25 citations · 49 across the 16 of their papers we have counts for
19 papers
End-to-End Differentiable Diffusion Conditioning for Physics-Informed Optimization
Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz +5
Optimizing high-dimensional physical systems under expensive evaluations and complex constraints is a central challenge in science and engineering. In offline model-based optimizat…
Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications
Ricardo Luna Gutierrez, Sahand Ghorbanpour, Ejaz Rahman +6
Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively high costs and limited experim…
Fast 3D Surrogate Modeling for Data Center Thermal Management
Soumyendu Sarkar, Antonio Guillen-Perez, Zachariah J Carmichael +6
Reducing energy consumption and carbon emissions in data centers by enabling real-time temperature prediction is critical for sustainability and operational efficiency. Achieving t…
DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads
Antonio Guillen-Perez, Avisek Naug, Vineet Gundecha +8
The increasing energy demands and carbon footprint of large-scale AI require intelligent workload management in globally distributed data centers. Yet progress is limited by the ab…
LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers
Avisek Naug, Antonio Guillen, Vineet Kumar +7
Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock gre…
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…
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves
Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour +5
The industrial multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices in c…
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…
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…
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels
Alexander Shmakov, Avisek Naug, Vineet Gundecha +5
Bayesian Optimization (BO), guided by Gaussian process (GP) surrogates, has proven to be an invaluable technique for efficient, high-dimensional, black-box optimization, a critical…
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…
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters
Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour +4
Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to maximize energy generation. Traditional controllers have shown limitations to capture complex…