Events


Seminar Series: Time - The next frontier in Machine Learning
Jan
22

Seminar Series: Time - The next frontier in Machine Learning

Join us for the OxWoCS Distinguished Speaker Series. Professor Mihaela van der Schaar is giving her talk: "Time: The next frontier in Machine Learning".

Speaker: Prof. Mihaela van der Schaar
Title: Time - The next frontier in Machine Learning
Time: Monday 22nd Jan. 2-3pm
Venue: Department for Statistics (24-29 St. Giles) - Large Lecture Theatre
Registration:https://forms.office.com/e/Az5r1xVA4b

In this talk, I aim to illuminate the underemphasized yet critical dimension in machine learning: time. I contend that time harbors the potential to revolutionize machine learning methodologies and their applications in numerous domains from healthcare to engineering to finance. This presentation underscores the opportunities and challenges that emerge from integrating temporal dynamics into machine learning models, enriching prediction accuracy, inference robustness, causality, and conceptual understanding.

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela is personally credited as inventor on 35 USA patents, many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.

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Seminar Series: <a href="https://talks.ox.ac.uk/talks/id/58e794a9-cb72-443b-9d1b-7636abe42f94/" target="_blank">Axioms as rules: How to reason with mathematical theories</a>
Nov
28

Seminar Series: Axioms as rules: How to reason with mathematical theories

Sign-Up Link: https://forms.office.com/e/UAVpHCAabq

One of the advantages of using sequent systems as a framework for logical reasoning is that the resulting calculi are often simple, have good proof theoretical properties (like cut-elimination, consistency, etc) and can be easily implemented, eg using rewriting.

Hence it would be heaven if we could add axioms in mathematical theories to first order logics and reason about them using all the machinery already built for the sequent framework. Indeed, the general problem of extending standard proof-theoretical results obtained for pure logic to certain classes of non-logical axioms has been the focus of attention for quite some time now.

The main obstacle for this agenda is that adding non-logical axioms to systems while still maintaining the good proof theoretical properties is not an easy task. In fact, adding naively axioms to sequent systems often result in non cut-free systems. One way of circumventing this problem is by treating axioms as theories, added to the sequent context. This is already in Gentzen’s consistency proof of elementary arithmetic. Now the derivations have only logical axioms as premises, and cut elimination applies.

But we can do better by transforming axioms into inference rules. In this talk, we will propose a systematic way of adding inference rules to sequent systems. The proposal will be based on the notions of focusing and polarities. We will also discuss how our framework escalates to hypersequents and systems of rules, and the application of this to modal logics, proofs explanation and SMT solvers.

This is a joint work with Dale Miller, Sonia Marin, Marco Volpe, Francesca Poggiolesi and Yoni Zohar.

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Panel on Female Leadership and Scientific Entrepreneurship
Nov
8

Panel on Female Leadership and Scientific Entrepreneurship

  • Lecture Theatre A, Wolfson Computer Science Building (map)
  • Google Calendar ICS

We are thrilled to extend an invitation to you for a panel discussion centered around female leadership, moving from science to entrepreneurship, and global partnerships. This enlightening event is a collaborative effort between the Oxford Women in Computer Science Society (OxWoCS) and Oxford Chinese Students and Scholars Association (Oxford CSSA). Refreshments will be provided in the Atrium after the panel (5:00 - 5:30PM)

Sign up here: https://forms.gle/2zjM6xQjYCvjhbtS7

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PhD and Masters Panel
Nov
2

PhD and Masters Panel

In collaboration with OxWEST, join us to gain insights into Masters and DPhil applications, interviews, and the overall graduate experience from our panel experts from Computer Science and Engineering!

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Seminar Series: Developing novel methods for acquiring, reconstructing and analyzing MRI scans of the brain
Oct
30

Seminar Series: Developing novel methods for acquiring, reconstructing and analyzing MRI scans of the brain

We are delighted to invite you to attend a talk by Professor Karla Miller this Monday at 3:30pm. Prof. Karla Miller develops novel methods for acquiring, reconstructing and analyzing MRI scans of the brain. Much of her research focuses on techniques for studying brain function and connectivity. Her current work characterises tissue microstructure with MRI in conjunction with complementary technologies like microscopy. She also helped to establish the UK Biobank brain imaging protocol and is undertaking studies with this data as an external user. She will be speaking about her work and career progression, with refreshments provided after the event.

Register here!

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Seminar Series: Learning to read X-Ray: applications to heart failure monitoring
Jun
13

Seminar Series: Learning to read X-Ray: applications to heart failure monitoring

Prof Polina Golland

We propose and demonstrate a novel approach to training image classification models based on large collections of images with limited labels. We take advantage of availability of radiology reports to construct joint multimodal embedding that serves as a basis for classification. We demonstrate the advantages of this approach in application to assessment of pulmonary edema severity in congestive heart failure that motivated the development of the method.

Speaker Bio

Polina Golland is a Sunlin (1966) and Priscilla Chou professor of Electrical Engineering and Computer Science at MIT and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Her primary research interest is in developing novel techniques for medical image analysis and understanding. With her students, Polina has demonstrated novel approaches to image segmentation, shape analysis, functional image analysis and population studies. She has served as an associate editor of the IEEE Transactions on Medical Imaging and of the IEEE Transactions on Pattern Analysis. Polina is currently on the editorial board of the Journal of Medical Image Analysis. She is a Fellow of the International Society for Medical Image Computing and Computer Assisted Interventions (MICCAI) and of the American Institute for Medical and Biological Engineering (AIMBE).

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OxWoCS Alumni Social
May
13

OxWoCS Alumni Social

Seeking OxWoCS Alumni! We are excited to announce our first alumni social, please spread the word to any alumni pals! Register your interest using this form and save the date. If you can't make this date but keen to join for future events, please also fill in the below form.

Sign up for alumni events here: https://forms.gle/7VowhQzYfaSPpBCX6

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Join the Committee! OxWoCS AGM and Hustings
May
11

Join the Committee! OxWoCS AGM and Hustings

If you've enjoyed some of the OxWoCS events and would like to be more involved join the committtee by filling out the form to stand for a position! The OxWoCS AGM is a friendly, inclusive environment and we encourage everyone who has enjoyed the OxWoCS events to get involved. As a personal aside, being part of this committee has been one of the best and most rewarding things I've done at Oxford, and I've made amazing friends on the committee. So do consider standing for a position :)

If you have questions and want to know more, we are hosting a 'meet the committee' lunch in the Computer Science department in the week before the AGM, so come along and ask any questions you might have.

Sign up: https://forms.office.com/e/3NPcyGdV2r

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GenSTEM - a new Gender Equity in STEM event
May
3

GenSTEM - a new Gender Equity in STEM event

An event run by societies for women and other minoritized groups studying and working in STEM at the University of Oxford 

 

We are pleased to invite you to join us at this new event which will combine a platform for those in minoritized groups to showcase their research through lightning presentations, to hear from a panel debating the key issues facing minoritized groups and to network with your peers. Attendees will have the opportunity to: 

 

  • Attend a panel discussion on gender equity in STEM. Conformed panellists include Professor Susanna-Assunta Sansone, Dept. of Eng Science, Dr Zhanet Zaharieva, CEO Quantum Dice and Professor Sonia Contera, Dept. of Condensed Matter Physics. 

  • Hear lightning presentations by researchers from minority groups with a follow up networking session, 

  • Attend a career and society exhibition to browse and make new connections, 

  • Practice and develop networking skills. 

  • Enjoy free refreshments as you circulate the event 

 

GenSTEM has evolved from the successful 'Women in STEM Networking' event run for the past 3 years. After discussions with societies and with the MPLS EDI team we agreed that this new event would better reflect the changing objectives of societies and offer more diversity in scope for representation and audience.  

This new event is for 'women in...' plus any other minoritized groups or societies studying and working in STEM, and men and other groups/societies are all cordially invited to attend as allies and audience.  

If you would like to attend, please register here

 

We are also looking for people to participate in the lighting presentation sessions!  

Presenters will each be given 5-minutes to talk about their research with no time for Q&A. Attendees will then have an opportunity to talk directly with presenters after the session in a designated networking area.  

If you are interested in showcasing your work on this platform, please register your interest here.

 

For more details kindly click here.

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Oxbridge Women in Computer Science Conference 2023
Apr
29

Oxbridge Women in Computer Science Conference 2023

We are incredibly excited to announce the ninth Oxbridge Women in Computer Science Conference, which will be held on the 29th of April 2023 in Cambridge, UK. The annual conference brings together computer scientists of all age-groups and career stages at Cambridge and Oxford, with the aim of encouraging collaboration via formal and informal discussion. Highlights of the conference include talks by key speakers from academia and a panel-discussion on overcoming obstacles faced by minorities in computer science.

Registration

Everyone is welcome to register for attending the conference, including people from other Universities. We will prioritise members of the Women@CL and OxWocs networks due to capacity reasons. Remaining places will be allocated to members of the computing department and then on a first-come-first-serve basis.

There is no registration fee; and breakfast, lunch, refreshments and a conference dinner will be provided free of charge. Coach transportation will be provided free of charge for all participants travelling from Oxford. The deadline for registration is the 22nd of March 2023 via the following form.

Call for Submissions

We strongly encourage current undergraduates, postgraduates, research assistants and postdocs to submit abstracts of their research for presentation, demonstration and/or the poster session. We have two types of presentations of different lengths. Full presentations will be 20 minutes followed by 10 minutes of Q&A. Lightning talks will be 5 minutes. We also accept submissions for demonstrations/workshops. There will be no proceedings for the conference, so posters and papers can be under submission for other conferences/workshops. Submission of abstracts is done via this form (different to the registration form).

The Reviewing Committee will review all presentation and poster abstracts. Notifications of acceptance will be sent by the 29th of March.

We will be booking a coach to and from Oxford so need numbers ASAP, so please sign up at your earliest convenience! If you have any questions, please email me: madeleine.wyburd@cs.ox.ac.uk

The aim of the conference is to promote research within the community and get valuable feedback in a constructive and friendly environment. We hope to see many of you join us for the conference!

For more information please visit https://www.oxbridge2023.com/

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International Women's Day Lunch
Mar
8

International Women's Day Lunch

International Women's Day Lunch

Join us for a lunch in the Department on the 8th of March to celebrate International Women’s Day together! The lunch will start at 1 pm in the atrium, and will be provided by Vaults & Garden! Please register by Wednesday the 1st of March, so we have an idea of numbers for the food order.

As we expect a lot of interest in this event, this event is primarily aimed at people that identify as women or non-binary.

Sign up before 1st of March: https://forms.office.com/e/Ms53wtxecF

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Grace Hopper Scholarship Application
Mar
7
to 28 Apr

Grace Hopper Scholarship Application

Deadline to apply 28th April

OxWoCS and the Oxford Computer Science Department are able to award two scholarships to attend the 2023 Grace Hopper Celebration (https://ghc.anitab.org/), which will take place from the 26-29th September 2023 in Florida. The scholarship includes the conference ticket, travel costs and accomodation for the duration of the event.

We are looking for Wom*n in Computer Science who have contributed extraordinarily to community building and inclusion as well as representation of minorities in computer science and related fields at Oxford. Students from University of Oxford MPLS departments enrolled for the academic year 2022-23 are eligible to apply.

Apply here: https://forms.gle/izdp9KQrxSEwwkAEA

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Seminar Series: Intelligent systems in cancer care
Feb
14

Seminar Series: Intelligent systems in cancer care

Intelligent systems in cancer care

Abstract: The breakthrough successes of deep learning-based solutions in various fields of research and practice have attracted a growing number of researchers to work in the field of medical image analysis. However, are we really solving the right problems?

A key component of Intelligent medical systems is their perception. While the current state of the art in interventional healthcare largely relies on conventional imaging modalities, we challenge common practice by proposing intelligent medical systems based on novel biophotonics-based techniques that go beyond human perception with modern machine learning methods.

Promising as they may appear, however, these systems can only be as ‘intelligent’ as the validation that was conducted on the algorithms used. Although validation is the basis for measuring all scientific progress as well as a key prerequisite for successful clinical translation, current common practice in the entire field of medical image analysis is heavily flawed, with strategies and metrics used frequently not reflecting the underlying medical problem. We challenge these shortcomings and propose solutions compiled by an international consortium of medical and machine learning experts from over 70 institutions worldwide.

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Seminar Series: Computer science meets big data and healthcare: a journey from PhD in machine learning to Associate Professorship in labour monitoring
Nov
29

Seminar Series: Computer science meets big data and healthcare: a journey from PhD in machine learning to Associate Professorship in labour monitoring

Computer science meets big data and healthcare: a journey from PhD in machine learning to Associate Professorship in labour monitoring

Abstract: This talk will focus on both the career journey and the science behind it, building up to the Oxford Labour Monitoring group that Georgieva is leading. She journeyed from a BSc in Applied Mathematics in Sofia (Bulgaria), through a PhD in Machine Learning and Artificial Neural Networks in Portsmouth (UK), to a junior post-doc at Oxford in fetal monitoring based at the JR Hospital, to now as a successful leader of a multidisciplinary research group, which is about to test for the first time at the bedside her data-driven decision support tool for triage at the onset of labour (the Fit for Labour test).

Across the globe, each day, we continue to have term babies arrive at delivery wards in good condition in utero, only to be born hours later with neurological injuries. The consequences are profound and life-long for the babies, parents, siblings, and their wider family. Clinical staff involved in the obstetric management are severely impacted in multiple ways. On the other hand, Caesarean section to avoid oxygen deprivation during labour carries multiple risks for mother, fetus, future pregnancies; as well as costs. But achieving safe spontaneous delivery is sometimes challenging due to poorly understood and complex fetal physiology, and often, conflicting healthcare needs for mother and baby.

Oxford Labour Monitoring is committed to preventing injury of babies during labour and delivery, caused by lack of oxygen in utero - rare but devastating events. Our work will potentially benefit families, clinicians and healthcare systems by reducing brain injuries, the deaths of babies during labour or after birth and unnecessary medical interventions in childbirth.

Our multidisciplinary team is focused on developing and implementing novel technologies for continuous monitoring and risk assessment of the fetus in-utero at the onset of and during labour. We employ a range of data-science methods to provide automated and data-driven analysis of physiological signals alongside clinical risk factors (for example, fetal or maternal age, co-morbidities, maternal temperature, etc). We already have a prototype decision support tool, derived from a large birth cohort (100,000 term deliveries) by systematic analysis of computer-based fetal monitoring characteristics (a time series called CTG) and clinical risk factors in relation to perinatal outcomes. In tests 'off-line' with the data, the current prototype has shown to perform better than clinicians in clinical practice. We have developed a tablet app that runs OxSys in real time data at the John Radcliffe Hospital, analysing all CTGs as they are being taken (maternity admission unit, delivery suite or wards). We are continuously improving the app's interface in collaboration with the clinicians. The app takes in information from the user about any risk factors if present and modifies the analysis accordingly."

Speaker Bio of Prof. Antoniya Georgieva: "I have developed my career in biomedical research, building on my expertise in machine learning, computing and mathematics, but specialising in intrapartum (in labour) fetal monitoring. I am now leading an ambitious programme to develop data-driven decision-support software in this clinical field. I am uniquely positioned to achieve this by working with the world’s largest and most complete birth cohort of routine labour data (100,000 deliveries).

I obtained a BSc (Hons) in Applied Mathematics from the Technical University of Sofia (Bulgaria) and a PhD in Computer Science from Portsmouth University. I joined the Nuffield Department of Obstetrics and Gynaecology and the Institute of Biomedical Engineering at Oxford for a post-doctoral position in 2007. In 2016 I was awarded a NIHR Career Development Fellowship to grow my independent research group. In the same year, I became a Research Fellow at Wolfson College and also joined the newly formed Big Data Institute at Oxford.""

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OxWoCS Annual Christmas Dinner
Nov
28

OxWoCS Annual Christmas Dinner

Celebrate Christmas with OxWoCS and enjoy a festive formal dinner at St Peter’s College with like-minded friends. Tickets will be released on 30th Oct at noon so set a reminder in your calendar (https://www.eventbrite.co.uk/e/438683262447)!

Dinner Run-down:
18:45-19:30 Drinks reception & Networking
19:30-22:30 Formal dinner (3 course dinner with St Peter's mints, truffles and coffee)

Due to the popularity of this event and limited capacity, we initially ask only women and non-binary people to purchase tickets.

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OxWoCS x Cohere AI
Nov
10

OxWoCS x Cohere AI

Save the date!

OxWoCS is teaming up with Cohere AI to organize an event to learn about career opportunities at the company 🙂

A little bit about the company, Cohere is kickstarting a new chapter in machine learning by giving developers and businesses access to NLP powered by the latest generation of large language models. Come join us to learn more about the company, followed by a delicious complimentary dinner 😋

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Internship Panel &amp; Pizza
Nov
2

Internship Panel & Pizza

We will be hosting a panel of students that have done internships in the past and will share their experience with you! Afterwards there will be informal networking accompanied by free pizza in the Computer Science Department. If you have already done internships yourself, it would be great if you could come along as well to share your experience during the networking.

Sign up here: https://forms.gle/9tW1vbxEsbcSAkyn6

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Lunch Break
Oct
28

Lunch Break

Break for lunch at the Computer Science department. Come and meet the committee with other members of OxWoCs and enjoy a free Taylors lunch in the CS department!

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Diagnosing Infectious Diseases Using Deep Learning and Time Series Imaging with Wearable Monitors in Resource-Limited Settings
Oct
18

Diagnosing Infectious Diseases Using Deep Learning and Time Series Imaging with Wearable Monitors in Resource-Limited Settings

Dr. Ping Lu

Tetanus is a life-threatening infectious disease still common in low- and middle- income countries (LMICs) that kills up to 500,000 people every year. Artificial intelligence, including machine learning and deep learning methods, has revolutionized healthcare for diagnosing and classifying the severity of infectious diseases. Time series imaging is a popular technology which transforms time series data into images.  Deep learning and time series imaging can improve intervention times and symptom classification for infectious diseases with high mortality. This work is highly valuable for inexperienced or overloaded staff in LMICs, such as Vietnam, because it could avoid unnecessary ICU admissions and reduce treatment delays.

Speaker Bio

Dr. Ping Lu has been awarded the degree of PhD in Biomedical Engineering at the University of Bern (Switzerland) with the Latin honors magna cum laude. Particular focus was given on advanced medical image analysis of the human facial nerve based on machine learning technologies, in order to improve cochlear implantation image-guided planning.

Having joined the Institute of Biomedical Engineering at the University of Oxford in 2018, Ping worked on the SmartHeart project and developed spatio-temporal convolutional networks for cardiac motion estimation and regional analysis of left ventricular function, to characterize differences between healthy and diseased hearts.

Her current research focuses on the development of medical technologies for improving care in low- and middle- income countries (LMICs). The work is improving timely intervention and rapid triage for infectious diseases with high mortality – as a result the expected impact on quality of life is high particularly for the most disadvantaged populations who are disproportionately affected by the diseases she is working on. She collaborates with clinicians at Oxford University Clinical Research Unit - focusing on deep learning applied to healthcare using time-series sensor data. She leads the development of deep learning models for the prediction of infectious disease.

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Jul
18

OxWoCS Virtual International Conference for Machine Learning (ICML)

OxWoCS session on "Highlighting Wom*n Researchers in ML" aims to highlight the distinguished work of several Wom*n Researchers in machine learning. From ML for Space, ML for healthcare, interpretability, climate change, and more, we’ll have you covered with exciting research work and candid discussion about further possibilities along with distinguished researchers in the field. Whether you're looking for expert guidance for kickstarting with ML, publishing papers at top conferences such as ICML or exploring distinct research applications, this workshop is going to cover all these aspects! Join Dr. Tingting Zhu, Royal Academy of Engineering Fellow, Charlotte Dean, Professor of Structural Bioinformatics, Nicola Dinsdale, Postdoctoral Researcher, Gunshi Gupta, PhD student in Autonomous Intelligent Machines and Systems and Kelsey Doerksen, PhD student in Autonomous Intelligent Machines and Systems, for a 1-hour panel discussion on their research and paths to becoming women leaders in ML, followed by an informal networking session via zoom!

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Jun
16

Improving Robustness to Distribution Shifts: Methods and Benchmarks

Shiori Sagawa

Machine learning models deployed in the real world constantly face distribution shifts, yet current models are not robust to these shifts; they can perform well when the train and test distributions are identical, but still have their performance plummet when evaluated on a different test distribution. In this talk, I will discuss methods and benchmarks for improving robustness to distribution shifts. First, we consider the problem of spurious correlations and show how to mitigate it with a combination of distributionally robust optimization (DRO) and controlling model complexity---e.g., through strong L2 regularization, early stopping, or underparameterization. Second, we present WILDS, a curated and diverse collection of 10 datasets with real-world distribution shifts, that aims to address the under-representation of real-world shifts in the datasets widely used in the ML community today. We observe that existing methods fail to mitigate performance drops due to distribution shifts in WILDS, even though these methods have been successful on existing benchmarks with different types of distribution shifts. This underscores the importance of developing and evaluating methods on diverse types of distribution shifts, including directly on shifts that arise in practice.

How can you join?

(Registration closes 2 hours before the beginning of the seminar)

Speaker Bio

Shiori Sagawa is a fourth-year PhD student at Stanford University, advised by Percy Liang. She studies robustness to distribution shifts, and to this end, she has developed methods based on distributionally robust optimization, analyzed these algorithms in the context of deep learning models, and recently built a benchmark on distribution shifts in the wild. She is an Apple PhD Scholar in AI/ML.

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OxBridge Women in Computer Science Conference
May
7

OxBridge Women in Computer Science Conference

The Oxbridge Women in Computer Science Conference is an annual conference that brings together junior and senior female computer scientists at Oxford and Cambridge, encourages collaboration through formal and informal discussion, and provides a perfect opportunity for young researchers to present their research and get valuable feedback in an open, friendly and informal environment.

The conference is free to attend for computer scientists at all UK universities and this year will be held at the University of Oxford. It will include talks by key speakers from industry and academia who will share their experiences with the participants.

This years conference will be held on 7th May 2022 at the Mathematical Institute, University of Oxford and the registrations are now open!

For more information and registration: womencs.com

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