Computer Science Colloquium: Student Summer Research Presentations

"Chinese Word Segmentation"
Albert Ji, '21

We investigated using machine learning for the word segmentation problem. We trained a model just with Chinese, and then another with several languages other than Chines. We found that a multi-lingual model works much better than a Chinese-only model, that is, training the model with multiple languages improves the performance. Our work suggests that the concept of being a word is somehow transportable across languages.

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"Differentially Private Hypothesis Testing"
Kaiyan Shi, '20

A general differentially private framework on hypothesis testing. After subsampling data and performing public hypothesis test on each sub-dataset, we aggregate them differentially privately to obtain a final resulting p-value for the dataset. We adopt this framework on several models to see its power and its quality in application.

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"Gradual Verification with Recursive Predicates"
Henry Blanchette, '20

Two common strategies are used to verify that programs meet their specification: proofs at compile time ("static" verification) and assertions at run time ("dynamic" verification). I'll present work on an implementation of gradual verification that allows less precise specification, and thus is a mix of the two. The goal is to offer partial static verification when full static verification is infeasible.

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"Differentially Private Confidence Intervals"
Monica Moniot, '20

Tuesday, October 8, 2019 at 4:30pm to 6:00pm

Eliot, 314

Event Type

Lecture

Audience

Open to the Public, Faculty, Students, Staff

Department
Computer Science, Division of Mathematical and Natural Sciences
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