Upcoming events

ML Seminar: Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery

Vertical Reasoning Enhanced Learning, Generation and Scientific Discovery

CS&E Colloquium: Toward Practical Quantum Computing Systems with Intelligent Cross-Stack Co-Design

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Hanrui Wang (MIT), will be giving a talk titled "Toward Practical Quantum Computing Systems with Intelligent Cross-Stack Co-Design".

Abstract

Quantum Computing (QC) has the potential to solve classically hard problems with greater speed and efficiency, and we have witnessed exciting advancements in QC in recent years. However, there remain substantial gaps between the application requirements and the available devices in terms of reliability, software framework support, and efficiency. To close the gaps and fully unleash quantum power, it is critical to perform AI-enhanced co-design across various technology stacks, from algorithm and program design, to compilation, and hardware architecture.

In this talk, I will provide an overview of my contributions to the architecture and system supports for quantum computing. At the algorithm and program level, I will introduce QuantumNAS, a framework for quantum program structure (ansatz) design for variational quantum algorithms. QuantumNAS adopts an intelligent search engine and utilizes the noisy feedback from quantum devices to search for program structure and qubit mapping tailored for specific hardware, leading to notable resource reduction and reliability enhancements. Then, at the compilation and control level, I will discuss Q-Pilot, a compilation framework for the Field-Programmable Qubit Array (FPQA) implemented by the emerging reconfigurable atom arrays. This framework leverages movable atoms for routing 2Q gates and generates atom movements and gate scheduling with high scalability and parallelism. On the hardware architecture and design automation front, I will present SpAtten, an algorithm-architecture-circuit co-design aimed at Transformer-based quantum error correction decoding. SpAtten supports on-the-fly error pattern pruning to eliminate less critical inputs and boost efficiency. Finally, I will conclude with an overview of my ongoing work and my research vision toward building software and hardware supports for practical quantum advantages.

Biography

Hanrui Wang is a Ph.D. Candidate at MIT EECS, advised by Prof. Song Han. His research focuses on architecture and system-level supports for quantum computing, and AI for quantum. His work appears in conferences such as MICRO, HPCA, QCE, DAC, ICCAD, and NeurIPS and has been recognized by the QCE 2023 Best Paper Award, ICML RL4RL 2019 Best Paper Award, ACM Student Research Competition 1st Place Award, Best Poster Award at NSF AI Institute, Best Demo Award at DAC University Demo, MLCommons Rising Star in ML and Systems, and ISSCC 2024 Rising Star. His work is supported by the Qualcomm Innovation Fellowship, Baidu Fellowship, and Unitary Fund. He is the creator of the TorchQuantum library, which has been adopted by the IBM Qiskit Ecosystem and PyTorch Ecosystem with 1.2K+ stars on GitHub. He is passionate about teaching and has served as a course developer and co-instructor for a new course on efficient ML and quantum computing at MIT. He is also the co-founder of the QuCS "Quantum Computer Systems" forum for quantum education.

CS&E Colloquium: Emily Tseng

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Emily Tseng (Cornell Tech), will be giving a talk.

ML Seminar: Scientific Innovations in the Age of Generative AI

Scientific Innovations in the Age of Generative AI

CS&E Colloquium: The marriage of (provable) algorithm design and machine learning

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Sandeep Silwal (MIT), will be giving a talk titled "The marriage of (provable) algorithm design and machine learning".

Abstract

The talk is motivated by two questions at the interplay between algorithm design and machine learning: (1) How can we leverage the predictive power of machine learning in algorithm design? and (2) How can algorithms alleviate the computational demands of modern machine learning?
 
Towards the first question, I will demonstrate the power of data-driven and learning-augmented algorithm design. I will argue that data should be a central component in the algorithm design process itself. Indeed in many instances, inputs are similar across different algorithm executions. Thus, we can hope to extract information from past inputs or other learned information to improve future performance. Towards this end, I will zoom in on a fruitful template for incorporating learning into algorithm design and highlight a success story in designing space efficient data structures for processing large data streams. I hope to convey that learning-augmented algorithm design should be a tool in every algorithmist's toolkit.
 
Then I will discuss algorithms for scalable ML computations to address the second question. I will focus on my works in understanding global similarity relationships in large high-dimensional datasets, encoded in a similarity matrix. By exploiting geometric structure of specific similarity functions, such as distance or kernel functions, we can understand the capabilities -- and fundamental limitations -- of computing on similarity matrices. Overall, my main message is that sublinear algorithms design principles are instrumental in designing scalable algorithms for big data. 
 
I will conclude with some exciting directions in pushing the boundaries of learning-augmented algorithms, as well as new algorithmic challenges in scalable computations for faster ML.

Biography

Sandeep is a final year PhD student at MIT, advised by Piotr Indyk. His interests are broadly in fast algorithm design. Recently, he has been working in the intersection of machine learning and classical algorithms by designing provable algorithms in various ML settings, such as efficient algorithms for processing large datasets, as well as using ML to inspire algorithm design.

CS&E Colloquium: Modern Algorithms for Massive Graphs: Structure and Compression

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Zihan Tan (Rutgers University), will be giving a talk titled "Modern Algorithms for Massive Graphs: Structure and Compression."

Abstract

In the era of big data, the significant growth in graph size renders numerous traditional algorithms, including those with polynomial or even linear time complexity, inefficient. Therefore, we need novel approaches for efficiently processing massive graphs. In this talk, I will discuss two modern approaches towards this goal: structure exploitation and graph compression. I will first show how to utilize graph structure to design better approximation algorithms, showcasing my work on the Graph Crossing Number problem. I will then show how to compress massive graphs into smaller ones while preserving their flow/cut/distance structures and thereby obtaining faster algorithms.

Biography

Zihan Tan is a postdoctoral associate at DIMACS, Rutgers University. Before joining DIMACS, he obtained his Ph.D. from the University of Chicago, where he was advised by Julia Chuzhoy. He is broadly interested in theoretical computer science, with a focus on graph algorithms and graph theory.

Thirst for Knowledge: AI in Health and Medicine

Join the Department of Computer Science & Engineering (CS&E) for this all-alumni event to discuss AI in health and medicine, featuring Chad Myers, Ju Sun, Yogatheesan Varatharajah, and Qianwen Wang. Enjoy hosted beverages and appetizers, and the chance to reconnect with former classmates, colleagues, instructors, and friends. All alumni of the University of Minnesota CS&E programs (Computer Science, Data Science, MSSE) are invited to attend, and guests are welcome. 

There is no charge to attend our event, but pre-registration is required. 

About the Program

While tools like ChatGPT allow the public to use AI for various tasks, computer scientists around the world are hard at work applying AI to some of the most critical problems in society. CS&E researchers are applying AI techniques to combat problems in the healthcare space - like clinician burnout, disease prediction, and data imbalance issues in biomedical data science.

Learn more about our AI efforts at z.umn.edu/AIforchange 
Check out our medical AI initiatives at z.umn.edu/MedicalAIPrograms 

CS&E Colloquium: Caiwen Ding

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Caiwen Ding (University of Connecticut), will be giving a talk.

CS&E Colloquium: Yu Chen

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Yu Chen (EPFL), will be giving a talk.

MSSE Information Session (Virtual)

Interested in learning more about the University of Minnesota's Master of Science in Software Engineering program?

Reserve a spot at an upcoming virtual information session to get all your questions answered.

Info sessions are recommended for those who have at least 1-2 years of software engineering experience.

During each session, MSSE staff will review:

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

RSVP for the next information session now