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King’s Women in Informatics Conference - 26 April 2022

Please note that this event has passed.

Join the King’s Women in Informatics Conference to learn about the exciting research conducted by King’s PhD students and staff who identify themselves as women or non-binary, have a chat about a career in research, and network over refreshments.

The line-up of the event is as follows:

16:00 – 17:00 - Welcome and presentations - (Strand Building S-1.04)

17:00 – 17:30 - AMA (Ask Me Anything) - (Strand Building S-1.04)

17:30 – 18:00 - Refreshments and networking - (King’s Building staff common room)

Please register for the conference in advance. This is due to limited room capacity.

If you would like to submit a question for the AMA session before or during the conference, please, fill in this form.   




Abstract: Automation in cloud security and access control (Anna Bamberger)

Recent years have seen a multitude of changes across the online and software development work environment with accelerated moves towards a fully distributed cloud computing model. In the light of these developments, security and regulated access control authorization, both move to the forefront of system design and execution. Distributed systems have a potentially unlimited amount of data including highly sensitive information, so access control to such systems must not only ensure smooth operational functionality, but it must also guarantee that access is only issued to authorised parties and only at the level such authorisation is granted. A list of use case scenarios for the creation of access control rules is not a desirable solution in the cloud model due to the associated administrative burden as well as the sheer impossibility of being able to predict all possible combinations in such a vast data pool.

A modern access control system should therefore be able to automatically detect and incorporate changes in characteristics related to principals and their environments to generate a more granular and more appropriate access control decision. This mechanism should run all while simultaneously easing the manual burden placed on system administrators who, besides the time commitment, would traditionally also need to exercise personal judgment to solve such ambiguous requests, thereby adding bias and larger room for error to what should be objective decisions. This presentation aims to explore the benefits and application functionalities of a such system.

Biography: Anna Bamberger

Anna’s experience includes extensive cloud consulting, architecture, and security implementation within the Amazon Web Services Professional Services division, where she focused on the UK public health sector. A very significant contribution of hers includes building an innovative platform for the integration of genetic sequencing tool Hail, to enable a fast and cost-efficient sequencing solution, navigating a complex architecture deployment as well as highly challenging big data computational requirements.

Prior to AWS Anna worked with Nomura Insurance Solutions on building a non-negative equity forecast based on Black Scholes as well as preparing alternative asset sourcing solutions for a German client to anticipate a drop in solvency ratio due to expiring durations and low interest rates.

Anna is a current EPSRC funded PhD student at King’s College London, where her research focuses on automating the generation of access control policies with machine learning as well as exploring the ever more dynamic nature of access requests and categories, a development requiring a shift in the traditionally static nature of security control. Anna holds a BSc in Computer Science with Intelligent Systems, also from KCL, and is a Security Engineering Consultant with Bank of NY Mellon.


Abstract: What's all the fuss about PhDs in Computer Science? (Mackenzie Jorgensen)

You may be wondering: is a PhD the right next step for me after my undergrad or master’s studies? Do I like research? Wait, what really IS computer science research like? Will a PhD set me on a good career trajectory? What if I'm unsure if I'll stay in academia afterwards? If you are thinking about one or all of these questions, Mackenzie will do her best to answer these questions. She will discuss how she was first introduced to research whilst studying for her undergraduate degree, why she decided to pursue a PhD, how research has taken her across the Atlantic Ocean, and what career paths she is thinking about after finishing her PhD.

Biography: Mackenzie Jorgensen

Mackenzie is a Computer Science PhD candidate funded by the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence at King’s College London. Her research focus is on mitigating the harms that can result from algorithmic decision making. She graduated from Villanova University with a Bachelor of Science in May 2020, where she studied Computer Science and Philosophy. As an undergraduate, she completed research projects in the U.S. and Germany in the fields of big data analytics for healthcare, multi-agent communication and coordination, and hate speech moderation through machine learning.

Apart from her research, she enjoys most of her time outdoors whether that is running, walking neighborhood dogs, biking in London or hiking and kayaking in Seattle (her hometown). She enjoys traveling within the US and abroad. In London, you will typically find her out and about at coffee shops, bookshops, food markets and restaurants.


Abstract: Striving for gender fairness in AI (Madeleine Waller)

Fairness and bias within AI is widely discussed due to high profile cases of systems being unfair, particularly making decisions based on protected personal characteristics, for example gender or sex. Historical data used in cases such as recruitment which can mean previous discrimination or societal trends are reinforced. In addition, the people designing, creating and regulating such systems may have potential to hinder the overall fairness as they are not being created with the benefit of historically disadvantaged groups in mind. In this talk, Madeleine will discuss examples of discriminatory AI systems, potential ways to make them fairer and draw on personal experiences as a female within AI academia to suggest ways to improve the gender imbalance within the field, which should in turn improve the fairness of AI in general.

Biography: Madeleine Waller

Madeleine Waller graduated with a BSc in Computer Science at King's College London in 2021 and is now a first year PhD candidate at the Safe and Trusted AI Centre for Doctoral Studies at KCL. Her project is looking at explainability of decision-making AI systems, specifically to explain the potential unwanted bias of a system or individual decision to a stakeholder. Specifically, Madeleine has done previous research in predictive analytics in child social services, looking at the legal, ethical and societal issues of such systems. She has also done research into smart personal assistants such as Alexa and whether they treat users differently based on personal characteristics.


Abstract: Natural language processing for personalised language learning (Dr Zheng Yuan)

One of the biggest challenges faced by the education system has been the presumption that every learner has the same interest and aptitude to learn, leading to ‘one-size-fits-all’ learning, teaching, and assessment. Personalised education intends to address the distinct learning needs, enhancing the experience of learning and teaching, as well as providing equal opportunities for learners and teachers worldwide. Language education is the process and practice of teaching or learning a second or foreign language. In this talk, Dr Yuan will look at how natural language processing technologies can help with personalised language learning. Particular tasks that she will mention include automated feedback generation (grammatical error correction and automated assessment), personalised learning strategy creation, and task selection.

Biography: Dr Zheng Yuan

Dr Zheng Yuan is a Lecturer at King's College London and a Visiting Researcher at the University of Cambridge. Her research focuses on machine learning and deep learning for natural language processing, including real-world applications. Previously, Dr Yuan was a Research Associate at the Department of Computer Science and Technology of the University of Cambridge, focusing on the development of educational applications for second language learners. She holds a PhD in Natural Language Processing and an MPhil in Advanced Computer Science from the University of Cambridge, and a BSc(Eng) in Telecommunications Engineering with Management from Queen Mary University of London and Beijing University of Posts and Telecommunications.


Abstract: AI for good (Dr Elizabeth Black)

Let's start with a brief introduction to the work done by Dr Elizabeth Black, as an academic in the Department of Informatics, which includes doing artificial intelligence (AI) research, working to promote diversity and inclusion within our discipline and the higher education sector more generally, and training PhD students in the area of safe and trusted (AI).

Dr Black will talk about the importance of diversity for ensuring that the AI systems we develop are truly of benefit to society, and will introduce you to her main area of research: computational argumentation. Computational argumentation is a key subfield of AI for formalising reasoning and decision-making in the presence of conflicting information, using evaluative principles that are familiar in everyday reasoning and debate. This means that we can use computational argumentation to allow humans and AI systems to engage in joint reasoning and decision-making, so that AI systems can justify their choices and explain their reasoning to human users, and humans can challenge and influence these AI systems’ reasoning.

Biography: Dr Elizabeth Black

Dr Elizabeth Black is a Reader in Artificial Intelligence in the Department of Informatics at King’s College London. Her research focusses on computational models of argument dialogue and how these can be applied in domains such as healthcare and multi-agent systems, and in systems to support human reasoning and decision-making.

Dr Black is Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence, which brings together world-leading experts from King's College London and Imperial College London to deliver a unique 4-year PhD programme, focussed on the use of model-based (symbolic) artificial intelligence (AI) techniques for ensuring the safety and trustworthiness of AI systems. She is on the Board of Directors of the European Association for Multi-Agent Systems, and a member of the steering committee of the COMMA international conference series on Computational Models of Argument. Dr Black has a particular interest in matters relating to equality, diversity and inclusion, and has been involved in many initiatives to create a more inclusive and supportive environment for staff and students.

Rita Borgo_cropped

Abstract: The worth of a picture (Dr Rita Borgo)

We have often heard the quote “a picture is worth a thousand words”, the question then is: “which words is it worth?”. In her talk, Dr Borgo will guide you through her research work in data visualization, its power of expression when faced with the challenge of diving through a sea of data, as well as its limitations when faced with the challenges related to human interpretation. Dr Borgo will show the importance of research in human factors in visualization as a means and strength to create and develop tools that enhance human natural abilities.

Biography: Dr Rita Borgo

Dr Rita Borgo is a Senior Lecturer in Data Visualization at the Informatics Department in King’s College London. She is the Head of the Human Centred Computing (HCC) research group and Deputy Director of the Centre for Urban Science and Progress (CUSP) - London. Her research focus is on data visualization and visual analytics with an emphasis on the role of human factors in visualization. She has followed an ambitious program of developing new data visualization techniques for interactive mining, rendering, and manipulation of large multi-dimensional and multivariate datasets. Novel in all aspects of her research is the aims at providing solutions that involve humans in the loop of intelligent reasoning while reducing the burden of inspection of large complex data.

Dr Borgo's current research activities include: visualization for explainable AI, multimedia visualization, augmented and extended reality, and urban data visualization.

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