Han Guo [interview with UNC Endeavors]

April 9, 2020

Han Guo is a senior double-majoring in computer science and statistics and analytics within the UNC College of Arts & Sciences. He researches how to build language technologies like automated image captioning.

April 8th, 2020

Q: When you were a child, what was your response to this question: “What do you want to be when you grow up?”

A: I remember Thomas Edison making a significant impact on me when I was pretty young, so I thought I would become an inventor like him. Later, I came across books on Bill Gates, Paul Allen, and Mark Zuckerberg, and it became clearer to me that I wanted to do something related to technology. Actually, one night in primary school, after reading the book on Zuckerberg, I dreamt of myself coding — and that’s why I learned to code. The single most significant impact, though, came from Steve Jobs. His biography, which I bought right after he passed away, was life-changing. To this day, I have read or listened to the book about five times, and I reread it every year.

Q: Share the pivotal moment in your life that helped you choose your field of study.

A: When I was in high school, I was pretty interested in behavioral economics and statistics. This helped me develop a passion for using numbers to understand the fundamentally random nature of the world. In my first year of college, I worked on a project with my research advisor. I was tasked with using numeric representations of words to understand the evolution of conversation in the Wikipedia talk pages. At the same time, Cornell University professor Lillian Lee gave a talk at UNC on the use of machine learning and statistics to study sentiments and natural language. I was so hooked by the presentation that I fell in love with machine learning and language understanding.

a group from the neurolanguage processing lab pose at a local brewery

Guo (first row, left) hangs out with members of the UNC Natural Language Processing Lab at a local brewery.

Q: Tell us about a time you encountered a tricky problem. How did you handle it and what did you learn from it?

A: One of the more recent technical problems I had was measuring the uncertainties in modern machine learning systems. My usual go-to approach is to read loads of papers and, if possible, talk to people who are experts. In this case, I ended up spending half of the winter break going through the PhD thesis of a professor at Oxford.

Q: Describe your research in 5 words.

A: Teaching machines to understand language.

Q: What are your passions outside of research?

A: Reading papers, books, and the news. Some of the books are directly related to my research, but I enjoy reading things on the economy and mathematics, as well as biographies. Books help me connect my knowledge to other disciplines.

Research UNCovered delves into the lives of UNC researchers from all disciplines and career levels, showcasing not only their research prowess but personal experiences in academia and beyond. Know someone we should feature? Nominate a researcher.

Kaur receives Google Faculty Research Award

March 30, 2020

Jasleen KaurAssociate Professor Jasleen Kaur received a 2019 Google Faculty Research Award from Google AI. Kaur’s proposal was one of only 150 to be funded out of 917 applications from 50 countries.

Google Faculty Research Awards provide unrestricted gifts to support world-class technical research in computer science, engineering, and related fields. The awards are structured as seed funding to support one graduate student for one year. The awards are very competitive; on average, around 15 percent of applications receive funding, and Kaur’s research proposal was one of only eight to be funded in the area of networking. The review process for 2019 involved 1,100 reviewers from across Google.

Many enterprises like Google, Facebook, and Amazon maintain large-scale, private networks to connect datacenter clusters. Those networks are equipped with instruments that monitor the health and performance of the network, but when anomalies and outages occur, manually diagnosing the problem can require much time and effort.

Kaur’s proposal will use unsupervised learning methods to analyze performance data collected on Google’s B4 network. Unsupervised learning is a branch of machine learning where a computer attempts to draw inferences from input data without any output data. In other words, rather than being given raw data and corresponding conclusions and determining how to classify the raw data, the computer will be asked to analyze only the raw data in search of patterns that will help in manual diagnosis.

The goal of Kaur’s project is to derive models that can help identify the causes of anomalies in monitored data. If successful, the research will enable more efficient and accurate diagnosis of the root causes of anomalies in wide-area enterprise networks like Google’s.

Tar Heel Reader enables virtual learning for students with disabilities amid COVID-19 outbreak

March 30, 2020
Tar Heel Reader home page
Tar Heel Reader home page

On March 18, Professor Gary Bishop of the Department of Computer Science logged in to check on Tar Heel Reader, an online library of beginner-level books that he has maintained since 2008. In the midst of social distancing to slow the spread of COVID-19 and with many of the site’s users out of school, Bishop expected to see a significant decline in usage.

Instead, Bishop found that overall usage had slightly increased. With students no longer accessing the site together in classrooms, peak usage has decreased. But, Bishop says, students are spreading out their visits throughout the day, using it later into the evening and even logging in on weekends. For Bishop, this is the latest instance in more than a decade of Tar Heel Reader exceeding his most ambitious hopes for the platform.

Tar Heel Reader is an online, open-source library of free, easy-to-read, accessible books. In addition to reading, users can log in and create their own books using public domain photos from Flickr, enabling teachers and caregivers to write books on topics that their students will find interesting. The site, developed by Bishop and Professor Karen Erickson of the Center for Literary and Disability Studies in the Department of Allied Health Sciences, grew out of a need for books for beginning readers with disabilities. It quickly grew popular with unexpected demographics, including students without disabilities, adults learning second languages, and even students of Latin. The site now includes books in 27 different languages, and users come from more than 200 countries and territories. Tar Heel Reader hit 10 million books read in January 2017, less than a decade after its launch.

Tar Heel Reader audience March 2020 vs January 2020
Comparison of Tar Heel Reader site visits before (orange) and during (blue) social distancing measures

Tar Heel Reader users are able to navigate the site and read books using a wide variety of input devices, including a single switch. Users with limited internet access can collect books for offline reading. The site includes instructional resources, and users have created their own tutorials on YouTube and other sites.

Gary Bishop and Karen Erickson
Gary Bishop, left, and Karen Erickson

In a time of social distancing and stay-at-home orders, Tar Heel Reader exhibits a different type of virality, spreading among educators through positive reviews and conference testimonials. Erickson attributes the current surge in usage to Tar Heel Reader’s promotion by educators as a solution for students with disabilities who cannot otherwise engage in “virtual” learning. She also credits positive reviews for Tar Heel Shared Reader, a shared reading interface launched in 2019 that enables adults to help engage students more actively in reading and gain more meaning from texts.

Bishop says he has received several messages from Tar Heel Reader users citing COVID-19 distancing measures, including positive reviews for the site and requests to become book creators from countries hit hard by the pandemic. Users from the United States, Italy, Spain and the Netherlands have made up more than 80 percent of the site’s usage since March 16.

To learn more about Tar Heel Reader and start reading, visit tarheel reader.org.

Pearl Hacks 2020 unites and empowers women in tech

March 3, 2020

Hundreds of women and non-binary students met in Chapel Hill for Pearl Hacks, a 24-hour hackathon facilitated by the UNC Department of Computer Science. The event was held from February 21 to 23 at the Frank Porter Graham Student Union.

Held for the seventh time since its inception in 2014, Pearl Hacks is one of the nation’s longest-running coding competitions for women and non-binary students in high school and college. Participants came from schools all over the East Coast to form teams and build projects in 24 hours or less. Teams were aided by tech industry mentors and workshops on a variety of topics, some of which were not related to tech. The event produced 71 unique submissions, many of which came from first-time coders.

The submissions’ topics varied from politics and advocacy to daily convenience to healthcare to financial literacy. Projects submitted included Fridge-Track, a web app that logs the contents of a refrigerator and will alert the user via SMS message when an item is nearing expiration; Once Upon a Code, an interactive game that makes programming accessible and enjoyable to young girls; and pear (short for Private Emergency-Assisted Response Application), a mobile app designed to support survivors of sexual violence and trauma and help them navigate different resources with ease. Many of the projects shared a focus on supporting and building community among women.

Pearl Hacks 2020 was made possible by generous support from CapTech Consulting, Twitter, Avalara, Capital One, Deutsche Bank, Genesys, nCino, Red Ventures, Wells Fargo, Appian, Bandwidth, Barings, Cisco, Credit Suisse, Fidelity, Google, Google Cloud, Macy’s, MetLife, Optum, RENCI, Splunk, Vanguard, WillowTree, The Carolina Inn, CS + Social Good Club at UNC, UNC Gillings School of Global Public Health, Guayaki, IBM, Innovate Carolina, Kaleido, Microsoft, Monster Energy, Palantir, Rewriting the Code, Samsung, SAS, and Sticker Mule.

For more information about Pearl Hacks, please visit pearlhacks.com.

Pearl Hacks preps for 7th annual female and non-binary hackathon

February 7, 2020
Pearl Hacks preps for 7th annual female and non-binary hackathon Buy Photos

Pearl Hacks, a beginner-friendly hackathon for female and non-binary students 18 or older, will host nearly 400 participants on Feb. 21-23, 2020. Photo courtesy of Pearl Hacks.

Pearl Hacks will soon host over 400 participants on campus for UNC’s only hackathon designed specifically for female and non-binary students.

Founded in 2014 by UNC alumna and current Google employee Maegan Clawges, Pearl Hacks is a beginner-friendly hackathon open to female and non-binary students 18 or older and will take place from Feb. 21 to 23.

“Pearl Hacks is sort of a cross between a competition and a conference,” Director of External Workshops and UNC junior Tylar Watson said.

The three-day event offers a number of experiences for students. During the 36-hour project-building hacking competition, participants also have the opportunity to attend workshops led by mentors, local community members and corporate sponsors like Twitter and Google.

“There’s like two things happening that you can participate in,” Watson said. “So if you are working on a project and you get stuck, you can say ‘OK, I’ll go do a workshop for the next hour.’”

Pearl Hacks hosts workshops and sponsor fairs that give participants the chance to meet with recruiters and professionals in the technology field, said Prasiddhi Jain, a junior biostatistics and computer science major and director of hacker experience for Pearl Hacks.

“We have a lot of mentors from companies like Google and Microsoft, so a lot of big name companies,” Jain said. “So you develop relationships, and you develop skills to talk to a lot of different kinds of people.”

While competition and networking are large components of Pearl Hacks, Jain said the goal of the weekend is to create a positive environment for those involved.

“It’s like a safe, inclusive space for people to just explore their interests in technology,” junior and Vice President of Logistics Hannah Cao said. “A lot of people associate hackathons with making a project and being really experienced. With Pearl Hacks, it’s just to create a space safe space for women and non-binaries, which is not very present in the computer science community.”

This year’s event will be the seventh annual hackathon, and Jain said the Pearl Hacks team has been working on making the weekend as inclusive as possible.

“We are trying to frame a lot of our workshops, our panel discussions, projects and events throughout Pearl Hacks toward being more beginner-friendly,” Jain said. “So we’re hoping that a lot of people that are attending — that haven’t had any coding experience or do identify themselves as beginner-friendly — really learn a lot from it and have a positive experience.”

In anticipation for the main event, Pearl Hacks is also hosting a number of “first look” beginner workshops from Feb. 10 to 13. These events are open to all genders.

As the weekend approaches, Cao said the Pearl Hacks team intends to strengthen and share their message that tech is for everyone.

“Tech is such a buzzword right now, and Pearl Hacks creates that safe, inclusive, welcoming space for people to just explore their interests,” Cao said. “There’s no commitment, there’s no pressure.”


A 48-hour marathon [UNC Discover video profile of Global Game Jam 2020]

February 7, 2020

Carolina was one of more than 900 Global Game Jam host locations last weekend, with nearly 90 students developing prototypes of video games over the span of two days.

By Rob Holliday, University Communications, Friday, February 7th, 2020

With USB cables and power strips spread through Sitterson Hall last weekend, dozens of Carolina students settled in for a long weekend in front of their computer screens for work that would never result in a letter grade.

Gathered in groups around tables, the students were on a mission to develop video games — and they only had 48 hours to do it. The students’ efforts were part of an international event called Global Game Jam.

Carolina was one of more than 900 Global Game Jam host locations across 119 countries. Nearly 90 people — most of them Carolina students — participated in Chapel Hill event, which was hosted by the UNC-Chapel Hill Game Development Club.

Because of time limitations, the groups only designed prototypes of their games, which are often turned into more fully fleshed-out versions later. The event encourages teams to collaborate, learn from each other and develop a new set of skills.

“The purpose of a ‘hackathon,’ a game jam or any sort of timed event like this is to learn. It’s to get better,” said Scott Amaranto, a Carolina sophomore and Global Game Jam participant. “That’s much more apparent and helpful with a team. You can share knowledge. You can push each other to be better.”

Press the play button above to learn more

Announcing the Bloomberg Data Science Ph.D. Fellowship Winners for 2019-2020 [Hao Tan was one of only four fellows for 2019-2020]

February 5, 2020

Four exceptional doctoral students, who are working in broadly-construed data science, including natural language processing (NLP), vision-and-language tasks, machine learning, and artificial intelligence, visited Bloomberg’s Global Headquarters in New York City in 2019 as part of the Bloomberg Data Science Ph.D. Fellowship. Bloomberg has benefited significantly from its first class of Fellows in 2018-2019, as well as its Data Science Research Grant Program, which builds relationships with academic researchers around the globe.

The goal of this fellowship is to engage professionals early in their careers and to provide support and encouragement for groundbreaking publications in both academic journals and conference proceedings. Today, we are pleased to announce the second class of Bloomberg Data Science Ph.D. Fellows.

A committee of Bloomberg’s data scientists from across the organization selected the Fellows based on their proposals’ technical resiliency and strengths, and recommendation letters from their academic advisors, some of whom accompanied the Fellows on their visit to Bloomberg. The committee’s decisions were based in part on the candidate’s diverse academic focus, with priority given to machine learning, NLP, information retrieval, knowledge graph, and quantitative finance; the quality of the ideas presented in the proposal; the candidate’s achievements and experience; and the idea’s potential business impact.

Bloomberg’s Ph.D. Fellows and Data Science Grant recipients for 2019-2020 accompanied by some of their academic advisors and members of Bloomberg’s Data Science team (Photographer: Lori Hoffman/Bloomberg)

“Each project will advance the state of the art in their respective academic areas,” explained Songyun Duan, Head of Machine Learning Incubation in Bloomberg’s Office of the CTO. “The results will be published in top-tier conferences.”

During their Fellowship, each of the Fellows will work to advance their research and explore real-world applications that contribute to the innovative work leveraging data science and machine learning in Bloomberg’s products and services. The Fellows will also participate in an internship during the summer of 2020, during which they will collaborate with a Bloomberg team under the guidance of their research advisor to implement their research into one of the company’s applications to solve real-world problems and refine workflows.

As an introduction to Bloomberg, the Fellows traveled to New York City for three days to meet with their mentors and the company’s data science and AI engineering teams. During their visit, they learned more about the variety and depth of the company’s data science research and how the organization operates.

Katherine Keith, a Ph.D. student in Computer Science in the College of Information and Computer Sciences at the University of Massachusetts Amherst, was already familiar with Bloomberg through an internship during the summer of 2019 with Bloomberg’s Data Science team in the Office of the CTO, which offered a hands-on introduction to Bloomberg’s research. Through the relationships she developed during her internship, she published a paper at ACL 2019 titled “Modeling financial analysts’ decision making via the pragmatics and semantics of earnings calls” together with Amanda Stent, NLP Architect in Bloomberg’s Office of the CTO.

During her fellowship, Keith will further her research focused on improving natural language processing methods for computational social science applications. “I’m interested in improving social measurements of text, such as event extraction and extracting semantic and pragmatic signals from text, and improving methods that use these measurements in descriptive and causal inferences,” Keith said.

Contagion risk exists within the financial system when market participants utilize contracts to interact with each other, and the complex financial products that bind these networks create new ‘spiral’ risks that can potentially be measured and controlled with algorithms. Applying financial network analysis to real-world problems has been a challenge though, and Ariah Klages-Mundt, a Ph.D. candidate in applied math at Cornell University’s Center for Applied Mathematics, hopes to overcome hurdles by developing numerical methods and machine learning tools for sensitivity analysis and probabilistic measure of network risks. “I look forward to working with Bloomberg to see first-hand how these tools could realistically be used within the finance industry,” said Klages-Mundt.

Hao Tan, a fourth-year Ph.D. student in the University of North Carolina at Chapel Hill’s NLP Research Group (which is led by Assistant Professor Mohit Bansal), has worked on building the mapping from words and phrases to visual concepts, like objects and relationships, by designing tasks, building neural models, and developing training methods to learn this connection. At Bloomberg, where billions of data points are processed daily, Tan plans to explore the possibility of including visual information like images, videos and data plots by adapting methods developed for natural images to structural figures.

Machine learning algorithms can be used to solve sequential decision-making problems, as most interactive systems – like search engines – are improved through a recurrent loop with various stages involving learning from new data, improving the features and the model, and then testing the new system.

“My goal is to fundamentally speed up this process through new counterfactual inference techniques that move both learning and evaluation from ‘online’ to ‘offline’,” said

Yi Su, a Ph.D. student in the Department of Statistics and Data Science (DSDS) at Cornell University, where she is advised by Professor Thorsten Joachims. “Since logged historical data is both biased and partial, machine learning algorithms on this partial information data can be highly sub-optimal. I’m interested in developing new estimators and algorithms to work on this partial-information setting.”

The 2018-2019 Ph.D. Fellows have all completed their internships and have had their fellowships renewed. Bloomberg has already benefited from the academic collaboration with strong Ph.D. candidates with interests similar to the company’s Data Science and AI Engineering teams. This included facilitating publications in top-tier conferences and improving the quality of the company’s products, where applicable, said Duan.

Notably, 2018-2019 Fellow Huazheng Wang, a Ph.D. candidate in the Department of Computer Science of University of Virginia School of Engineering and Applied Science, won the prestigious Best Paper Award at SIGIR 2019 for his work on “Variance Reduction in Gradient Exploration for Online Learning to Rank.” This research, which was supported by Bloomberg, was performed together with his three other students and Professor Hongning Wang, his faculty advisor at the University of Virginia.

Out of 79 applications from doctorate students at universities in the United States, European Union and United Kingdom, a committee of Bloomberg’s data scientists from across the organization selected these four Fellows for the 2019-2020 academic year:

Bloomberg Ph.D. Fellow Yi Su

Yi Su (Cornell University)
Off-Policy Evaluation and Learning for Interactive Systems
Search engines, recommender systems, and most other user interactive systems go through a recurrent loop of improvement. This loop typically involves learning from newly collected data, making improvements to the features and the model, and then testing the new system in an online A/B test. My goal is to fundamentally speed up this process through new counterfactual inference techniques that move both learning and evaluation from “online” to “offline.”

Bloomberg Ph.D. Fellow Katherine Keith (Photographer: Lori Hoffman/Bloomberg)

Katherine Keith (University of Massachusetts Amherst)
Constructing Subjective Knowledge Bases
The field of information extraction (IE) has made great strides in constructing knowledge bases by extracting facts from large collections of unstructured text. IE methods have been used in many applied settings, including my recent work building a database of police fatality victims. However, extracting facts implies discerning between different realities in order to determine what is “true”. Social scientists, journalists, policy makers, and financial investors may be more interested in understanding a populations’ shared and conflicting subjective beliefs and how they vary temporally and spatially. This leads to the following research questions:

  • Can we extract propositions representing authors’ stated beliefs in order to construct subjective knowledge bases?
  • Once we have extracted subjective propositions for individual authors, can we infer belief communities by clustering authors with similar beliefs?
Bloomberg Ph.D. Fellow Hao Tan (Photographer: Lori Hoffman/Bloomberg)

Hao Tan (UNC Chapel Hill)
Summarizing Salient Content in Structured Documents with Figures
Describing content in structured images, i.e., plots, charts, and diagrams, is crucial when summarizing or searching over, for example, complex financial or legal news documents. It helps a layperson to understand the salient information inside these complex figures and also enables visually-impaired people to “see” the figure. It also enhances pure-text paragraph summarization systems by providing additional valuable information from figures inside the news/legal document and allows search/retrieval over documents containing such structured figures. Hence, we propose models that learn to generate informative and comprehensive summaries for such structured figures in complex documents, that capture salient and logically entailed (verified) information.

Bloomberg Ph.D. Fellow Ariah Klages-Mundt (Photographer: Lori Hoffman/Bloomberg)

Ariah Klages-Mundt (Cornell University)
Learning Cascade Risks in Complex Economic Networks: Methods to Make Financial Network Analysis Practical for Application
Computational and sensitivity problems currently present a barrier to using network models to quantify risks in networks of interacting firms. For instance, sensitivity from parameter uncertainty is not currently well understood and network computations can be algorithmically hard. I am developing numerical methods and machine learning tools to address these problems. This work will help make financial network analysis practical for application in industry.

Bansal receives DARPA Director’s Fellowship and Microsoft Investigator Fellowship

January 30, 2020

The Defense Advanced Research Projects Agency (DARPA) awarded the prestigious Director’s Fellowship to assistant professor Mohit Bansal, naming him one of the top performers among the agency’s Young Faculty Award recipients.

UNC Vice Chancellor for Research Terry Magnuson said, “We are exceedingly proud of Dr. Bansal’s well-deserved recognition and ground-breaking work.”

DARPA Director’s Fellowships are given to only a few recipients of the agency’s already selective Young Faculty Award recipients, who have demonstrated exceptional project performance through the 24-month base period of the original award. The Young Faculty Award identifies and engages rising research stars in junior faculty positions at U.S. academic institutions. Bansal was one of only 28 recipients of DARPA’s Young Faculty Award in 2017, across all science and engineering fields and U.S. universities, and his performance since then has put him in an even more select group of 13 researchers who were awarded the Director’s Fellowship, which provides up to $500,000 on top of the initial $500,000 Young Faculty Award.

“This award is a great recognition of the consequential nature of Dr. Bansal’s innovations in natural language processing. This is a great honor for him and for UNC,” said Jaye Cable, senior associate dean for natural sciences in the College of Arts & Sciences.

The Young Faculty Award has supported Bansal’s research on developing life-long learning-based artificial intelligence models that continually revise their neural architecture, use feedback from common-sense knowledge bases, and automate several expensive manual decisions in multi-task learning, and the Director’s Fellowship will help his lab continue important research in this direction of life-long and self-learning models. Bansal is the director of the UNC-NLP Lab, which focuses on natural language processing and generation, multimodal and grounded machine learning, and deep learning-based text analysis.

Bansal has also concurrently received the Microsoft Investigator Fellowship, a two-year fellowship that recognizes higher education faculty in the United States whose exceptional talent identifies them as distinguished scientists and teachers. This fellowship program is designed to empower researchers who plan to make an impact with research and teaching using the Microsoft Azure cloud computing platform. Fellows receive an unrestricted gift of $200,000 over two years to support their research.

Bansal is one of 15 fellows in the program’s inaugural group, with around 300 applications received (all full-time faculty at U.S. universities were eligible to apply).

“This exciting Microsoft fellowship will help us further advance our research goals of developing human-like NLG, Q&A, and dialogue systems with multimodal grounding, personality, and generalizable knowledge skills,” Bansal said.

Other benefits of the program include invitations to attend multiple events during the two-year term. The goal is to enable the members of the cohort to make connections with other faculty from leading universities and Microsoft and participate in the greater academic community (the full list of winners can be found on the Microsoft Research website). More details can be found in this Microsoft Research blog post.

Karlekar, Guo recognized by CRA for excellence in undergraduate research

January 22, 2020
Han Guo and Sweta Karlekar meet with their advisor Mohit Bansal
Han Guo (left) and Sweta Karlekar (right) meet with Dr. Mohit Bansal in Brooks Building

Each year, the Computing Research Association (CRA) recognizes undergraduate students at North American Universities who show outstanding potential in an area of computing research. This year, two UNC Computer Science students were honored by the CRA with 2020 Outstanding Undergraduate Researcher Awards. Sweta Karlekar and Han Guo were recognized as Runner-up and Finalist, respectively.

Karlekar’s research has been focused in the areas of NLP and ML. Her first project detected signs of Alzheimer’s Disease through identifying linguistic characteristics in patient interviews. Using visualization techniques, in her second project Karlekar examined linguistic commonalities to support a data-driven approach in the treatment of victims of domestic abuse.

Karlekar reflected fondly upon her undergraduate research experiences, “research gave me the chance to prove myself. The freedom for creativity and mobility. Research allowed me to build my next steps brick by brick, and through research I was provided the foundation I needed to grow professionally.”

Guo’s research has been sequential, building on skills each next step. His first project focused on automated image captioning, followed by a project that applied automated text summarization of news articles. Today, Guo’s research lies at the intersection of machine learning and NLP, and he’s interested in building robust NLP systems that are interpretable. Guo says his commitment to becoming an expert in AI has reinforced his desire to continue research moving forward.

“I want to stay in front of technology, I enjoy learning and understanding how things work and am always considering how areas could be integrated together. Through research, you are shaping the future of technology.”

Karlekar and Guo, who will graduate in May, expressed sincere gratitude for the mentorship of assistant professor Mohit Bansal, director of the UNC Natural Language Processing (NLP) group, where they completed their research.

Bansal, who nominated the students for this award, said, “I am very proud of Sweta and Han for this prestigious achievement. They have been an important and beloved part of our UNC-NLP lab for several years and have pursued exciting, advanced projects with real-world impact. These students are great examples of the extremely strong undergraduate researchers we have here at UNC.”

The CRA Outstanding Undergraduate Researcher Award has been announced annually since 1995. Awardees are nominated by the chair of the department and require a faculty recommendation. Students submit a research summary, explaining the significance of their research project(s) to the field and/or society, their specific contributions, and challenges they addressed. Nominees have often been involved in multiple projects as undergraduates, authored papers, and presented at major conferences. Past awardees have continued their impact, making significant contributions to industry, research, and academia.

For the full list of 2020 awardees and their accomplishments, visit the CRA website.

Graduating senior snags a prestigious role at Amazon [profile of CS major Selina Zhang]

December 10, 2019

Selina Zhang’s risky decision to change her major toward the end of her junior year paid off. She snagged a job as a software engineer at tech giant Amazon and will start her career in Seattle after she graduates on Dec. 15.

By Emilie Poplett, University Communications, Monday, December 9th, 2019

After Selina Zhang graduates from Carolina on Dec. 15, she’ll start her career at one of the biggest, most influential companies in the world: Amazon.

Soon, she will be a software engineer at the tech giant, but the story of how she got there isn’t what you might expect.

Zhang came to Carolina intent on becoming a doctor. Since both of her parents work in the medical field, it seemed like an obvious choice.

But by the middle of her junior year at UNC-Chapel Hill — several semesters into her pre-med track — something kept nagging at her. She was still thinking about the introductory computer science class she took as an elective during her first year.

Zhang took the class to diversify her resume and to strengthen her medical school applications. And, Zhang admits, she wasn’t sure she would like computer science at all. But there was something about working with computers that excited her in a way that pre-med classes didn’t.

“I’d coded the quadratic formula on my calculator, so I thought it would be no issue, but it was actually pretty hard. There was a steep learning curve,” she said, “but I really liked solving puzzles.”

The joy of the challenge kept bringing her back to computer science. And just before the second semester of her junior year, Zhang made a risky move: She switched her major and left medicine behind.

“When I first joined the major, I had zero friends and didn’t know anybody, but they very quickly adopted me,” she said. “They were much further along in their careers than I was because I was still pretty behind at this point. But they really helped me not just catch up, but excel.”

Zhang then became a teaching assistant for the course that changed her trajectory three years before. Among 60 other TAs for assistant professor Kris Jordan’s Computer Science 110 course, she found a true passion for coding and a network of friends who understood how her mind worked.

A former Carolina varsity gymnast, Zhang said coding is a lot like perfecting a new skill in her sport.

“In gymnastics, with a skill, you have to know exactly what’s wrong and how to fix it. When you’re coding, you think the same way,” she said. “And kind of like gymnastics, you’re learning how to be a better person, not a better coder.”

When she heads out to Seattle after graduation, Zhang said she’ll have the chance to reconnect with many of the friends she met as part of the computer science TA staff. Several of those friends work at Amazon, while others are at Microsoft or tech startups in the city.

“It will be cool to be in that area and just have that network,” she said. “I think what Kris [Jordan] has done with the 110 team is something very special. You’re making friends that you can continue to network with even in your post-grad life.”

Because of those friends, she said, she now has the opportunity to pursue a career in a field she loves.

“I definitely think the big thing that Carolina offers is community,” Zhang said. “Once you find that community, it really changes your college experience.”

Although moving out west and starting a new job sounds a little intimidating, there’s one thing Zhang said she can always hold on to.

“No matter where you go,” she said, “Carolina never leaves you.”