Upcoming events

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

This week's talk is part of the Cray Distinguished Speaker Series. This series was established in 1981 by an endowment from Cray Research and brings distinguished visitors to the Department of Computer Science & Engineering every year. 

This week's speaker is Miryung Kim from the University of California, Los Angeles.

Abstract

We are at an inflection point where software engineering meets the data-centric world of big data, machine learning, and artificial intelligence.   As software development gradually shifts to the development of data analytics with AI and ML technologies, existing software engineering techniques must be re-imagined to provide the productivity gains that developers desire. We conducted a large scale study of almost 800 professional data scientists in the software industry to investigate what a data scientist is, what data scientists do, and what challenges they face. This study has found that ensuring correctness is a huge problem in data analytics.

We argue for re-targeting software engineering research to address new challenges in the era of data-centric software development. We showcase a few examples of my group's research on debugging and testing of data-intensive applications: e.g., data provenance, symbolic-execution based test generation, and fuzz testing in Apache Spark. We then conclude with open problems in software engineering to meet the needs of AI and ML workforce.

Biography

Miryung Kim is a Full Professor in the Department of Computer Science at the University of California, Los Angeles. She is known for her research on code clones---code duplication detection, management, and removal solutions. Recently, she has taken a leadership role in defining the emerging area of software engineering for data science. She received her B.S. in Computer Science from Korea Advanced Institute of Science and Technology and her M.S. and Ph.D. in Computer Science and Engineering from the University of Washington.  She received various awards including an NSF CAREER award, Google Faculty Research Award, Okawa Foundation Research Award, and Alexander von Humboldt Foundation Fellowship. She was previously an assistant professor at the University of Texas at Austin and also spent time as a visiting researcher at Microsoft Research. She is the lead organizer of a Dagstuhl Seminar on SE4ML---Software Engineering for AI-ML based Systems. She is a Keynote Speaker at ASE 2019, a Program Co-Chair of ESEC/FSE 2022, and an Associate Editor of IEEE Transactions on Software Engineering.

The last day to receive a 100% tuition refund for canceling full semester classes is Monday, January 25.

This is also the last day to add full semester classes without instructor approval.

View the full academic schedule on One Stop.

Prospective students can RSVP for an information session to learn about the following graduate programs:

• Computer Science M.S.
• Computer Science MCS
• Computer Science Ph.D.
• Data Science M.S.
• Data Science Post-Baccalaureate Certificate

During the information session, we will go over the following:

• Requirements (general)
• Applying
• Prerequisite requirements
• What makes a strong applicant
• Funding
• Resources
• Common questions
• Questions from attendees

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

The last day to cancel full semester classes and not receive a "W" is Monday, February 1. This is also the last day to receive a 75% tuition refund for canceling full semester classes.

In addition, this is the last day to add classes without college approval and to change grade basis (A-F or S/N) for full semester classes.

View the full academic schedule on One Stop.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

The last day to receive a 50% tuition refund for canceling full semester classes is Monday, February 8.

View the full academic schedule on One Stop.

Save the date for the Spring 2021 CSE Virtual Career Fair. Mark your calendar for Tuesday, February 9, 2021 and start preparing over winter break.

Spring 2021 CSE Virtual Career Fair
Tuesday, February 9, 2021
11 a.m.-6 p.m. Central Time
Virtual fair platform: Career Fair Plus

Similar to the Fall 2020 fair, the Spring fair will be held via the virtual platform, Career Fair Plus. The platform provides features that will allow you to connect personally with employers.

Virtual Career Fair features

• No waiting in line! Students can pre-register for a time to speak with employers to video chat.
• Speak with employers from the comfort and safety of your home.
• Research employers prior to the fair, as well as during the fair by entering the employer's group chat and info session room.

Download the Career Fair Plus app

Download the Career Fair Plus App in the App Store for Apple devices or the Google Play Store for Android devices. Once downloaded, search for the University of Minnesota, then College of Science and Engineering Career Fair. Alternatively, you can view the event online.

You can use the app now to begin researching employers. Winter break is a great time to begin researching employers, and new employers are being added each day! Additional instructions on how to sign-up to speak with employers will be provided soon after the beginning of spring semester.

Get your resume ready and prepare for interviews

Visit the CSE Career Fair website for resume and cover letter writing guides, interviewing tips, advice from employers, and more.

Career counselors are also available over winter break for individual appointments. Please reach out by scheduling a virtual appointment or by sending an email to csecareer@umn.edu.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Sarah Dean (University of California, Berkeley), will be giving a talk titled "Reliable Machine Learning in Feedback Systems".

Abstract

Machine learning techniques have been successful for processing complex information, and thus they have the potential to play an important role in data-driven decision-making and control. However, ensuring the reliability of these methods in feedback systems remains a challenge, since classic statistical and algorithmic guarantees do not always hold.

In this talk, I will provide rigorous guarantees of safety and discovery in dynamical settings relevant to robotics and recommendation systems. I take a perspective based on reachability, to specify which parts of the state space the system avoids (safety) or can be driven to (discovery). For data-driven control, we show finite-sample performance and safety guarantees which highlight relevant properties of the system to be controlled. For recommendation systems, we introduce a novel metric of discovery and show that it can be efficiently computed. In closing, I discuss how the reachability perspective can be used to design social-digital systems with a variety of important values in mind.

Biography

Sarah is a PhD candidate in the Department of Electrical Engineering and Computer Science at UC Berkeley, advised by Ben Recht. She received her MS in EECS from Berkeley and BSE in Electrical Engineering and Math from the University of Pennsylvania. Sarah is interested in the interplay between optimization, machine learning, and dynamics in real-world systems. Her research focuses on developing principled data-driven methods for control and decision-making, inspired by applications in robotics, recommendation systems, and developmental economics. She is a co-founder of a transdisciplinary student group, Graduates for Engaged and Extended Scholarship in computing and Engineering, and the recipient of a Berkeley Fellowship and a NSF Graduate Research Fellowship.

The last day to receive a 25% tuition refund for canceling full semester classes is Monday, February 15.

View the full academic schedule on One Stop.