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Neil Dhir

Neil Dhir
Siemens · Automation and Control Technology Field

PhD

About

26
Publications
2,301
Reads
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47
Citations
Citations since 2016
25 Research Items
47 Citations
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201620172018201920202021202202468101214
201620172018201920202021202202468101214
201620172018201920202021202202468101214
Introduction

Publications

Publications (26)
Preprint
Full-text available
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed. This problem arises in many areas of science including biology, operations research and healthcare. We propose Causal Entropy Optimization (CEO), a framework that generalizes Causal Bayesian Optimiza...
Conference Paper
Full-text available
In this paper, we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particular attention is paid to a recently published approach: dynamic causal Bayesian optimisation (Aglietti et al., 2021, DCBO). We propose that DCBO can act as a blue agent w...
Conference Paper
Full-text available
Selecting a suitable embedding space is a key issue in modelling nonlinear dynam- ics. In classical phase-space reconstruction, which relies on time-delay vectors, the embedding space is highly dependent on two discrete parameters (for the univariate case), the values of which greatly affect model performance. They also determine the complexity of...
Preprint
Full-text available
This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time. This problem arises in a variety of domains e.g. system biology and operational research. Dynamic Causal Bayesian Optimization (DCBO) brings together ideas from seq...
Conference Paper
Full-text available
In this paper we combine Gaussian process regression and impedance control, to illicit robust, anthropomorphic, adaptive control of a powered ankle prosthesis. We learn the non-linear manifolds which guide how locomotion variables temporally evolve, and regress that surface over a velocity range to create a manifold. The joint set of manifolds, as...
Preprint
Full-text available
This paper studies an instance of the multi-armed bandit (MAB) problem, specifically where several causal MABs operate chronologically in the same dynamical system. Practically the reward distribution of each bandit is governed by the same non-trivial dependence structure, which is a dynamic causal model. Dynamic because we allow for each causal MA...
Conference Paper
Full-text available
This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time. This problem arises in a variety of domains e.g. system biology and operational research. Dynamic Causal Bayesian Optimization (DCBO) brings together ideas from seq...
Preprint
Full-text available
Cybercriminals are rapidly developing new malicious tools that leverage artificial intelligence (AI) to enable new classes of adaptive and stealthy attacks. New defensive methods need to be developed to counter these threats. Some cybersecurity professionals are speculating AI will enable corresponding new classes of active cyber defence measures -...
Preprint
Full-text available
One of the Greater London Authority's (GLA) response to the COVID-19 pandemic brings together multiple large-scale and heterogeneous datasets capturing mobility, transportation and traffic activity over the city of London to better understand 'busyness' and enable targeted interventions and effective policy-making. As part of Project Odysseus we de...
Preprint
During the COVID-19 pandemic, policy makers at the Greater London Authority, the regional governance body of London, UK, are reliant upon prompt and accurate data sources. Large well-defined heterogeneous compositions of activity throughout the city are sometimes difficult to acquire, yet are a necessity in order to learn 'busyness' and consequentl...
Conference Paper
Full-text available
There is an urgent need for non-intrusive tests that can detect early signs of Parkinson's disease (PD), a debilitating neurodegenerative disorder that affects motor control. Recent promising research has focused on disease markers evident in the fine-motor behaviour of typing. Most work to date has focused solely on the timing of key presses witho...
Conference Paper
Full-text available
One of the Greater London Authority's (GLA) response to the COVID-19 pandemic brings together multiple large-scale and heterogeneous datasets capturing mobility, transportation and traffic activity over the city of London to better understand 'busyness' and enable targeted interventions and effective policy-making. As part of that ongoing project 1...
Conference Paper
Full-text available
With ever more data becoming available, there has been a recent drive to develop modelling tools for heterogeneous data sets such as electronic healthcare records. Therein appears both Bayesian non-parametric latent feature models, as well as methods for automatically determining the statistical data type (e.g. ordinal or categorical) of the attrib...
Preprint
Full-text available
With ever more data becoming available, there has been a recent drive to develop modelling tools for heterogeneous datasets such as electronic healthcare records. Therein appears both Bayesian nonparametric latent feature models, as well as methods for automatically determining the statistical data type (e.g. ordinal or categorical) of the attribut...
Chapter
In this chapter we present the use of Bayesian nonparametric methods in the realm of prostheses control. More specifically we are interested in change; change in the driving functions of human locomotion behavior in response to endogenous and exogenous influences.
Preprint
Full-text available
We present a general method for fitting finite mixture models (FMM). Learning in a mixture model consists of finding the most likely cluster assignment for each data-point, as well as finding the parameters of the clusters themselves. In many mixture models, this is difficult with current learning methods, where the most common approach is to emplo...
Preprint
Full-text available
Most diseases have different heterogeneous effects on patients. Broadly, one may conclude what manifested symptoms correspond to which diagnosis, but usually, there is more than one disease progression pattern. Because there is more than one pattern, and because each pattern may require a bespoke (and personalised) therapeutic intervention, time-se...
Conference Paper
Full-text available
We present the first steps towards a type inferential general latent feature model: the nested latent feature model (NLFM). The NLFM combines automatic type inference and latent feature modelling for heterogeneous data. By combining inference over type, as well as over latent features, during runtime, we condition each latent parameter on all other...
Preprint
Full-text available
We present an instance of the optimal sensor scheduling problem with the additional relaxation that our observer makes active choices whether or not to observe and how to observe. We mask the nodes in a directed acyclic graph of the model that are observable, effectively optimising whether or not an observation should be made at each time step. The...
Article
Full-text available
'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be predicted by exploiting their correlation with observed signals acquired from other sensors. For humans, our hands move synchronously with our legs, and we can exploit t...
Poster
Full-text available
What: Finding the strange attractors of deterministic nonlinear dynamical systems. How: What: Use univariate time-series measurements to reconstruct qualitative features of the phase-space, using the Takens' embedding theorem. Why: Accurately reconstructing the phase-space is concurrent to completely describing of states of the system. Result: Meth...
Thesis
Full-text available
Robots need to be able to adapt to their surroundings. Robots whose core function relates to the rehabilitation and assistance of humans, need to be able to adapt to humans. But not all humans, one human in particular: their user. This is an adaptive control problem which speaks of the need for powered prostheses to have anthropomorphic adaptive ca...
Conference Paper
Full-text available
Human locomotion and activity recognition sys-tems form a critical part in a robot’s ability to safely andeffectively operate in a environment populated with humanend users. Previous work in this area relies upon strongassumptions about the labels in the training data; e.g. thatare noise-free and that they exist at all. Our approach doesnot predefi...
Article
Full-text available
Improving activity recognition, with special focus on fall-detection, is the subject of this study. We show that Kalman smoothed in-painting of missing pose information and task-specific dimensionality reduction of activity feature vectors leads to significantly improved activity classification performance. We illustrate our findings by applying co...

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Cited By

Projects

Projects (4)
Project
Academic work related to cybersecurity.
Project
Use state-space modelling for simulating neurodegenerative diseases such as Parkinson's disease (PD), Alzheimer's (AD) and multiple sclerosis (MS).