activity
20172026
most citedTheoretical analysis of deep neural networks for temporally dependent observations

4 citations · 12 across the 11 of their papers we have counts for

collaborators

15 papers

cs.CL2026

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…

stat.ME2025

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…

stat.ME2025

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…

stat.ME2024

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…

stat.ML20224 cited

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…

stat.AP2022

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…