Role Description

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Q: Could you describe your current position and the fields you work in?

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A: I was in the healthcare and AI space. My company was using machine learning to detect early onset of Alzheimer’s disease. I realized early on that I wanted to get a PhD, so I found good advisors during my undergraduate studies and continued on through my PhD. My advice for future students is if you want to get a PhD, start early—get internships or research experience. That really helped me.

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On the research side, I manage between half a million and a million dollars in research projects annually, all focused on AI and machine learning. Right now, I have a contract with Accenture, where we're developing a career coach and another smaller project.

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I’m also the Associate Chair at the Department of Systems and Enterprises, where I manage the corporate education business of the department. We primarily serve the defense industry, which has been one of the major sponsors of my research. Over the years, I’ve managed between five and six million dollars in research for them.

Additionally, I am part of the College of Professional Education, which extends beyond my department to serve a larger university community. I also direct the Center for Complex Systems and Enterprises, which serves as a common denominator for my various research activities.

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Career Journey

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Q: Can you give us a quick overview of your career journey, including what college you went to and what got you interested in your work?

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A: I was an undergraduate student at Stevens Institute of Technology, where I did both my undergraduate and master’s degree in four years. Stevens has a 4+1 program (four years undergrad, one year master’s), but I took 23 credits per semester and finished in four years. Then, I transitioned straight into my PhD, also at Stevens.

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I wanted to help people using machine learning and AI, so I focused on eating detection—tracking when a person eats, what they eat, and how much they eat. I used Android watches for wrist motion, Google Glass for head motion, and in-ear microphones for chewing sounds. I stayed at Stevens because I liked the topic and enjoyed conducting experiments and analyzing data using data science and machine learning.

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Emerging Career Opportunities

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Q: What are some interesting career opportunities emerging in machine learning over the next 5-10 years?

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A: Right now, large language models like ChatGPT are big. In the future, there might be jobs specifically for prompting ChatGPT effectively. The job would involve asking the right questions and verifying the answers, because while AI is powerful, it can make mistakes. Generative AI is another growing field—models that generate images, text, or even new breeds of animals based on data.

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Specific Advice

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Q: Do you have any advice for young people interested in your field? Are there specific skills or experiences they should pursue?

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A: Definitely get internships, whether in industry or academia. I was a student at Bergen County Academies, where we had senior internships instead of school on Wednesdays. I did mine at Stevens, working on an app for AIDS patients in Africa. That’s how I got to know Stevens.

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If you’re interested in AI or machine learning, start learning how to code. There are tons of resources online for Python, Java, and other languages. Also, try to find a mentor—someone who’s already been through it and can guide you. Having a mentor can prevent you from feeling overwhelmed.

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Reflection

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Q: Is there anything you would have done differently in your career journey?

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A: Maybe more networking. During my PhD, I was very focused—coding 12-15 hours a day—and I didn’t talk to many people. I was lucky that the CEO of my last company reached out to me for a job, but now that I’m job hunting again, I see how important networking is. Interviews can be tough when you don’t have a strong network.

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Short and Long Term Goals

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Q: What are your short-term and long-term goals?

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A: Short-term, I want to get a job. Long-term, I want to improve society with AI and machine learning. My past projects focused on helping diabetes patients manage insulin and detecting early Alzheimer’s disease. AI is a powerful tool for doctors and patients, but it shouldn’t be trusted 100%—it still makes mistakes.

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Comparing Machine Learning and AI​

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Q: What’s the main difference between machine learning and AI?

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A:  Machine learning is more about the math behind AI. AI is broad, while machine learning focuses on data-driven models. Deep learning is a subset of machine learning that involves neural networks. For example, using a neural network is deep learning, using a random forest model is machine learning, and using a combination of techniques is general AI.

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Intersection Between AI and Healthcare

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Q: How do you use machine learning in your healthcare work?

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A: First, we collect data. During my PhD, I worked on eating detection. There were groups in Georgia using in-ear microphones and groups in Texas using wrist sensors, but no one was combining the two. So, we collected our own data, analyzing gyroscope movements and chewing sounds to detect eating behaviors.

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For ViewMind, we used AI to analyze eye-tracking data from VR headsets to detect early signs of Alzheimer’s. The process is similar—we collect data, find patterns, and build machine learning models to interpret the results.

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Typical Dat at Viewmind

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Q: What was your day-to-day like at ViewMind?

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A: I researched papers on eye tracking and Alzheimer’s, coded new features, and built machine learning models. The goal was to find patterns that could help detect Alzheimer’s early.

 

Importance of Coding

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Q: How important is coding, and is there a specific programming language you recommend for beginners?

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A: Learning Python or Java is important. Machine learning requires coding, and there are many online resources to help you start. Debugging is one of the biggest challenges in coding, and Java has better debugging tools, but Python is dominant in machine learning because of its extensive libraries.

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If you asked me 10-15 years ago, I’d say R. But now, Python dominates. About 95% of machine learning work is done in Python because of its libraries and community support. If you want to do software engineering, Java is a good choice because of its structured syntax and debugging tools.

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Importance of Math

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Q: Do you use math a lot in your work?

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A: It depends on the problem. You can use calculus, but sometimes machine learning tools handle the math for you. For example, you can install a random forest package in Python and use it without diving into the underlying calculus. However, understanding the math helps when fine-tuning models.