Instructor Daniela Ganelin's Work in AI Has Always Had One Goal: Make the World Freer, Safer, and Fairer
Daniela Ganelin has a Bachelor's in Computer Science and Math and a Master’s in Computer Science (AI) from MIT. She has extensive research experience in topics such as economic disparities in online education, diagnosing dementia with machine learning, creating AI-generated images, and improving recommendation engines. In addition to being an instructor, Daniela is also Inspirit AI’s Director of Curriculum.
How did you choose what you studied in college? How did you end up studying AI?
“I loved math and linguistics in high school, and in college, I planned to double-major in cognitive science and math. I took some algorithms classes that I loved, and I ended up as a computer science and math double major. I first encountered AI during an internship after my freshman year at a computational cognitive science lab in Paris. During the next few summers, I learned more about AI through internships in Boston and Taipei.”
What research/industry work within AI have you been focused on? What excites you about the future of AI?
“I've worked with disease prediction, recommender systems, image generation, and online learning analytics. Most recently, my focus has been in K12 AI education - I also trained as a teacher during college. I created and taught a high-school course on AI and Society at an international school, and I've led curriculum development at Inspirit AI this summer.
One aspect that I always try to emphasize is the social impact of AI: we're building amazing technology - but is it making the world freer, safer, and fairer, or the opposite? I hope that more people learn and think about the ways in which AI can reinforce biases or damage privacy, even as it improves healthcare or cities.”
What has been the highlight of your teaching experience with Inspirit AI?
“I've loved teaching the project on AI Ethics: Criminal Justice! Every session, I've been blown away by students' thoughtfulness as they not only create machine learning models and learn tricky mathematical concepts, but also connect the impact of AI bias with broad-ranging social issues.”
