LLM Safety & Adversarial Testing
ActiveQuality-diversity evolutionary framework for discovering diverse vulnerabilities in LLMs. Accepted at ICLR 2026 Workshop AIWILD.
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Last updated: 2026-03
Quality-diversity evolutionary framework for discovering diverse vulnerabilities in LLMs. Accepted at ICLR 2026 Workshop AIWILD.
Designing compute abstractions for environments where power, connectivity, and time are all intermittent. Scheduling and fault-tolerance driven by orbital physics.
Building production-ready multi-agent systems for autonomous data pipeline management. Industry-agnostic framework handling data quality, orchestration, and monitoring.
Reference implementations of acceleration techniques: speculative decoding, KV-cache compression, custom Triton kernels. 8.1x speedup, 88% peak bandwidth on A100.
Mechanistic interpretability research for production AI safety. Circuit tracing techniques for understanding model internals and sandbagging detection.
George Sebestyen - Satellite systems architecture and distributed networks
Anil Ananthaswamy - Mathematical foundations of machine learning