UGSRP 2019 Talk Series Schedule
"How to Give Good Talks" presented by Prof. Henry Yuen
Graduate Student Panel
"How to Write Great Research Papers" presented by Prof. Joseph Jay Williams
First Talk: Jeffrey Yi Nian Niu (Supervisor: M. Hoffman)
Title: Corner Cases in Popular Bioinformatics Software
Abstract: The increased availability of high-throughput sequencing technologies over the years has led to the development of hundreds of software tools in genomics research. Each of these tools relies on a number of file formats. While some file formats have been rigorously defined by various working groups, others remain vague, causing confusion among users. In this presentation, I will discuss our work on the BED format which is widely used for gene annotation and visualization. We first aim to provide a rigorous definition of the format that meets the needs of the users. We will then use this definition to test various tools' ability to properly parse the format. Finally, I will discuss future extensions to this work including a file validator and defining different levels of conformity to our definition.
Second Talk: Alex Cann (Supervisor: F. Ellen)
Title: Non-Blocking Mergeable Search Trees
Abstract: Non-Blocking Mergeable Search Trees are a Concurrent Data Structure supporting the operations Insert, Delete, Search, Split and Merge in an asynchronous system. My talk discusses the challenges with the design of this data structure and an approach to addressing them.
Third Talk: Ryan Marten (Supervisor: S. Dickinson)
Title: Homogeneity in images and its use in object segmentation
Abstract: I will present my work from this summer on training a convolutional neural network to classify regions as homogeneous or heterogeneous. This problem is complex and interesting because even humans can find that there is sometimes no definite right classification for a given image region.
Fourth Talk: Yizhe Cheng and Bence Weisz (Supervisor: A Farzan)
Title: Automated Program Synthesis
Abstract: Writing a correct program is often tedious and difficult. To simplify the work of programmers, we present an algorithm, based on a counterexample-guided refinement loop (CEGAR), for synthesizing correct programs from a set of specifications. Moreover, we discuss a representation of programs, a technique for finding and generalizing counter examples, and a method for determining the existence of a proven correct program.
First Talk: Jing Xie
Title: Moral Sense Acquisition from Text
Abstract: We present the problem of moral sense acquisition by asking whether machines can distinguish right from wrong given simple textual input. For example, the act of laughing at someone's mistake would be frowned upon, but laughing in general would not. We draw from work in social psychology and natural language processing to construct models that predict the moral sentiment of a stated situation. We describe preliminary methodologies and show that a simple approach based on verb schematization is often most competitive. We also discuss future directions to extend this research.
Second Talk: Lara Schull
Title: Evolution of the Moral Lexicon
Abstract: How does the lexicon evolve? One possibility is that the lexicon is driven by changing sociocultural needs that are hard to predict.The other possibility is that the lexicon is shaped by cognitive principles in adaptation to these needs, with predictable trajectories over time. We explore these possibilities by tracking how the moral lexicon evolved over a period of 1000 years in English. We present a suite of models that postulate different hypotheses about word emergence and discuss preliminary results on the predictability of lexical evolution above and beyond chance.
Third Talk: Ben Prystawski
Title: Gender Differences in Child Linguistic Input Reflect Implicit Biases in Text
Abstract: In recent years, word embeddings have been shown to reflect implicit biases in society, such as the stereotypical association of nurses as female and engineers as male. Using a corpus of child-directed speech, I will present evidence that caretakers speak to children
differently depending on the gender of the child and that these differences correlate with the biases quantified by word embeddings. I will also compare these findings across languages.
Fourth Talk: Yuya Asano
Title: Randomized Experiments with Machine Learning
Abstract: When researchers run randomized experiments, they have different goals. For example, they not only want to help users quickly but also want scientific (statistical) evidence to claim which option is better. However, it is difficult to balance out those goals. I will discuss how machine learning could potentially help researchers achieve all of the goals at the same time and how machine learning algorithms would behave under different conditions.