533 citations · 644 across the 24 of their papers we have counts for
26 papers
DP-OPD: Differentially Private On-Policy Distillation for Language Models
Fatemeh Khadem, Sajad Mousavi, Yi Fang +1
Large language models (LLMs) are increasingly adapted to proprietary and domain-specific corpora that contain sensitive information, creating a tension between formal privacy guara…
Robustness Evaluation for Video Models with Reinforcement Learning
Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha +5
Evaluating the robustness of Video classification models is very challenging, specifically when compared to image-based models. With their increased temporal dimension, there is a…
Coordinated Robustness Evaluation Framework for Vision-Language Models
Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha +5
Vision-language models, which integrate computer vision and natural language processing capabilities, have demonstrated significant advancements in tasks such as image captioning a…
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
Soumyendu Sarkar, Avisek Naug, Antonio Guillen +7
Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimiz…
Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +5
We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks, RLAB, that allows users to select from various distortion filters to create…
SustainDC: Benchmarking for Sustainable Data Center Control
Avisek Naug, Antonio Guillen, Ricardo Luna +8
Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate ch…
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +3
We present a generic Reinforcement Learning (RL) framework optimized for crafting adversarial attacks on different model types spanning from ECG signal analysis (1D), image classif…
A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration
Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez +5
There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data…
Sustainability of Data Center Digital Twins with Reinforcement Learning
Soumyendu Sarkar, Avisek Naug, Antonio Guillen +4
The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address t…
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
Soumyendu Sarkar, Avisek Naug, Ricardo Luna +6
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporat…
N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
Sajad Mousavi, Ricardo Luna Gutiérrez, Desik Rengarajan +5
We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination. This method involves refining model outputs thro…
Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness
Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi +6
We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. Our framework allows users to customize the types of d…
PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability
Avisek Naug, Antonio Guillen, Ricardo Luna Gutiérrez +8
The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation…
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning
Alireza Shamsoshoara, Fatemeh Lotfi, Sajad Mousavi +2
This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environ…
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
Seokmin Choi, Sajad Mousavi, Phillip Si +3
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. Ho…
An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification
James Belen, Sajad Mousavi, Alireza Shamsoshoara +1
Atrial Fibrillation (AF) is among one of the most common types of heart arrhythmia afflicting more than 3 million people in the U.S. alone. AF is estimated to be the cause of death…
ECG Language Processing (ELP): a New Technique to Analyze ECG Signals
Sajad Mousavi, Fatemeh Afghah, Fatemeh Khadem +1
A language is constructed of a finite/infinite set of sentences composing of words. Similar to natural languages, Electrocardiogram (ECG) signal, the most common noninvasive tool t…
HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya
Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of more than 3 million people in the U.S. and over 33 million people around the wor…
An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations
Alireza Shamsoshoara, Fatemeh Afghah, Abolfazl Razi +3
This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaste…
Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks
Sajad Mousavi, Atiyeh Fotoohinasab, Fatemeh Afghah
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimo…
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian +3
The high rate of false alarms in intensive care units (ICUs) is one of the top challenges of using medical technology in hospitals. These false alarms are often caused by patients'…
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep expert…
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in car…
A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs, A Genetic-Algorithm Approach
Mohammad Zaeri-Amirani, Fatemeh Afghah, Sajad Mousavi
High false alarm rate in intensive care units (ICUs) has been identified as one of the most critical medical challenges in recent years. This often results in overwhelming the clin…