Thought Leadership Events, University of Essex Series

We are delighted to launch a new series of thought leadership events with the University of Essex. 

Join us each month to expand and enhance your own knowledge plus build on your networks.

Explore as well our Thought Leadership overview

Coming up next!

Dr Mays Al-Naday, School of Computer Science and Electrical Engineering, University of Essex

Semantic based networking for digital transformation

“The adoption of the Internet of Things (IoT) for digital transformation is sketching new forms of distributed data and decentralised machine learning, localised with data sources, or in a proximity to adhere to such requirements as data privacy and possibly latency. The new form of data and machine learning is opening new horizons in future networks (such as 6G) while introducing novel requirements that leverage the role of data semantics at the network layer.

In this talk, I will share our vision on how future networks can find and exchange semantically-relevant data for such applications as machine-learning. Specifically, I will show how state-of-the art information-centric networks can be extended with novel networking services to enable semantic-based mapping and delivery of context-relevant, not necessarily exact copies, of data.”

Dr Amit Kumar Singh, School of Computer Science and Electrical Engineering, University of Essex

Attacks and defences for Edge Devices: side and covert channels perspective

Previous Thought Leadership Talks in the University of Essex series

Professor Stuart Walker, Essex University and Dave Townend, BT
Building dense urban mobile networks and the journey beyond 100GHz

Recording available now on the presentation page or on YouTube

In this talk we explored the challenges with densifying mobile networks and the role wireless backhaul has to play in building future mobile network architectures.

Zilong Liu, School of Computer Science and Electrical Engineering, University of Essex
6G for Future V2X Communications
Recording available now on the presentation page or on YouTube

Among many emerging vertical industries, connected autonomous vehicles (CAVs) with the aid of vehicle-to-everything (V2X) communication and networking are deemed to transform our travel experience with numerous far-reaching societal and economic benefits. With 5G communication networks rapidly rolling out globally, it is time to ask how 6G will help revolutionize our future V2X communications.

Somdip Dey, School of Computer Science and Electrical Engineering, University of Essex

AI, IoT and Blockchain being the future of food security
Recording available now on YouTube
Outline:

• Introduction to the problem of food wastage and food security around the world
• Brief introduction of AI and machine learning and their advancement
• State-of-the-art of using blockchain and AI/ML in food supply chain and reduction of food wastage
• Introduce FoodSQRBlock and SmartNoshWase frameworks – frameworks using blockchain, AI, IoT and Cloud to digitize food supply chain for easy accessibility and traceability while helping stakeholders in the supply chain to reduce food waste and catering for food security

Prof Maria Fasli, Executive Dean, Faculty of Science and Health, University of Essex on "Collaboration in Data Science Projects"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Maria Fasli is a Professor of Computer Science (Artificial Intelligence) and the Executive Dean of the Faculty of Science and Health at the University of Essex. She is also the Founding Director of the Institute for Analytics and Data Science (IADS) at the University of Essex and the Director of the ESRC Business and Local Government Data Research Centre (BLGDRC). She obtained her BSc in Informatics from the Technological Education Institute of Thessaloniki in 1996, and her PhD in Computer Science from the University of Essex in 2000.

She has held research and academic positions at the University of Essex since 1999 and became Professor in 2012. In 2009, she became the Head of the School of Computer Science and Electronic Engineering at Essex, a post which she held until the end of 2014. In August 2014, she was appointed in her current role as Director of IADS. In 2016, she was awarded the first UNESCO Chair in Analytics and Data Science.

Her research interests lie in artificial intelligence techniques for analyzing and modeling complex systems and structured and unstructured data in various domains. Her research has been funded by National Research Councils in the UK and other organisations including businesses. She has worked with a range of companies in data analytics related projects. Maria has published over 130 papers in the field of artificial intelligence, modelling and learning from data and has delivered keynote talks at international conferences. She is also passionate about education and pedagogic innovation and in 2005, she was awarded a National Teaching Fellowship by the Higher Education Academy (UK) for her innovative approaches to education.

Collaboration in Data Science Projects

Dr Zoë Webster, AI Director at BT on "Establishing an AI Centre of Enablement"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Zoë has a background in computer science (with a MSc and PhD in AI) and in developing novel AI technologies across a few verticals. From a focus on AI at QinetiQ and SEA, Zoë moved into supporting innovation more broadly at Innovate UK, leading and implementing innovation strategies in ICT, enabling technologies and high value manufacturing. She also had responsibility for Innovate UK’s Horizon Scanning Unit. Zoë moved to BT in Nov 2020 to turn her focus back to AI as AI Director in BT’s Data and AI team.

Establishing an AI Centre of Enablement

There are a number of questions to consider when setting up a team to accelerate the development and adoption of AI at scale. Not all of these centre on the technology. This talk will discuss some of the questions that have focused my attention here at BT and the perspectives that are important in addressing them.

James Grant, Lecturer in Statistics at Lancaster University on "Multi-armed Bandits"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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James Grant is a Lecturer in Statistics at Lancaster University where he completed his PhD in 2019. His research considers mathematical models of decision-making and learning, and combines ideas from Statistics, Operational Research, and Machine Learning. He is particularly interested in multi-armed bandit problems, online optimisation, recommender systems, and time series.

University profile

Multi-armed Bandits

In modern data science applications, there is often the opportunity to observe the effects of a decision and revise it, and to iterate this process repeatedly, experimenting in order to learn an optimal action. Multi-armed bandits provide mathematical models of such settings, where designing an optimal sequence of decisions can be highly challenging. This talk will give an introduction to multi-armed bandit models, and explore the best techniques used to tackle the problems.

Libby Kinsey, Ocado Technology on "Scaling Data Science"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Libby Kinsey recently joined Ocado Technology as Head of Data Science - Strategy and Operations. 

Prior to that she was Lead Technologist for AI at Digital Catapult and co-founder of Project Juno. Libby re-trained in machine learning in 2014 after 12 years working in technology, mostly in venture capital.

LinkedIn

Twitter @libbykinsey

Twitter @OcadoTechnology

Scaling Data Science

Data science already powers insights, products and capabilities in every part of Ocado Technology’s solutions, i.e. we know how to do data science at scale. The next big challenge is about reducing effort to value. Scaling data science is hard.

Tuba Islam, Google Cloud on "ML Journey into production - What are the challenges and how to tackle them?"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Tuba Islam is a machine learning specialist at Google Cloud, based in London, primarily focusing on deep learning, forecasting, natural language processing and the automation of machine learning solutions. 

Before Google, she was a data scientist at SAS, working globally across Europe and the US. She started her career in research and development at the National Research Institute of Electronics and Cryptology in Turkey implementing speech recognition engine for Turkish. She has an engineering background in electronics and holds a master’s degree in digital signal processing.

She delivered successful projects across various industries such as rogue trader fraud detection in capital markets, smart metering analytics in utilities, hazard detection and readmission prediction in healthcare, credit risk in banking, churn prediction in telecom, rate making in insurance, demand forecasting in retail, pharmacovigilance analysis in life sciences.

LinkedIn

ML Journey into production

What are the challenges and how to tackle them?

In this talk, we will share the key challenges that the organizations are likely to face when they move their machine learning models from experimentation stage into production and provide recommendations on how to handle these challenges with the right process and technology in place.

James Hamilton, TUI on "It’s not all about the model – challenges applying artificial intelligence"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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James Hamilton

I have spent my entire career applying data, analytics and modelling to deliver value in different organisation across a range of sectors including retail, public sector and now travel. My greatest satisfaction comes from building and developing analytics teams and in coming up with innovative solutions to new complex problems.

In my current role I head the data science capability and am also lead product owner for data driven solutions in the commercial part of our business, mostly focus on automated pricing. 

Prior to this I worked as head of customer investment strategy at Tesco and as an analytics and modelling consultant at PwC. I have a maths degree from Exeter and a masters in operational research from Lancaster.

It’s not all about the model – challenges applying artificial intelligence

In TUI we have a very successful automated pricing system that has delivered significant benefits over a number of years. We continue to develop it incrementally and our current focus is on better use of our data and applying artificial intelligence.

We have experienced a number of challenges in applying these approaches and I will talk about our experiences, how we are overcoming them and why if we get to training a new model we have already done the hard part.

Samuel Madden, MIT CSAIL on "Outlier and Data Debugging"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Samuel Madden is a Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory. His research interests include databases, distributed computing, and networking. Research projects include the C-Store column-oriented database system, the CarTel mobile sensor network system, and the Relational Cloud "database-as-a-service". Madden is a leader in the emerging field of "Big Data", heading the Intel Science and Technology Center (ISTC) for Big Data, a multi-university collaboration on developing new tools for processing massive quantities of data. He also leads BigData@CSAIL, an industry-backed initiative to unite researchers at MIT and leaders from industry to investigate the issues related to systems and algorithms for data that is high rate, massive, or very complex.

Madden received his Ph.D. from the University of California at Berkeley in 2003 where he worked on the TinyDB system for data collection from sensor networks. Madden was named one of Technology Review's Top 35 Under 35 in 2005, and is the recipient of several awards, including an NSF CAREER Award in 2004, a Sloan Foundation Fellowship in 2007, best paper awards in VLDB 2004 and 2007, and a best paper award in MobiCom 2006. He also received a a "test of time" award in SIGMOD 2013 (for his work on Acquisitional Query Processing in SIGMOD 2003), and a ten year best paper award in VLDB 2015 (for his work on the C-S

LinkedIn

Outlier and  Data Debugging

Rapidly developing areas of information technology are generating massive amounts of data. Human errors, sensor failures, and other unforeseen circumstances unfortunately tend to undermine the quality and consistency of these datasets by introducing outliers -- data points that exhibit surprising behaviour when compared to the rest of the data. In this talk I’ll describe some recent tools we’ve built at MIT CSAIL to identify these outliers in data, including a tool called AutoOD designed to automate many aspects of adapting existing outlier detection methods to complex datasets.

Merve Alanyali, Head of Data Science Academic Partnerships and Research at LVGI, attending the panel on 'Panel on "Data quality and data anomalies'

 Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Merve Alanyali is Head of Data Science Academic Partnerships and Research at Liverpool Victoria General Insurance with more than five years’ experience in academic research. She draws on an interdisciplinary background in computer science, complex systems and behavioural science. 

Prior to joining LVGI, she worked at The Alan Turing Institute as a research associate leading a group of data scientists on a short-term collaborative project with one of the top electronics companies. She completed her doctoral degree on “Quantifying human behaviour using online images” at the University of Warwick with Chancellor’s International Scholarship and The Alan Turing Institute Enrichment Scheme funding. She was awarded a double degree Master’s degree in Complex Systems Science by the University of Warwick and Chalmers University of Technology, Sweden. 

Her work has been featured by television and press worldwide including coverage in Financial Times and Bloomberg Business.

LinkedIn

Twitter @mervealanyali

Panellist on: Data Quality and Data Anomalies

Alex Healing, Future Cyber Defence Research at BT on "Human-Machine Collaborative Analytics"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Alex Healing is a senior research manager at BT Labs where he is responsible for the Future Cyber Defence programme. His team design and build AI and visualisation tools that allow security analysts to better understand and respond to evolving cyber threats. He has published work in several conferences and journals, is co-inventor of over twelve inventions, and won the title of Young IT Professional of the Year at the UK IT Industry Awards in 2012. He has a degree in Artificial Intelligence and Computer Science from the University of Edinburgh.

Human-Machine Collaborative Analytics

The use of AI is clearly a critical part of analysing data at the scale that today’s IT systems allow, but sadly the human user in the system is often an afterthought. This talk will touch on the challenge, opportunity and progress made so far to create more human-centric AI systems for data analysis, involving visual interfaces for presenting machine learning results, and to help analysts better explore and generate insight from data.

Faisal Nazir, AWS on "The importance of explainability in AI"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Faisal Nazir has over 22 year’s experience in engineering, development, integration and strategy consulting. In the past Faisal has worked for Motorola, Cisco, Redhat, Booz Allen and now AWS. Faisal has a Masters in Quantum Physics from Imperial College London.

Faisal is currently working as a Machine Learning Specialist for Amazon also has interests in Blockchain Technologies and Quantum Computing and actively works on projects in all three disciplines.

LinkedIn 

The importance of explainability in AI

Data scientist could potentially wield great power over the lives of everyday people. This power comes from how they develop ML models that can be used to make life-changing decisions. Explainability - having knowledge of why an model makes an inference - is the field that tries make sense of a models decision. We will discuss what tooling is available to Data Scientists to help them find out what is going on with the models they train.

Rob Claxton, BT Senior Manager – Big Data, Insight & Analytics on "Are we nearly there yet? Model baselines and Performance"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Rob Claxton is a Senior Manager at BT Applied Research. He graduated from the University of York in 1993 with a MEng in Electronic Systems Engineering and has spent his career with BT in a variety of technology roles including work on signalling systems and software development for BT’s Intelligent Network platform. 

In 2005 Rob joined BT Research where he currently leads the Big Data, Insight and Analytics team and played a key role in BT’s adoption of big data technology. 

His team are actively applying machine learning and AI to real-world problems including natural language processing and machine vision. Rob also leads the AI Governance work stream at the TM Forum where he is helping to develop a framework for the safe operation of AI deployed at scale.

LinkedIn

Are we nearly there yet? Model baselines and Performance

The process of building models has a strong emphasis on ‘digital performance testing’. In other words, how accurate is my model? Whilst this makes sense when building prototypes or testing the state of the art, it can lead to problems when developing models for production. In this talk we turn the tables and ask the question, how good does my model need to be?

Blaise F Egan, BT Data Science and Statistics Specialist on " Statistics; the original data science"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Blaise Egan acts as a statistical advisor to senior management across the BT Group. 

He has been involved in high-profile legal cases and many projects where a deep knowledge of statistical science is required. He is a Chartered Statistician and a Council member of the Royal Statistical Society. He holds degrees in mathematics (BSc, Queen Mary College, 1978), Statistical Applicastion in Business and Government (MSc, University of Westminster, 1993) and an MPhil in Bayesian Statistics (Queen Mary University of London, 2007).

LinkedIn

Statistics: the original data science

Statistical science is the oldest of the components of what is now called Data Science. I will give a quick run-through of some of the bigger landmarks on the road to where we are now and how we got here.

Michael Free, BT on "Models that cheat - Making sure it really works"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Michael Free, is an AI Research Manager in BT’s Applied research department. With his work focussing on business applications of Deep Learning, he has led machine learning projects across a variety of fields such as natural language understanding on customer interaction data, image recognition for infrastructure management and future chatbot technology. His main interest lies in how to best use this technology in an enterprise context, on real problems with limited training data, utilising unsupervised learning techniques to create useful functionality.

Current main projects involve utilising reinforcement learning for dialogue management, content recommendation systems and collaborative work with universities on speech analytics/NLP.

Models that cheat - Making sure it really works

Machine Learning has made great strides in hard, unstructured problems with the advent of deep learning. However, such progress does not come free of issues. Often treated as black box solutions, interpretability and explainability are complex issues when building deep learning models, and poorly framed experiments and ‘dodgy data’ have led to a litany of models that don’t really work in practise – they merely ‘cheat’ the limited test you’ve given them.

I’ll discuss these issues in this talk, with examples of where things have gone wrong – and methods we can use to mitigate these issues and ensure our models “really work” when we test them.

Tim Whitley opening TFNetworkAutumn21 'Data Science - The beating heart of AI'

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Prof Tim Whitley is a BT Distinguished Engineer and serves as MD Applied Research for BT and MD of BT’s Technology Campus ‘Adastral Park’ in Suffolk, England. He is accountable for all aspects of BT’s Global Research activities, which includes applied research, technology and partnerships with world leading universities.

From 2007 to 2011 he was BT Group Strategy Director. Tim holds a BSc in Physics and a PhD in Optical Fibre Systems. Tim is a Board member for the New Anglia Local Enterprise Partnership and a BT visiting Professor with the School of Computer Science and Electronic Engineering at the University of Essex.

In December 2016, Professor Whitley was appointed as a member of the Engineering and Physical Sciences Research Council (EPSRC) by the UK Government.

In February 2018 Tim was admitted as BT's William Pitt Fellow at Pembroke College Cambridge.

Tim joined BT in 1981 as a Telephone Engineering apprentice in North Wales and has held roles in Research, Technical Architecture, Strategic Analysis and Corporate Strategy.

Welcome to 'Data Science - The beating heart of AI'

Subhash Talluri, AWS on "Data Science on AWS"

 

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Subhash Talluri is a specialist solutions architect in AI/ML working for AWS. He brings cross-functional expertise at the intersection of engineering; cloud computing, machine learning, computer science and business to provide quality and scalable solutions.

He'd like to position himself as a full stack data scientist but well knowing that such unicorns do not exist he is pursuing an alternative strategy. There are essentially five elements to a data scientist: data engineering, data visualization, machine learning, big data and cloud computing. He aims to be a specialist in two of these areas (machine learning, cloud computing) and a generalist in the other three. This allows him to handle the complete data lifecycle. From identifying the right problem, translating the problem in terms of data, getting the data, building the pipelines, analysing data, building models, presenting findings and putting models in production, he aspires to handle it all. 

Web

LinkedIn

Data Science on AWS

AWS has been continually expanding its service portfolio to support virtually any cloud workload, including many services and features in the area of artificial intelligence. In the context of data science projects on AWS, the benefits of cloud computing include agility, cost savings, elasticity, faster innovation and smooth transition from prototype to production. Amazon SageMaker is a fully managed offering that addresses every aspect of machine learning by its modular design. All machine intelligence is powered by data. However, not all data are created equal. We need to critically evaluate machine-learning products from a standpoint that prioritizes the quality of the data streaming into them. This necessitates the need for a data lake or a data platform with considerations for horizontal scalability, a single source of truth, data governance and appropriate security frameworks.

Ben Taylor, CTO & Co-founder, Rainbird Technologies on "Bias, transparency and governance in automated decision making"

 

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Ben Taylor, an authority on artificial intelligence – with an encyclopedic knowledge of its past and present – he is passionate about solving complex challenges through the use of innovative technologies. Where some see problems, he sees solutions.

Earlier in his career, while Director of Technology at a motor insurance start-up, he led the technical development of an award-winning AI system that revolutionised the motor insurance industry. As the co-founder and CTO of Rainbird Technologies, Ben is the driving force behind the fusion of human expertise and automated decision-making. He continues to push the boundaries of the platform’s capabilities, enhancing and developing it to serve a variety of data-driven processes. He holds a degree in artificial intelligence from the University of Sussex and is an active member of the All Party Parliamentary Group on Artificial Intelligence (APPG AI).

LinkedIn @Rainbird Technologies

Twitter  @rainbirdAI

Bias, transparency and governance in automated decision making

As they look at the great landscape of AI, organisations are getting to picture machine learning in finer detail. But the closer they get to the detail, the more they notice a chasm emerging between prediction and automated decision. And for no organisation does that chasm pose greater danger than those who operate in regulated industries.

In this talk, Ben Taylor, CTO of Rainbird, will discuss the challenges of turning data-first prediction into automated decision making, and how organisations can overcome them. He’ll discuss bias in data and decision making, the importance of transparency and how to embed governance into automation.

Professor Alessio Lomuscio, Imperial College London on "Towards verifying neural systems"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Alessio Lomuscio, Professor of Safe Artificial Intelligence at the Department of Computing, Imperial College London, Royal Academy of Engineering Chair in Emerging Technologies, ACM Distinguished Member

He leads the Verification of Autonomous Systems Lab, developing methods and tools for the verification of AI systems so that they can be deployed safely and securely in applications of societal importance.

At present the team are contributing to the following research areas:

• Verification of neural systems and autonomous systems realised via machine-learning.
• Explainability and Fairness in AI systems.
• Parameterised verification of robotic swarms.
• Logic-based verification of multi-agent systems.

LinkedIn

Towards verifying neural systems

A key difficulty in the deployment of AI solutions, including machine learning, remains their inherent fragility and difficulty of certification and explainability. Formal verification has long been employed in the analysis and debugging of traditional computer systems, including hardware and networks, but its deployment in the context of AI-systems remains largely unexplored.

Dr Detlef Nauck, BT on " Programming with Data"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Dr Detlef Nauck is the Head of AI & Data Science Research.

He runs BT’s AI and Data Science research programme where he focuses on making best use of data through AI and machine learning. A key part of his work is to establish best practices in Data Science and Machine Learning for conducting data analytics professionally and responsibly. 

He has a keen interest in AI Ethics and explainable AI as means to tackling bias and increasing transparency and accountability in AI. He has a PhD and a post-doctoral degree in Computer Science with a focus on Data Science and Machine Learning. He is also a visiting professor at Bournemouth University.

LinkedIn

Programming with Data

Experienced Data Scientists know that the biggest challenge to using insights from data in an operational setting is to get hold of good quality data. Pretty much every talk or blog mentions that 80% of the effort in any data science project are spent on data access and data wrangling. My take is that this effort will approach 99% soon simply because the analytics and machine learning parts are becoming largely automated. It is time that we shift focus from techniques and software to data and make it clear to ourselves what we are doing in Data Science and in particular in Machine Learning – we are programming with data.

Dr Raoul-Gabriel Urma, CEO Cambridge Spark on "Skills for a Data World"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Dr Raoul-Gabriel Urma is the CEO and founder of Cambridge Spark. Its mission is to empower organisations to achieve business goals by educating their current and future workforce in Data Science and AI. In particular, Cambridge Spark has developed K.A.T.E.®, a proprietary AI-powered learning platform for Data Science, with the support of the UK innovation agency.

He is author of several programming books, including the best-seller “Modern Java in Action: lambdas, streams, functional and reactive programming” which sold over 50,000 copies globally and with a second edition published in November 2018 as well as "Real-World Software Development" published in December 2019.

Raoul holds a PhD in Computer Science from Cambridge University as well as a MEng in Computer Science from Imperial College London and graduated with first-class honours, having won several prizes for technical innovation. His research interests lie in the area of programming languages, compilers, source code analysis, machine learning and education.

He was nominated an Oracle Java Champion in 2017. He is also an international speaker having delivered over 100 talks covering Emerging Technologies, Entrepreneurship, Java and Python. Raoul has advised and worked for several organisations on large-scale software engineering projects including at Google, Oracle, eBay and Goldman Sachs.

Cambridge Spark

Twitter: @CamridgeSpark

Skills for a Data World

Data is all around us and transforming how organisations are having to operate. As a result, data is significantly impacting the future of work. In this talk, we will cover what are the key skills for individuals and organisation to invest in across all roles for a successful future.

Kes Ward, University of Lancaster on 'Anomaly Detection at the Edge '

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Kes is a mathematics PhD student looking at anomaly detection in real-time data streams. 

He is based at the Statistics and Operational Research for Industry (STOR-i) centre for doctoral training at Lancaster University. 

His work is part-funded by BT to develop new methods for finding changes in network traffic flowing through sensors. This helps identify problems as soon as they happen, so they can be fixed early before causing more issues.

Anomaly Detection on the Edge

In an Internet of Things where everything is collecting and analysing its own data, we need edge analytics to help us sort the meaningful from the muck without breaking the computational bank. In this talk I will present a new statistical method for finding anomalies of different shapes and sizes in a real-time data signal, while working under extremely tight computational constraints.

Dr Matloob Khushi, UoS on "AI: The Fourth Age"

Conference TFNetworkAutumn21

Data Science - The beating heart of AI
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Dr. Khushi is the Senior Lecturer in AI at the University of Suffolk - UoS and the Course Leader of M.Sc. AI and Data Science. 

He has over 20 years of industry and academic experience. He earned his PhD in AI and Data Science from the University of Sydney in which he developed novel algorithms for solving big genomic data and health informatics.

Dr. Khushi has earned various awards for his research achievements and has authored more than 50 research papers. His ResearchGate score is 23.6 which is higher than 80% of the researchers world-wide. During his postdoc (2014-2017) at the Children’s Medical Research Institute, Australia, he developed automated AI-based algorithms for developing cancer drugs expediting the discovery process. 

Some of his research publications caught international and Australian media attention. He has also developed solutions for the financial industry for the prediction of stock, forex and commodities markets. Dr. Khushi has supervised more than 100 research theses in various domains of AI, big data and data science.

YouTube Channel

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AI: The Fourth Age

In this talk Matloob will talk about how AI has transformed our society and has defined new norms of livings. He will also share some of the outcomes of his AI research.

iSee teamed up with TFNetworkAutmn21 Conference on "Data Science - The beating heart of AI"

Workshop: 13th - 14th October 2021

At the Tommy Flowers Network Autumn '21 Conference on Data Science - The beating heart of AI

Download the agenda

Registration is closed now

The iSee project is funded by national European funding councils and co-ordinated by the CHIST-ERA programme.

What do users want when explaining an AI system? 

An Introduction to the iSee Project and Co-Creation Activities

Can you explain the outcomes of AI models for your business? Not everyone can.

There are increasing legal pressures and social obligations for businesses to be able to explain the outcomes of their complex intelligent systems. It is difficult to follow best practice or industry guidelines for a very new and growing aspect of research and development. However, selecting and developing an appropriate explanation strategy is a daunting task. 

To address these challenges, we introduce the iSee project. iSee aims to enable Intelligent Sharing of Explanation Experience by users for users. 

The project will enable the recommendation of the most appropriate explanation strategy to suit the needs of an individual AI system, task and user group. The recommendation will be underpinned by a state-of-the-art AI platform, extracting knowledge from the past explanation experiences and adapting them to ensure compatibility. 

We are employing a User-Centred Design methodology to capture input from a range of stakeholders., making sure that iSee actually supports the users it is built for.

Find out more about iSee and our aims on our website or watch our user story video on YouTube 

 

Geraldina Iraheta, Chief Commercial Officer at the Digital Catapult

Conference TFNetworksummer21
Smart Connected Manufacturing - Making it happen
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Digital leader, technologist and strategic thinker, Geraldina Iraheta is the Chief Commercial Officer at Digital Catapult.

Passionate about turning innovative ideas into concrete business benefit, Geraldina works with leading corporations to drive a culture of innovation and deliver growth through the adoption of leading-edge digital technology.

Bringing a wealth of experience from her global career, including working with startups in Silicon Valley, where she has performed key roles at Deutsche Telekom, T-Mobile, and Critical Path Inc. Her experience spans a broad range of sectors including mobile telcoms, fintech, manufacturing, creative, and digital health.

She is also a strong advocate of women in digital industry leadership. Her broad business experience also includes extensive work with startups, academia and investors, both in the UK and internationally.

Panel Discussion: "Testbed as a Service" and creating a business case

Gary Bruce, Research Manager and Ecosystems Architect, BT on "Digital Business Marketplace - Partner Ecosystems for the Industry 4.0 age!"

Conference TFNetworksummer21
Smart Connected Manufacturing - Making it happen

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Gary has over 30 years of experience in the Telecommunications and Software industry, with roles at Sun Microsystems and now BT. Throughout his career, Gary has worked on network signalling protocols, APIs, open source software and frictionless BSS ecosystems.

He has worked in various standards activities within the ITU-T, ETSI 3GPP, IETF, OMG, Parlay, JAIN and the TM Forum, and is at home leading and collaborating with a diverse set of people and skillsets from many leading-edge companies. Gary initiated and leads the Digital Business Marketplace within the TM Forum and is now working on commercialising the technology within BT.

Digital Business Marketplace - Partner Ecosystems for the Industry 4.0 age!

Mohammad Zoualfaghari, Research Manager - IoT Architect & Technical Lead, BT on "Value-Added-Services for Industry 4.0"

Conference TFNetworksummer21
Smart Connected Manufacturing - Making it happen

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Dr Mohammad Zoualfaghari, PhD, MEng, MIET, MIEEE is a research manager and Internet of Things (IoT) architect in BT Applied Research. In the last few years, Mo has been the architect and head of technical design & delivery for the UK’s flagship smart cities pilots, such as Milton Keynes (MK:SMART) and Manchester (CityVerve). He leads a couple of technical teams at one of BT's international centre of excellence - BT-Ireland Innovation Centre (BTIIC)- in Ulster University. Mo also leads the flagship Smart Manufacturing initiative in the Digital Business Marketplace (DBM) Catalyst, including Intel, AWS, Ulster University and 26 other companies around the world, as part of Digital Transformation World at TM Forum.

Mo provides over 10 years of experience in the telecommunication industry and recently initiated and lead Zero-Touch Onboarding (ZTO) research, which can address a critical need for the industry, as IoT begins to scale up. This award-winning project helps to onboard millions of devices automatically and securely. His recent work provides device-to-cloud security and illustrates how vertical industries looking to become Smart can be digitally enabled by abstracting and digitizing their products and services which leverage IoT, 5G, Secure Device Onboarding and AI. This comprehensive technology has been innovated proudly in the UK Telco industry, for the very first time in the world.

Prior to BT, Mo was the software and hardware development lead of a successful SME, Meal2Go Ltd, which was acquired by Just-Eat Plc in 2013. He was also an Assistant Lecturer in the Department of Electronic, Electrical and Systems Engineering at the University of Birmingham (UK).

Mo received his Doctor of Philosophy (PhD) and Master of Engineering (MEng, 1st Class) degrees, both in Electrical, Electronic and Communications Engineering from the University of Birmingham (UK), in 2015 and 2009, respectively. He is a transactions reviewer of IEEE Communications Society, Elsevier and JCSE, and is the registered inventor of many patents, book chapters, journal, and conference papers. Mo is also an active member of the Institution of Engineering and Technology (IET) and the Institute of Electrical and Electronics Engineers (IEEE).

Value-Added-Services for Industry 4.0

Mehdi Daoudi, Chief Revenue Officer at VRtuoso, VRtuoso on "Upskill & Reskill workforce with Virtual Reality"

Conference TFNetworksummer21
Smart Connected Manufacturing - Making it happen

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Mehdi is Chief Revenue Officer at VRtuoso, the world's first turnkey Virtual Reality learning solution for enterprises. 

Prior to joining VRtuoso, Mehdi has spent 15 years in the Learning & Development industry consulting with a broad range of client organisations across the globe and through partnering with them to deliver impactful learning experiences that engage their people and help them acquire the skills and knowledge they need.

At VRtuoso, we collaborate, challenge and empower based on established learning science to drive high-performance solutions that lead to behavioural change and learning transfer.

Upskill & Reskill workforce with Virtual Reality

Stephen Douglas, Head of 5G Strategy, Spirent Communications on "Accelerating 5G With the Network Digital Twin"

Conference TFNetworksummer21
Smart Connected Manufacturing - Making it happen

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Stephen works for Spirent's strategy organisation helping to define technical direction, new innovative solutions and market leading disruptive technologies. Currently Stephen leads Spirent’s strategic initiatives for 5G and future networks and represents Spirent on a number of Industry and Government advisory boards.

With over 24 years’ experience in telecommunications Stephen has been at the cutting edge of next generation technologies and has worked across the industry with service providers, network equipment manufacturers and start-ups, helping them drive innovation and transformation. Stephen is an ardent believer in connected technology and strives to challenge, blur and break down the silos which prevent innovation and business success.

Accelerating 5G With the Network Digital Twin

Ian Bouquet-Taylor, Operations Director of Ae Aerospace on "Digitising the SME - A private 5G network to drive quality and efficiency"

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Smart Connected Manufacturing - Making it happen

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Ian joined AE Aerospace in December 2019, to help us transform our business for the next phase of our growth to £10m. Originally trained as a pattern maker, he has worked in a series of challenging roles in automotive, consumer goods and aerospace. During his career he has worked for GKN, Johnson Controls, Bentley, Rolls Royce, and Meggitt, and covered quality, manufacturing, engineering, planning, project management and supply chain.

The last 10 years have been focussed on business transformation and supply chain support leading to time spent in a consultancy role with one of the world’s leading quality consultancies, training businesses and individuals in managing the transition of ISO9001 and AS9100.

He was one of the architects of the Department of Business, Energy and Industrial Strategy backed NMCL/SC21 C&G a £52m programme aimed at raising the competitiveness of automotive and aerospace sector suppliers and was responsible for developing the syllabus of subjects for the improvement modules now being delivered to companies like AE Aerospace.

At AE Ian is responsible for managing the development of processes and tools across engineering, production control, quality manufacturing and customer support to raise our efficiency, competitiveness and to implement digital technologies that support this aspiration. Most recently this is focussed on installing our private 5G network supported by the Department of Digital, Culture, Media and Sport alongside WM5G, Ericsson and BT.

Outside work, Ian is a keen club cyclist, photographer, homebrewer, and guitar player, he also always seems to have a list of DIY jobs to do. When he finally manages to sit down, he enjoys reading, particularly historical fiction and has several collections of authors to keep him occupied over the next few years.

Digitising the SME - A private 5G network to drive quality and efficiency

Dan Brooks, Strategy Lead -Digital Manufacturing at NPL on "Confidence in data to support the adoption of Industrial Digital Technologies"

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Smart Connected Manufacturing - Making it happen

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Dan Brooks is Strategy Lead for Digital Manufacturing at the National Physical Laboratory (NPL).

He has more than 10 years’ experience in developing and deploying advanced technologies within manufacturing, throughout industries including Aerospace, Automotive and Pharmaceutical. He joined NPL 3 years ago from Jaguar Land Rover Powertrain where he was Principal Engineer, tasked with identifying new technologies to transform the way in which JLR make engines, researching a range of topics including automation and the intelligent use of data.

He now leads digital manufacturing at NPL, shaping science and engineering research to ensure NPL delivers impact to UK industry, particularly to ensure measurement and data science is at the heart of technology adoption in manufacturing. In addition, he represents NPL on multiple technical committees in Aerospace and Pharmaceutical manufacturing and leads NPL’s contribution to the UK’s Manufacturing Made Smarter challenge.

Dan holds an undergraduate degree in Engineering and is currently studying for his MBA with Manchester Metropolitan University.

Confidence in data to support the adoption of Industrial Digital Technologies