1 citations · 3 across the 11 of their papers we have counts for
13 papers
Toward Autonomous Radio Follow-up of Multi-messenger Transients with RADAR: From Alert Parsing to Inference and Observation Scheduling
Mihael Hategan-Marandiuc, Tanner O'Dwyer, Alessandra Corsi +6
Multi-messenger astronomy (MMA), the joint study of cosmic sources through gravitational waves (GWs), electromagnetic (EM) radiation, neutrinos, and cosmic rays, is rapidly reshapi…
COMPASS: Steering Distributed Vector Search with Scientific Knowledge Graphs
Song Young Oh, Amal Gueroudji, Seth Ockerman +5
Vector databases use hashing to partition data across "shards," logical units for distributed execution. This placement, however, destroys semantic locality, forcing each query int…
Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data
Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji +5
Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations,…
StreamGuard: Low-Overhead Resilience for Real-time HPC Data Streams
Hai Duc Nguyen, Bogdan Nicolae, Tekin Bicer +4
Real-time scientific workflows operate on continuous data streams and must produce timely, high-quality results despite executing on complex, failure-prone infrastructure. Hardware…
When More Cores Hurts: The Vector Database Scaling Paradox in HPC
Seth Ockerman, Song Young Oh, Amal Gueroudji +12
Vector databases have been designed and optimized for cloud environments; however, emerging scientific AI workloads (e.g., molecular search, meteorological trajectory detection, an…
Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents
Chih-Hsuan, Yang, Tanwi Mallick +5
Large Language Models (LLMs) in multi-agent systems (MAS) have shown promise for complex tasks, yet current training methods lack principled ways to connect system-level evaluation…
A Distributed Framework for Causal Modeling of Performance Variability in GPU Traces
Ankur Lahiry, Ayush Pokharel, Banooqa Banday +5
Large-scale GPU traces play a critical role in identifying performance bottlenecks within heterogeneous High-Performance Computing (HPC) architectures. However, the sheer volume an…
Exploring Distributed Vector Databases Performance on HPC Platforms: A Study with Qdrant
Seth Ockerman, Amal Gueroudji, Song Young Oh +5
Vector databases have rapidly grown in popularity, enabling efficient similarity search over data such as text, images, and video. They now play a central role in modern AI workflo…
LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology
Renan Souza, Timothy Poteet, Brian Etz +5
Modern scientific discovery increasingly relies on workflows that process data across the Edge, Cloud, and High Performance Computing (HPC) continuum. Comprehensive and in-depth an…
PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows
Renan Souza, Amal Gueroudji, Stephen DeWitt +5
Large Language Models (LLMs) and other foundation models are increasingly used as the core of AI agents. In agentic workflows, these agents plan tasks, interact with humans and pee…
PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
Seth Ockerman, Amal Gueroudji, Tanwi Mallick +4
Spatiotemporal graph neural networks (ST-GNNs) are powerful tools for modeling spatial and temporal data dependencies. However, their applications have been limited primarily to sm…
Scalable GPU Performance Variability Analysis framework
Ankur Lahiry, Ayush Pokharel, Seth Ockerman +3
Analyzing large-scale performance logs from GPU profilers often requires terabytes of memory and hours of runtime, even for basic summaries. These constraints prevent timely insigh…
Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
Rafael Ferreira da Silva, Deborah Bard, Kyle Chard +108
The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensit…