CS Events

Computer Science Department Colloquium Measuring Uncertainty
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Speaker: Diana Kim

Bio

Diana Kim is a Ph.D. graduate in computer science at Rutgers University (2016-2022) and a postdoctoral researcher at Vision CAIR group of KAUST in Saudi Arabia (2023-current). Her research interest is interpreting massive art patterns on the latent space of various deep neural nets by using language models and fine-grained art principal semantics. Her works were published in several AI conferences (ICSC-2018, ICCC-2019, and AAAI 2018, 2022). She likes to teach students: a mentor for undergraduate research internships and a teacher for recitation classes at Rutgers.

Location : CoRE 301

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Event Type: Computer Science Department Colloquium

Abstract: Understanding probability is key to critical reasoning and rational decisions. In this lecture, we will learn mathematical machinery to compute probability from building a probability space and axioms to using the tools: partitioning sample space (Bayes Theorem), tree diagrams, and induction. For empirical probability computation, we will learn how relative frequency reveals the probability hidden in data; the Galton board will be presented and analyzed. The convergence of relative frequency will be proved by using Chebyshev's inequality. From the derivation, we can understand the relation between the number of data and reliability in probability inference from data.

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Contact  Professor Richard Martin

Zoom Info: https://rutgers.zoom.us/j/91565767482?pwd%3DMGdsUWc3NWxvZyt3U0t1TGgwSi80QT09

ID: 91565767482
Passcode: 934306


‪(US) +1 301-715-8592‬
Passcode: 934306

Passcode: 934306