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Andrew Owens’ research group uses visual illusions to test the limits of diffusion models

EECS graduate students Daniel Geng, Aaron Park, and Ziyang Chen use ambiguous image generation to understand diffusion models.

Fifteen papers by ECE researchers to be presented at the Conference on Neural Information Processing Systems

Topics of accepted ECE NeurIPS papers include diffusion models, large language models, multi-armed bandit models, and more.

ECE faculty design chips for efficient and accessible AI

Faculty specializing in architecture, hardware, and software innovation accelerate machine learning across a range of applications.

Andrew Owens receives NSF CAREER Award for research to improve machine perception systems

Prof. Owens’ research will help fully autonomous systems interact with their environments without human supervision.

Matthew Raymond recognized for research using ML techniques to design new types of medicine

Doctoral student Matthew Raymond wants to facilitate the development of new and groundbreaking nanomedicines.

Can Yaras recognized for his research aimed at efficient algorithms for LLMs

Doctoral student Can Yaras wants to reduce the carbon footprint of AI.

Leveraging artificial intelligence for early detection of lung cancer

Predictive models developed by an interdisciplinary U-M research team have improved early lung cancer detection beyond traditional measures, with the potential to save lives.

Hun-Seok Kim appointed as inaugural Samuel H. Fuller Early Career Professor of Electrical and Computer Engineering

Prof. Kim is a world leader in efficient algorithm and VLSI design for wireless communication, signal processing, computer vision, and machine learning.

New textbook teaches students about matrix methods and their real world applications

Linear Algebra for Data Science, Machine Learning, and Signal Processing, written by ECE Professors Jeffrey Fessler and Raj Nadakuditi, provides an accessible and interactive guide to matrix methods.

OptoGPT for improving solar cells, smart windows, telescopes and more

Taking advantage of the transformer neural networks that power large language models, engineers can get recipes for materials with the optical properties they need.

Fourteen papers by ECE researchers to be presented at the International Conference on Machine Learning

Accepted papers for the ICML conference span topics including deep representation learning, language model fine-tuning, generative modeling, and more.

GenAI diffusion models learn to generate new content more consistently than expected

Award-winning research led by Prof. Qing Qu discovered an intriguing phenomenon that diffusion models consistently produce nearly identical content starting from the same noise input, regardless of model architectures or training procedures.

Linking online and offline social networks to better predict real world impact

Prof. Lei Ying leads a new MURI that is focused on the interplay between online and offline networks and how they could impact disruptive behavior and events.

Improving generative AI models for real-world medical imaging

Professors Liyue Shen, Qing Qu, and Jeff Fessler are working to develop efficient diffusion models for a variety of practical scientific and medical applications.

Neural Collapse research seeks to advance mathematical understanding of deep learning

Led by Prof. Qing Qu, the project could influence the application of deep learning in areas such as machine learning, optimization, signal and image processing, and computer vision.

Understanding attention in large language models

How do chatbots based on the transformer architecture decide what to pay attention to in a conversation? They’ve made their own machine learning algorithms to tell them.

Designing Synthetic Human Gut Microbiome with AI

Prof. Al Hero was interviewed and gave a presentation about his research using machine learning to improve our understanding of the human gut

Machine learning begins to understand the human gut

The new computer model accurately predicts the behavior of millions of microbial communities from hundreds of experiments, an advance toward precision medicine.

Teaching Machine Learning in ECE

With new courses at the UG and graduate level, ECE is delivering state-of-the-art instruction in machine learning for students in ECE, and across the University

Immune to hacks: Inoculating deep neural networks to thwart attacks

The adaptive immune system serves as a template for defending neural nets from confusion-sowing attacks

Qing Qu receives CAREER award to explore the foundations of machine learning and data science

His research develops computational methods for learning succinct representations from high-dimensional data.

Using neural networks and machine learning to design the first universal decoder for the next generation of wireless systems

PhD student Mohammad Vahid Jamali has been awarded a Qualcomm Innovation Fellowship to work on developing a single neural decoder that can decode several channel codes at once.

$7.5M MURI to make dynamic AI smarter and safer

Researchers from four U.S. institutions aim to pull the best from control theory and machine learning to build safer mobile, intelligent systems.

Profiles in ECE: Rucha Apte (MS ECE 2021)

From the internships that inspired her interest in signal & image processing and machine learning to late night study sessions at the Duderstadt to her background in classical dance, Master’s student Rucha Apte shares her journey with us.

Qing Qu uses data and machine learning to optimize the world

A new faculty member at Michigan, Qu’s research has applications in imaging sciences, scientific discovery, healthcare, and more.

Fairer AI for long-term equity

Prof. Mingyan Liu is a key member of a project to mitigate bias in Artificial Intelligence and Machine Learning systems for long-term equitable outcomes.

New machine learning method improves testing of stem-like tumor cells for breast cancer research

To improve the prediction and identification of stem-like cancer cells, Prof. Euisik Yoon’s group developed a method that is 3.5 times faster than the standard approach.

Xueru Zhang awarded Rackham Predoctoral Fellowship

Zhang is working to improve data security and address important ethical issues related to AI and discriminatory data sets.

Hun-Seok Kim receives CAREER Award to facilitate Internet of Things connectivity

Kim takes an interdisciplinary approach to tackle challenges in heterogeneous classes of energy-efficient and versatile communication systems.

Machine Learning takes over the EECS Atrium

Students in EECS 545: Machine Learning, taught by Prof. AL Hero, presented their final projects in a poster session sponsored by KLA.

Creating a place where kids of all abilities can play together

Prof. Hun-Seok Kim helped design iGYM, an augmented reality system that allows disabled and able-bodied people to play physical games together.

Enabling large-scale testing of cancer drugs with machine learning

Prof. Euisik Yoon and his team developed a new machine learning tool that enables large-scale testing of cancer drug effectiveness with microfluidics.

Machine Learning and Systems: A conversation with 2020 Field Award winners Al Hero and Anders Lindquist

Hero and Lindquist took a few minutes to talk about the impact of machine learning on Signal Processing and Control Systems, and what they plan to do about it

Laura Balzano receives NSF CAREER Award to improve machine learning for big data applications

Her research deciphering messy data sets will first tackle applications in genetics and computer vision.

Crafting better digital systems with ECE PhD student Jie-Fang Zhang

Zhang is recognized with the Chia-Lun Lo Fellowship for his work designing hardware solutions that could help support computer vision and machine learning.

Blue Sky: Up to $10M toward research so bold, some of it just might fail

Inspired by startup funding models, Michigan Engineering reinvents its internal R&D grant structure.

Jason Corso on artificial intelligence

The most exciting use of AI for me focuses around a better collective use of our available resources, says Prof. Corso.

Mingyan Liu, 2018 Distinguished University Innovator, talks about her company and data science commercialization

Mingyan Liu, recipient of the 2018 Distinguished Innovator of the Year award, gave a talk about her startup company and participated on a panel discussing data science commercialiation.

Laura Balzano partners with 3M to advance research in big data

Prof. Laura Balzano received a 2018 3M Non-Tenured Faculty Award to advance her research in Big Data.

Students win prizes for improving image processing techniques for liver cancer detection and much more

Students in EECS 556: Image Processing, explore methods to improve image processing in applications such as biomedical imaging and video and image compression

Andrew Wagenmaker awarded NSF Fellowship for machine learning

Wagenmaker will utilize the award as he pursues his doctoral degree at the University of Washington.

$1.6M toward artificial intelligence for data science

DARPA is trying to build a system that can turn large data sets into models that can make predictions, and U-M is in on the project.

Mingyan Liu: Confessions of a pseudo data scientist

Liu’s most recent research involves online learning, modeling of large-scale internet measurement data, and incentive mechanisms for security games.

Steven Parkison earns NSF Fellowship to design tools for the future of autonomous cars

The goal of Steven’s research is to improve vision-based perception systems on cars and to create an extra layer of safety.

Jason Corso receives Google Faculty Research Award

Prof. Corso believes that this research could make it easier to search for certain types of videos on the web.

Research in machine learning earns Notable Paper Award at AISTATS 2014

Prof. Scott’s research is in the field of machine learning, and his paper builds upon “supervised pattern classification.”

Students to use IBM Watson Cognitive Computing System in class

Michigan is one of seven universities IBM is partnering with to give students access to the technology.