About

Fadel Adib is an Associate Professor at MIT with a joint appointment in the Media Lab and EECS. His research focuses on systems and networking within electrical engineering and computer science. His work involves developing groundbreaking sensors, energy transducers, and physical substrates for computation, addressing shared challenges facing humanity through innovative system design. As part of his research, he explores the intersection of electrical engineering and computer science to create advanced systems that sense, process, and transmit energy and information.

Research topics

• Computer Science
• Geology
• Telecommunications
• Artificial Intelligence
• Oceanography
• Materials science
• Electronic engineering
• Remote sensing
• Physics
• Biomedical engineering
• Internal medicine
• Cardiology
• Acoustics
• Optoelectronics
• Medicine
• Environmental science
• Engineering

Selected publications

• Piezo-Ultrasonic Backscatter: Low-Power High Throughput Underwater Networking

  2026-05-08

  articleOpen accessSenior author
  PublisherDOI

• Bringing All Modulations to Underwater Backscatter via PDM-Synthesis

  2026-05-08

  articleOpen accessSenior author

  Underwater backscatter is an emerging technology for low-cost and low-power communication and sensing in underwater environments. However, past underwater backscatter systems have been largely limited to simple on-off modulation schemes, which inherently limit their robustness, adaptivity, and spectral efficiency.

  PublisherDOI

• Non-Line-of-Sight 3D Object Reconstruction via mmWave Surface Normal Estimation

  2025-06-23 · 3 citations

  articleOpen accessSenior author

  This paper presents the design, implementation, and evaluation of mmNorm, a new and highly-accurate method for non-line-of-sight 3D object reconstruction using millimeter wave (mmWave) signals. In contrast to past approaches for millimeter-wave-based imaging that perform backprojection for 3D object reconstruction, mmNorm reconstructs the surface by estimating the object's surface normals. To do this, it introduces a novel algorithm that directly estimates the surface normal vector field from mmWave reflections. By then inverting the normal field, it can reconstruct structural isosurfaces, then solve for the exact surface through a novel mmWave optimization framework.

  PublisherOA PDFDOI

• Wave-Former: Through-Occlusion 3D Reconstruction via Wireless Shape Completion

  ArXiv.org · 2025-11-18

  preprintOpen accessSenior author

  We present Wave-Former, a novel method capable of high-accuracy 3D shape reconstruction for completely occluded, diverse, everyday objects. This capability can open new applications spanning robotics, augmented reality, and logistics. Our approach leverages millimeter-wave (mmWave) wireless signals, which can penetrate common occlusions and reflect off hidden objects. In contrast to past mmWave reconstruction methods, which suffer from limited coverage and high noise, Wave-Former introduces a physics-aware shape completion model capable of inferring full 3D geometry. At the heart of Wave-Former's design is a novel three-stage pipeline which bridges raw wireless signals with recent advancements in vision-based shape completion by incorporating physical properties of mmWave signals. The pipeline proposes candidate geometric surfaces, employs a transformer-based shape completion model designed specifically for mmWave signals, and finally performs entropy-guided surface selection. This enables Wave-Former to be trained using entirely synthetic point-clouds, while demonstrating impressive generalization to real-world data. In head-to-head comparisons with state-of-the-art baselines, Wave-Former raises recall from 54% to 72% while maintaining a high precision of 85%.

  PublisherOA PDFDOI

• Demo: Leveraging Underwater Backscatter for Long-Term Environmental Sensing

  2025-11-03

  articleOpen accessSenior author

  This demo presents BlueTag, a permanently deployed underwater sensor system based on backscatter communication. BlueTag is a battery-powered CTD (conductivity, temperature, depth) sensor that transmits measurements every 15 minutes to a remote base station via underwater backscatter. The base station archives these measurements and publishes them online. Unlike prior underwater backscatter systems limited to short-term laboratory experiments, BlueTag has been deployed in the Charles River in Boston, MA since July 9th, 2025, marking the first long-term underwater backscatter deployment of its kind to sense meaningful environmental data. Live data from this deployment is available publicly at https://sk-exp-server.mit.edu/.

  PublisherOA PDFDOI

• Mobile Underwater Backscatter Networking

  2025-08-27 · 2 citations

  articleOpen accessSenior author

  Underwater backscatter is a promising technology for ultra-low-power underwater networking, but existing systems break down in mobile scenarios. This paper presents EchoRider, the first system to enable reliable underwater backscatter networking under mobility.

  PublisherOA PDFDOI

• 6D Self-Localization of Drones Using a Single Millimeter-Wave Backscatter Anchor

  2025-05-19 · 5 citations

  articleSenior author

  We present the design, implementation, and evaluation of MiFly, a self-localization system for autonomous drones that works across indoor and outdoor environments, including low-visibility, dark, and GPS-denied settings. MiFly performs 6DoF self-localization by leveraging a single millimeter-wave (mmWave) anchor in its vicinity - even if that anchor is visually occluded. MiFly's core contribution is in its joint design of a mmWave anchor and localization algorithm. The low-power anchor features a novel dual-polarization dual-modulation architecture, which enables single-shot 3D localization. Mm Wave radars mounted on the drone perform 3D localization relative to the anchor and fuse this data with the drone's internal inertial measurement unit (IMU) to estimate its 6DoF trajectory. We implemented and evaluated MiFly on a DJI drone. We collected over 6,600 localization estimates across different trajectory patterns and demonstrate a median localization error of 7 cm and a 90<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile less than 15 cm, even in low-light conditions and when the anchor is fully occluded (visually) from the drone. Demo video: voutu.be/LfXfZ26tEok

  PublisherDOI

• MiNav: Autonomous Drone Navigation Indoors Using Millimeter-Waves

  Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · 2025-09-03

  articleOpen accessSenior author

  We present the design, implementation, and evaluation of MiNav, a system capable of accurate, efficient and fully autonomous drone navigation in challenging indoor environments, including those where vision-based systems fail. MiNav builds on recent literature in millimeter-wave (mmWave) backscatter localization and makes the leap to full end-to-end autonomous mmWave-based navigation. MiNav leverages a mmWave radar mounted on a drone and one or more mmWave backscatter tags deployed in the environment. To enable autonomous navigation, our design introduces key innovations. First, MiNav derives a novel Joint DOP-SNR formulation to probabilistically model uncertainty in localization, and uses this uncertainty to generate an RF-Navigation Map that maximizes the accuracy and reliability of mmWave backscatter localization throughout an environment. It then applies a RF-aware Autonomous Path Planning technique that jointly optimizes for navigation efficiency and localization performance. We built an end-to-end real-time implementation of MiNav consisting of a custom built drone and mmWave backscatter tags. We tested it in practical indoor environments. We run over 165 successful autonomous missions across different tag deployments and demonstrate a median 3D navigation error of 9.1 cm. Our results also show that in comparison to baseline implementations that rely on more classical uncertainty metrics, MiNav achieves a 20% increase in navigation reliability and nearly 3x improvement in self-tracking in millimeter-wave backscatter localization. Finally, we demonstrate first of its kind capabilities, such as fully autonomous, end-to-end mmWave-based drone navigation and path planning in featureless and dark environments. Demo video: http://y2u.be/EpnWibRcxBI

  PublisherOA PDFDOI

• High-Fidelity Through-Occlusion 3D Reconstruction via Millimeter-Wave Surface Normal Estimation

  GetMobile Mobile Computing and Communications · 2025-01-20

  articleSenior author

  The past few years have witnessed growing interest in millimeter wave (mmWave) based reconstruction in the mobile community [3, 5, 7, 14]. Unlike classical vision-based imaging systems, which are limited to line-of-sight, these mmWave-based systems can operate in through-occlusion scenarios, enabling them to sense objects in closed boxes and beneath clutter. This is because mmWave signals can traverse through many everyday occlusions (e.g., cardboard, fabric, etc.) [1, 11], and reflect off objects behind these occlusions, allowing them to produce images of the occluded objects. This capability, combined with the recent emergence of low-cost commercial mmWave radars, has the potential to enable many promising applications. For example, pick-and-place robots can leverage through-occlusion reconstructions to find and manipulate hidden objects, such as those beneath clutter or within a closed box. Similarly, Augmented Reality (AR) devices could leverage them to perceive occluded objects and display them to the user, truly augmenting our human perception. Smart home devices can use them for through-occlusion gesture recognition, to enable non-verbal commands even when users are hidden from view.

  PublisherDOI

• Scalable and Low Power Localization for Underwater Robots

  2025-11-03 · 1 citations

  articleOpen accessSenior author

  Localization is a critical task for underwater robots, yet today's underwater localization systems are limited by their accuracy, scalability, and/or energy consumption (i.e., longevity). We present the design, implementation, and evaluation of EchoBLUE- an accurate, scalable, and low-power localization system for underwater robots.

  PublisherDOI

Recent grants

• PFI-TT: Bridging the Information Gap in Supply Chain using Internet of Things (IoT)

  NSF · $366k · 2021–2024

• CPS: Small: Scaling Cyber-Physical Systems to the Low-Power Internet of Things

  NSF · $339k · 2017–2020

• CAREER: Wirelesss Sensing for In Vivo Medical Devices

  NSF · $658k · 2019–2024

• RAPID: Wireless Positioning for Mitigating COVID19 Surface Transmissions

  NSF · $100k · 2020–2021

• NeTS: Small: Enabling Long-Range Underwater Backscatter via Van-Atta Acoustic Networks

  NSF · $600k · 2023–2026

Frequent coauthors

• Dina Katabi

  Massachusetts Institute of Technology

  16 shared

• Tara Boroushaki

  Massachusetts Institute of Technology

  11 shared

• Laura Dodds

  Massachusetts Institute of Technology

  9 shared

• Waleed Akbar

  Massachusetts Institute of Technology

  9 shared

• Sayed Saad Afzal

  IIT@MIT

  9 shared

• Aline Eid

  University of Michigan–Ann Arbor

  9 shared

• Unsoo Ha

  Massachusetts Institute of Technology

  8 shared

• Nazish Naeem

  Massachusetts Institute of Technology

  6 shared

Education

• Ph.D., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2010

• M.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2006

• B.S., Electrical Engineering and Computer Science

  University of California, Berkeley

  2004