Antonia Creswell
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Google DeepMind Staff Research Scientist
I've been working in the field of AI for over 10 years! At the start of my career I focused on generative image models and pivoted 3 years ago to focus on reasoning in large language models. I was an author on DeepMinds first large language model paper (Gopher) and have had many high profile papers in this area since. A few months ago I left DeepMind to start my own company, BobbyChat (https://www.bobby-chat.com/), an AI-based coaching app designed to help women navigate the workplace. Bobby in on a mission to make coaching accessible to all women in the workplace. Bobby helps women with everything from overcoming imposter syndrome to improving confidence; (re-)learning to trust their instincts and reducing self blame; as well as encouraging positive self talk to developing in their career! If you are interested in joining our team on this journey please reach out!
Imperial College London
Doctor of Philosophy - PhD, Deep Learning and Computer Vision
October 1, 2015 – January 1, 2019
Imperial College London
Biomedical Engineering (Electrical Stream) MEng, Biomedical/Medical Engineering
January 1, 2011 – January 1, 2015
The Godolphin School
3 A levels: Math A*, Physics A, Chemistry A., Math, Physics, Chemistry
January 1, 2006 – January 1, 2011
Google DeepMind
Staff Research Scientist
May 1, 2025 – Present
Google DeepMind
Senior Research Scientist
April 1, 2024 – May 1, 2025
BobbyChat
Founder
May 1, 2023 – May 1, 2024
Greater London, England, United Kingdom
DeepMind
Senior Research Scientist
May 1, 2021 – May 1, 2023
DeepMind
Research Scientist
January 1, 2019 – May 1, 2021
Computer Vision Research Intern
March 1, 2017 – June 1, 2017
Magic Pony Technology, London
Cortexica Vision Systems
Deep Learning Research Intern
May 1, 2016 – September 1, 2016
London Area, United Kingdom
Imperial College London
Deep Learning And Computer Vision PhD Student
October 1, 2015 – January 1, 2019
London Area, United Kingdom
KNYTTAN Ltd
Computer Vision Research Intern
July 1, 2015 – September 1, 2015
London Area, United Kingdom
UC Davis, Dept. Computer Science
Computer Vision Researcher
November 1, 2014 – June 1, 2015
Imperial College London
Video Game Developer
July 1, 2014 – September 1, 2014
Imperial College London Bioengineering Dept. (Top BioEng Dept. in Europe)
Undergraduate Research Opportunity
June 1, 2013 – July 1, 2013
Imperial College London
Enginneering student
October 1, 2011 – June 1, 2015
Big Bang National Science and Engineering Competition
Finalist
January 1, 2010 – January 1, 2010
London
Spinnaker Sailing Club
Qualified Sailing Instructor
January 1, 2009 – January 1, 2012
National Youth Squad (International Topper Class Association)
Squad Member
January 1, 2009 – January 1, 2010
GBR
Junior Development Squad
Squad member
January 1, 2008 – January 1, 2009
UK
Standford's CS25 Transformer Lecture: In-Context Learning & Faithful Reasoning
February 1, 2023 – February 1, 2023
The definitive lecture series on transformer: "The bulk of this class will comprise of talks from researchers discussing latest breakthroughs with transformers and explaining how they apply them to their fields of research. The objective of the course is to bring together the ideas from ML, NLP, CV, biology and other communities on transformers, understand their broad implications, and spark cross-collaborative research." -- CS25
NeurIPS Tutorial: Pay Attention to What You Need: Do Structural Priors Still Matter in the Age of Billion Parameter Models?
December 1, 2021 – December 1, 2021
Deep learning has led to some incredible successes in a very broad range of applications within AI. However, deep learning models remain black boxes, often unable to explain how they reach their final answers with no clear signals as to “what went wrong?” when models fail. Further, they typically require huge amounts of data during training and often do not generalize well beyond the data they have been trained on. But AI has not always been this way. In the “Good-Old days”, GOFAI did not require any data at all and the final solutions were interpretable, but the AI’s were not grounded in the real world. Further, unlike deep learning where a single general algorithm could be used to learn to solve many different problems, a single GOFAI algorithm can only be applied to a single task. So can we have our cake and eat it too? Is there a solution to AI out there that requires a limited amount of data, is interpretable, generalises well to new problems and can be applied to a wide variety of tasks? One interesting, developing area of AI that could answer this question is NeuroSymbolic AI which combines deep learning and logical reasoning in a single model. In this tutorial we will explore these models in the context of “structure” identifying how varying degrees of structure in a model affects its interpretability, how well it generalises to new data as the generality of the algorithm and the variety of tasks it can be applied to.
Comparison of External Ankle Fixations
June 1, 2013 – August 1, 2013
Modeled the Ankle Fixations using SolidWorks to be imported into Marc Mentat for finite element analysis. Ran a FEA on a simplified model of the Ankle fixation. External Fixations are used to repair broken bones where the bones are displaced or where there is a joint that must be stabilised.
JAVTRAK (Javelin tracking and warning system)
October 1, 2012 – June 1, 2013
Part of the Rio Tinto Sports Innovation Challenge. The project task was to prevent people getting injured or killed by a thrown Javelin. We met this objective by creating JAVTRACK which detects in real time the flight path and landing location of the javelin as well as a warning system to notify people of the danger. Video imaging of the flight path is processed to predict the angle and (ideally also) the distance the javelin will travel. This is transmitted by Arduino to control LED which will light up the area where the javelin is set to land. (The Project Abstract) This was only possible because we extracted as much information from as little data as possible.
Heart Rate Sound Monitor
January 1, 2012 – January 1, 2012
Designed and built a circuit that took a heart sound to produce a digital signal for integration with other technology.
First Aid at Sea
RYA
June 24, 2026 – Present
Dinghy Instructor
RYA
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards advanced AI research and engineering, particularly in Deep Learning and Computer Vision. While this demonstrates strong analytical capabilities, the direct alignment with a 'Data Analyst' role, which typically focuses on data manipulation, visualization, statistical analysis, and reporting, is not immediately clear. The projects are highly technical and research-oriented, rather than business-focused data analysis. The breadth of skills is deep in AI/ML but lacks explicit mention of common data analyst tools and methodologies (e.g., SQL, Tableau, advanced Excel, A/B testing, business intelligence).
Soft Skills & Operational Fit
The candidate's experience as a Founder and Sailing Instructor suggests leadership, problem-solving, and communication skills. Their involvement in competitive engineering and sailing indicates a driven and resilient work attitude. However, specific operational fit for a Data Analyst role is not explicitly detailed in the provided data.