Cycle in Actuated-Controlled Corridor Using Sparse Time Series Models
Bahman Moghimi, Abolfazl Safikhani, Camille Kamga +2
Traffic signals as part of intelligent transportation systems can play a significant role toward making cities smart. Conventionally, most traffic lights are designed with fixed-ti…
Spatio-temporal Modeling of Yellow Taxi Demands in New York City Using Generalized STAR Models
Abolfazl Safikhani, Camille Kamga, Sandeep Mudigonda +2
A highly dynamic urban space in a metropolis such as New York City, the spatio-temporal variation in demand for transportation, particularly taxis, is impacted by various factors s…
Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models
Abolfazl Safikhani, Ali Shojaie
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to allow for piecewise stationarity, where the model is allowed to change at…
Structural Break Detection in High-Dimensional Non-Stationary VAR models
Abolfazl Safikhani, Ali Shojaie
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to allow for piecewise stationarity, where the model is allowed to change at…