21 citations · 53 across the 14 of their papers we have counts for
15 papers
A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP
Xi Yang, Aurelie Lozano, Naoki Abe +6
Despite rapid progress in AI agents for enterprise automation and decision-making, their real-world deployment and further performance gains remain constrained by limited data qual…
Think Locally, Explain Globally: Graph-Guided LLM Investigations via Local Reasoning and Belief Propagation
Saurabh Jha, Rohan Arora, Bhavya +7
LLM agents excel when environments are mostly static and the needed information fits in a model's context window, but they often fail in open-ended investigations where explanation…
Story of Two GPUs: Characterizing the Resilience of Hopper H100 and Ampere A100 GPUs
Shengkun Cui, Archit Patke, Hung Nguyen +11
This study characterizes GPU resilience in Delta, a large-scale AI system that consists of 1,056 A100 and H100 GPUs, with over 1,300 petaflops of peak throughput. We used 2.5 years…
ITBench: Evaluating AI Agents across Diverse Real-World IT Automation Tasks
Saurabh Jha, Rohan Arora, Yuji Watanabe +40
Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a…
Automated Compliance Blueprint Optimization with Artificial Intelligence
Abdulhamid Adebayo, Daby Sow, Muhammed Fatih Bulut
For highly regulated industries such as banking and healthcare, one of the major hindrances to the adoption of cloud computing is compliance with regulatory standards. This is a co…
Vulnerability Prioritization: An Offensive Security Approach
Muhammed Fatih Bulut, Abdulhamid Adebayo, Daby Sow +1
Organizations struggle to handle sheer number of vulnerabilities in their cloud environments. The de facto methodology used for prioritizing vulnerabilities is to use Common Vulner…
Attack Techniques and Threat Identification for Vulnerabilities
Constantin Adam, Muhammed Fatih Bulut, Daby Sow +3
Modern organizations struggle with insurmountable number of vulnerabilities that are discovered and reported by their network and application vulnerability scanners. Therefore, pri…
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Prithwish Chakraborty, James Codella, Piyush Madan +22
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability…
Question-Driven Design Process for Explainable AI User Experiences
Q. Vera Liao, Milena Pribić, Jaesik Han +2
A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI)…
Phenotypical Ontology Driven Framework for Multi-Task Learning
Mohamed Ghalwash, Zijun Yao, Prithwish Chakraborty +2
Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest…
A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
Parthasarathy Suryanarayanan, Bhavani Iyer, Prithwish Chakraborty +11
Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient ou…
ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction
Mohamed Ghalwash, Zijun Yao, Prithwish Chakrabotry +2
Increased availability of electronic health records (EHR) has enabled researchers to study various medical questions. Cohort selection for the hypothesis under investigation is one…
Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients
MingYu Lu, Zachary Shahn, Daby Sow +2
The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate t…
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
Rui Li, Zach Shahn, Jun Li +5
Counterfactual prediction is a fundamental task in decision-making. G-computation is a method for estimating expected counterfactual outcomes under dynamic time-varying treatment s…
Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records
Prithwish Chakraborty, Fei Wang, Jianying Hu +1
In recent years, we have witnessed an increased interest in temporal modeling of patient records from large scale Electronic Health Records (EHR). While simpler RNN models have bee…