Orangutan Tracking, a project with International Animal Rescue. As
animal welfare and ecological research project, orangutans in Borneo are implanted with VHF
they can be followed through the jungle. Unfortunately the tracking signal has a limited range
navigating the jungle to pick up signals is extremely cumbersome. Better coverage can be
flying above the canopy with a drone mounted receiver and homing system.
||Image Processing. An project for the University
Cambridge where the problem was to automatically detect and extract printers ornaments
millions of scanned 16th, 17th, and 18th century books.
||Airborne GPR. In many parts of the world unexploded ordnance and landmines
significant danger to the local population. A huge variety of systems and sensors have been
tackle the project of landmine detection yet the clearing rate is still fairly low and the risks
very real. In this project I worked on an integrated UAV based Ground Penetrating Radar
use in landmine detection as well as utilities, law enforcement, etc. The worlds first system of its kind.
||GPR-ML. Given a stream of Ground Penetrating Radar (GPR) data in the context of
demining (see previous project) there is still
significant challenge of identifying landmines amongst the subsurface clutter. This project was
the challenge through the use of real and simulated data and exploiting recent advances in
||Anomaly Detection. A project with a Fortune 500 engineering company where the
find anomalies and recurring patterns in event streams and explore their predictive power.
||Swarming Systems. A project in collaboration with Bristol
Robotics Lab whose aim is to demonstrate a true swarming capability (i.e., not
applications that involve maximizing area coverage.
||Accurate Drone Positioning. In any drone application good positioning or
order for the sensor data to be processed sensibly. There are many ways of estimating position.
project looked at integrating RTK and monocular visual SLAM into a custom drone platform.
||Airborne EMI sensor. The two main sensor modalities used to detect landmines
Ground Penetrating Radar (GPR)
and EMI (i.e., metal detector). This project explored the feasibility of having a UAV mounted
passive EMI sensor. A working prototype was built, to my knowledge the first in the world.
While a scientist at BAE Systems Research I worked on a wide range of projects. Unfortunately,
the nature of the work there is little I can share publicly.
Projects were typically related to my specific areas of interest. This includes data fusion,
analysis and visualization, machine learning (with deep learning in particular), Integrated
Health Management (IVHM) systems, computational engineering, and autonomous systems.
||Orchid. A collaboration between the
of Southampton, University of Oxford, University of Nottingham, The Australian Center for Field
Robotics, BAE Systems, Secure Meters UK, and Rescue Global.
Rather than issuing instructions to passive machines, humans and software agents will
flexibly establish a range of collaborative relationships with one another, forming
collectives (HACs) to meet their individual and collective goals.
||Human Mobility Analysis: It is well known that humans generally follow very
predicable mobility patterns, both spatially and temporally. Lots of work has looked at
predicting those patterns but much less so on looking specifically at departures from those
patterns. What can we learn from those departures from routine? How predictable are they?
This work was a collaboration with the Agents,
and Complexity group at University of Southampton. The approach was based around a
framework to analyse an individual’s mobility patterns and identify departures from routine.
able to detect both spatial and temporal departures from routine based on heterogeneous
(GPS, Cell Tower, social media, ..) and outperforms existing state-of-the-art predictors.
Applications include mobile digital assistants (e.g., Google Now), mobile advertising (e.g.,
LivingSocial), and crowdsourcing physical tasks (e.g., TaskRabbit).
||GALLOP: Genetic Algorithms for Linguistic Learner Optimization is a python
package I developed for
feature selection and hyperparameter optimization for Natural Language Processing models.
Gallop provides a custom genetic algorithm that can be used to optimize the
hyperparameters of models such as Timbl, SVMLight, and CRF++. Gallop also
evolutionary selection of features or feature groups and can be run on a standalone machine
or Torque compatible
||DECODE: Decision Environment for Complex Design Evaluation. An ESPRC funded research project where we developed an
design environment and manufacturing
process. Close ties with the Microsoft
Institute for High
Performance Computing, Rolls-Royce, Airbus, and the BBC.
Multiple aircraft have been built and flown successfully and our work has been covered
extensively by the media. Eventually evolved into the 2Seas 3i EU project.
||The SULSA project was about designing, building, and flying the worlds
first fully 3D printed aircraft. It was printed in just 4 separate pieces using selective laser sintering
assembled without any screws or traditional fastners. Its elliptical wings were modeled after
iconic Spitfire and the whole
||MDOW was a project in collaboration with Airbus
that took a non-conventional view to solving the Multidisciplinary
Optimization problem in aircraft design. The fundamental being to use an expert system
coordinate the activities of the domain level optimizers instead of a numeric optimizer. This
for more traceability and a human-in-the-loop.
||The Operational MOdel Order REduction for Nanoscale IC Electronics
project was a Transfer of Knowledge collaboration between the Technical University of Chemnitz,
University of Antwerp, Technical University of Eindhoven and NXP
Semiconductors. It involved developing accurate metamodels for integrated circuits.
studied included parameterization, parameter screening, nonlinearity, and combination of
optimization and statistics.
||h2O was a research project at Emory University,
Atlanta and part of the larger HARNESS
with The University of Tennessee and Oak Ridge National Laboratory. HARNESS (Heterogeneous
Reconfigurable Networked SyStem) was an experimental Metacomputing System aiming at providing a
dynamic, fault-tolerant computing environment for high performance computing applications.