Machine Learning by the People, for the People

Abstract: Typical analysis of learning algorithms considers their outcome in isolation from the effects that they may have on the process that generates the data or the entity that is interested in learning. However, current technological trends mean that people and organizations increasingly interact with learning systems, making it necessary to consider these effects, which fundamentally change the nature of learning and the challenges involved.

WACM Bi-Weekly Luncheon

The WACM bi-weekly social lunch event is back again this spring and is scheduled every alternate Tuesday from 12 to 1 pm @ CS 2310. Professors Andrea and Shuchi will be joining us for the lunch along with other female faculty of our Department occasionally dropping by!! This is a great platform to interact with fellow female computer science students and the Professors. Do you have a class that ends at 12.15 or begins at 1 pm?

Tackling the complexities of financial time series data analysis and prediction using Apache Spark/ML

Bloomberg receives large volumes of high-frequency financial time series from a wide variety of sources. We strive to provide to our clients real-time, reproducible and top quality analytics derived from this data. In this talk, we will present to you the challenges associated with running analytics on top of this complex data and the solutions we have built for anomaly detection, prediction, aggregation and scoring of data.

Faculty Candidate Talk: Mathematical Models of Adaptation in Human-Robot Collaboration

The goal of my research is to improve human-robot collaboration by integrating mathematical models of human behavior into robot decision making. I develop game-theoretic algorithms and probabilistic planning techniques that reason over the uncertainty in the human internal state and its dynamics, enabling autonomous systems to act optimally in a variety of real-world collaborative settings.

Student Assistant

Location: Health Sciences Learning Center 2112, 750 Highland Ave, Madison, WI

Degree/experience required:

  • Sophomore/Junior student in Computer Sciences or related fields
  • Preferred course taken: CS 301, CS367 or CS200, CS 300 and CS 400

Responsibilities:

Cybersecurity Student Assistant (two positions)

The University of Wisconsin-Madison Office of Cybersecurity provides a variety of IT security services for UW-Madison and UW System. These services include network traffic analysis, conducting network/host scanning, and reporting. Students will have the opportunity to enhance professional skills such as critical analysis, quality assurance, and customer support/service.

Training will be provided.

Some of the job duties are:

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