University of Wisconsin–Madison


Focus on new faculty: Jiamian Hu, using computer models to improve materials for many applications


For materials scientist Jiamian Hu, the culture of interdisciplinary research collaboration at the University of Wisconsin-Madison is a major selling point for the university.

“That’s actually one of the very important reasons that I wanted to come to Wisconsin,” says Hu, who will join the materials science and engineering faculty in fall 2017. “I found that the collaboration barrier here is very low. It’s a very interdisciplinary culture here within the college and across the entire university.”

That low barrier for collaboration is especially attractive for Hu because his research centers on computational modeling, and in that field: “Collaboration is basically everything to us,” Hu says.

That’s because a computational model needs to be validated by experiments. In return, modeling can provide guidance to experiments on how to achieve or optimize a desirable property or functionality.

An award-winning researcher of inorganic materials, Hu was hired through the college’s Grainger Institute for Engineering. He comes from Tsinghua University in China via a post-doctoral stint at Penn State.

Hu primarily studies the properties of inorganic materials using the computational modeling method known as the “phase-field” method. He models the evolutional microstructure of materials—structures that are larger than the atomic scale but small enough that the naked eye cannot discern them.

“We call it the mesoscale. It typically ranges from nanometers to microns,” says Hu. “Through computer modeling, we find how you can arrange a microstructure in such a way that a material will have the functionalities or properties you need. So basically, we are trying to make existing materials much better.”

Those materials include everything from metals to polymers, and from soft materials to ceramics.

Hu is especially excited about the prospect of collaborating with current MS&E faculty like Professor Dane Morgan and Professor Izabela Szlufarska, who also do computational modeling, but on different time and spatial scales than Hu’s microstructure-focused models.

“I’m excited to see if we can collaborate and do some multi-scale modeling of materials,” says Hu.

To date, most of Hu’s phase-field method research has modeled the magnetoelectric properties of materials that are a combination of magnets and ferroelectric materials. Hu says these materials have unique properties that open up new opportunities for electronics.

“Addressing these materials almost does not require any electric currents, which means the heat production is minimal,” Hu says. “That enables us to potentially produce many different types of energy-efficient devices that could eventually save a large amount of energy for our industry.”

Additionally, the materials offer a method for converting magnetic fields into electric fields, and vice versa, for all sorts of potential applications. These range from new and much less cumbersome medical imaging equipment to smaller, more powerful and more energy-efficient electronic devices.

Hu plans to continue pursuing his research into magnetoelectric materials, but he also plans to expand his computational modeling methods to other materials and applications while at UW-Madison.

“In the future I’m planning not to limit myself into a specific area because I’m treating my phase-field model as a tool,” Hu says. “The tool can describe a microstructure and its evolution dynamics in any material system.”

In addition to his research, Hu may teach thermodynamics and kinetics of materials. He says he’s excited to share his knowledge and experience on a wide variety of topics with students.

“Hopefully I can teach other courses eventually, as well,” he says. “Teaching is the best way to learn, and Wisconsin also has a lot of resources for teaching and designing courses. It’s very cool how many resources Wisconsin has for teaching, learning and research.”

Author: Will Cushman

Allen to lead Grainger Institute for Engineering energy thrust

todd_allen-larger-photo-jpeg-crop-825wJanuary 11, 2017

Given the great scope and complexity of the energy challenges facing society, innovative research collaborations across disciplines hold the most potential to produce transformative technological breakthroughs.

Through the Grainger Institute for Engineering, an incubator for transdisciplinary research in the University of Wisconsin-Madison College of Engineering, the college is poised to drive advances that help solve technological challenges in several areas, including energy and sustainability, which is the newest focus area in the institute.

Todd Allen, a senior visiting fellow at the policy think tank Third Way, is returning to UW-Madison to lead the energy and sustainability thrust area in the Grainger Institute for Engineering.

Allen served as a faculty member in the UW-Madison Department of Engineering Physics for 10 years before taking a leave of absence in 2013 to serve as deputy director of science and technology at the Idaho National Laboratory.

“I’m excited to return to UW-Madison in this new role as thrust lead in the institute,” Allen says. “It’s a great opportunity to create some new, innovative approaches to cross-disciplinary research collaboration and education at the university, which will enable us to make a greater impact on energy issues.”

While working at Idaho National Laboratory, Allen was in charge of overseeing all energy and national security research at the lab, and he focused on bringing together researchers from disparate groups for fruitful collaborations.

For example, Allen points to a successful collaboration between a staff member in the lab’s national security group and researchers working on nuclear fuel issues.

The staff member was working on harnessing advanced digital image processing to identify subtle changes in an image that might indicate a security threat. On the nuclear fuel side, researchers want to ensure the fuel maintains its integrity and doesn’t crack or leak, so they conduct visual exams to see if cracks might be forming.

“It’s a big national lab, and the nuclear fuel people had never met the national security people,” Allen says. “But once you figure out how to connect these different researchers, they really wanted to work together because the nuclear researchers saw how they could improve their ability to understand what was going on in the fuel by connecting to the technology that the nuclear security staff member was working on for a totally different reason.”

Similarly, Allen aims to create opportunities to bring together UW-Madison faculty members from various disciplines to tackle big energy challenges in new ways. “The significant energy and sustainability challenges are bigger than a typical single faculty member’s group, which tends to focus deeply on certain technical areas,” Allen says. “With the Grainger Institute for Engineering thrust, the idea is to figure out clever combinations of people’s skills in order to address these big energy problems.”

Dan Thoma, director of the Grainger Institute, says Allen’s outstanding track record as a UW-Madison faculty member combined with his leadership experience at Idaho National Laboratory make him an excellent fit for this new role.

“In his time away from campus, Todd has been contributing to the national discourse on energy issues and has made a lot of contacts,” Thoma says. “He has the connections and the leadership ability to influence the national conversation on energy topics, and the right skill set to lead the development of large-scale multidisciplinary programs in the area of energy and sustainability.”

Allen’s background is in nuclear engineering, and in addition to his responsibilities as a thrust lead in the institute he will rejoin the engineering physics faculty and run his own research group. His research interests include fuels and materials for nuclear energy systems, with a focus on radiation damage and corrosion.

“The engineering physics department has hired a junior faculty member, Adrien Couet, in the same research area, and I want to be very supportive and helpful to him as he builds up his research program,” Allen says.

Beyond developing large-scale research programs that help solve critical technological challenges, Allen ultimately wants the cutting-edge work at UW-Madison to help influence national energy policy.

“I want to help make better connectivity between the faculty members and the work they’re doing at UW-Madison and the people in policy space, so our work can have an even greater positive impact on society,” he says.

Author: Adam Malecek

UW-Madison engineers part of $140 million ‘Manufacturing USA’ clean-energy initiative

Several University of Wisconsin-Madison engineers are among leading researchers around the country who will participate in the newly created Reducing Embodied-Energy and Decreasing Emissions (REMADE) Institute.

Led by the Rochester Institute of Technology (RIT) Golisano Institute for Sustainability, the institute was created under the U.S. Department of Energy Manufacturing USA initiative and announced Jan. 4, 2017.

The REMADE Institute is a national coalition of leading universities, national laboratories and industries that will forge new clean-energy initiatives deemed critical in keeping U.S. manufacturing competitive.

Under the RIT-led Sustainable Manufacturing Innovation Alliance, the institute will leverage up to $70 million in federal funding that will be matched by more than $70 million in private cost-share commitments from industry and other consortium members. In all, 26 universities, 44 companies, seven national labs, 26 industry trade associations and foundations and three states (New York, Colorado and Utah) are engaged in the effort.

The institute will focus its efforts on driving down the cost of technologies essential to reuse, recycle and remanufacture materials such as metals, fibers, polymers and electronic waste and aims to achieve a 50-percent improvement in overall energy efficiency by 2027. These efficiency measures could save billions of dollars in energy costs and improve U.S. economic competitiveness through innovative new manufacturing techniques, small business opportunities and offer new training and jobs for American workers.

The Grainger Institute for Engineering at UW-Madison led the university’s participation in the initiative. UW-Madison engineers involved in the institute includes civil and environmental engineer Andrea Hicks, an expert on quantifying the environmental impact of products and processes; chemical and biological engineer George Huber, who is among the world’s leading biofuels researchers; and chemical and biological engineer Victor Zavala, whose expertise centers around developing models for evaluating technological systems.

Researchers in the REMADE Institute also will develop and implement an education and workforce development program that will fill workforce gaps identified by its industry, government and academic partners and build the next generation of the recycling and remanufacturing workforce. Susan OttmannJames TinjumFrank RathWayne Pferdehirt and Paul Miller of the UW-Madison Department of Engineering Professional Development will contribute to the institute’s efforts in workforce development.

REMADE Institute partners have the following five-year goals:

  • 5 to 10 percent improvement in manufacturing material efficiency by reducing manufacturing material waste
  • 50-percent increase in remanufacturing applications
  • 30-percent increase in efficiency of remanufacturing operations
  • 30-percent increase in recycling efficiencies
  • A targeted 50 percent increase in sales for the U.S. manufacturing industry to $21.5 billion and the creation of a next-generation recycling and manufacturing workforce.

Manufacturing USA is a network of regional institutes, each with a specialized technology focus. Also known as the National Network for Manufacturing Innovation (NNMI), the consortium brings together academia, industry and federal partners with a goal to increase U.S. manufacturing competitiveness and promote a robust and sustainable national manufacturing research and development infrastructure. The institutes are tasked with bridging the gap between basic research and product development in key technology areas regarded as critical to U.S. manufacturing. Since 2012, 13 research institutes have been established, with two more planned for later in 2017. The UW-Madison College of Engineering also is a partner in the $320 million Digital Manufacturing and Design Innovation Institute, which was created in 2014.

Focus on new faculty: Po-Ling Loh, applying abstract math to real-world situations

po-ling-loh-copy-825wDecember 1, 2016

A new assistant professor in the University of Wisconsin-Madison Department of Electrical and Computer Engineering uses highly conceptual calculations to tackle concrete problems like medical imaging or modeling how diseases spread.

Po-Ling Loh, who joined the faculty in fall 2016 and is also a fellow at the Grainger Institute for Engineering, arrives at Madison with a strong background in theoretical statistics, honed and refined during her graduate studies and two years as an assistant professor at Pennsylvania University. Her deep affection for advanced analytics extends all the way back to her early education.

“At the beginning of undergrad, I loved math, especially abstraction. I just thought it was really cool. But now I feel like it’s good to have applications to the real world,” says Loh.

Some of those applications include reconstructing accurate images from medical scans. Loh is looking forward to collaborating with researchers in the medical school to help doctors improve their diagnoses based on fewer measurements.

“I’m exciting about engaging with people here. I have some colleagues in ECE who have been talking to people in radiology. I think that would be a good place for me to plug in,” says Loh.

Her interest in health also extends to global problems, such as how infections take hold and potentially become epidemics. Because multiple random interacting processes determine how pathogens proliferate, describing diseases with accurate mathematical models is no simple task.

“We’re learning that you can dream up a model, but once you start talking to someone in the field, you realize that your model needs to be adjusted. Maybe you don’t have the full information that you thought you did in the mathematical world,” says Loh.

Bridging the gap between the conceptually clean mathematical world and the messy circumstances on planet earth can be challenging, but that practical ethos motivates Loh to both pursue engineering research and educate future engineers about the power of math.

“I’m teaching the undergraduate probability and stochastic processes class. The students are interested in the math not just as an end in itself, but so that they can apply it to real world problems, whether they’re going to be working in communications systems or building devices,” says Loh.

Allowing students to pursue their own passions is a central principle of her mentoring philosophy. She relishes opportunities to work on new problems based on the questions raised by her students. That research relationship is partly what set Loh down the path of mathematically modeling disease epidemics, after her trainee became interested in HIV spreading in Africa.

Loh’s enthusiasm to explore new ideas has her seeking out collaborations across campus. She plans to work with Varun Jog, who also joined the ECE faculty at the beginning of fall semester in 2016. They bring a complementary skillset to the department.

“Varun and I have started collaborating. His interest is also very mathematical and we both like working together. We’re thinking about problems that bring in both of our strengths,” says Loh.

Joining the faculty at UW-Madison represents something of a homecoming for Loh, who grew up on Madison’s west side. After spending time away in California and on the East Coast for her education, Loh is thrilled to be back among the friendly citizens of the City on Four Lakes.

“My favorite thing about Madison is the people,” she says. “People here are genuinely nice in a way that would seem really weird if you were in another state.”

Author: Samuel Million-Weaver

Focus on new faculty: Shiva Rudraraju, studying mechanics and morphology evolution in materials

Photo of Shiva RudrarajuIn January 2017, Shiva Rudraraju will join the Department of Mechanical Engineering as an assistant professor. Also an affiliate of the college’s Grainger Institute for Engineering, Rudraraju comes from the University of Michigan, where he’s been a graduate student, postdoctoral fellow and research scientist for 10 years.

Rudraraju is a computational physicist. His research focuses on two broad areas: using computational physics to study the response and evolution of materials, and developing/leveraging high-performance computing algorithms for solving problems in materials physics.

“I’m really excited about what the College of Engineering and the Grainger Institute have to offer in terms of their collaborative environment,” says Rudraraju.

Rudraraju received his undergraduate degree in India. At Michigan, he studied failure in composite materials, especially in the context of aerospace structures. That research was in collaboration with NASA and Boeing. Following his PhD, Rudraraju was heavily inspired by the work done at the Computational Physics Group in Michigan. This helped him look at broader aspects of computational physics, primarily driven by high-performance computing, to understand multiscale processes in biological, structural and functional materials. In addition to collaborating with other computational physicists at Michigan and through the Department of Energy’s PRISMS center, Rudraraju collaborates with researchers at the University of Oxford on modeling growth and morphology evolution in biology.

Continuing that collaboration is one of Rudraraju’s three main goals when he arrives at UW-Madison in January.

“Going forward, a significant part of my research focus will continue to be in biology, especially looking at multi-physics processes and growth phenomena in biology,” Rudraraju says. “I’ll continue my exciting collaborations with the Computational Physics Group at Michigan and with the University of Oxford, where we are looking into the shape evolution in sea shells. This work has implications for understanding evolutionary dynamics.”

Rudraraju’s second goal is to continue his work in materials science, especially on multi-scale modeling which can potentially lead to the discovery of new structural and functional materials. He’ll especially focus on modeling the mechano-chemical processes in metallic alloys and battery materials.

Rudraraju is also very excited to leverage his strength in high-performance computing for developing new collaborations across the College of Engineering campus. He says that the Grainger Institute’s emphasis on transdisciplinary collaboration was a factor in his decision to come to Wisconsin from Michigan.

“The institute has this very active role in terms of collaborating in the discovery of materials and in advanced manufacturing,” Rudraraju says. “With my background, I want to look for active collaborations across the Grainger Institute and beyond.”

In addition to his research plans, Rudraraju plans to teach courses on mechanics, numerical methods and applications of high-performance computing, reflecting his evolving interests from his undergraduate years in India to his present research on high-performance computing driven computational physics.

“When I was younger, my interest was primarily in physics,” Rudraraju says. “I knew I wanted to work on physics, especially mechanics and its applications, hence I chose to study and later continue research in mechanical engineering.”

After his undergraduate years, Rudraraju pursued internships at the Helmholtz-Zentrum Dresden-Rossendorf and Technische Universitat (TU) Freiberg, both in Germany, where he was introduced to the computational aspects of mechanics.

“It was a very organic route starting from my interest in physics, extending to computational mechanics and computational physics, and then to applications in materials and biology,” he says. “So I’m still primarily driven by that curiosity in physics.”

Author: Will Cushman

Focus on new faculty: Varun Jog, mining big data for big attitudes




Varun Jog puts a modern spin on the old proverb “people are known by the company they keep” by applying advanced mathematical tools to analyze interpersonal connections within social networks.

People’s social interactions dramatically shape how they see the world. Even if someone initially doesn’t hold strong feelings about a topic, conversations with friends, colleagues and acquaintances can cause extreme viewpoints to develop.

“Community dynamics push and polarize opinions,” says Jog, who in fall 2016 joined the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison as an assistant professor.

Jog hopes to understand how this process plays out, and to develop models to predict which direction public sentiment might swing.

“Opinions evolve over time,” says Jog. “Each person is kind of independent, but once opinions start getting influenced by social networks, they start getting more extreme.”

Understanding the dynamics within social networks is much more complicated than simply scrolling through a list of someone’s Twitter followers. Picking out communities from complicated interconnected webs of interactions requires advanced mathematical techniques.

Furthermore, some people within a network exert outsized influence on a community as a whole. Identifying the opinion leaders can be useful to predict how public attitudes may form, or make marketing more effective.

“One way to market could be to give out free samples, but how do you select who receives the product?” says Jog. “I’m interested in if there’s a principled approach to finding these key people.”

Learning about networks also offers promise to the medical field. Understanding interpersonal connections can help predict how diseases spread, which is one research focus of Jog’s collaborator, Po-Ling Loh, another recent addition to the electrical and computer engineering faculty. They plan to work together on projects that play to both of their strengths as mathematically minded engineers.

Jog looks forward to working with multiple faculty members within the ECE department, as well as across campus. In fact, the opportunity to do multidisciplinary research is one of Jog’s favorite aspects of UW-Madison.

“There’s this collaborative atmosphere here. People work together a lot, even outside their area,” Jog says.

Jog also values the opportunity to train future engineers, and says teaching is probably the most satisfying thing he has ever done.

Throughout his training, Jog hasn’t limited his scope. Instead he’s always tackled challenging questions about unwieldy datasets. Jog started investigating networks during his time as a postdoctoral scholar at the University of Pennsylvania. Before then, his PhD studies at UC Berkeley focused on more abstract mathematical concepts related to information theory and geometry. In the future, he will continue to seek out interesting problems from across a wide array of disciplines.

“Eventually, I’m interested in working on questions related to genetics,” says Jog. “Information about genes makes up some of the most challenging kinds of big data.”

Author: Sam Million-Weaver

Focus on new faculty: Dimitris Papailiopoulos, speeding up machine learning pipelines



The world of science and technology is currently being reshaped by machine learning. At the heart of machine learning are algorithms whose aim is to mimic the complex processes of human learning, but on a near-unimaginable scale.

Although computers already are learning our preferences, enhancing our online experiences, and allowing for seamless automation to an unimaginable extent, for assistant professor Dimitris Papailiopoulos, who joined the electrical and computer engineering faculty in fall 2016, it is about how we can enable computers to learn concepts even faster.

In several modern applications, it can take up to several weeks for a computer to “learn” a certain concept. For instance, in driverless vehicles, the machine needs time to learn the difference between humans and cars, and intersections and cul de sacs—intuiting the same automatic connections that any human needs to make while driving. However, if researchers like Papailiopoulos can streamline this computational learning process, these novel machines will become much more efficient, accurate, and easier to interact with.

“In contemporary applications, learning algorithms are required to go through millions of examples to learn a concept—this is called the training phase,” he says. “For several modern machine-learning pipelines, this is a major computational bottleneck. The goal of my research is to significantly speed up this training phase.”

The current focus of his research is to reduce the typical duration of training from approximately a week, to only a day or two. Essentially, he’s trying to get computers to learn concepts that are in some cases as complex as those learned by humans, but at an extremely larger scale and rate.

Papailiopoulos, who grew up in Greece, developed an early interest in mathematics and computers, so electrical and computer engineering offered a perfect fit. He received his engineering diploma and master’s degree from the Technical University of Crete in Chania, Greece. He spent seven years there before moving to the United States, where he attended USC as a graduate student. He then followed his advisor to the University of Texas at Austin, where he received his PhD. After his PhD, he spent two years as a postdoctoral researcher at the University of California at Berkeley, where he was a member of the AMPLab.

During his PhD, Papailiopoulos mainly worked on large-scale data processing and data storage problems, which has led to several theoretical and practical innovations in the field of coding theory.

In many ways, his work with coding theory and data storage has been the foundation of his research. He started working on machine-learning problems a little more than three years ago, and is excited about the potential impact of developing and implementing new ideas in the field.

An academic at heart, Papailiopoulos moved to UW-Madison because of its strong research environment.

“I chose UW-Madison because I needed a place that would give me the academic and intellectual freedom to work on the things that I wanted to pursue,” he says. “I was also impressed by how excited people here were about my work and what I have to bring to the table.”

Like many new faculty members, he was also impressed by the highly collaborative and open environment on campus. “I think it’s one of the things that makes UW-Madison special, and isn’t necessarily true for many places; there’s a lot of opportunities for interdisciplinary and cross-disciplinary research here,” he says. “We have, for example, the Wisconsin Institute for Discovery, that brings together faculty from many disciplines like computer science, biology, electrical and computer engineering, statistics, with all sorts of backgrounds—this offers a fantastic space for researchers to collaborate with one another. You’ll find people from the social sciences and engineering working on a joint project, and I think that’s a very beautiful thing that you don’t find in many places.”

Papailiopoulos was hired through the college’s Grainger Institute for Engineering and is affiliated with the Wisconsin Institute for Discovery (WID) on campus. He’s part of the optimization group at the WID, which allows him to interact with people who use the same mathematical tools that he does, but often for entirely different applications.

“The optimization group offers a lot of intellectual diversity. You are exposed to concepts that may have been applied to, say, industrial and systems engineering, and you can get ideas about how you can apply those same concepts to some of the computational work in the field of machine learning,” Papailiopoulos says. “There are also all sorts of events organized by the group, which is another way to get your ideas out there, and get feedback from people of various scientific backgrounds.”

He is currently teaching a special topics class, ECE 901: Large-scale Machine Learning and Optimization. The class delves into algorithmic challenges associated with machine learning, specifically when applied to large data sets, which is very close to Papailiopoulos’s research interests.

“Machine-learning is a cutting-edge field right now, so there’s a lot of fascinating open problems,” he says. “I enjoy getting to share them with the students, and hearing interesting feedback and ideas on how we can resolve some of the existing algorithmic challenges.”

Madison has become increasingly high-tech, rated No. 5 in the country for high-tech jobs. For Papailiopoulos, Madison is a “hidden gem,” integrating both Midwestern charm with a growing tech scene.

“There’s a lot of tech industry here, and lots of young professionals inside and outside of the university, which creates a very lively and highly intellectual environment,” he says. “There are exciting things happening in Madison.”

Author: Lexy Brodt

Focus on new faculty: Benjamin Peherstorfer, developing mathematical tools to make tractable large-scale numerical simulations in engineering






How do scientists and engineers solve numerical problems that would require years upon years to solve on today’s fastest supercomputers?

They don’t.

Instead, with the assistance of mathematics, scientists and engineers can rely on approximations that provide answers that are sufficiently accurate for many applications and that can be computed in seconds on laptop computers.

Reducing the complexity of numerical simulations with mathematical tools is what keeps Benjamin Peherstorfer busy at his desk all day. Peherstorfer, hired through the college’s Grainger Institute for Engineering, is the newest faculty member in the Department of Mechanical Engineering. Peherstorfer takes complex computer models from engineers and scientists, develops mathematical tools to reduce them to only the very essential components, and performs the mathematical analysis to ensure the error introduced by this approximation is within an acceptable tolerance.

While approximations may seem not ideal, Peherstorfer says the computer models in engineering and science have become so complex that simply buying a larger computer is not an option. “Using reduced models is really not about making it fast, but about making it possible at all,” Peherstorfer says.

So, what are these incredibly complex problems Peherstorfer is interested in?

One is related to numerical simulations of liquid rocket engines (LREs). Peherstorfer uses his computer modeling methods to account for the uncertainty inherent in these problems: “In virtually any engineering system, uncertainties are introduced because of incomplete data, measurement errors, or tiny variations in the manufacturing process,” Peherstorfer explains. “So how do these small variation affect the system? To estimate the effects, we perform millions of numerical simulations to make statistical statements on how likely it is for the rocket engine to fail, for example. Reduced models are essential to make these computations tractable.”

To complicate Peherstorfer’s research, he works in the context of inverse problems, as opposed to forward problems.

“In forward problems, you have the inputs and you feed them into your numerical simulation and to get your output. In inverse problems, you have the output and you would like to know what inputs gave rise to that output.” Peherstorfer says.

A common real-world example of an inverse problem is x-ray computed tomography, where an object is imagined based on how it scatters incoming x-rays. A computationally very demanding inverse problem is imaging subsurface Earth based on seismic waves. Roughly speaking, inverse problems require more computing power and are more difficult to answer than forward problems, Peherstorfer says.

“When you now think that you additionally want to quantify uncertainties in inverse problems, then you can again see the need for developing cheap and certified reduced models,” he says.

Peherstorfer has long been interested in computational problems in science and engineering and harnessing the power of computers to help find solutions—or at least approximations—to them. He received his bachelor’s, master’s and PhD in computer science at the Technical University of Munich (TUM) in Germany. There, he was a member of the scientific computing group and worked on machine learning to detect patterns in data streams in real-time.

Peherstorfer comes to UW-Madison from a postdoctoral position at Massachusetts Institute of Technology (MIT) in its aerospace computational design laboratory. While at UW-Madison, he plans to work on multi-fidelity modeling to combine multiple computational models for uncertainty quantification in inverse problems. In addition, he’s establishing research collaborations that go beyond the department and college.

“I am absolutely excited about the incredible collaboration opportunities at UW-Madison,” Peherstorfer says. “UW-Madison provides a very collaborative environment, which is a great opportunity for me and my interdisciplinary research that cuts across math, computer science, and engineering.”

Author: Will Cushman

Advanced nano-cutter to boost emerging materials research at UW-Madison

The University of Wisconsin-Madison College of Engineering is the new home of a unique machine that is capable of 3D milling precise to one nanometer. The machine, called the ROBONANO α-0iB, is the first of its kind in North America, and it brings extremely advanced technological capabilities that could represent the future of advanced manufacturing.

The ROBONANO, which is on a multi-year loan from the Japanese robotics manufacturer FANUC, arrived on Sept. 1, 2016, and is housed in the laboratory of Sangkee Min, an assistant professor of mechanical engineering at UW-Madison and a faculty member in the Grainger Institute for Engineering. Officials from FANUC traveled from Japan for a ribbon-cutting ceremony and open house for the ROBONANO, held Sept. 11, 2016. The ROBONANO’s extremely precise capabilities offer Min and colleagues exciting new research opportunities, which he hopes will open up improved and novel approaches to the manufacturing of everything from semiconductors to toys and mobile devices to scientific instruments.

The ROBONANO’s superiority over previous generations of similar machines is obvious: Its ability to cut at the nanoscale is two orders of magnitude more precise than most machines used in advanced manufacturing today.

Image of Sangkee Min and ROBONANOThe ROBONANO is a 5-axis machine that uses non-contact air bearings, which gives it nearly limitless configurations for cutting, scribing and milling materials. Where it’s truly exceptional, however, is in its nano precision. Many materials have different properties at the nanoscale, meaning the ROBONANO can potentially handle emerging and existing materials in new and useful ways.

Min will use the machine’s unique capabilities to explore its suitability for manufacturing emerging materials, as well as currently available materials like synthetic sapphire, which is a promising shatter-proof alternative to glass for screens on devices such as tablets and smartphones. Synthetic sapphire—which is made from heating aluminum oxide to extremely high temperatures—currently is difficult to manufacture at large scales because it is very brittle and difficult to handle. However, Min has already conducted initial research on synthetic sapphire with the ROBONANO machine in Japan and discovered that the material sometimes behaves ductile when handled at the extremely tiny nano level. “Many materials have different properties at the nanoscale that create all sorts of different possibilities that aren’t possible with conventional machines,” he says.

It’s primarily these differences in the physical properties of materials at the nanoscale that Min wants to explore, both in emerging materials and in materials like sapphire that require alternative handling methods to become truly manufacturable.

Min also hopes to explore how the machine can help open up new possibilities for manufacturing design. Most designers are constrained by manufacturing limitations that can choke creativity and slow innovation. Min points to smartphone design as a prime example of this “design for manufacturing” paradigm leading to stale product lines.

“The design of the Apple iPhone has not changed very much since from the first one to the latest iPhone that was just announced,” Min says. “It’s the same for a lot of products. Vehicles are the same. A Ford looks like a Ford.”

That’s because manufacturers have long-term investments in supply chains that are difficult and costly to switch on a dime. The capital risk for changing a manufacturing process is often too high. Min says he hopes his research with the ROBONANO will identify ways to speed up the process and becomes one of the enabling technologies for a new manufacturing paradigm—what Min calls “manufacturing for design.”

“I want to be able to ask the manufacturer, ‘what is your perfect design?’ And be able to provide that,” Min says.

Among successes he’s already had: He recently helped a national laboratory vastly improve the imaging capabilities of a microscopic instrument it manufactures.

The ROBONANO has existed more than for 10 years in Japan, where the semiconductor industry is already using it to improve its products. Min says that the semiconductor industry is one among many industries that can benefit from the ROBONANO’s capabilities. He’s also been approached by the toymaker Lego and other well-known brands to help improve their products. “The opportunities are almost limitless for improving products and manufacturing processes with this machine,” Min says.