Oberlin College Computer Science

Join professors Kate Jones-Smith (physics & astronomy), Chelsea Martinez (chemistry), Cynthia Taylor (computer science), and Leslie Kwakye (neuroscience) for a panel discussion on their careers in the sciences.

We invite scientists and non-scientists alike to attend and participate in what we hope will be an interesting, informative, and fun event.

When: Tuesday, February 11 at 4:45pm to 6:00pm

Where: Norman C. Craig Lecture Hall, Science Center

The session is part of the Roots & STEM events series exploring the human makeup of science, technology, engineering, and mathematics.

https://calendar.oberlin.edu/event/women_in_stem_panel_part_2

Peter F. and Dan B. will be hosting Python Night on Tuesday, December 3rd at 7pm.

Come an learn new tricks for programming in Python including:

• list comprehensions
• higher-order functions (+ map, filter, reduce)
• sets and other useful data structures
• with statements/context managers
• decorators
• the power of the python syntax

Matt Kretchmar

On Thursday, November 14 there will be a talk by Dr. Matt Kretchmar of Denison University entitled Automated Identification of Text Message Authors: Was that really you who sent that text message?

Reception with light refreshments at 4:00pm in King 225, talk to follow at 4:30pm in King 239

Abstract: This talk is about the application of machine learning techniques to the problem of classifying authors of text messages. We use kernel-based support vector machines to build an automated classifier that uses statistical idiosyncrasies to distinguish one sender from others.

The talk is aimed at general undergraduate students in both mathematics and computer science.

Wednesday, November 6 NOON King 237

Thomas G. Dietterich of Oregon State University will present:

Opportunities for Machine Learning in Ecological Science and Ecosystem Management
How can computer science address the many challenges of managing the
earth's ecosystems sustainably?  Viewed as a control problem, ecosystem
management is challenging for two reasons. First, we lack good models
of the function and structure of the earth's ecosystems.
Second, it is difficult to compute optimal management policies because ecosystems
exhibit complex spatio-temporal interactions at multiple scales.

This talk will discuss some of the many challenges and opportunities
for machine learning research in computational sustainability. These
include sensor placement, data interpretation, model fitting,
computing robust optimal policies, and finally executing those
policies successfully.  I'll provide examples from current work
and discuss open problems in each of these areas.

All of these sustainability problems involve spatial modeling and
optimization, and all of them can be usefully conceived in terms of
facilitating or preventing flows along edges in spatial networks. For
example, encouraging the recovery of endangered species involves
creating a network of suitable habitat and encouraging spread along
the edges of the network. Conversely, preventing the spread of
diseases, invasive species, and pollutants involves preventing flow
along edges of networks.  Addressing these problems will require
advances in several areas of machine learning and optimization.

Information Session for CSCI Majors  Thursday, Oct. 31

12:15-1:15 King 327  Food and Drinks Provided

Have your questions answered by Staff and Faculty FROM AIT.

For more information about the program visit www.ait-budapest.com