Big Data

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

The Rising Star Award is based on an individual’s whole body of work in the first five years after the PhD.

A new application for an ongoing NSF project could bolster contract tracing efforts.

Graphical online simulation could spur more targeted COVID-19 protection measures.

A major defense project pivots to explore how to encourage COVID-safe behavior effectively.

The algorithm can pick out weak signals from nuclear weapons materials, hidden in ordinary radiation sources like fertilizer.

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

A wrist-worn device detected disrupted sleep 24 hours before study participants began shedding flu viruses.

The analytical model, called Accelerometer, can be applied in the early stages of an accelerator’s design to predict its effectiveness before ever being installed.

Karl (BSE MSE PhD EE) talks about how his time at Michigan helped prepare him for his dream job at Intel and a career advancing embedded memory technology and circuits.

How changing the rules of computing could lighten Big Data’s impact on the internet.

A team at Michigan proposed an approach to generating realistic and high-fidelity stock market data to enable broader study of financial markets.

Balzano is honored for her all-around excellence in teaching, mentorship, and curriculum development.

This round of funding strongly encourages pioneering work with the potential for major expansion.

Thin film transistors stacked on top of a state-of-the-art silicon chip could help shrink electronics while improving performance.

Neuhoff, an internationally recognized expert in information theory, source coding, and image processing, retired earlier this year.

The team's new tool will combine of software and data to make gathering structured data dramatically easier.

Erie provided database repairs that were previously performed exclusively by human programmers.

The frame in which a human marks out the boundaries of an object makes a huge difference in how well AI software can identify that object through the rest of the video.

Balzano will work with Portuguese researcher Mário Figueiredo to develop new machine learning methods impacting medical diagnosis and treatment.

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

Human-generated responses could remotely assist autonomous vehicles decision’s during times of uncertainty.

Applications include managing large networked systems, such as sensor networks, power grids, or computer networks.

Electrical and Computer Engineering (ECE) faculty and students at Michigan are part of the revolution in data science that is happening today.

The papers provide data-driven solutions to hospital infection and the use of machine learning in healthcare.

Balzano uses statistical signal processing, matrix factorization, and optimization to unravel dynamic and messy data.

MDST brings together students from many fields to get their hands dirty with real data science problems and tools.

Foofah is a tool that can help to minimize the effort and required background knowledge needed to clean up data.

When it comes to their smartphones, immigrants struggle to apply instinctive caution, according to a study by a team of University of Michigan researchers.

This award is given to the VLDB paper published ten years earlier that has had the most influence since its publication.

The researchers identified movement between industry, academia, and government work, tracked the growth of important organizations, and built predictive models for career transitions and employer retention.

Even though we interact with different web services in different ways, there are clues in the data that can indicate trends and identify a unique profile.

New tools could fight crime, protect financial system

Hero and his team may have discovered a better way to facilitate communication using a twist on the classic game of 20 Questions.

Lecture part of highest professorial honor bestowed on U-M faculty.

The new partnership will provide scalable storage and an analytic software framework available to all U-M researchers.

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.

Increasingly, market manipulators are attacking market integrity through complex computer-controlled attacks.

Verdict can make databases deliver answers more than 200 times faster while maintaining 99 percent accuracy.

With Codeon, developers can request help by speaking their requests aloud within the context of their Integrated Development Environment (IDE).

Kurator is a hybrid intelligence system leveraging mixed-expertise crowds to help families curate their personal digital content, including videos and photos.

Researchers are working to design a successful movie that will attract the interest of a targeted demographic by leveraging user ratings, reviews, and product characteristics.

The identified changes in social media behavior may point to real events and changes, some of which can benefit from intervention.

Centralizing available data in the intelligent systems community through a COmputer Vision Exchange for Data, Annotations and Tools, called COVE.

The Big Data Summer Bootcamp is a six-week interdisciplinary training and research program at the University of Michigan.

Prof. Emily Mower Provost is collaborating to develop new technologies that provide individuals with insight into how the disease changes over time.

QuadMetrics offers a pair of services to help companies both assess the effectiveness of their security and decide the best way to allocate (or increase) their security budget.

CSE students and faculty will collaborate as a part of a larger team to help respond to the crisis.

Clark proposed framing the issue of collecting data from a network of different sensors as an optimization problem, making a solution easier to formulate for different systems.

Prof. Wiens will continue to use machine learning techniques to study the disease.

The team used machine learning to extract information from NBA sports data for automatically recognizing common defense strategies to ball screens.

He has built software systems for information extraction, database integration, and feature engineering and applied these to problems in the social sciences.

Her primary research interests lie at the intersection of machine learning and healthcare.

Prof. Mozafari is passionate about building large-scale data-intensive systems that are more scalable, more robust, and more predictable.

The software enables users to ask questions about the hosts and networks that compose the Internet and get an immediate reply.

Such tactics, even if effective in reducing crime, raise civil liberty concerns.

The purpose of the ECFHP is to help Intel connect with the best and brightest early career faculty members who show great promise.

MIDAS is the new focal point for the multidisciplinary discipline of data science at Michigan, and part of Michigan’s $100M Data Science Initiative.

Students could use sensors or other data collection tools to pursue a goal of their choosing.

His paper detailed his use of big data analysis to solve a major problem of cyber security.

Yoon is leading a team that will design new light sources with lasers capable of zooming in on individual neuron circuits within the brain.

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

Nataraj is using big data techniques to transform the field of medical imaging

Lu plans to design and fabricate a computer chip based on so-called self-organizing, adaptive neural networks.

Nadakuditi’s research has applications in biomedical signal processing, wireless communications, geophysical signal processing, array processing, and finance.

The technique involves a visualization method that renders clinical flow cytometry data more interpretable to pathologists.