MIT alum brings predictive algorithms to the auction house
Kelly Shen, a 2017 MIT graduate, develops algorithms at Sotheby's to forecast art prices based on market trends and artist popularity. She also works on cataloging and managing website traffic for live auctions. Shen credits her MIT math and computer science training, along with her passion for drawing, for her role in the emerging field of art intelligence.
Shen’s dual background in mathematics and computer science, sharpened by MIT’s Project Laboratory in Mathematics, taught her to translate complex puzzles for varied audiences—a skill she now applies to auction forecasting. Her role also involves technical infrastructure, such as managing live-auction web traffic and cataloging works, blending data precision with art-world logistics. Beyond her day job, she remains active in MIT alumni networks, including serving as vice president of the Association of MIT Alumnae. Her stated pragmatism—prioritizing audience engagement over algorithmic elegance—reflects a broader industry shift toward user-centric tech applications in traditional markets like fine art.
This convergence of predictive algorithms and auction houses could reshape how art is valued and traded, affecting collectors, artists, and smaller galleries who may rely on data-driven pricing. While such tools may increase market transparency and efficiency, they could also reinforce existing popularity biases, potentially overshadowing emerging or unconventional artists. Buyers and sellers alike may need to adapt to a landscape where quantitative forecasts complement—or challenge—human expertise.