AcaRevival Initiative

Experienced academic misconduct or bullying? We're building a real weapon against it.

Read Manifesto →
MS

Mac Schwager

Aeronautics and Astronautics

Stanford University

No ratings yetBe the first to rate
Loading...

About Mac Schwager at Stanford University (Stanford)

Mac Schwager is an Associate Professor in the Department of Aeronautics and Astronautics at Stanford University, where he directs the Multi-Robot Systems Lab (MSL). His research focuses on distributed algorithms for control, estimation, and learning in teams of autonomous robots, including quadrotor swarms, aerial camera networks, autonomous cars, and heterogeneous multi-robot systems. Schwager’s work explores how groups of robots can coordinate safely and efficiently in dynamic, uncertain environments, addressing topics such as cooperative manipulation, distributed task allocation, vision-based navigation, adversarial robustness, and human-swarm interfaces. His group integrates theory and experimentation to bridge scalable algorithms with real-world robotic platforms, and has produced advances in agile swarm control, environmental monitoring with aerial robots, autonomous drone racing, and trust and deception modeling in multi-agent interaction. Schwager received his PhD and MS from MIT and his BS from Stanford, and his research has appeared in top robotics and control venues. He remains a leading contributor to distributed autonomy and multi-robot coordination.

Research Areas

multi-robot systemsdistributed controlswarm roboticsautonomous dronesdecentralized estimationrobot learning

Academic Impact Matrix

Research output metrics for Mac Schwager aggregated from public academic databases. Student lab experience data is pending.

Academic data verified · April 2026 · Next sync: May 2026

Research Output

Total Citations23,301

Top 5% globally

Publications921

Highly prolific researcher

h-index49

Established scholar

i10-index133

Broad impact

Lab Environment

No lab data yet for Mac Schwager

+ Contribute First Review
  • Supervisionawaiting data
  • Responsivenessawaiting data
  • Fundingawaiting data
  • Communicationawaiting data
  • Work-Life Balanceawaiting data

Frequently Asked Questions

Not sure how to interpret mixed signals? A structured decision guide can help you think through high-risk supervision choices more clearly. Download the free guide.