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Paulo Tabuada

PROFESSOR

UCLA

United States

Research Interests

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About

Professor Paulo Tabuada is a faculty member at UCLA, specializing in control theory and decentralized optimization. His recent research explores vulnerability in optimization frameworks, deep learning networks, and IoT system synthesis. He has contributed significantly to understanding state-tracking and verification in multi-agent systems, making notable advancements in the application of robust control methods.

Articles (18)

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2026

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2021

Collaborators (9)

Andrew Taylor

Teaching Assistant

California Institute of Technology

UNITED STATES

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Joel Burdick

Professor

California Institute of Technology

UNITED STATES

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Xiao Tan

KTH Royal Institute of Technology

SWEDEN

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Ersin Daş

Assistant Professor

Illinois Institute of Technology

UNITED STATES

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Aaron Ames

Professor

California Institute of Technology

UNITED STATES

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Mani Srivastava

Professor

UCLA

UNITED STATES

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Yasser Shoukry

Assistant Professor

University of California, Irvine

UNITED STATES

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Konstantinos Gatsis

University of Southampton

UNITED KINGDOM

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Suhas Diggavi

UNITED STATES

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Paulo Tabuada

Research Interests

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About

Articles (18)

Safe Navigation Under State Uncertainty: Online Adaptation for Robust Control Barrier Functions

Deep Learning From a Control Vantage Point: Feedforward, Residual, Transformers, Large Language Models, And Selective State-Space Models

A Duality-Based Optimization Formulation of Safe Control Design With State Uncertainties

Decentralized Optimization Resilient Against Local Data Poisoning Attacks

Towards a Performance-Driven Device-Edge-Cloud Relationship

Static Event-Triggered Newton-Based Extremum Seeking

Correction to “Universal Approximation Power of Deep Residual Neural Networks Through the Lens of Control”

Controlled Invariant Sets: Implicit Closed-Form Representations and Applications

Universal Approximation Power of Deep Residual Neural Networks Through the Lens of Control

Synthesis of Large-Scale Instant IoT Networks

Learning to control known feedback linearizable systems from demonstrations

Sharp Performance Bounds for PASTA

Being Correct Is Not Enough: Efficient Verification Using Robust Linear Temporal Logic

Decentralized Secure State-Tracking in Multi-Agent Systems

On the computational complexity of the secure state-reconstruction problem

Safety and Stability Guarantees for Control Loops With Deep Learning Perception

Sampled-Data Stabilization With Control Lyapunov Functions via Quadratically Constrained Quadratic Programs

Cloud-Based Quadratic Optimization With Partially Homomorphic Encryption

Collaborators (9)

Andrew Taylor

Joel Burdick

Xiao Tan

Ersin Daş

Aaron Ames

Mani Srivastava

Yasser Shoukry

Konstantinos Gatsis

Suhas Diggavi

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