Women in Data Science Conference

Women in Data Science Conference

WiDS Aberdeen is an independent event that is organized by the Aberdeen Centre for Health Data Science to coincide with the annual WiDS Worldwide conference organized by Stanford University and an estimated 200+ locations worldwide. All genders are invited to attend WiDS regional events which feature outstanding women doing outstanding work.

The video below features WiDS Ambassadors from around the world, explaining the purpose of the conference.

For our conference in Aberdeen, we have secured a fantastic line-up of female speakers from academia, industry and the NHS, from North East Scotland and beyond. See the agenda below, and information on our speakers.

We will have a limited number of in-person attendees, with a number of tickets reserved for students, so if you would like to attend the conference in person, please make sure you register early! Alternatively, you can register to attend online. 

If you have any questions about our conference, please get in touch: ACHDS@abdn.ac.uk.

We look forward to seeing you on June 1st!

WiDS Aberdeen Organising Team: Dimitra Blana, Hannah Wilson, Andra Stefan, Emma-Louise Tarburn

Our conference will take place at ONE Tech Hub in Aberdeen, and online, on Thursday June 1st 2023.


Date: Thursday, June 1st, 2023

Location: ONE Tech Hub (Schoolhill, AB10 1JQ) / online

9:30 - 10:00 Breakfast and Networking
Session 1 (Health Data Science)  
10:00 - 10:15 Welcome
10:15 - 10:45 Keynote Speaker: Jess Butler
10:45 - 12:15 Technical Talks
12:15 - 13:15 Lunch
Session 2  
13:15 - 14:45 Technical Talks
14:45 - 15:05 Coffee Break
Session 3  
15:05 - 15:35 Keynote Speaker: Karen Jewell
15:35 - 16:20 Careers Panel
16:20 - 16:30 Closing Remarks

Download the detailed agenda here.



We are grateful to our sponsors for supporting our conference:

Canon Medical logo

TL Tech logo

DaSH logo

Research Data Scotland logo

We are also grateful to Opportunity North East for providing us with the amazing venue, and One HealthTech Aberdeen for co-organising the morning session, focused on Health Data Science.

Registration Link

Click here to book your in-person or online ticket:

Keynote Speakers


Technical Talks - Session 1

Ale Aranceta-Garza

Ale Aranceta-GarzaAle is a new Lecturer in Biomedical Engineering and is establishing the area of Rehabilitation Engineering at the University of Dundee. She is interested in muscle movement, motor control, and rehabilitation with the aim of understanding the roles of health (for example for prevention of musculoskeletal disorders), disease (for example to understand progression during motor neuron disease), and injury (for example to address issues following amputation). She also has experience developing assistive medical technology from lab to market.

Carmen Brack

Carmen BrackCarmen’s PhD project is entitled “Proactive Frailty Identification in Primary Care”, the primary aim of which is to determine the best way to identify frail older people living in NHS Highland. She completed both BSc and Master in Public Health at the University of Aberdeen which allowed her to hone her research interests which are centred around frailty and ageing as well as the health of older informal caregivers.

Clarisse De Vries

ClClarisse De Vriesarisse is currently a postdoctoral researcher at the University of Aberdeen evaluating healthcare Artificial Intelligence (AI) tools in real-world settings as part of the Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD) programme. Her work, which has focussed on evaluating AI for breast cancer screening, has directly led to a currently ongoing prospective study aiming to determine whether AI could be implemented into the screening pathway. She previously completed a PhD in Medical Imaging, an MSc in Medical Physics, and a BSc in Electrical Engineering.

Yining Hua

Yining HuaYining is a lecturer at the School of Natural Computing Sciences, University of Aberdeen and also an honorary researcher at University of Glasgow. She specialises in applied AI, the Internet of things (IoT), and distributed/lightweight autonomous systems. Yining is energetic in interdisciplinary research, especially in tackling real-world challenges with AI techniques and lightweight computational/IoT devices. Project topics include the lightweight microvascular lesions detection, federated system with lightweight devices, and energy efficiency in electric vehicles and autonomous driving.

Ana Klimovich-Gray

Ana Klimovich-GrayAna studies how processing of language in the human brain adapts to different situations and environments in both typical and atypical populations. She is also deeply interested in using current neuroimaging methods for assessment, early diagnosis and remediation of developmental and acquired disorders of language. 

Liz White

Liz WhiteLiz White is a Data Engineer in healthcare, working at CorporateHealth International. Her focus is on capsule endoscopy and the integration of AI into capsule delivery for the benefit of patients and clinicians. 

Technical Talks - Session 2

Viktoria Eriksson

Viktoria ErikssonWith a PhD in Sociology, Viktoria currently works with community development in the third sector. Having a diverse career background, from doing community-led research to designing Scottish Government interactive data dashboards, Viktoria’s passion is understanding and improving inequalities.

Ana Giocarlan

Ana GiocarlanAna is a lecturer in Computing Science at University of Aberdeen and an expert in persuasive technology and human-centred computing, with strong research interests in behavioural sciences. Her research focus is on investigating theory-informed adaptive interventions and intelligent systems to understand, motivate, and support behaviour change in a variety of contexts, including health, sustainability, and education, while taking into consideration personal, social, and cultural factors.

Dimitra Gkatzia

Dimitra GkatziaDimitra is an Associate Professor at the School of Computing at Edinburgh Napier University. She received her PhD in Computer Science from Heriot-Watt University (Edinburgh, UK) in 2015. Dimitra is interested in making computers and robots interact in a human-like way using natural language, while at the same time respecting the privacy of the users. She is interested in exploring data-driven Natural Language Generation (NLG) for low-resource domains/languages, i.e. domains where parallel data is hard to acquire and annotate as well as domains where tools that are useful for NLG are not available.

Pam Johnston

Pam JohnstonPam is a lecturer and data scientist at Robert Gordon University. Her PhD thesis, "Beyond the pixels: learning and utilising video compression features for localisation of digital tampering", is about how features of compression learned by neural networks can be applied to detect tampering in videos. Her current research interests are in computer vision and deep learning.

Arabella Sinclair

Arabella SinclairArabella is a Lecturer (Assistant Professor) in the Department of Computer Science at the University of Aberdeen, where she is a member of the Computational Linguistics group. Her research interests fall at the intersection of computational psycholinguistics, natural language processing, and AI. She explores language use in interactive settings, with a particular interest in how speakers adapt to one another in dialogue. Current work involves investigating adaptive, audience aware models of language, and gaining an increased understanding of human and model linguistic behaviour.

Catalina Vallejos

Catalina VallejosCatalina is group Leader at MRC Human Genetics Unit (Edinburgh) and Fellow of the Alan Turing Institute. Her focus is on the development of novel statistical methodologies to address and study sources of heterogeneity in complex biomedical data: heterogeneity across individuals in a population (e.g. response to treatment), heterogeneity in terms of the type of data we collect (e.g. health records & genomics) and heterogeneity that is introduced by the data collection process (e.g. measurement error).