5 citations · 15 across the 10 of their papers we have counts for
11 papers
Recursive Self-Improvement on a Budget: Scoring Code Edits Before They Run
Erfan Darzi, Sree Bhargavi Balija, Sheng He
A coding agent that rewrites its own scaffold has to choose which proposed edit to expand before that edit has been evaluated, and the search then has to return one agent. Each run…
Host-Side Telemetry for Performance Diagnosis in Cloud and HPC GPU Infrastructure
Erfan Darzi, Aldo Pareja, Shreeanant Bharadwaj
Diagnosing GPU tail latency spikes in cloud and HPC infrastructure is critical for maintaining performance predictability and resource utilization, yet existing monitoring tools la…
Predictable LLM Serving on GPU Clusters
Erfan Darzi, Shreeanant Bharadwaj, Sree Bhargavi Balija
Latency-sensitive inference on shared A100 clusters often suffers noisy-neighbor interference on the PCIe fabric, inflating tail latency and SLO violations. We present a fabric-agn…
The Trust Fabric: Decentralized Interoperability and Economic Coordination for the Agentic Web
Sree Bhargavi Balija, Rekha Singal, Ramesh Raskar +4
The fragmentation of AI agent ecosystems has created urgent demands for interoperability, trust, and economic coordination that current protocols -- including MCP (Hou et al., 2025…
Foundation AI Model for Medical Image Segmentation
Rina Bao, Erfan Darzi, Sheng He +6
Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across various tasks with high acc…
A Survey for Large Language Models in Biomedicine
Chong Wang, Mengyao Li, Junjun He +14
Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicin…
Tackling Heterogeneity in Medical Federated learning via Vision Transformers
Erfan Darzi, Yiqing Shen, Yangming Ou +2
Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the…
The Hidden Adversarial Vulnerabilities of Medical Federated Learning
Erfan Darzi, Florian Dubost, Nanna. M. Sijtsema +1
In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks. Our analysis uncovers a novel exploitation avenue: using gradien…
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks
Erfan Darzi, Nanna M. Sijtsema, P. M. A van Ooijen
This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggrega…
Exploring adversarial attacks in federated learning for medical imaging
Erfan Darzi, Florian Dubost, N. M. Sijtsema +1
Federated learning offers a privacy-preserving framework for medical image analysis but exposes the system to adversarial attacks. This paper aims to evaluate the vulnerabilities o…
Using image-extracted features to determine heart rate and blink duration for driver sleepiness detection
Erfan Darzi, Armin Mohammadie-Zand, Hamid Soltanian-Zadeh
Heart rate and blink duration are two vital physiological signals which give information about cardiac activity and consciousness. Monitoring these two signals is crucial for vario…