Seminars:

Monday 13th March

A Hybrid Computing Ecosystem For Practical Quantum Advantage (Gokul Subramanian Ravi) | 10AM

Data Structures for Fast Systems (Alex Conway) | 12PM

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) | 12PM

Events:

Call to Join the MS Planning Committee

Stanford Postdoc Lab Opening

CIERA Conference | March 13 -17th

CEH Seminar: Dr. Phebe Vayanos | March 13th 11AM
Dow - 2023 BEST Symposium | April 30th

News

CS Seminars

Monday / CS Seminar

March 13th / 10:00 AM

Mudd 3514

Title: A Hybrid Computing Ecosystem For Practical Quantum Advantage
Speaker: Gokul Subramanian Ravi

Abstract:
As quantum computing transforms from lab curiosity to technical reality, we must unlock its full potential to enable meaningful benefits on real-world applications with imperfect quantum technology. Achieving this vision requires computer architects to play a key role, leveraging classical computing principles to build and facilitate a hybrid computing ecosystem for practical quantum advantage.

First, I will introduce my four research thrusts toward building this hybrid ecosystem: Classical Application Transformation, Adaptive Noise Mitigation, Scalable Error Correction and Efficient Resource Management.

Second, from the Classical Application Transformation thrust, I will present "CAFQA: A classical simulation bootstrap for variational quantum algorithms", which enables accurate classical initialization for VQAs by searching efficiently through the classically simulable portion of the quantum space with Bayesian Optimization. CAFQA recovers as much as 99.99% of the accuracy lost in prior state-of-the-art classical initialization, with mean improvements of 56x.

Third, from the Scalable Error Correction thrust, I will present "Clique: Better than worst-case decoding for quantum error correction", which proposes the Clique QEC decoder for cryogenic quantum systems. Clique is a lightweight cryo-decoder for decoding and correcting common trivial errors, so that only the rare complex errors are handled outside the cryo-refrigerator. Clique eliminates 90-99+% of the cryo-refrigerator I/O decoding bandwidth, while supporting more than a million physical qubits.

Finally, I will conclude with an overview of other prior and ongoing work, along with my future research vision toward practical quantum advantage.

Biography:

Gokul Subramanian Ravi is a 2020 NSF CI Fellows postdoctoral scholar at the University of Chicago, mentored by Prof. Fred Chong. His research targets quantum computing architecture and systems, primarily on themes at the intersection of quantum and classical computing. He received his PhD in computer architecture from UW-Madison in 2020 and was advised by Prof. Mikko Lipasti. He was awarded the 2020 Best ECE Dissertation Award from UW-Madison and named a 2019 Rising Star in Computer Architecture. His quantum and classical computing research have resulted in publications at top computer architecture, systems, and engineering venues (such as ASPLOS, ISCA, MICRO, HPCA, TACO, ISLPED, QCE, IISWC), as well as two granted and three pending patents. His co-authored work was recognized as the Best Paper at HPCA 2022 and as a 2023 IEEE Micro Top Picks Honorable Mention.

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Monday / CS Seminar

March 13th / 12:00 PM

Mudd 3514

Title: Data Structures for Fast Systems

Speaker: Alex Conway

Abstract:

In this talk, I'll show how algorithms can be used to solve decades-old problems in systems design. I'll present an algorithmic approach to co-designing TLB hardware and the paging mechanism to increase TLB reach without the fragmentation issues incurred by huge pages. Along the way, I'll introduce a new hash-table design that overcomes existing tradeoffs, and achieves better performance than state-of-the-art hash tables both in theory and in practice. Key to these results are "tiny pointers," an algorithmic technique for compressing pointers.

Biography:
Alex Conway is a senior researcher at VMware. He received his PhD from Rutgers, where he was advised by Martín Farach-Colton. His work has primarily focused on randomized data structures and their use in storage systems, and covers the full research stack, from theory to systems to product. He is the co-creator and research lead of SplinterDB, an enterprise-grade key-value store deployed in VMware products.

Future CS Seminars:

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

Title: Designing Formally Correct Intermittent Systems
Speaker: Milijana Surbatovich

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 / 12:00 PM
Mudd 3514

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

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

The pay rate is $16/hour and meals will be provided.  Even if you’re only available for a half day, your help will be greatly appreciated!  You must currently be in the Northwestern Payroll system (note: all graduate students already are; undergraduates who have student temp or work-study positions also are).   Responsibilities may include: moderating Zoom rooms for remote attendees, signing folks in at tables in the front, general crowd control during meal times, etc.

If you have availability during this week to help out on site, please contact Madeline Wilson (madeline.wilson@northwestern.edu) with your availability each day from 8am – 5:30pm by Tuesday, March 7.

If you are a grad student, please fill out the Permission to Work form and notify your advisor. https://www.northwestern.edu/tgs/about/for-staff/funding/graduate-student-permission-to-work-request.html

Once you express interest we will follow up with required paperwork. Please reach out if you have any questions.

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 our minority scientists and Ph.D. recruiting team, demonstrates our commitment to a diverse work force.

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 at their website. All applications are due by April 30th, 2023.

Participants in the conference may be considered for future employment at Dow. However, participation neither obligates the student to apply for employment, nor guarantees future consideration for employment by Dow. For those wishing to learn more about opportunities, please visit our careers page.

For further information, please contact the symposium chair Karena Lekich.

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Northwestern Department of Computer Science

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