2022夏季研讨会系列
HMC is inviting speakers and organizing events as part of our Stauffer Lecture Series. We have a great group of scholars that will discuss the scholarship they have produced and/or their specific research interests. 研讨会将于每周四上午十一时举行.m. We encourage our Harvey Mudd community to join us in these informative and fun seminars!
May 26 | Katherine Van Heuvelen, Associate Professor of Chemistry, 太阳2平台
Location:通过电子邮件共享的缩放链接
研讨会主题: 负责任的研究行为
Abstract: The responsible and ethical conduct of research (RCR) is critical for excellence, 以及公众的信任, 在科学和工程领域. Education in RCR is considered essential in the preparation of future scientists and engineers.
June 2 | Brian Shuve, Assistant Professor of Physics, 太阳2平台
Location: Shan 1430
研讨会的主题:揭开宇宙隐藏的一面
Abstract: More than 80% of matter in the universe is invisible: it doesn’t interact with light, and it’s not made out of any of the known elementary particles. What could this dark matter be, and where did it come from? Delving into the origins of dark matter during the universe’s earliest moments, I will describe what we can infer about the properties of dark matter particles and how we might look for them in terrestrial experiments. Remarkably, evidence for hidden states of matter could be buried in existing experimental datasets because the data have not been examined in the right way. I will then show how we are leading new searches for hidden states of matter in data from high-energy particle colliders.
June 23 | Jamie Haddock, Assistant Professor of Mathematics, 太阳2平台
Location: Shan 1430
研讨会的主题张量模型、方法和医学
Abstract当前对效率的需求是前所未有的, 定量, and interpretable methods to study large-scale (often multi-modal) data. 我们感兴趣的一个关键领域是张量分解, which seeks to automatically learn latent trends or topics of complex data sets, providing practitioners a view of what is “going on” inside their data. This talk will survey tools for topic modeling on matrix and tensor data. These tools are of interest across the many fields and industries producing, capturing, 分析大数据, but are of particular interest in applications where expert supervision is available and often essential (e.g., medicine). We will describe an application of these methods to medical data; an ongoing application to cardiovascular imaging data.
6月30日|斯托夫社交活动
波巴欢乐时光，斯普拉格户外帐篷，中午
7月7日:阿尔贝托·索托和乔·沃思, Postdoctoral Fellows in the Program in Interdisciplinary Computation (PIC), 太阳2平台
Location: Shan 1430
题目与摘要(Dr. Soto):
追逐猎物和其他运动行为
Locomotion is one of the most important behaviors animals engage in. It plays an outsize role on the outcome of predator-prey interactions. 捕食者必须决定何时以及如何追捕逃跑的猎物, while prey have to time and direct their motion just right to escape the approaching predator. To understand the role of locomotion on these dynamic interactions the Soto Lab is working to develop a bio-inspired experimental testbed for aquatic predator-prey interactions. We’re also collaborating with marine biologists to investigate the locomotor behaviors of white sharks off the coast of southern California.
题目与摘要(Dr. Wirth):
自动化微生物分类系统基因组方法
The classification of living organisms is a problem that predates modern biology. 几个世纪以来, scientists have sought useful methods for both categorizing microbes and refining what the word “species” means in the microbial world. 不像植物和动物这样的高等生物, microbes cannot be classified by easily observed features such as sexual reproduction or physical characteristics. 在过去的三百年里, the species definition as it applies to microorganisms has changed as technological advances allow for an ever-increasing amount of detail to be gleaned from Earth’s oldest and most abundant life forms. 在这次演讲中, I will discuss why microbial taxonomy matters as well as software I have developed that allows microbiologists with minimal computational experience to perform sophisticated taxonomic analyses.
7月14日:CS开放日
计算机科学系.
7月21日| Sarah Kavassalis, Postdoctoral Fellow in the Program in Interdisciplinary Computation (PIC), 太阳2平台
Location: Shan 1430
研讨会主题: 我们能否预测 understand 利用机器学习解决空气污染问题?
Abstract: Simulating the composition of the atmosphere on a regional or global scale is traditionally done by large, deterministic models requiring significant computational time. 即使是在最高分辨率下, these models are only able to represent a small portion of the chemical reactions we believe are regulating key pollutants and climate forcing agents, like ozone. I will present some of the work my students have done this year on creating data driven models, which are potentially much more computationally efficient to run and have shown excellent performance. These machine learning models have their own downsides however, as they are only as good as the data they are built on (and that data is often biased). I’ll share some of our progress on dealing with biased and sparse datasets as well as some insights from 可辩解的 机器学习技术.