College researchers ranked among top 2% in the world
Eighteen researchers from the College of Engineering & Applied Science have made the latest list of the top 2% of researchers in the world, The list is compiled by Stanford-Elsevier, which ranks researchers by how often their work is cited in other scientific publications, giving a gauge of their impact on their respective fields. The rankings are drawn from a massive database of the world’s top researchers.
Here are the researchers included in each list:
2024 list
Pradeep Rohatgi
Michael Nosonovsky
George Hanson
Konstantin Sobolev
Lingfeng Wang
Deyang Qu
Ali Reza
Rohit Kate
Yongjin Sung
Yi Hu
Robert Cuzner
Career-long top 2%
Pradeep Rohatgi
Michael Nosonovsky
George Hanson
Lingfeng Wang
Konstantin Sobolev
Deyang Qu
Brian S.R. Armstrong
Robert Cuzner
Krishna Pillai
Ali Reza
Hugo López
Anoop K. Dhingra
Rohit Kate
Yi Hu
Yongjin Sung
Zeyun Yu
Jun Zhang
Devendra Misra
PhD student studies how edge computing can add up to energy savings
Monika Gawande, PhD student, electrical engineering, researches edge computing, a framework to reduce energy use by processing data locally. Inspired by growing up in India with unreliable electricity, she chose UWM for its strong energy research and access to advanced tools. She aims to one day contribute to the field of energy efficiency.
“With edge computing,” she said, “you don’t have to send all data to the cloud. Processing done at the location where the data is generated can save significant amounts of energy needed by data centers.”
She said that UWM has been a great fit for pursuing her education.
“I chose UWM because it was an R1 research university and many professors were working on energy-related research,” she said. “UWM has provided me access to advanced tools and technologies that I never had access to before. It’s been a great place to learn to explore and to grow in terms of research.”
Return of the alumni featured at events in September
Representatives from four companies – Komatsu, Milwaukee Tool, Rockwell Automation and GE HealthCare – hosted events in September, giving students the chance to meet and ask questions of those who had finished their degrees and were now in the working world.
GE HealthCare College of Engineering & Applied Science alums came back to the EMS building to greet students. Alumni at Rockwell Automation coordinated a tour of their facility for undergraduate students.Alumni from Milwaukee Tool made their annual trek to campus in September, showing off their products and taking questions from students. College of Engineering & Applied Science alumni at Komatsu hosted a group of students on a tour of their company.
Slavens’ goal to protect hand health of manual wheelchair users with new grant
For people who use manual wheelchairs, pushing the wheels is not just transportation – it’s independence and physical activity. But the repetitive force required can take a toll over time on the nerves in hands and wrists.
Brooke Slavens, professor of mechanical and biomedical engineering, has received a new grant from the National Institutes of Health to examine how the mechanics and strength of the arms affect the median nerve, which controls movement and feeling in parts of the hand and fingers, in adults who use manual wheelchairs.
The $3.25 million, five-year award is co-led with a collaborator at the Shirley Ryan AbilityLab in Chicago.
Early warning system for risk
When the median nerve becomes compressed or irritated, it can lead to carpal tunnel syndrome (CTS) with symptoms such as pain, numbness, or weakness in the hand. Studies estimate that between 49% and 73% of wheelchair users experience CTS, far higher than in the general population.
“Our goal is to better understand how the physical demands of wheelchair propulsion interact with arm and hand strength,” Slavens explained. “If the muscles don’t have enough strength to meet those demands, it could potentially lead to chronic nerve injury.”
This research will involve a large, cross-sectional study of manual wheelchair users with spinal cord injuries. By identifying the point where physical demand outweighs the user’s strength, the researchers hope to determine who might be at highest risk for median nerve compression – before symptoms appear.
Affects millions of users
The long-term goal is prevention: giving clinicians the tools to help wheelchair users maintain mobility without developing painful secondary conditions. This could involve training strategies, strength-building, or redesigned wheelchair use techniques.
“Millions of adults in the U.S. use a wheelchair,” Slavens said. “If we can reduce the risk of nerve injury and improve comfort and function, we can make daily life better for many people, while also protecting their long-term health.”
The project is supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) of the National Institutes of Health.
Sung’s research recognized at UWM Employee Excellence Awards
When you think of a microscope or a medical scan, you probably imagine peering into a hidden world — structures too small to see with the naked eye. But for Yongjin Sung, those tools are just the beginning.
He’s building imaging systems that reveal far more than ever before, transforming how we see the microscopic world.
Sung’s work was recognized with the “Office of Research/UWM Foundation Research Award” and presented at the UWM Employee Excellence Awards on Oct. 15.
Sung invented “snapshot optical tomography,” a technique that captures a full 3D image of a specimen in a single shot — no scanning required. That innovation opened the door to “4D” chemical imaging, allowing scientists to observe how materials or biological samples change over time.
His research, supported by the National Science Foundation, National Institutes of Health and the U.S. Department of Defense, has biomedical, semiconductor, and pharmaceutical applications.
Working with Massachusetts General Hospital, Sung also helped pioneer next-generation X-ray technologies, including phase contrast and dark-field imaging that provide much more detail than conventional X-rays. He even co-invented a motion-free CT system that is a potential game changer for clearer, faster scans and patient comfort.
Now, Sung is collaborating with researchers at Stanford and the University of California, San Francisco, to create a microscopic version of the PET scan. This breakthrough could allow scientists to track diseases at the cellular level, offering new insight into how illness begins — and how it might be stopped.
The UWM Excellence Awards were established in 1978 and continue to recognize and encourage UWM assistant and associate professors who have demonstrated potential to achieve distinction in their academic disciplines.
AI can put data centers on an energy diet with smart hardware
Data centers have a large appetite for electricity – and a bad habit of wasting it. Surprisingly, AI – the very thing that data centers power – could also provide the energy diet they need.
With electricity demand climbing, Feng Guo, assistant professor of electrical engineering, said better energy efficiency could yield significant cost savings.
Guo specializes in developing high-efficiency and high-power-density devices that manage energy performance – equipment that can also be used in electrified transportation systems.
Our students and faculty are making waves of impact.
Here, they are using AI to make and conduct smart devices to save energy on a nationwide scale.
Electricity usually arrives from the grid as alternating current (AC) over transmission lines. But inside a data center, nearly all the equipment runs on direct current (DC). Before it reaches the servers, the power must pass through a host of devices like transformers, converters and controllers, stepping through multiple stages to change the current or the voltage.
Along the way, each conversion leaks energy.
Devices guide the electricity journey
Smart hardware can dramatically improve energy efficiency. Guo (front) holds a component of semiconductor testing, while Chaimanekorn displays the paralleled power electronics converters which increase the output current and power.
Data centers are raising their DC voltage to pack more power into the same space and boost efficiency. This requires redesigning equipment, collectively called power electronics, needed to shepherd the flow of electricity in ways that achieve that.
Researchers feed AI models vast amounts of electrical data, from voltage and current to frequency patterns. From the data, AI develops a deep understanding of how these systems behave. That knowledge informs smarter designs for converters and other crucial components that waste less energy from the start.
And that’s only the beginning. After engineers model the system, then AI can figure out the most efficient playbook.
Think of AI as a conductor, orchestrating “smart” components that generate, route, and control power. It can even take on tricky jobs like fault detection and managing renewables.
“Each time you train the model, you’re asking a slightly different question,” Guo said. “It’s a process that strengthens the model. You start with the ocean, then zoom in, little by little.”
GPUs, edge computing, and the push for energy-smart AI
Originally designed for video games, graphics processing units (GPUs) now power much of today’s AI – from voice assistants to self-driving cars. Unlike regular computer chips, GPUs have thousands of small cores that handle many simple tasks at once.
GPUs that are built into larger devices process data locally – at single locations – called “the edge.” This approach saves power, speeds results, and keeps information more private because it doesn’t have to be sent to the cloud, said Roger Shen, assistant professor of electrical engineering.
Edge computing can handle small, local AI models that later can contribute to larger shared models through a process called “federated learning,” while preserving data security.
Making Waves of Impact
See how computing on the ‘edge,’ rather than in the cloud, offers speed, data privacy, and efficiency.
A GPU, just larger than the palm of your hand, was created for gaming, but can perform many simple, repetitive tasks simultaneously, accelerating mathematical calculations. They can be deployed at individual locations where they can process AI “at the edge” – right were the data is generated.
Another key to energy savings is through better-tailored algorithms, Shen said.
A tradeoff
Efficient algorithms can reduce the number of calculations needed and handle larger data sets without overwhelming servers.
“My focus is on the algorithm side,” Shen said. “This provides a promising solution to the energy consumption issue because an embedded system is simpler – it’s just focused on a specific task.”
There’s always a tradeoff, Shen noted. Using less power often means a model is less precise. But not every task requires perfection. In manufacturing, for example, AI may only need to decide a yes-or-no question: “Is this part defective or not?”
In such a case, a small loss in accuracy is inconsequential and worth the savings in speed and energy.
Embedded AI processing can flag defects in real time and prevent breakdowns before they happen, shaping the next wave of smart manufacturing.
Shen has seen the payoff firsthand while working with a local company.
“Our job was to dig into their production data to see if there was some circumstance that explained their problem,” he said.
AI speeds the hunt for better rechargeable batteries
Rechargeable batteries power everything from electric vehicles to laptop computers. They are in-demand, but far from perfect. Improving them means finding the ideal mix of elements from the periodic table, each with unique properties alone and in combination.
Like drug discovery, the search can be overwhelming: thousands of possible materials, only a few with the right traits.
In a battery, the electrodes at each end and the electrolyte in the middle drive the electrochemical reactions that store and release energy.
Making Waves of Impact
With all the past research in the data pool, AI can help find the chemical ‘needle in the haystack’ for next-gen batteries.
Professor Junjie Niu, industrial & manufacturing engineering (left), and Osman Shovon, PhD student in materials science & engineering, work with a tap density tester, which measures how tightly a powder, like a cathode or anode material, can be packed.
The challenge is to identify materials that boost energy density and electron flow so batteries last longer and charge faster.
“If you had to choose materials armed with only the periodic table, it would probably take you 100 years,” said Professor Junjie Niu, industrial & manufacturing engineering, whose lab researches improvements for energy storage.
“AI is good for the selection of new materials to use in these parts of the batteries,” Niu said. “It helps us narrow the field to the top five or ten possibilities to meet the performance requirements and then my students focus on only the most promising.”
Different applications – say, a car battery versus a phone battery – require different qualities. So AI also scans and compiles data from past studies, accelerating the screening process and pointing to new directions.
Success lies in asking informed questions, he said.
“We’re not asking AI for the answers. We’re using what we know to ask for specific clues within certain parameters. Then we validate through our experiments.”
Record attendance at IPIT and WisDOT’s annual Southeast Wisconsin Transportation Symposium
Tall vehicles – those that have a higher ground clearance than sedans – have larger-than-usual blind spots, and it poses a particular risk for pedestrians, according to a study by master’s student Joely Overstreet and her advisor, Professor Xiao Qin, both in civil & environmental engineering at UWM. The two are investigating solutions.
The presentation was among the UWM research in the breakout sessions at the annual Southeastern Wisconsin Transportation Symposium at UWM on Oct. 10.
UWM’s Institute for Physical Infrastructure and Transportation (IPIT) and WisDOT co-hosted the symposium, now in its fifth year, to bring together researchers, students and transportation professionals to showcase related research and share innovative practices.
Xiao Qin, professor and director of UWM’s Institute of Physical Infrastructure and Transportation, opens the symposium. Now in its fourth year, the symposium attracted record attendance with 220 regitrants. Joely Overstreet, master’s student, civil & environmental engineering, presents her work on pedestrian visibility in tall vehicles.Bryan Porter, dean of the UWM Graduate School, joined the symposium to present on traffic psychology and behavior.Tom Shi, assistant professor, civil & environmental engineering (left), and PhD student Muhammad Fahad are studying whether illuminating crosswalks with blue light rather and white light affects pedestrian safety. The hypothesis is that blue light at around 7000 Kelvin does a better job of capturing the attention of drivers approaching crosswalks. It’s a data intensive study that also involved Xiao Qin, professor, civil & environmental engineering, and Tian Zhao, associate professor, computer science.
Attendance topped last year’s with over 230 registrants from across the state and multiple disciplines, said Xiao Qin, who also is IPIT director.
IPIT currently has 34 affiliated faculty members across five colleges at UWM. There are 31 active projects addressing issues such as traffic safety, urban mobility, infrastructure preservation.
Symposium breakout sessions covered a wide range of other topics, such as:
Artificial Intelligence in Transportation (Xiao Qin, UWM)
From Behavior to Breakthroughs: How Technology is Shaping Safer Driving Habits (Bryan Porter, Dean, UWM Graduate School)
Transportation Demand Management Opportunities in the Region (Dana Shinners, Southeastern Wisconsin Regional Planning Commission)
Advancing AV/CV Readiness in Wisconsin: Panel discussion and demonstration (Xiaopeng Li, UW-Madison, and Tom Shi, UWM)
Speakers included WisDOT Secretary Kristina Boardman and Victoria Sheehan, executive director of the Transportation Research Board of the National Academies of Sciences, Engineering and Medicine.
From pixels to predictions: Students can uncover AI uses in the lab
Cancer research often demands patience – hours of labeling 3D images, tracing tumor boundaries pixel by pixel before the real work can even begin.
In Associate Professor Mahsa Dabagh’s lab, artificial intelligence is reshaping how researchers predict and treat disease. Dabagh, a biomedical engineering faculty member, studies how blood flows through and feeds tumors and how subtle biological and environmental cues can signal cancer’s next move.
“AI helps us spend less time exploring the data and more time using it to design solutions,” Dabagh said.
Making Waves of Impact
A newcomer to AI seizes the opportunity to fast-track the time-consuming task of segmentation.
Biomedical engineering student Miles Smith started by manually labeling medical scans of tumors. Now he’s training AI to do the time-consuming job faster and smarter. ‘Learning these methods will be useful no matter where I end up,’ he said.
Her team is creating a deep-learning system that doesn’t just scan for tumors – it builds a virtual model of them. By analyzing MRI and biopsy images alongside detailed patient data such as protein activity, family medical history, and lifestyle factors, the tool can recognize not only what a tumor looks like, but how it might behave.
“Research in the last decade shows that one data source is rarely enough for accurate prediction,” Dabagh said. “AI’s strength is in pulling together all those layers of information to create a more complete picture.”
Undergraduate contributions
Biomedical engineering senior Miles Wehner’s summer role in Dabagh’s lab was to work on the first step of the project, labeling tumors in MRI scans, a process that can take an hour per patient. Going slice by slice through hundreds of 3D images, Wehner had to carefully trace and label the tumor areas pixel by pixel.
He soon saw the potential for AI to speed things up. Now he’s building a platform that uses both his own segmented scans and publicly available datasets to automate the entire process, bringing AI into the process earlier than the final predictions.
“It’s still a lot of work to train our models,” Dabagh said, “but it’s worth it. It can improve clinicians’ predictions and give them personalized guidance for each patient’s treatment. That means saving more lives.”
For Wehner, who only started coding through a summer program at UWM that introduces students to AI and programming, the research opened new doors. The program, Cyberinfrastructure Comprehensive, Applied and Tangible Summer School, or CIberCATSS, is funded by the National Science Foundation through 2026 and students with a faculty mentor can be admitted.
“They say AI is the future. Learning these methods will be useful no matter where I end up,” he said. “Doing this research helped me see what’s possible.”
