20 women doing fascinating work in AI, machine learning and data science – Siliconrepublic.com

20 women doing fascinating work in AI, machine learning and data science – Siliconrepublic.com

Gender balance in AI conference line-ups has been noticeably poor. But women in AI exist and their work is multifaceted and extraordinary.

Artificial intelligence (AI) is the hot ticket in tech at the moment. Unfortunately, too many events and conferences delving into the subject, and its related disciplines of machine learning (ML) and data science, feature few women – if at all – among their speakers.

To prove it doesn’t have to be like this, and as a follow-on from International Women’s Day, we at Silicon Republic have found 20 terrific women in AI, ML and data science who we think should be turning up in speaker line-ups across the country (and around the world).

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Alessandra Sala

Alessandra Sala is head of analytics research at Nokia Bell Labs and technology advisory board member at CeADAR, Ireland’s Centre for Applied AI. She’s also the chief ambassador of Women in AI Ireland.

Women in AI is a non-profit working towards gender-inclusive AI for the benefit of global society. The group’s most recent Dublin meet-up took place at Trinity Business School and posed challenging questions around ethically driven AI design.

An active, visible and well-connected member of Ireland’s AI community, Sala’s research at Nokia Bell Labs focuses on distributed algorithms and complexity analysis with an emphasis on graph algorithms and privacy issues in large-scale networks.

Gillian Armstrong

Gillian Armstrong is a proven dab hand at tech events, having co-organised AI Con in Belfast in late 2019 as well as ServerlessDays Belfast at the beginning of this year.

A solutions architect with Liberty IT where she is helping to bring machine learning and serverless systems to the world of enterprise, Armstrong was recently named a global AWS Machine Learning Hero.

She is most excited by applied AI and exploring how software engineers and data scientists can work closer together with the right tools. She’s also passionate about ethical AI and human-centred design, and eagerly shares what she learns with others online and off.

Sarah Jarvis

Head of data science at Prowler, Sarah Jarvis was named among Re-Work’s 30 influential women advancing AI last year. She spoke last summer at CogX, the festival of AI and emerging technology, about building “a principled decision-based AI system” at the Cambridge-based company.

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Prowler is building a platform that allows for complex decision-making in environments such as smart cities, logistics businesses and interconnected financial systems. Jarvis believes the future of tech-enabled decision-making will have to be instantaneous but also scalable and transmittable. She also believes there needs to be trust and, for that to happen, those who build AI need to be able to explain the how and why behind their tech’s decisions.

Jennifer Cruise

Head of data science at the Aon Centre for Innovation and Analytics in Dublin, Jennifer Cruise believes she’s in the “most exciting” industry for delving into data, and it’s true that insurance has deep roots in the history of analytics.

A woman speaking to her colleague at work. | Women in AI

Jennifer Cruise, head of data science at ACIA. Image: Luke Maxwell/Siliconrepublic.com

Cruise has a solid academic background in maths, including a master’s degree in the subject, but she believes this kind of work requires a fusion of creative thinking with logic, data science and machine learning.

She has previously spoken at events on the challenges that businesses face relating to data, including how to deal with the abundance of information that is now available and, of course, the key issues of skills and resources.

Abeba Birhane

Abeba Birhane is a cognitive science PhD candidate at University College Dublin (UCD) and Lero, the Irish software research centre. Her interdisciplinary research intersects embodied cognitive science, dialogism, complexity science, critical data studies and philosophy of technology.

A thoughtful contributor on topics of AI, ethics and data science, Birhane explores the dynamic and reciprocal relationships between people – both as individuals and as a society – and the ubiquitous digital technology we encounter daily as well as newer, emerging technologies.

A woman wearing a scarf and a carrying a backpack stands in a corridor of trees in a snowy park. | Women in AI

Image: Abeba Birhane

Birhane teaches subjects such as critical thinking and ethics and, last year, she was on stage at Code Mesh LDN and other events, urging audiences to embrace uncertainty.

Niamh Donnelly

Niamh Donnelly was awarded a master’s in computer science from UCD followed by an AI Award for a student project in 2018. She then joined Akara Robotics just as the company was spinning out from a robotics research team at Trinity College Dublin.

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At Akara, Donnelly leads the development of the AI behind Stevie, a social robot and recent Time magazine cover star. She’s working to improve Stevie’s ability to communicate and interact with people autonomously in social settings and she brings this research to the field, observing Stevie in nursing homes surrounded by people and even calling games of bingo.

Rozenn Dahyot

Prof Rozenn Dahyot is a Trinity statistics professor whose research dives into deep learning and computer vision. She’s a principal investigator with the AIMapIT project at the Science Foundation Ireland (SFI) funded Adapt research centre, leading research on data analysis and visualisation.

AIMapIT’s AI system can support the discovery, detection and GPS mapping of stationary objects such as traffic lights, antennas, road signs or even individual trees. This technology could be used by utility companies to conduct a fully automated inventory of their assets, or it could aid autonomous vehicles in navigating ever-changing cityscapes.

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Dahyot is also president of the Irish Pattern Recognition and Classification Society, which runs the Irish Machine Vision & Image Processing Conference, and is leading the organisation of the 29th European Signal Processing Conference in Dublin next year.

Keelin Murphy

Dr Keelin Murphy previously led the Delphi project to develop a smart brain monitoring system at Infant, the Irish Centre for Maternal and Child Health.

Delphi set out to use AI to help detect the severity of brain damage in infants as soon as possible, enabling early intervention and appropriate tailored therapies. As a research fellow on the project, Murphy could apply her expertise from her PhD thesis on automated medical image analysis.

Two women in a hospital setting watch as an infant's head, connected to a number of sensors, is lifted by a healthcare practitioner.

From left: Infant director and professor of neonatal physiology at UCC, Geraldine Boylan, with Delphi project research fellow Dr Keelin Murphy. Image: Clare Keogh

She now works as a postdoctoral researcher for Radboud University Medical Centre, bringing her knowledge on applying machine and deep learning to the analysis of medical images to focus on chest X-rays.

Alison O’Shea

PhD student Alison O’Shea was instrumental in Infant winning Best Application of AI in an Academic Research Body at both the 2018 and 2019 AI Awards. Primarily trained as an electrical engineer, she develops deep-learning algorithms for physiological signal processing, and her project at the Cork research centre focuses on the detection of seizure events in neonatal EEG signals.

A young woman stands in a hospital corridor smiling and holding a closed laptop.

Alison O’Shea. Image: UCC

While completing her PhD in University College Cork (UCC), O’Shea works as a machine learning engineer at Qualcomm and, in 2018, she was recognised by Google as one of 20 Women Techmaker Scholars in the EMEA region. A publication arising from her PhD research was named as best publication of 2019 in the UCC School of Engineering.

Susan Leavy

Multi-disciplinarian Dr Susan Leavy holds an MPhil in gender and women’s studies, an MSc in AI, and a BA in philosophy and English literature. She also earned a PhD in computer science for her work detecting gender bias in political media coverage using ML and natural language processing (NLP). She then worked internationally in the investment banking sector, managing the design and development of large-scale trading platforms.

Now assistant professor at the School of Information and Communication in UCD, her research interests concern AI and digital policy, developing interdisciplinary frameworks for the governance and regulation of machine learning algorithms. Her postdoctoral research with Insight, the SFI-funded research centre for data analytics, and the UCD School of English, Drama and Film explored the use of AI and text mining for cultural analytics.

Nicole Baker

With more than two decades’ experience in academia, pharma, regulation and clinical research, Nicole Baker recently turned her focus to developing AI solutions for clinical safety. She participated in Enterprise Ireland’s 2019 New Frontiers Entrepreneur Development Programme and from this came Biologit, an early-stage technology start-up.

Headshot of a smiling woman in a navy turtleneck against a white background.

Nicole Baker. Image: Trinity College Dublin

Biologit’s aim is to help keep patients safe by simplifying the detection of adverse events from drug development to post-market, and to provide those developing and dispensing medicines with accurate information about safety, efficacy and the quality of medicinal products. Its first product is a cloud-based tool using NLP to monitor adverse events in medical literature faster and with greater accuracy.

Georgiana Ifrim

Dr Georgiana Ifrim is actively shaping the next generation of machine learning talent coming out of Ireland as co-lead of ML Labs, the SFI centre for research training in machine learning, which has been designed to address the urgent industry demand for ML skills.

Ifrim is also an assistant professor at UCD and researcher at SFI’s Insight and VistaMilk research centres. Her research focuses on developing scalable predictive models for ML and data mining applications, and she has developed new methods for sequence learning, time series classification, text mining and real-time prediction for news and social streams.

Claire Gormley

Dr Claire Gormley is also co-director of a new SFI centre for research training, this one focusing on the foundations of data science. Like her UCD colleague Ifrim, she is a funded investigator at the Insight and VistaMilk research centres. Statistical methods developed through her research have been applied in a wide range of fields, from social science and genetics to metabolomics and orthopaedics.

A woman wearing a green top and spectacles smiles while standing in front of a well-stocked bookcase.

Claire Gormley. Image: UCD

Much of this work is computationally intensive and involves working with big data sets, and one of the biggest challenges Gormley identifies is separating the good from the bad when it comes to a world of data. “Lots of good data is the holy grail, but lots of bad data is very dangerous,” she says.

Claudia Orellana-Rodriguez

Salvadoran Claudia Orellana-Rodriguez is the lead research engineer at RecSys Labs, a project at the Insight centre seeking to build an online recommendations engine that is explainable, transparent and, most importantly, preserves user privacy.

She is also co-founder and CEO of Libre AI, a company leveraging AI and ML for clients in the commercial, public and social sectors; and co-organiser of the Dublin chapter of Women in Machine Learning and Data Science (WiMLDS).

Orellana-Rodriguez is committed to investigating AI as both a tool and medium for creativity and she shows her appreciation for the creative side of AI as co-founder of Cueva, an online gallery dedicated to AI art.

Shana Chu

Shana Chu is CEO and founder of Styl.Wrap, an Enterprise Ireland-backed start-up with a data-driven solution for one of online retail’s most wasteful challenges.

According to Chu, 41pc of online shoppers buy multiple sizes of garments in order to check the fit at home. With her own experience as an online shopper and a garment technologist, Chu devised a solution that uses machine learning and an AI algorithm to analyse shoppers’ buying history and fabric specifications in order to better predict the fit. The result is a sizing recommendations engine that can reduce the cost and carbon footprint of shipping multiple sizes to try, and tackle the issue of overproduction in the fashion industry.

Sita Karki

Dr Sita Karki works as an Earth observation computational scientist at both the Irish Centre for High End Computing (ICHEC) and NUI Galway. She uses satellite images to study the past and present condition of the environment, writing programmes to process these big data sets.

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Karki is interested in studying natural hazards using remote sensing techniques and has developed early warning systems for both rainfall-induced landslides and harmful algal blooms.

Currently, she is working on two projects using data from the Copernicus satellite and remote sensing of water. The Macro-MAN project is a follow-on from the Sea-MAT project studying macroalgal blooms in transitional and coastal Irish waters, while Infer is a three-year project developing algorithms to support the monitoring of water quality of Irish surface waters.

Medb Corcoran

Medb Corcoran drives the artificial intelligence R&D work of Accenture Labs in Ireland, finding ways to address critical business problems by applying leading-edge AI techniques, including ML, NLP, knowledge representation and reasoning. Prior to this role, she worked at Accenture’s Dublin RD&I centre, The Dock, overseeing teams prototyping and piloting advanced analytics and AI projects across various industries and sectors.

A woman in a pink a blazer and colourful top smiles on a cushioned bench below a striking and colourful abstract painting.

Medb Corcoran. Image: Ruth Medjber/ruthlessimagery.com

Corcoran’s own background is in mathematics, finance, data science and AI, and she now has a hand in guiding third-level education in data science. She was invited to be part of an industry working group designing the first MSc in artificial intelligence in Ireland.

Patricia Scanlon

Soapbox Labs was founded in 2013 by Dr Patricia Scanlon, who was inspired to improve speech recognition for children from observing her young daughter interact with voice-led technology.

Turning entrepreneur after a 20-year career with IBM and Bell Labs, Scanlon put her research and expertise on speech recognition to work to develop an AI-driven, child-specific solution. By 2018, she was recognised by Forbes as one of the world’s top women in tech.

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Soapbox Labs uses deep neural net-based speech-recognition technology to assess children’s speech in real-world noise environments. It’s built to integrate easily into third-party apps and web services, enabling a huge range of applications, from childhood literacy and language learning through to voice control in gaming.

Suzanne Little

Dr Suzanne Little is an associate professor at Dublin City University’s School of Computing and a researcher in multimedia semantics and video analysis at the Insight base there.

She teaches data management and visualisation all the while continuing her research on a number of industry, national and European projects. One such project is the Smart Stadium set-up at Croke Park, in partnership with Intel and Microsoft.

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Little sees the smart instrumented stadium as “a microcosm of a smart city” and, with Croke Park, her research team identified scenarios they could monitor through sensoring and technology, and are now looking at how AI can be used to act on this data.

Ivana Dusparic

Dr Ivana Dusparic is an assistant professor in future cities and the internet of things at Trinity College Dublin and a funded investigator at Connect, the SFI research centre for future networks and communications.

Her research explores the use of AI for the autonomous optimisation of large-scale urban infrastructures, especially intelligent transport systems. For example, she has studied how to use machine learning on linked systems such as traffic lights to help keep transport and pedestrians flowing efficiently.

Dusparic understands that machine learning requires more than just software and that insights from psychology, education and ethics have an important role to play too.

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