Mo Alloulah
alloulah at outlook dot com

I am the founder and CEO of RADAREYE. Previously, I was a member of technical staff at Nokia Bell Labs, where I worked on sensing and perception. My track record includes 17+ years experience in tech-based R&D, having envisioned, led, and delivered systems in multiple industrial settings in areas spanning signal processing and machine learning.

Before that I did a PhD at Lancaster University, an MSc with distinction at The University of Manchester where I ranked 1st and won the National Instruments award, and a BSc at the University of Balamand (Dean's honour lists).

LinkedIn  /  Strava  /  GitHub

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Current Research

My research focuses these days on self-supervised and multi-modal machine learning techniques for vision+radar perception, including representation learning. I continue to occasionally contribute to wireless problems, particularly those intersecting with machine learning.

I have led a programme (over a period of 4 years) that envisioned and delivered a self-supervised learning algorithm for automotive radar perception. Our algorithm uses contrastive learning and domain-specific radar augmentations to provide substantial improvements over supervised learning: 70% in label efficiency and 5.8% in mAP. Performance characterised in the real-world under nontrivial MIMO radar artefacts.

BootstrappingAutoRadar Bootstrapping Autonomous Driving Radars with Self-Supervised Learning
Yiduo Hao, Sohrab Madani, Junfeng Guan, Mohammed Alloulah, Saurabh Gupta, Haitham Hassanieh
To appear IEEE/CVF CVPR, 2024  
arXiv

Bounding box estimation from radar-only for autonomous perception by distillation from vision using contrastive learning and domain-specific radar augmentations.

lookRadiateLearn Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence
Mohammed Alloulah, Maximilian Arnold
IEEE/CVF CVPR, 2023  
arXiv / Slides

Automatic localisation of objects in the environment by tapping into the cross-modal information captured simultaneously by vision and radar.

For our synthetic dataset MaxRay, contact my ex-employer Nokia Bell Labs.

radio-visual_representationLearning Self-Supervised Radio-Visual Representation Learning for 6G Sensing
Mohammed Alloulah, Akash Deep Singh, Maximilian Arnold
IEEE ICC, 2022  
arXiv

Building a radar classification model by distillation from vision using contrastive learning.

joint_comms_sensing Future Millimeter-Wave Indoor Systems: A Blueprint for Joint Communication and Sensing
Mohammed Alloulah, Howard Huang
IEEE Computer, 2019  
PDF

Position paper on why and how should we care to build a unified communication-perception network.

Prior Research

locnets Benchmarking Learnt Radio Localisation under Distribution Shift
Maximilian Arnold, Mohammed Alloulah
arXiv, 2022  
arXiv

Benchmarking 8 flavours of real-world learnt RF localisation across three performance axes: (i) learning, (ii) proneness to distribution shift, and (iii) localisation, as well as against classic maximum likelihood estimation.

imulet IMULet: A Cloudlet for Inertial Tracking
Mohammed Alloulah, Lauri Tuominen
ACM HotMobile, 2021  
PDF / Slides

Practical inertial navigation system using deep learning and cloud-device partitioning.

dit Deep inertial navigation using continuous domain adaptation and optimal transport
Mohammed Alloulah, Maximilian Arnold, Anton Isopoussu
arXiv, 2021  
arXiv

In-situ adaptation of deep inertial navigation through spatial diversity, optimal transport, and domain adaptation.

sleep On goodness of WiFi based monitoring of sleep vital signs in the wild
Kamran Ali, Mohammed Alloulah, Fahim Kawsar, Alex X Liu
IEEE TMC, 21  
DOI

Measuring breathing and body motion markers wirelessly towards sleep monitoring.

ePerceptive ePerceptive: energy reactive embedded intelligence for batteryless sensors
Alessandro Montanari, Manuja Sharma, Dainius Jenkus, Mohammed Alloulah, Lorena Qendro, Fahim Kawsar
ACM SenSys, 2020  
DOI

Best-effort inferencing using a variable instantaneous energy budget by means of knobs at featurisation-level as well as at neural network-level.

kinphy KinPhy: a kinetic in-band channel for millimetre-wave networks
Mohammed Alloulah, Zoran Radivojevic, René Mayrhofer, Howard Huang
ACM SenSys, 2019  
PDF / Slides

How would future networks in mm-wave bands exchange information using vibrational (i.e. kinetic) modulation in order to support security and spontaneous interaction applications.

degradable_inference Degradable inference for energy autonomous vision applications
Alessandro Montanari, Mohammed Alloulah, Fahim Kawsar
ACM UbiComp/ISWC, 2019  
DOI

Degradable vision inference by means of knobs at featurisation- and neural network-level.

human_radar On Indoor Human Sensing Using Commodity Radar
Mohammed Alloulah, Anton Isopoussu, Fahim Kawsar
ACM UbiComp/ISWC, 2018  
DOI

Thoughts and guidelines on how to build sensing models for commodity radar HW indoors.

sensium A Cross-Layer Coding for Scalable ECG streaming
Mohammed Alloulah, Mark Dawkins, Alison Burdett
EAI MobiCASE, 2016  
PDF / Slides

Joint compression and wireless coding scheme for robust and ultra-low power medical physiological sensing.

abu An efficient CDMA core for indoor acoustic position sensing
Mohammed Alloulah, Mike Hazas
IEEE IPIN, 2010  
DOI

Indoor acoustic localisation chip. Part of two further technical reports here and here.

Past Work (proprietary)

polar_tx Ultra-Low Power WiFi Modulator

Traditional direct conversion RF transmitters are not suitable for IoT devices with stringent power budget. A polar transmitter in wideband WiFi channels is challenging. We made it work with couple key innovations. Achieved 3x power reduction.

Silicon + several patents.

ofdm_phy OFDM PHY Chip

Led work on multiple architecture-aware PHY implementations, part of a terrestrial DVB-T2 chipset. Keywords included inter-symbol interference (ISI) and inter-carrier interference (ICI).

Silicon in the field + several patents.


Adapted from the webpages of these lady and gentleman.