28 citations · 57 across the 14 of their papers we have counts for
16 papers
Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis
Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang +1
Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with recurrent state-space dynamics. H…
Communication-Efficient Personalized Adaptation via Federated-Local Model Merging
Yinan Zou, Md Kamran Chowdhury Shisher, Christopher G. Brinton +1
Parameter-efficient fine-tuning methods, such as LoRA, offer a practical way to adapt large vision and language models to client tasks. However, this becomes particularly challengi…
AoI-based Scheduling of Correlated Sources for Timely Inference
Md Kamran Chowdhury Shisher, Vishrant Tripathi, Mung Chiang +1
We investigate a real-time remote inference system where multiple correlated sources transmit observations over a communication channel to a receiver. The receiver utilizes these o…
Remote Safety Monitoring: Significance-Aware Status Updating for Situational Awareness
Tasmeen Zaman Ornee, Md Kamran Chowdhury Shisher, Clement Kam +1
In this study, we consider a problem of remote safety monitoring, where a monitor pulls status updates from multiple sensors monitoring several safety-critical situations. Based on…
Online Learning of Whittle Indices for Restless Bandits with Non-Stationary Transition Kernels
Md Kamran Chowdhury Shisher, Vishrant Tripathi, Mung Chiang +1
The restless multi-armed bandit (RMAB) framework is a popular approach to solving resource allocation problems in networked systems. In this paper, we study optimal resource alloca…
RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget
Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang +1
Machine learning (ML) algorithms deployed in real-world environments are often faced with the challenge of adapting models to concept drift, where the task data distributions are s…
Computation and Communication Co-scheduling for Multi-Task Remote Inference
Md Kamran Chowdhury Shisher, Adam Piaseczny, Yin Sun +1
In multi-task remote inference systems, an intelligent receiver (e.g., command center) performs multiple inference tasks (e.g., target detection) using data features received from…
Goal-Oriented Status Updating for Real-time Remote Inference over Networks with Two-Way Delay
Cagri Ari, Md Kamran Chowdhury Shisher, Yin Sun +1
We study a setting where an intelligent model (e.g., a pre-trained neural network) infers the real-time value of a target signal using data samples transmitted from a remote source…
Timely Communications for Remote Inference
Md Kamran Chowdhury Shisher, Yin Sun, I-Hong Hou
In this paper, we analyze the impact of data freshness on remote inference systems, where a pre-trained neural network blue infers a time-varying target (e.g., the locations of veh…
On the Monotonicity of Information Aging
MD Kamran Chowdhury Shisher, Yin Sun
In this paper, we analyze the monotonicity of information aging in a remote estimation system, where historical observations of a Gaussian autoregressive AR(p) process are used to…
Goal-Oriented Communications for Remote Inference under Two-Way Delay with Memory
Cagri Ari, Md Kamran Chowdhury Shisher, Elif Uysal +1
We study the design of a goal-oriented sampling and scheduling strategy through a channel with highly variable two-way random delay, which can exhibit memory (e.g., Delay and Disru…
Context-aware Status Updating: Wireless Scheduling for Maximizing Situational Awareness in Safety-critical Systems
Tasmeen Zaman Ornee, Md Kamran Chowdhury Shisher, Clement Kam +1
In this study, we investigate a context-aware status updating system consisting of multiple sensor-estimator pairs. A centralized monitor pulls status updates from multiple sensors…
Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling
Md Kamran Chowdhury Shisher, Bo Ji, I-Hong Hou +1
In this paper, we consider a remote inference system, where a neural network is used to infer a time-varying target (e.g., robot movement), based on features (e.g., video clips) th…
How Does Data Freshness Affect Real-time Supervised Learning?
Md Kamran Chowdhury Shisher, Yin Sun
In this paper, we analyze the impact of data freshness on real-time supervised learning, where a neural network is trained to infer a time-varying target (e.g., the position of the…
A Local Geometric Interpretation of Feature Extraction in Deep Feedforward Neural Networks
Md Kamran Chowdhury Shisher, Tasmeen Zaman Ornee, Yin Sun
In this paper, we present a local geometric analysis to interpret how deep feedforward neural networks extract low-dimensional features from high-dimensional data. Our study shows…
The Age of Correlated Features in Supervised Learning based Forecasting
Md Kamran Chowdhury Shisher, Heyang Qin, Lei Yang +2
In this paper, we analyze the impact of information freshness on supervised learning based forecasting. In these applications, a neural network is trained to predict a time-varying…