AI Career Spotlight: Svetlana Stoyanchev
Artificial intelligence is shaping the future, but what do real careers in AI actually look like?
In this career spotlight series, we showcase the career paths, daily work, and impact of people working in AI. Whether you’re an aspiring researcher, an engineer, or simply interested in AI, these stories will give you a firsthand look at the possibilities ahead of you.
Today, we speak with Svetlana Stoyanchev who’s a Senior Research Engineer & Team Lead at Toshiba Research. She’s spent over two decades in the field of human-computer dialogue, figuring out how we can speak to machines.
“Given the rapid pace of change in this field, it’s essential to keep an open mind and adapt to new developments.”
With a PhD from Stony Brook University and postdoc at Columbia, her work since has spanned industry and academia.
Tell us a bit about your job
As a researcher at an industrial lab, I engage in a variety of tasks, including writing research proposals, conducting experiments, programming prototype systems, and writing and reviewing research papers. I collaborate with university professors to supervise Masters and PhD students while also pursuing my own research agenda. My current project focuses on designing a natural language interface that enables users to communicate with a robot in a virtual environment. I aim to create an interactive system that communicates naturally, allowing for corrections, and clarifications, and continuously learns from the user. What I love most about working in a research laboratory is the opportunity to collaborate with a brilliant and supportive team, generate new ideas and continuously learn new things.
How did you get into the field of AI? What excites you about working in AI?
While pursuing my Master’s in Computer Science at New York University, long before the era of deep learning, I took AI courses where we explored logical problem solvers, applied decision trees to play chess, and used syntactic parsers for language translation. I was captivated by the ability to create systems that can reason and solve problems autonomously. Driven by an interest to apply AI to language, I joined a PhD program at Stony Brook University, where my journey working on human-computer dialogue began.
Tell us about a career choice you’ve made along the way
Throughout my career, I have navigated between academia and industry. Before starting my PhD, I worked as a software engineer, where I learned to build systems. This experience proved invaluable for my research on dialogue, which often requires system integration to evaluate research questions through interactions with real users. I have also held research positions at Columbia University and the Open University, which deepened my insight into academic research and prepared me for work in industrial research labs. While I find theoretical questions fascinating, I am equally interested in practical applications and exploring how AI can impact society.
How did you develop the leadership skills you need for your role?
In my current and past roles, I have honed my ability to identify and formulate research questions, which has been instrumental in developing my vision for impactful research. Effective communication with colleagues and collaborators at every stage of a research project has enhanced my communication skills, allowing me to discuss ideas and present research outcomes clearly. Supervising Masters and PhD students has provided me with valuable experience in delegation, communication, and collaborative problem-solving. Additionally, organizing workshops and serving as a program co-chair for conferences have been excellent opportunities for building my leadership skills.
What’s your best piece of advice for anyone early on in their AI career?
AI is an exciting field, whether you’re working on cutting-edge research questions or applying solutions to real-world problems. Given the rapid pace of change in this field, it’s essential to keep an open mind and adapt to new developments. For those considering an academic career, I recommend choosing a research topic that will remain relevant as more powerful models emerge.
What are you excited for in the future of AI?
It’s incredible to witness advancements in language processing and computer vision research with the rise of self-supervised language and vision models. I am curious to see if scaling the current transformer architecture will be sufficient for the emergence of the new AI capabilities and what other methods might lead to the further advancements.
