Following a national search, Hongfeng Yu has been named director of the Holland ComputingCenter.

Yu, associate professor in the School of Computing, started his new position Aug. 15. His appointment follows two years as the centers interimdirector.

The Holland Computing Center, the University of Nebraskas high-performance computing core, is home to the fastest resources in the state. Its computing and cyberinfrastructure systems and services, located at the Schorr Center in Lincoln and the Peter Kiewit Institute in Omaha, are key to advancing education, research and discovery at NU.

The center is integral to some of the most visible research initiatives harnessing strengths from all four university campuses, including precision agriculture efforts in the Institute of Agriculture and Natural Resources, the Quantitative Life Sciences Initiative, the National Strategic Research Institute and the Center for Brain, Biology and Behavior. At the University of Nebraska-Lincoln, the center is expected to play a key role in facilitating Grand Challenges projects and progress toward the aims of the N2025 StrategicPlan.

As director, Yu will shape the future of research computing for Nebraska. He will lead long-term strategic planning and investment in high-performance and high-throughput computing, collaborate with campus entities and individual researchers, pursue federal and philanthropic funding opportunities, and represent the center on the regional, national and international stage. Hell also create new collaborations across NUs four campuses and ensure the center is equipped with the technical expertise necessary to help researchers andstaff.

With his nationally recognized expertise in high-performance computing and deep understanding of the University of Nebraskas diverse research priorities and strengths, Hongfeng is ideally positioned to lead the center, said Bob Wilhelm, vice chancellor for research and economic development. His leadership will be crucial as our researchers push the boundaries of discovery through cross-disciplinary work that requires, more than ever, the centers advanced computing resources and capacity to process and store large datasets.

Yu, who joined Nebraska in 2012, is an expert in big data analysis and visualization, high-performance computing, and user interfaces and interaction. His work has produced scalable algorithms and systems that have helped scientists across the country find accurate, efficient visualizations for applications in climate modeling, geophysical analysis, medical imaging, plant phenotyping andmore.

Yus laboratory has received funding from the National Science Foundation, the U.S. Department of Agriculture and the Department of Energy, among others. This includes a $476,952 award from NSFs Faculty Early Career Development Program, which supported Yus work to create software tools that expand network visualizationcapabilities.

Yu holds bachelors and masters degrees in computer science from Zhejiang University in China and a doctorate from the University of California, Davis. He was a postdoctoral researcher with Sandia NationalLaboratories.

Yu succeeds founding director David Swanson, who died in a car accident in 2019. In May, the center launched Swan, a new supercomputer named in honor of Swanson. Swan provides cutting-edge resources at no cost to NU researchers, instructors andstudents.

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Yu named director of Holland Computing Center | Nebraska Today | University of NebraskaLincoln - Nebraska Today

BLOOMINGTON, Ind. -- Quantum science and engineering can save energy, speed up computation, enhance national security and defense, and innovate health care. With a grant from the National Science Foundation, researchers from Indiana University (both Bloomington and IUPUI campuses), Purdue University and the University of Notre Dame will develop industry- and government-relevant quantum technologies as part of the Center for Quantum Technologies. Purdue will serve as the lead site.

"The Center for Quantum Technologies is based on the collaboration between world experts whose collective mission is to deliver frontier research addressing the quantum technological challenges facing industry and government agencies," said Gerardo Ortiz, Indiana University site director, scientific director of the IU Quantum Science and Engineering Center and professor of physics. "It represents a unique opportunity for the state of Indiana to become a national and international leader in technologies that can shape our future."

"This newly formed center is unique in many aspects," said Ricardo Decca, professor and chair of the Department of Physics at IUPUI. "It brings together experts in many scientific disciplines -- computer science, physics, chemistry, materials science -- from three universities and four campuses and companies developing the next generation of quantum-based information and sensing systems. The future seems very bright."

Given the wide applicability of quantum technologies, the new Center for Quantum Technologies will team with member organizations from a variety of industries, including computing, defense, chemical, pharmaceutical, manufacturing and materials. The center's researchers will develop foundational knowledge into industry-friendly quantum devices, systems and algorithms with enhanced functionality and performance.

"Over the coming decades, quantum science will revolutionize technologies ranging from the design of drugs, materials and energy harvesting systems, to computing, data security, and supply chain logistics," IU Vice President for Research Fred Cate said. "Through the CQT, Indiana will be at the forefront of transferring new quantum algorithms and technologies to industry. We are also looking forward to educating the quantum workforce for the future through the corporate partnerships that are integral to the funding model of the CQT."

Committed industry and government partners include Accenture, the Air Force Research Laboratory, BASF, Cummins, D-Wave, Eli Lilly, Entanglement Inc., General Atomics, Hewlett Packard Enterprise, IBM Quantum, Intel, Northrup Grumman, NSWC Crane, Quantum Computing Inc., Qrypt and Skywater Technology.

Additionally, the Center for Quantum Technologies will train future quantum scientists and engineers to fill the need for a robust quantum workforce. Students engaged with the center will take on many of the responsibilities of principal investigators, including drafting proposals, presenting research updates to members, and planning meetings and workshops.

The center is funded for an initial five years through the NSF's Industry-University Cooperative Research Centers program, which generates breakthrough research by enabling close and sustained engagement between industry innovators, world-class academic teams and government agencies. The IUCRC program is unique in that members fund and guide the direction of research through active involvement and mentoring.

Other academic collaborators include Sabre Kais, center director and distinguished professor of chemical physics at Purdue; Peter Kogge, the University of Notre Dame site director and the Ted H. McCourtney Professor of Computer Science and Engineering; and David Stewart, Center for Quantum Technologies industry liaison officer and managing director of the Purdue Quantum Science and Engineering Institute.

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3 research universities to collaborate with industry, government to develop quantum technologies: News at IU: Indiana University - IU Newsroom

Human languages are notoriously complex, and linguists have long thought it would be impossible to teach a machine how to analyze speech sounds and word structures in the way human investigators do.

But researchers at MIT, Cornell University, and McGill University have taken a step in this direction. They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own.

When given words and examples of how those words change to express different grammatical functions (like tense, case, or gender) in one language, this machine-learning model comes up with rules that explain why the forms of those words change. For instance, it might learn that the letter a must be added to end of a word to make the masculine form feminine in Serbo-Croatian.

This model can also automatically learn higher-level language patterns that can apply to many languages, enabling it to achieve better results.

The researchers trained and tested the model using problems from linguistics textbooks that featured 58 different languages. Each problem had a set of words and corresponding word-form changes. The model was able to come up with a correct set of rules to describe those word-form changes for 60 percent of the problems.

This system could be used to study language hypotheses and investigate subtle similarities in the way diverse languages transform words. It is especially unique because the system discovers models that can be readily understood by humans, and it acquires these models from small amounts of data, such as a few dozen words. And instead of using one massive dataset for a single task, the system utilizes many small datasets, which is closer to how scientists propose hypotheses they look at multiple related datasets and come up with models to explain phenomena across those datasets.

One of the motivations of this work was our desire to study systems that learn models of datasets that is represented in a way that humans can understand. Instead of learning weights, can the model learn expressions or rules? And we wanted to see if we could build this system so it would learn on a whole battery of interrelated datasets, to make the system learn a little bit about how to better model each one, says Kevin Ellis 14, PhD 20, an assistant professor of computer science at Cornell University and lead author of the paper.

Joining Ellis on the paper are MIT faculty members Adam Albright, a professor of linguistics; Armando Solar-Lezama, a professor and associate director of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of CSAIL; as well as senior author

Timothy J. ODonnell, assistant professor in the Department of Linguistics at McGill University, and Canada CIFAR AI Chair at the Mila -Quebec Artificial IntelligenceInstitute.

The research is published today in Nature Communications.

Looking at language

In their quest to develop an AI system that could automatically learn a model from multiple related datasets, the researchers chose to explore the interaction of phonology (the study of sound patterns) and morphology (the study of word structure).

Data from linguistics textbooks offered an ideal testbed because many languages share core features, and textbook problems showcase specific linguistic phenomena. Textbook problems can also be solved by college students in a fairly straightforward way, but those students typically have prior knowledge about phonology from past lessons they use to reason about new problems.

Ellis, who earned his PhD at MIT and was jointly advised by Tenenbaum and Solar-Lezama, first learned about morphology and phonology in an MIT class co-taught by ODonnell, who was a postdoc at the time, and Albright.

Linguists have thought that in order to really understand the rules of a human language, to empathize with what it is that makes the system tick, you have to be human. We wanted to see if we can emulate the kinds of knowledge and reasoning that humans (linguists) bring to the task, says Albright.

To build a model that could learn a set of rules for assembling words, which is called a grammar, the researchers used a machine-learning technique known as Bayesian Program Learning. With this technique, the model solves a problem by writing a computer program.

In this case, the program is the grammar the model thinks is the most likely explanation of the words and meanings in a linguistics problem. They built the model using Sketch, a popular program synthesizer which was developed at MIT by Solar-Lezama.

But Sketch can take a lot of time to reason about the most likely program. To get around this, the researchers had the model work one piece at a time, writing a small program to explain some data, then writing a larger program that modifies that small program to cover more data, and so on.

They also designed the model so it learns what good programs tend to look like. For instance, it might learn some general rules on simple Russian problems that it would apply to a more complex problem in Polish because the languages are similar. This makes it easier for the model to solve the Polish problem.

Tackling textbook problems

When they tested the model using 70 textbook problems, it was able to find a grammar that matched the entire set of words in the problem in 60 percent of cases, and correctly matched most of the word-form changes in 79 percent of problems.

The researchers also tried pre-programming the model with some knowledge it should have learned if it was taking a linguistics course, and showed that it could solve all problems better.

One challenge of this work was figuring out whether what the model was doing was reasonable. This isnt a situation where there is one number that is the single right answer. There is a range of possible solutions which you might accept as right, close to right, etc., Albright says.

The model often came up with unexpected solutions. In one instance, it discovered the expected answer to a Polish language problem, but also another correct answer that exploited a mistake in the textbook. This shows that the model could debug linguistics analyses, Ellis says.

The researchers also conducted tests that showed the model was able to learn some general templates of phonological rules that could be applied across all problems.

One of the things that was most surprising is that we could learn across languages, but it didnt seem to make a huge difference, says Ellis. That suggests two things. Maybe we need better methods for learning across problems. And maybe, if we cant come up with those methods, this work can help us probe different ideas we have about what knowledge to share across problems.

In the future, the researchers want to use their model to find unexpected solutions to problems in other domains. They could also apply the technique to more situations where higher-level knowledge can be applied across interrelated datasets. For instance, perhaps they could develop a system to infer differential equations from datasets on the motion of different objects, says Ellis.

This work shows that we have some methods which can, to some extent, learn inductive biases. But I dont think weve quite figured out, even for these textbook problems, the inductive bias that lets a linguist accept the plausible grammars and reject the ridiculous ones, he adds.

This work opens up many exciting venues for future research. I am particularly intrigued by the possibility that the approach explored by Ellis and colleagues (Bayesian Program Learning, BPL) might speak to how infants acquire language, says T. Florian Jaeger, a professor of brain and cognitive sciences and computer science at the University of Rochester, who was not an author of this paper. Future work might ask, for example, under what additional induction biases (assumptions about universal grammar) the BPL approach can successfully achieve human-like learning behavior on the type of data infants observe during language acquisition. I think it would be fascinating to see whether inductive biases that are even more abstract than those considered by Ellis and his team such as biases originating in the limits of human information processing (e.g., memory constraints on dependency length or capacity limits in the amount of information that can be processed per time) would be sufficient to induce some of the patterns observed in human languages.

This work was funded, in part, by the Air Force Office of Scientific Research, the Center for Brains, Minds, and Machines, the MIT-IBM Watson AI Lab, the Natural Science and Engineering Research Council of Canada, the Fonds de Recherche du Qubec Socit et Culture, the Canada CIFAR AI Chairs Program, the National Science Foundation (NSF), and an NSF graduate fellowship.

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AI that can learn the patterns of human language - MIT News

About the University of Illinois at Chicago

UIC is among the nations preeminent urban public research universities, a Carnegie RU/VH research institution, and the largest university in Chicago. UIC serves over 34,000 students, comprising one of the most diverse student bodies in the nation and is designated as a Minority Serving Institution (MSI), an Asian American and Native American Pacific Islander Serving Institution (AANAPSI) and a Hispanic Serving Institution (HSI). Through its 16 colleges, UIC produces nationally and internationally recognized multidisciplinary academic programs in concert with civic, corporate and community partners worldwide, including a full complement of health sciences colleges. By emphasizing cutting-edge and transformational research along with a commitment to the success of all students, UIC embodies the dynamic, vibrant and engaged urban university. Recent Best Colleges rankings published by U.S. News & World Report, found UIC climbed up in its rankings among top public schools in the nation and among all national universities. UIC has nearly 260,000 alumni, and is one of the largest employers in the city of Chicago.

Teaching Track Faculty Openings in Computer Science

The Computer Science Department at the University of Illinois Chicago (UIC) seeks to hire full-time teaching faculty (Lecturer or Clinical Professor). Candidates would work alongside 17 full-time teaching faculty with over 150 years of experience and 13 awards for excellence. Standard teaching load is three course sections per semester.

UIC is one of the top-ten most diverse universities in the US (US News and World Report), a top 25 public and top 10 best value (Wall Street Journal and Times Higher Education), and a Hispanic-serving institution. The department seeks candidates interested in all areas of computer science. Submit applications online at https://jobs.uic.edu. Include:-A curriculum vitae,- Contact information for at least three references,- One-page statement on your teaching philosophy and how it is inclusive to a diverse student population,- Recordings of teaching activities (optional), and- recent teaching evaluations (optional).

For more information, send e-mail to cs-ntt-search@uic.edu. For fullest consideration, apply by 11/17/22. Applications will be accepted and reviewed until the positions are filled.

Qualifications:

The Lecturer track is a long-term career track that starts with Lecturer and offers opportunities for advancement to Senior Lecturer. Minimum qualifications include an MS in Computer Science or a closely related field.

The Clinical Professor track is a long-term career track that starts with Clinical Assistant Professor and offers advancement to Clinical Associate and Clinical Full Professor. Minimum qualifications include a PhD in Computer Science or a closely related field. Candidates interested in Computer Science Education research or collaborating in the departments existing Computer Science research are encouraged to apply.

The University of Illinois at Chicago is an affirmative action, equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, protected veteran status, or status as an individual with a disability.

Offers of employment by the University of Illinois may be subject to approval by the Universitys Board of Trustees and are made contingent upon the candidates successful completion of any criminal background checks and other pre-employment assessments that may be required for the position being offered. Additional information regarding such pre-employment checks and assessments may be provided as applicable during the hiring process.

As a qualifying federal contractor, the University of Illinois System usesE-Verifyto verifyemployment eligibility.

The University of Illinois System requires candidates selected for hire to disclose any documented finding of sexual misconduct or sexual harassment and to authorize inquiries to current and former employers regarding findings of sexual misconduct or sexual harassment. For more information, visithere.

University of Illinois faculty, staff and students are required to be fully vaccinated against COVID-19. If you are not able to receive the vaccine for medical or religious reasons, you may seek approval for an exemption in accordance with applicable University processes.

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Teaching Track Faculty Openings in Computer Science job with University of Illinois at Chicago | 37307439 - The Chronicle of Higher Education