Meet the amazing women training AI machines

For International Day of Women and Girls in Science, meet some of the women at AIML who are building great new things and leading the way in cutting-edge machine learning technology.

Story by Karen Smart

Women have played a vital part in the story of machine learning and early computing, whether that be Ada Lovelace—whose work on Charles Babbage’s mechanical general-purpose computer, the Analytical Engine, in 1842 led to the first published computer algorithm and Lovelace’s moniker as ‘the first computer programmer’—or the incredible Wrens of Bletchley Park, whose cryptanalysis work was central to the British code-breaking effort during the Second World War.

The incredible achievements of these early trailblazers continue today in machine learning research here at the Australian Institute for Machine Learning (AIML).

Dr Yifan Liu

dr yifan liu

Dr Yifan Liu

A swim at the beach may one day become safer, if Dr Yifan Liu has anything to do with it.

Focusing on the use of machine learning to recognise and understand objects in videos and images, Dr Liu’s work has wide-reaching application in items such as robotic vacuums, smartphones, and autonomous vehicles — potentially including shark-spotting drones.

“To be usable in the real world…the system must be very accurate [and] fast. That’s what I’m focusing on – bringing together different aspects of the technology to make that happen.”

Her dedication to the field of machine learning also caught the eye of one of the world’s largest tech companies. In 2020 Dr Liu was awarded a Google PhD Fellowship in recognition of her leading research in computer vision, placing her in an impressive field of just six Australian researchers to receive the award that year, and one of only 47 globally.

After completing her PhD in 2021, Dr Liu took up a lecturing position in the School of Computer Science, where she is now the main supervisor for two PhD students and two Masters students of her own.


Violetta Shevchenko

Violetta Schevchenko

Violetta Shevchenko

After completing her undergraduate studies in Russia and a double Masters degree in Finland, Violetta Shevchenko was certain of two things — she wanted to complete a PhD in machine learning, and she wanted to live in Adelaide. But it was a simple Google search that ultimately connected the dots.

“I had visited family in Adelaide before, and I really liked the city. So I just googled Adelaide + computer + vision.”

Violetta’s work with AIML focused on AI’s ability to understand questions about visual scenes, and she has recently submitted her PhD thesis for examination.

She explored “whether AI can process visual information – for example, from a photograph – and answer related natural language questions.”

The technology could have benefits for people with vision-impairment. “[A person] would be able to go to a shop, take a photo of an item of clothing, and the AI could tell them the colour, the shape, and other features of the product.”

Since submitting her thesis, Violetta has accepted an internship as an applied scientist with Amazon based here at Adelaide’s innovation precinct, Lot Fourteen.

“This job is a nice mixture of industry and research, and I’d like to continue my career in that path.”


Sofia McLeod

sofia mcleod

Sofia McLeod

Sofia McLeod spends a lot of time with her head in the stars, and her feet on the ground.

As a PhD researcher focusing on computer vision for space-related applications, she has developed a single camera-driven algorithm which can tell a spacecraft how long it will take until it comes into contact with a lunar surface beneath it during its landing sequence.

While acknowledging that it is still “early days” in her PhD journey, Sofia is already considering postdoctoral and industry research opportunities when she submits.

“I would be interested in exploring more computer vision space application projects, but I am equally considering pursuing research projects that have greater environmental impact, such as wildlife preservation or projects that help combat climate change.”

Lana Tikhomirov

Lana Tikhomirov

Lana Tikhomirov

Coming from a Psychology background, Lana first became interested in how complicated and unique the human-machine connection can be while studying Honours at Flinders University.

Her Honours project—which was completed in 2021—centred on ‘human factors’ research, with a specific focus on the cognition of poor task performance using automated decision aids (such as warnings from an autopilot on a flight).

Her PhD work—which she begins this year at AIML’s new Centre for Augmented Reasoning—will use a new AI system made for radiologists and aims to investigate ways in which researchers can improve usability and safety of brand-new technology.

Working under the supervision of Dr Lauren Oakden-Rayner (for her medical and machine-learning expertise) and Professor Carolyn Semmler (for her experience in the psychology-oriented elements), Lana has the best of both worlds to look forward to.

“I wanted to expand my skillsets and challenge myself by learning coding and machine learning to allow me to have a more nuanced understanding of the human-machine relationship.” The project offered by AIML, she says, “allows me to have a perfect balance of these two things, whilst providing a welcoming environment for people like me who are learning for the first time.”

Lana also welcomes the cross-disciplinary recruitment strategy implemented by the Centre for Augmented Reasoning.

Adopted to help address the poor representation of women in the machine learning sector, the centre’s Program Manager Dr Angela Noack says that the unique scholarships on offer will attract researchers from a more diverse range of specialisations—such as health, medicine, or the biological and earth sciences—who might not have otherwise considered a career in machine learning.

“Women are underrepresented in computer science at all levels,” Dr Noack says. “By offering an additional year of scholarship funding…researchers from other science disciplines can use that time to quickly get their core machine learning or mathematics skills up to speed before transitioning to a machine learning PhD.”

Lana agrees.

“I hope to see [more of it] in the future, especially so that other women can see they have a place in learning these skills for the future, even if it seems daunting.”

Tagged in women in AI, computer vision