Faculty Associate


The Department of Computer Sciences is seeking to add a faculty associate to its current roster of nine faculty associates. Duties of faculty associates include teaching (primarily) introductory and (occasionally) advanced computer science courses and participating in department service.


Begin date: January or August, 2019

Salary is negotiable; 9-month renewable appointment.


On Preserving Non-Discrimination When Combining Expert Advice

The emergence of online marketplaces has facilitated adaptive data collection but has also led to the need of adaptive decision making to counter non-stochastic patterns in the data. On the other hand, ethical concerns such as how to avoid discrimination have been only studied assuming that the input is i.i.d. In this paper, we explore the interplay between adaptivity and non-discrimination by studying the design of online learning algorithms that, when run on members of different groups, do not discriminate against some group.

WACM Bi-weekly lunches

The perfect gathering for all WACM members and Women in Tech to stay in touch, exchange updates about the advances in the relevant field and most of all meet people with similar goals and aspirations. Each cycle we choose a different theme or a topic for the day and use the luncheons as a way to let off some steam and go back to our schedules re energized, fresh and inspired.

Note: This is no longer a 'bring your own food'. There will be free food ordered by the bi-weekly chairs! Microsoft is generously sponsoring the bi-weekly luncheons this semester.

WACM Mentoring Kickoff Dinner & Resume Workshop

This year, Google is sponsoring the Mentoring Kickoff Dinner & Resume Workshop. There will be a short presentation by Googlers, and then we will split into groups of mentors/mentees. This is a great way to get your resume reviewed and informally chat with Google engineers. Food and swag will be provided!

Please RSVP by Tuesday, October 2th using the following link: https://bit.ly/2Q9Wf9V

Crack Open (Neural) Nets: Can We Make ML-Based Networked Systems More Trustworthy?

Machine learning (ML) solutions to challenging networking problems are a promising avenue but the lack of interpretability and the behavioral uncertainty affect trust and hinder adoption. A key advantage of ML algorithms and architectures, such as deep neural networks and reinforcement learning, is that they can discover solutions that are attuned to specific problem instances. As an example, consider a video bit rate adaptation logic that is tuned specifically for Netflix clients in the United States. Yet, there is a general fear that ML systems are black boxes.

Detection Games

AI technologies, such as machine learning, are seeing increasing adoption in adversarial settings. One important domain in which AI techniques are particularly promising is detection: for example, one can, in principle, use data to learn how to detect a host of malicious activities, including malware and intrusions. A key challenge in detection is how to trade off the consequences of a failure to detect malicious activity and the cost of false alarms, especially in the case when the malicious party makes deliberate attempts to avoid being detected.

SACM's CS Fall Picnic

The CS department Fall Picnic will be on Friday, Sept 28th at 4pm in Edward Klief Park! This picnic is for all CS undergraduates, graduates, faculty, and staff. Families are also invited. Past picnics have featured volleyball, frisbee, touch football, soccer, and croquet in addition to the food and drink. The picnics are well attended by faculty and students alike.

Come join us for food, conversation, and sports. SACM will be providing burgers, brats, soda, and snacks.

Machine Learning for Medical Imaging: Medical Image Imputation

Abstract: We present an algorithm for creating high resolution anatomically plausible images that are consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images.


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