Women Leading The AI Industry: “To attract more women to the field we need more strong female role models and an environment that welcomes diversity.” with Sergul Aydore and Tyler Gallagher

To attract more women to the field we need more strong female role models and an environment that welcomes diversity. The good news is that there are certainly initiatives on this. For example, there is a leading AI conference called Neural Information Processing Systems. The acronym for this conference, NIPS, created an unwelcoming environment for many women and the committee ultimately changed the name to NeurIPS in order to create a more accepting environment. I think this really proved the power of the female community in this field.

I had the pleasure of interviewing Sergul Aydore. Aydore is an Assistant Professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. Her research interests include developing, improving and understanding machine learning algorithms with possible but not limited application to neuroimaging. Previously, she was a machine learning scientist at Amazon’s demand forecasting team. There she built neural network models to predict the demands of millions of products to enable better in-stock positions. Before Amazon, she spent a year at JP Morgan as a Data Scientist. She was a postdoctoral research scientist at the Laboratory of Intelligent Imaging and Neural Computing in Columbia University. She finished her Ph.D in Brain Imaging Laboratory in University of Southern California under the supervision of Prof. Richard M Leahy. In her Ph.D studies, she explored mathematical approaches to identifying brain networks. Before USC, she received her BS and MS degrees from the department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey in 2007 and 2009, respectively.


Thank you so much for doing this with us! Can you share with us the ‘backstory” of how you decided to pursue this career path?

I have always been interested in mathematical theory and the application of engineering problems. To me, machine learning and AI are a perfect combination of these practices, and those who excel in both can lead the AI field. I am fortunate to have electrical engineering, signal processing in particular, training from amazing professors who helped me build the foundations of practical and theoretical skills. Working at companies like Amazon introduced me to broader application areas of AI. After three years in industry, I decided to go back to academia to further expand my AI research and train younger generations in this field.

What lessons can others learn from your story?

My career path is a bit unusual. Typically, graduated PhD students spend a few years on postdoctoral training in university settings before becoming a professor. My postdoctoral training in academia was only nine months and the majority of my training came from my time at Amazon. During that time, I also worked with some great researchers in INRIA, France and started a collaboration on a project that was unrelated to my Amazon work. For two years, I was working on AI projects at Amazon during the day and another project with the researchers at INRIA at night. I think it is my dedication that took me to where I am now. I advise others to be aware that there is not one particular path to get to the place they want to be. They can create their own path with hard work and dedication.

Can you tell our readers about the most interesting projects you are working on now?

In my group, we work on developing or improving AI models for various applications. One application is how to train an AI model if you do not have enough samples of data and the data is very high dimensional. This situation is very common in medical imaging. Typically, the data in medical imaging is very large but there are not enough samples due to the monetary and logistical expenses related to data collection. We showed that we can improve the performance of an AI model by grouping features (pixels in images) on fMRI data sets.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

I could not be where I am today without the support and training I received from my PhD advisor Professor Richard Leahy. Even several years after my graduation, I contact him to discuss my research plans and career moves. He taught me how to conduct high-quality research and more importantly, how to share it with the world. Each time I interact with my students, I keep his attitude and approach to education top of mind and do my best to replicate that.

What are the 5 things that most excite you about the AI industry? Why?

I have always been fascinated by the medical applications of AI. To count a few: abnormal detection in the human brain, brain-computer interface and breast cancer detection. I genuinely believe that these developments will make an impact on many patients’ lives.

I am also excited to see how products like Alexa or Google Home can improve the lives of the elderly or people with disabilities.

What are the 5 things that concern you about the AI industry? Why?

I have mentioned my excitement around Alexa and Google Home but it is important to note that data privacy is a big concern now. The amount of private data these products collect and if everyday users are aware of this data collection are important questions to consider.

In addition, witnessing how AI’s influence on social media platforms, such as recommended posts and news, can manipulate people’s political views and how these platforms can segregate society scares me as well. Further, there is some concern around the applications of AI in some fields such as criminal justice. At this time, AI models are not sophisticated enough to identify perpetrators based solely on facial features. While AI models can assist in the process, they should not be the final judge.

As you know, there is an ongoing debate between prominent scientists, (personified as a debate between Elon Musk and Mark Zuckerberg,) about whether advanced AI has the future potential to pose a danger to humanity. What is your position about this?

If we do not fix the privacy concerns and potential bias associated with AI then we may have cause for concern. However, I do not believe that AI poses other threats to humanity. While AI might displace some jobs, it will create new jobs just as we saw in the industrial revolution. I think it is our responsibility to educate the younger generations on AI so that they are well prepared for the future of work and can avoid the potential dangers associated with AI.

What can be done to prevent such concerns from materializing? And what can be done to assure the public that there is nothing to be concerned about?

I think we need more regulations at the state level to address any privacy and bias concerns involved with AI.

How have you used your success to bring goodness to the world? Can you share a story?

That is a big claim! I work hard everyday to train my students in AI research and the problems we work to solve are all for the general good. One example being our research in detecting lesions through MRI.

As you know, there are not that many women in your industry. Can you share 3 things that you would you advise to other women in the AI space to thrive?

I advise women to be engaged in AI activities at their school, company or in their towns. They can organize events, hold presentations on the subject or contribute to open source software platforms for AI such as Scikit-learn, Mxnet, Pytorch and Tensorflow.

I also advise women to be demanding. They should not be shy when asking for help when they believe it will enhance their career. We need more women leaders in the field and these leaders require engagement and hard work.

Can you advise what is needed to engage more women into the AI industry?

To attract more women to the field we need more strong female role models and an environment that welcomes diversity. The good news is that there are certainly initiatives on this. For example, there is a leading AI conference called Neural Information Processing Systems. The acronym for this conference, NIPS, created an unwelcoming environment for many women and the committee ultimately changed the name to NeurIPS in order to create a more accepting environment. I think this really proved the power of the female community in this field.

We also need more women to interact with each other through networking organizations. Organizations like Women in Machine Learning (WiML) and Women in Machine Learning and Data Science (WiMLDS) organize amazing high-quality events for those in the industry. I was recently a presenter at the 13th WiML Workshop, co-located with NeurIPS in Montreal and I had a chance to meet some amazing female researchers and enthusiastic future female leaders.

What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?

“Do the one thing you think you cannot do. Fail at it. Try again. Do better the second time. The only people who never tumble are those who never mount the high wire.” By Oprah Winfrey.

I think any academic or industry leader can understand the truth behind this quote.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. :-)

I would make the entry level barrier to AI lower and have already started making small steps in this direction. For example, I make all the material for my course, Programming in Python, public and I share my lectures on Youtube. My group is also started a machine learning reading series and we record each session and share the material and the videos on our website.

How can our readers follow you on social media?

They can follow me on Twitter. My handle is sergulaydore.

Thank you so much for joining us!