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Zicklin Undergrad Blazes Trail for Women in AI

March 11, 2022

Audite Talukder (BBA, ’23) is one of a kind.  

Audite Talukder (BBA '23), Zicklin school of business student; woman in black turtleneck

Audite Talukder (BBA, ’23)

Last fall, Audite (pronounced “O-D-T”), a computer information systems major, was the only Baruch College student accepted into the inaugural cohort of Break Through Tech AI, a free, highly competitive program that helps college women prepare for careers in artificial intelligence as machine learning engineers. Open to female students from all New York City colleges and universities, the program accepted just 40 applicants.  

Audite sat down for an interview with Zicklin News about her experience.  

Zicklin News: Tell us your story.  

Audite Talukder: I was born in Bangladesh and immigrated here with my family when I was about two years old. As a college student I started out at City College majoring in applied mathematics and minoring in computer science, but I didn’t feel it was the right place for me.  

ZN: How did you choose the Zicklin School?  

AT: I decided to transfer here because the Zicklin School offers a major in computer information systems, which combines business and technology and is very versatile. As the only child of low-income immigrant parents, one of my biggest setbacks has been navigating my academic and professional careers on my own, and I found that Zicklin is just the place for people like me. Here is where your disadvantage becomes your competitive advantage.  

ZN: Tell us more about what you mean by that.  

AT: Opportunity. Baruch and the Zicklin School offer a lot of resources and guidance to help first-generation students, low-income students, students of color, women, and others who traditionally haven’t been well represented in the workforce. A lot of doors opened for me once I came here, and I’m very grateful.  

ZN: Tell us about the Break Through Tech AI program.  

AT: It’s an 18-month program that started last June. The first couple of months were a bootcamp where they taught us basic skills in machine learning, data science, and AI through the Python programming language. In the fall, we were paired with a company and worked on a business challenge with a group of our peers from the program. For the spring semester, we worked on Kaggle competitions to build our portfolio and for the summer and the remainder of the program, we’ll continue working with our mentors for professional development.

ZN: What was your business challenge?   

AT: I worked on a project for Google with three of my peers. It was called Toddle, which stood for “Too Distracted Didn’t Listen Enough.” The challenge was that even the most attentive student can’t pay attention 100 percent of the time during an online class and take accurate notes. This is even more so for those who are listening to lectures in a non-native language. Our solution was to create an artificial intelligence agent that automatically extracts key points and provides a summary of spoken lectures. Using AWS Transcribe, a speech recognition service from Amazon, we converted audio files of college lectures into text files and then built an AI agent that uses natural language processing to produce a summary of the lectures from the audio transcript. 

ZN: Why did you apply for the Break Through Tech AI program?  

AT: Break Through Tech AI really aims to close the gender gap in machine learning and accelerate advances in artificial intelligence and machine learning by encouraging and supporting women on all levels and providing them a safe space where they can excel. But it’s not just about wanting more women in machine learning — we need more women in this field. Women are necessary for these workforces, especially in order to accelerate AI maturity since one of the biggest obstacles in machine learning is bias within algorithms. Sometimes we forget technology isn’t its own being entirely; it’s made by people, and a lot of the bias and prejudices that people carry are implemented into algorithms and then broadcasted and further reiterate systematic bias that’s prevalent in the real world.

ZN: Could you give an example?  

AT: Sure. In 2015 Amazon found out that the algorithm it was using to hire people was biased against women. The algorithm was based on the number of resumes submitted over the last 10 years, and because most of the applicants were men, the algorithm had been trained to favor men over women.  

ZN: How have your classes at Zicklin prepared you for the program? 

AT: Zicklin’s classes emphasize problem solving, which has helped me learn to navigate obstacles amid uncertainty. Also, teamwork — our classes have group projects, and just like the working world you have to collaborate with other people, learn to balance personalities, and find your end goal.  

ZN: Tell us about one of your favorite classes at the Zicklin School. 

AT: I really enjoy the cybersecurity class I’m taking right now with Prof. Adel Yazdanmehr. This class is an introduction to cybersecurity and touches on many of the core components of the field. The first few days of class we were shown just how far a cyber attack can go. We watched a video showing how two security researchers hacked a Jeep remotely while the driver was on the road. It wasn’t just a captivating way to open up cybersecurity for beginners; it also demonstrated just how far a cyber attack can go, especially done by the hands of the wrong people. I’m really excited to learn more.

ZN: What’s next for you?  

AT: This summer I have an internship lined up at American Express in information security. More generally, I want to continue learning. I feel like I know so little because there is still so much I have yet to learn. But I want to continue learning so I can find my novelty in my career, something I not only specialize in but enjoy. I want to make an impact and I want my work to be impactful.

ZN: Do you have any advice for other women who want to work with artificial intelligence or machine learning?  

AT: I would tell them to go for it. Men apply to jobs when they only meet part of the job criteria while women tend to apply to jobs if they meet the criteria almost perfectly, especially because of the fear of putting themselves out there and failing. But I think women are more than qualified to be a part of machine learning and AI. Imposter syndrome is real — I’m surrounded by brilliant women who don’t realize they’re brilliant — but it’s something we can overcome if we can keep pushing ourselves out there. Break Through Tech AI is a community that offers tons of support and resources. It’s a place to make mistakes, learn, and build confidence. I really encourage women to step out of their boundaries and apply for the program — this can not only jumpstart your career in machine learning and AI, but also make it easier for future generations of women to follow in the same footsteps. 

Learn more about Break Through Tech AI here.  


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