Bridging Massive-Scale ML & Autonomous Agents

I am a Research Scientist at Meta Platforms Inc. based in Menlo Park, CA, where I translate billion-scale user signals into measurable product wins. My work sits at the intersection of Massive-Scale Machine Learning and Autonomous Systems — bridging the gap between theoretical intelligence and practical, reliable application.

I believe the future of AI isn’t just about bigger models, but about smarter orchestration: systems that are robust, efficient, and impactful.

News

• Jun 2025 — Joined Meta Platforms Inc. as a Research Scientist in Menlo Park, CA.
• 2025 — Earned Ph.D. in Electrical Engineering from Louisiana State University.
• 2025 — “Joint Device and Training Scheduling for Wireless Federated Learning” published in the IEEE Internet of Things Journal.
• 2025 — “Temporal Contrastive Learning for Sensor-Based Human Activity Recognition” published in the IEEE Sensors Journal.
• 2025 — “DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game” presented at AAAI 2025.
• Nov 2024 — Invited talk at the University of Houston on client selection for wireless federated learning.
• 2024 — “Client Selection for Wireless Federated Learning with Data and Latency Heterogeneity” published in the IEEE Internet of Things Journal.
• 2024 — “Cost-Effective Federated Learning: A Unified Approach to Device and Training Scheduling” presented at IEEE ICC 2024.

Selected Publications

• DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game AAAI 2025
  Game-theoretic auctions for fair, stable device participation in decentralized training.
• Client Selection for Wireless Federated Learning with Data and Latency Heterogeneity IEEE Internet of Things Journal, 2024
  Device scheduling that balances learning performance with communication cost and latency.
• Joint Device and Training Scheduling for Wireless Federated Learning IEEE Internet of Things Journal, 2025
  A blueprint for edge AI deployment balancing communication, energy, and performance.
• Temporal Contrastive Learning for Sensor-Based Human Activity Recognition: A Self-Supervised Approach IEEE Sensors Journal, 2025
  Self-supervised representation learning for wearable sensor data.

See the full list on the Publications page or Google Scholar.

🚀 Impact at Scale (Meta)

At Meta, I architect and deploy machine learning systems that power core discovery and integrity experiences for billions of users, focused on turning complex signals into Tier-0 product wins.

• Scale: Designed graph-based user representation learning systems that scale to billions of nodes, unlocking new capabilities for signal-sparse populations.
• Efficiency: Architected offline evaluation frameworks and training pipelines that significantly reduced compute overhead while accelerating iteration velocity for the entire team.
• Product: Led multiple high-impact launches across Instagram and Facebook, driving global engagement wins and integrity improvements through novel feature engineering and model architecture optimizations.

⚡ The Frontier: Agentic Systems

Beyond industrial ML, I am deeply invested in Collaborative AI and Multi-Agent Systems. I believe the next leap in productivity comes from workflow consolidation — where specialized agents orchestrate complex tasks autonomously, moving beyond “chatbots” to “do-bots” that integrate deeply into tools and infrastructure.