Daniela Rus

About

Daniela Rus is a professor affiliated with the Department of Electrical Engineering and Computer Science (EECS) at MIT. Her research focuses on artificial intelligence and decision-making, combining intellectual traditions from computer science and electrical engineering to develop techniques for the analysis and synthesis of systems that interact with the external world through perception, communication, and action. Her work involves systems that learn, make decisions, and adapt to changing environments, contributing to advancements in AI for healthcare, life sciences, and societal applications. As a leading figure in her field, Daniela Rus's research encompasses a broad range of topics including robotics, machine learning, and intelligent systems. Her contributions aim to develop groundbreaking sensors, energy transducers, and physical substrates for computation, addressing shared challenges facing humanity through innovative system design and AI integration.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Machine Learning
• Control engineering
• Computer vision
• Human–computer interaction
• Mechanical engineering
• Mathematical analysis
• Mathematics

Selected publications

• LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping

  2020-10-24 · 1963 citations

  articleSenior author

  We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews p…

  PublisherDOI

• BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

  2023-05-29 · 995 citations

  article

  Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with camera features. However, the camera-to-LiDAR projection throws away the semantic density of camera features, hindering the effectiveness of such methods, especially for semantic-oriented tasks (such as 3D scene segmentation). In this paper, we propose BEVFusion, an efficient and generic multi-task multi-sensor fusion fr…

  PublisherDOI

• LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

  2021-05-30 · 513 citations

  articleSenior author

  We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness. LVI-SAM is built atop a factor graph and is composed of two sub-systems: a visual-inertial system (VIS) and a lidar-inertial system (LIS). The two sub-systems are designed in a tightly-coupled manner, in which the VIS leverages LIS estimation to facilitate initialization. The accuracy of the VIS…

  PublisherDOI

• Neural circuit policies enabling auditable autonomy

  Nature Machine Intelligence · 2020 · 273 citations

  DOI

• Liquid Time-constant Networks

  Proceedings of the AAAI Conference on Artificial Intelligence · 2021 · 257 citations

  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and boun…

  DOI

Recent grants

• Collaborative Research: An Expedition in Computing for Compiling Printable Programmable Machines

  NSF · $6.6M · 2012–2019

• NSFSaTC-BSF: TWC: Small: Enabling Secure and Private Cloud Computing using Coresets

  NSF · $486k · 2015–2020

• EFRI C3 SoRo: Soft, Strong, and Safe Configurable Robots for Diverse Manipulation Tasks

  NSF · $2.0M · 2018–2024

Frequent coauthors

• Sertaç Karaman

  Massachusetts Institute of Technology

  101 shared

• Mac Schwager

  Vaughn College of Aeronautics and Technology

  75 shared

• Ramin Hasani

  66 shared

• Alexander Amini

  64 shared

• Emilio Frazzoli

  61 shared

• Marcelo H. Ang

  56 shared

• Guy Rosman

  55 shared

• Mathias Lechner

  51 shared

Awards & honors

• 2025-26 EECS Faculty Award Roundup

Similar researchers at Massachusetts Institute of Technology

• Manolis Kellish-164
• Antonio Torralbah-133
• Anantha Chandrakasanh-115