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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

Many asylum seekers face long waits in Mexico due to a shortage of translators for Indigenous language speakers. Photo/iStock.

Imagine fleeing persecution at home, surviving a difficult journey, arriving in a new country to seek asylum, only to be turned away at the border because nobody speaks your language. This is the reality for hundreds of migrants coming into the United States from remote areas of Central America who do not speak common languages, such as Spanish or Portuguese.

A shortage of translators for Indigenous asylum seekers speaking traditional languages means many must wait for months or even years in Mexico to apply for asylum, creating a long backlog in an already overwhelmed immigration system.

Katy Felkner is developing a machine translation system for Mexican and Central American Indigenous languages to help asylum seekers at the border. Photo/Katy Felkner.

The U.S. immigration system is set up to handle English and Spanish, said Katy Felkner, a Ph.D. student in computer science at the USC Viterbi School of Engineering, but there are several hundreds of people a year who are minority language speakers, in particular, speaking Indigenous languages from Mexico and Central America, who are not able to access any of the resources and legal aid that exists for Spanish-speaking migrants.

In other cases, people are unable to explain the threats to their lives in their hometowns, which could be the basis for asylum. When migrants cannot understand or be understood, there is no way to establish the threat to their safety during a credible fear interview conducted by the U.S. Department of Homeland Security.

The statistics are staggering: asylum-seeking immigrants without a lawyer prevailed in only 13 percent of their cases, while those with a lawyer prevailed in 74 percent of their cases, according to a study in the Fordham Law Review.

Felkner, who conducts her research at the USC Information Sciences Institute (ISI) under Jonathan May, a research associate professor, is working on developing a solution: a machine translation system for Mexican and Central American Indigenous languages that can be used by organizations providing legal aid to refugees and asylum-seekers.

People are being directly adversely impacted because there arent interpreters available for their languages in legal aid organizations, said Felkner. This is a concrete and immediate way that we can use natural language processing for social good.

People are being directly adversely impacted because there arent interpreters available for their languages in legal aid organizations. Katy Felkner.

Felkner is currently working on a system for Kiche, a Guatemalan language, which is one of the 25 most common languages spoken in immigration court in recent years, according to The New York Times.

Were trying to provide a rough translation system to allow nonprofits and NGOs that dont have the resources to hire interpreters to provide some level of legal assistance and give asylum seekers a fair chance to get through that credible fear interview, said Felkner.

Felkners interest in languages began during her undergraduate degree at the University of Oklahoma, where she earned a dual degree in computer science and letters, with a focus on Latin. During her first year of college, she worked on a project called the Digital Latin Library, writing Python code to create digital versions of ancient texts.

Thats what got me thinking about language technology, said Felkner. I taught myself some basics of natural language processing and ended up focusing on machine translation because I think its one of the areas with the most immediate human impact, and also one of the most difficult problems in this area.

While Felkner and May are currently focused on developing a text-to-text translator, the end goal, years from now, is a multilingual speech-to-speech translation system: the lawyer would speak English or Spanish, and the system would automatically translate into the asylum seekers Indigenous language, and vice-versa.

Translation systems are trained using parallel data: in other words, they learn from seeing translation pairs, or the same text in both languages, at the sentence level. But there is very little parallel data in Indigenous languages, including Kiche, despite it being spoken by around one million people.

Thats because parallel data only exists when there is a compelling reason to translate into or out of that language. Essentially, said Felkner, if its commercially viableDisney dubbing films from English to Spanish, for instanceor stemming from a religious motivation.

In many cases, due to the influence of missionaries throughout Latin America, the only parallel data sourcethe same text in both languagesis the Bible, which doesnt give researchers much to work with.

Were really trying to push the lower bound on how little data you can have to successfully train a machine translation system. Katy Felkner.

Imagine youre an English speaker trying to learn Spanish, but the only Spanish youre ever allowed to see is the New Testament, said Felkner. It would be quite difficult.

Thats bad news for the data-hungry deep learning models used by language translation systems that take a quantity over quality approach.

The models have to see a word, phrase, grammatical construction a bunch of times to see where its likely to occur and what it corresponds to in the other language, said Felkner. But we dont have this for Kiche and other extremely low resource Indigenous languages.

The numbers speak for themselves. From English to Kiche, Felkner has roughly 15,000 sentences of parallel data, and 8,000 sentences for Spanish to Kiche. By contrast, the Spanish to English model she trained for some baseline work had 13 million sentences of training data.

Were trying to work with essentially no data, said Felkner. And this is the case for pretty much all low-resource languages, even more so in the Americas.

One tactic in existing low-resource work uses closely related, higher resource languages as a starting point: for instance, to translate from English into Romanian, you would start training the model in Spanish.

But since Indigenous languages of the Americas developed separately from Europe and Asia, the majority are low resource, and most of them are extremely low resource, a term Felkner coined to describe a language with less than around 30,000 sentences of parallel data.

Were really trying to push the lower bound on how little data you can have to successfully train a machine translation system, said Felkner.

But Felkner, with her background in linguistics, was undeterred. Over the past two years, she has worked on creating language data for the models using some tricks of the trade in natural language processing.

One tactic involves teaching the model to complete the abstract task of translation and then setting it to work on the specific language in question. Its the same principle as learning to drive a bus by learning to drive a car first, said Felkner.

To do this, Felkner took an English to Spanish model, and then fine-tuned it for Kiche to Spanish. It turned out, this approach, called transfer learning, showed promise even in an extremely low resource case. That was very exciting, said Felkner. The transfer learning approach and pre-training from a not-closely-related language had never really been tested in this extremely low resource environment, and I found that it worked.

She also tapped into another resource: using grammar books published by field linguists in the mid-to-late 70s to generate plausible synthetic data that can be used to help the models learn. Felkner is using the grammar books to write rules that will help her construct syntactically correct sentences from the dictionaries. The technical term for this is bootstrapping or data augmentation or colloquially, fake it til you make it.

We use this as pre-training data, to essentially teach the models the basics of grammar, said Felkner. Then, we can save our real data, such as the Bible parallel data, for the fine-tuning period when it will learn whats semantically meaningful, or what actually makes sense.

Finally, shes testing a technique that involves parsing nouns in the English and Kiche sides of the Bible, replacing them with other nouns, and then using a set of rules to correctly inflect the sentences for grammar.

For example, if the training data has the sentence: the boy kicked the ball, the researchers could use this approach to generate sentences like the girl kicked the ball, the doctor kicked the ball, the teacher kicked the ball, which can all become training data.

The idea is to use these synthetically-generated examples to essentially build a rough version of the system, so that we can get a lot of use out of the small amount of real data that we do have, and finetune it to exactly where we want it to be, said Felkner.

Working in extremely low-resource language translation is not easy, and it can be frustrating at times, admits Felkner. But the challenge, and the potential to change lives, drive her to succeed. Her work is being noticed, too: she was recently awarded a National Science Foundation Graduate Research Fellowship to continue working on the border translation project.

Within the next year, she plans to undertake a field trip to observe how legal aid organizations are working at the border, and where her system could fit into their workflow. She is also working on a demo website for the system, which she hopes to unveil in 2023, and once developed, she hopes the system could one day be applied to other Indigenous languages.

Hill climbing on high resource languages can make your Alexa, Google Home or Siri understand you better, but its not transformative in the same way, said Felkner. Im doing this work because it has an immediate humanitarian impact. As JFK once said, we choose to go to the moon not because it is easy, but because it is hard. I often think the things that are worth doing are difficult.

Published on August 24th, 2022

Last updated on August 24th, 2022

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Lost in Translation at the Border - USC Viterbi | School of Engineering - USC Viterbi School of Engineering

PLTW recognizes outstanding teachers and administrators across the U.S. who inspire, engage, and empower their students by creating transformative learning experiences in their schools through PLTW programs

INDIANAPOLIS, August 25, 2022--(BUSINESS WIRE)--Teachers and administrators across Project Lead The Ways (PLTW) network of schools empower and inspire their students every day. Each year, PLTW honors outstanding teachers and administrators who have made significant impacts in their classrooms, schools, and communities. These educators demonstrate exemplary leadership that inspires and provides opportunities for their students through PTLW programs.

PLTW applauds the unwavering commitment of educators in all that they do to inspire and prepare their students for a lifetime of opportunity. With educators in all 50 states, including D.C. and U.S. territories, over 80,000 PLTW teachers have transformed their classrooms through hands-on immersive learning. With PLTW Core Training, educators receive a dynamic and best-in-class professional development experience that empowers them to facilitate transformative learning in their classrooms.

Award winners were selected from across PLTWs national network of educators and divided into categories based on the pathways they teach or oversee, such as elementary and middle school programs PLTW Launch and PLTW Gateway and PLTW high school pathways in computer science, engineering, and biomedical science. The national teacher and administrator of the year awards signify the outstanding work and dedication of these educators, and PLTW celebrates their achievements.

Award winners were selected based on criteria that demonstrate the impact these educators have on their students including expanding access; exhibiting leadership within their classroom, school, district, and/or local community of educators; and ultimately empowering their students to develop key transportable skills for college and career success.

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"PLTW educators do incredible work facilitating learning experiences that help inspire and prepare students to reach their full potential. This award acknowledges and recognizes their accomplishments in continuing that mission," stated Dr. David Dimmett, PLTW Interim President and CEO. "We congratulate them and are proud of the work these educational leaders do to equip students with the knowledge and skills necessary for success beyond the classroom, no matter what career path they choose."

PLTW congratulates the 2022-23 National PLTW Teacher of the Year, Robert and Patricia Kern Teacher of the Year, and National PLTW Administrator of the Year Award recipients. Award winners are available in the National PLTW Awards Yearbook.

About PLTW

Project Lead The Way (PLTW) is a mission-driven organization that is transforming the learning experience for millions of PreK-12 students and thousands of teachers across the U.S. PLTW empowers students to develop in-demand, transportable knowledge and skills through pathways in computer science, engineering, and biomedical science. PLTWs teacher training and resources support teachers as they engage their students in real-world learning. Approximately 12,200 elementary, middle, and high schools in all 50 states and the District of Columbia offer PLTW programs. For more information on Project Lead The Way, visit pltw.org.

About PLTW National Awards

Teachers and administrators across PLTWs network of schools do amazing things every day to impact the lives of their students. Each year, PLTW honors those who have made significant impacts in their classrooms, schools, and communities through the PLTW National Awards. From outstanding administrators that demonstrate exemplary leadership to teachers who inspire, engage, and provide opportunities for brighter futures to students through PLTW programs.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220825005676/en/

Contacts

Jackie Yanchocik PLTW Director of Public Relations jyanchocik@pltw.org

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PLTW Announces 2022-23 Teacher and Administrator of the Year - Yahoo Finance