Seminars:

Monday 27th March

Designing Formally Correct Intermittent Systems (Milijana Surbatovich) | 12PM

Monday 3rd April

AI for Scientists: Accelerating Discovery through Knowledge, Data & Learning (Jennifer J. Sun) | 10am

Events:

Call to Join the MS Planning Committee

Stanford Postdoc Lab Opening

CIERA Conference | March 13 -17th

McCormick Summer Undergraduate Research Awards | Due April 3rd

CS Graduate Student Appreciation Brunch | April 5th 11AM

Tech Talk Series: Agile Methodology: A Glimpse into the Tech World's Development Tool | April 6th  2:30PM

IDEAL Workshop on Machine Learning, Interpretability, and Logic | April 10-14

Dow - 2023 BEST Symposium | April 30th

News

CS Seminars

Future CS Seminars:

Monday / CS Seminar
March 27th / 12:00 PM
Mudd 3514

Title: Designing Formally Correct Intermittent Systems
Speaker: Milijana Surbatovich 

Zoom Link

Livestream Link

Abstract:
"Extreme edge computing" is an emerging computing paradigm targeting application domains like medical wearables, disaster-monitoring tiny satellites, or smart infrastructure. This paradigm brings sophisticated sensing and data processing into an embedded device's deployment environment, enabling computing in environments that are too harsh, inaccessible, or dense to support frequent communication with a central server. Batteryless, energy harvesting devices (EHDs) are key to enabling extreme edge computing; instead of using batteries, which may be too costly or even impossible to replace, they can operate solely off energy collected from their environment. However, harvested energy is typically too weak to power a device continuously, causing frequent, arbitrary power failures that break traditional software and make correct programming difficult. Given the high assurance requirements of the envisioned application domains, EHDs must execute software without bugs that could render the device inoperable or leak sensitive information. While researchers have developed intermittent systems to support programming EHDs, they rely on informal, undefined correctness notions that preclude proving such necessary correctness and security properties. My research lays the foundation for designing formally correct intermittent systems that provide correctness guarantees. In this talk, I show how existing correctness notions are insufficient, leading to unaddressed bugs. I then present the first formal model of intermittent execution, along with correctness definitions for important memory consistency and timing properties.

I use these definitions to design and implement both the language abstractions that programmers can use to specify their desired properties and the enforcement mechanisms that uphold them. Finally, I discuss my future research directions in intermittent system security and leveraging formal methods for full-stack correctness reasoning.

Biography:
Milijana Surbatovich is a PhD Candidate in the Electrical and Computer Engineering Department at Carnegie Mellon University, co-advised by Professors Brandon Lucia and Limin Jia. Her research interests are in applied formal methods, programming languages, and systems for intermittent computing and non-traditional computing platforms broadly. She is excited by research problems that require reasoning about correctness and security across the architecture, system, and language stack. She was awarded CMU's CyLab Presidential Fellowship in 2021 and was selected as a 2022 Rising Star in EECS. Previously, she received an MS in ECE from CMU in 2020 and a BS in Computer Science from the University of Rochester in 2017.

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Monday / CS Seminar
April 3rd / 10:00 AM
Mudd 3514

Title: AI for Scientists: Accelerating Discovery through Knowledge, Data & Learning
Speaker: Jennifer J. Sun

Zoom Link

Livestream Link

Abstract:
With rapidly growing amounts of experimental data, machine learning is increasingly crucial for automating scientific data analysis. However, many real-world workflows demand expert-in-the-loop attention and require models that not only interface with data, but also with experts and domain knowledge. My research develops full stack solutions that enable scientists to scalably extract insights from diverse and messy experimental data with minimal supervision. My approaches learn from both data and expert knowledge, while exploiting the right level of domain knowledge for generalization. In this talk, I will present progress towards developing automated scientist-in-the-loop solutions, including methods that automatically discover meaningful structure from data such as self-supervised keypoints from videos of diverse behaving organisms. I will also present methods that use these interpretable structures to inject domain knowledge into the learning process, such as guiding representation learning using symbolic programs of behavioral features computed from keypoints. I work closely with domain experts, such as behavioral neuroscientists, to integrate these methods in real-world workflows. My aim is to enable AI that collaborates with scientists to accelerate the scientific process.

Biography:
Jennifer is a PhD candidate in Computing and Mathematical Sciences at Caltech, advised by Professors Pietro Perona and Yisong Yue. Her research focuses on developing scientist-in-the-loop computational systems that automatically convert experimental data into insight with minimal expert effort. She aims to accelerate scientific discovery and optimize expert attention in real-world workflows, tackling challenges including annotation efficiency, model interpretability and generalization, and semantic structure discovery. Beyond her research work, she has organized multiple workshops to facilitate connections across fields at top AI conferences, such as CVPR, and she has received multiple awards, such as best student paper at CVPR 2021.

CS Department Events

Other Events

All students awarded a McCormick Summer Research Award will be given a stipend of $4500, intended to defray summer living costs. It is expected that students will devote ~8 weeks of full-time effort to the project. If necessary for the project, students may apply for additional funds (up to $500) to cover research-related expenses. In this case, a budget should be provided explaining how the additional funds are to be used.

To submit your proposal, use this link. The deadline for submission is 5:00pm, April 3, 2023.

(Note that McCormick's summer research program is distinct from similar opportunities offered by Northwestern's Office of Undergraduate Research.)

Complete details, including application requirements: https://www.mccormick.northwestern.edu/students/undergraduate/research-opportunities/grants-awards.html#summer

Dates: The EXCEL programs run from June 24th to July 29th, with classes from Monday, June 26th through Friday, July 21st (four weeks). Instructors will lead a class every day for 90 mins Monday through Friday and hold office hours twice a week. Before the summer, they will be asked to design a syllabus for their course.

Compensation: Between $4,000-5,000 depending on experience and responsibilities. Your estimated class size would be 15-20 students. We are currently looking for instructors for our Calculus and Chemistry courses.

Qualifications:

How to Apply: Application Deadline: Monday, April 3, 2023

Materials will be reviewed as they are received. Upon review of materials, applicants who are further considered will be invited for an interview. Finalists will be given access to previous instructor’s course materials. If you have any additional questions please do not hesitate to contact us, Dr. Luke Flores, (l-flores2@northwestern.edu) WCAS contact or Ellen Worsdall, (e-worsdall@northwestern.edu) McCormick contact.

This year’s BEST symposium will be held in person in Midland, MI on July 24th – 27th, 2023. The symposium is primarily intended to introduce Black, Latinx, and Native American U.S. doctoral and postdoctoral scientists to the wide range of rewarding careers in industrial research, and in particular, the many opportunities with one of the world’s largest and leading materials science companies, Dow. This conference, developed jointly by their minority scientists and Ph.D. recruiting team, demonstrates our commitment to a diverse workforce.

This opportunity is for graduate students and post-doctoral scientists. Applicants must be pursuing degrees in: Chemistry, Chemical Engineering, Mechanical Engineering, Materials Science, Physics, or other closely related fields and should have a doctorate degree or expect to receive one by December 2024.

Additional information may be found on their website. All applications are due by April 30th, 2023. For further information, please contact the symposium chair Karena Lekich.

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Northwestern Department of Computer Science

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