About Me

Calder Katyal is a senior majoring in Applied Mathematics at Yale University, concentrating in Machine Learning. With the ability to learn extremely quickly and adapt to new learning environments, he is constantly exploring new technologies. Calder has researched and published in various fields of ML, such as multimodal generative AI, geometric learning, and convex optimization; he recently completed his senior thesis advised by Professor John Lafferty on chain-of-thought (CoT) supervision. In May 2025, Calder joined Character.ai as a multimodal research engineer intern, developing an end-to-end data preprocessing pipeline using Apache Spark, hundreds of H100 GPUs, and open-source models to curate a dataset of millions of videos and hundreds of thousands of hours of footage—reducing runtime by more than 4x. His efforts lead to both a full-time and part-time offer, which he accepted, working until November 2025 on Ovi, an open-source STOTA joint video-audio generation model developed with two other engineers at Character.ai. In Summer 2024, Calder worked as a Machine Learning Intern at the Palo Alto-based startup CloudChef, where he leveraged advanced computer vision techniques and frameworks, synthesizing a massive dataset with thousands of multimodal data points and training a high-performing embedding model for cooking footage despite limited compute and noisy data. He has previously worked for Appian, a multibillion cloud-computing and enterprise software company, reporting directly to the founder. Calder is experienced in Python, PyTorch, C/C++ and more, taking graduate-level ML courses at Yale. Most importantly, Calder is a well-rounded student, passionate about all things from math to journalism (publishing in The Atlantic while in high school). In his free time, you can find Calder reading, playing classical piano, or singing.