Harlene Samra Earns Inaugural Krulcik Scholarship

School of Computer Science junior Harlene Samra has so many doubts about whether she belongs in SCS that she's slapped an "imposter" sticker on her laptop. After all, when other people are pulling all-nighters, she's keeping a healthy sleep schedule. She binges TV shows. She works all weekend on assignments that might take someone else all of two hours. But her peers, professors and other admirers in SCS have no such doubts. In fact, they're so certain of her place in SCS that they've named her the inaugural recipient of the Scott Robert Krulcik Scholarship in Computer Science. Established by the Krulcik family to honor their son after his untimely death last winter, the merit-based Krulcik Scholarship acknowledges and rewards a current SCS undergraduate who clearly demonstrates the core traits, attitude and approach that Scott (CS 2018) embodied: a leader with a positive attitude, an insightful and compassionate scholar, an innovative contributor to the SCS community, and an inspiring peer mentor. Samra won't admit it, but she's all of those things. The Stamford, Connecticut, native didn't know she wanted to major in computer science until she was a senior in high school, took AP computer science and realized she "kind of liked it." She'd visited CMU the previous summer and was hooked. "The tour guides I met were really nice. I don't know. I just got here and I felt that vibe. I knew it was where I should be," she said. Trying desperately not to raise her own hopes, she launched a "Hail Mary" (her words) and applied to computer science schools — SCS as her top choice. "When I got in, I was so shocked that I fell out of bed," she recounts. But she had to be sure, so another campus visit ensued, during which she met Krulcik, who was then an SCS tour guide, as well as a host of other impressive students. It sealed the deal. She turned Tartan and joined the SCS community in 2017. Since then, Samra has done what she modestly calls "quite a few things" She was a tour guide for CMU's Office of Undergraduate Admission the summer after her freshman year, and now works as an SCS tour guide. She's been a teaching assistant (TA) for 15:151: Mathematical Concepts for Computer Science the past two fall semesters, and she TA'd 15:150: Functional Programming last spring. "I really like the idea of being a TA for someone who just came to college, because I think it's important that they have someone who is there for them at the beginning," Samra said. "And getting to know everyone in SCS is really easy when you're a TA because you learn all the students and the other TAs and there's a network." Beyond TAing, Samra participates in the club Teknowledge, of which she's president this year. The organization sends student volunteers into local middle schools to teach the Python programming language. In the same vein, she's also part of the CMU CS Academy, which has created an online high school computer programming curriculum that's free for any teacher who wants to use it. She joined the team the summer after her freshman year — while she was also a campus tour guide — and helped create content for the first class, a one-year Python course. She later started working on the organization's AP Computer Science Principles curriculum and currently belongs to the team developing a class that applies computer science to subjects like art and music. Samra says helping is simply in her nature. "I didn't realize it until recently, but everything I do is educating and helping other people. It's a good feeling when you help someone else understand something. That moment of clarity is a huge thing for me," she said. "Plus, I learn things in the process. I feel like by helping others — it's almost selfish — you're helping yourself in the end." That drive to help is what brought her to the scholarship committee's attention. "Having been Scott's academic advisor, I was especially attuned to nominating an outstanding student for this inaugural award," said Mark Stehlik, teaching professor, assistant dean for Outreach and co-founder of CMU CS Academy. "That wasn't easy, as we have so many great students. But Harlene closely mirrors what Scott brought to this place: a warm, sunny, positive disposition, and always willing to help. She's been an integral part of our CS Academy outreach project, echoing Scott's own contributions to that program." Even knowing that she's received the award hasn't really tamed Samra's imposter syndrome, though. "I'm not going to lie: I was kind of like 'Why me?' There are a lot of people who probably could have gotten this. But I am really grateful, because Scott was such an amazing person," she said. "It's wonderful to know that these professors saw in me what they saw in him. It gives you a little bit of a boost to know that something you're doing matters."

Girls of Steel LEGO Teams Win Big

Four teams fielded by Girls of Steel Robotics in the FIRST LEGO League (FLL) — a competition for elementary and middle school students — brought home several major honors from the Southwestern Pennsylvania FLL Championship Dec. 14 at Sewickley Academy. The teams, including 27 boys and girls in grades 4-8 from 18 local elementary or middle schools, competed in the championship's Scholastic Division. They were coached by Terry Richards, outreach program manager in the Robotics Institute (RI), and Bridget Soderna, a master's student in mechanical engineering, with assistance from Girls of Steel members in grades 8-12. The CM Blues team won the First Place Project Presentation Award, team Dot Dot Dot received the First Place Gracious Professionalism Award, the Green Gears team was awarded the First Place Champions Award and the Pittsburgh Purploids received the Second Place Project Research Award. This year's challenge required the teams to identify problems with buildings or public spaces in their communities, do research to find a solution, and then share that solution and refine it. The teams consulted with CMU faculty and staff and with staff at Kennywood, the Port Authority of Allegheny County and the Wilkins School Community Center. Their projects tackled such problems as "bunching" of buses on various routes, enhancing building accessibility, reducing flooding in the "bathtub" section of I-376 Downtown and reducing heat-related health emergencies at Kennywood. The Girls of Steel, now in its 10th season of competition in the FIRST Robotics Competition, is sponsored by CMU's Field Robotics Center.

Jim Herbsleb Will Lead CMU's Institute for Software Research

Martial Hebert, dean of Carnegie Mellon University's School of Computer Science, has named Jim Herbsleb as the new director of its Institute for Software Research (ISR). A faculty member for 17 years, Herbsleb has served as the institute's interim director since August. "Jim has a distinguished record of scholarly research in the area of collaboration in software engineering and has received many awards," Hebert said. "I look forward to working with him in his new position." Hebert selected Herbsleb based on the recommendations of a search committee led by Mary Shaw, the A.J. Perlis Professor of Computer Science. Herbsleb succeeds Bill Scherlis, who began a leave earlier this year. One of seven departments within the School of Computer Science, the ISR conducts research and offers educational programs in software engineering, societal computing, and privacy and security engineering. Its offerings include a variety of graduate degree programs as well as both on-campus and e-learning executive education programs. Herbsleb, who holds degrees in computer science, psychology and law, is best known for his research on collaboration and coordination in large-scale software engineering projects. He also has worked to develop and test a theory of coordination that unites the technical and human aspects of software development. He has addressed such topics as how development teams can function and collaborate even when they are geographically dispersed. He also has explored issues related to open-source development, both in individual projects and in large-scale ecosystems of interdependent projects. Herbsleb was a member of the technical staff at CMU's Software Engineering Institute from 1994 to 1996, then became part of the software production research department at Bell Laboratories, where he initiated and led the Bell Labs Collaboratory project. He joined the ISR faculty in 2002, where he eventually headed the Ph.D. program in societal computing. He received the 2016 Outstanding Research Award presented by the Association for Computing Machinery's Special Interest Group on Software Engineering (SIGSOFT). Other honors include SCS's 2013 Allen Newell Award for Research Excellence, the Most Influential Paper Award at the International Conference on Software Engineering (ICSE) in 2010, and a variety of distinguished paper and best paper awards at ICSE and other scientific conferences. Herbsleb earned a bachelor's degree in psychology and economics at Monmouth College, a master's in computer science at the University of Michigan, and both a Ph.D. in cognitive social psychology and a J.D. in law and psychology at the University of Nebraska.

New Book Offers Global Perspectives on Women in Computing

A new book edited by Carol Frieze and Jeria Quesenberry explores how different cultures address equity in computing. "Cracking the Digital Ceiling: Women in Computing Around the World" features a collection of essays that explore an unanswered question: Given that women are so poorly represented in computing in the U.S., what is happening to women in computing globally? The book challenges many western myths about why women are underrepresented in computing fields. "We found there's no correlation between a country's gender equity and women's participation in computer science," said Frieze, a faculty member in CMU's School of Computer Science and director of its Women@SCS and SCS4ALL initiatives. According to the data presented in the book, Scandinavian countries like Sweden have some of the lowest rates of women enrolled in computing, while some of the highest numbers are in Saudi Arabia. The editors said it often comes down to the difference between socio-cultural expectations and intellectual expectations. "What we found in India and Malaysia is that girls do not grow up thinking they're differently suited to jobs and studies in computing, even though you might find strong — and often restrictive — expectations for how women should behave socially," Frieze said. "It's typical in the west to encourage our children to pursue studies they love, which often means studies that fit western gender norms," said Jeria Quesenberry, an associate teaching professor in the Dietrich College's Information Systems Program. "This works against fields like technology in the U.S., where computing is often perceived as a boys' field. Some cultures encourage their young girls and boys to build on their math and science skills regardless of gender." "There's a growing awareness in the U.S. that computer science is changing our lives, it really does matter. A global perspective can help us recognize that women's participation in computing is shaped by cultural attitudes and influences, not innate ability. When we look to the future, women need to be there. It's amazing how long it's taking to happen," Frieze said. The editors hope the book reaches educators in K-12 and post-secondary fields, administrators, companies working to address diversity, and anyone who may have preconceived notions about STEM interventions or programs. "Change is hard, but it can happen when you pay attention," Frieze said. "You can change culture, and we saw that here at CMU when we focused on ensuring men and women were equally valued and received the same leadership opportunities, encouragement, visibility and mentoring. We worked to level the playing field and it paid off. CMU is a leader in sustaining higher than national averages for undergraduate women in computer science." "Cracking the Digital Ceiling: Women in Computing Around the World" is published by Cambridge University Press and is currently available online and in stores.

Seshan Named 2019 ACM Fellow

The Association for Computing Machinery (ACM) has named Srinivasan Seshan, professor and head of the Computer Science Department, one of 58 new ACM fellows who are being honored for fundamental contributions in such areas as artificial intelligence, cloud computing and wireless networking. The ACM cited Seshan "for contributions to computer networking, mobile computing and wireless communications." Seshan, who joined the CSD faculty in 2000, served as the department's associate head for graduate education from 2011 to 2015 and was named department head in 2018. His research focuses on improving the design, performance and security of computer networks, including wireless and mobile networks. He and his research group have developed ways to more efficiently transfer video content over the internet, and have worked on new architectures that would make the internet more trustworthy and better able to evolve as technology changes. "Computing technology has had a tremendous impact in shaping how we live and work today," said ACM President Cherri M. Pancake. "All of the technologies that directly or indirectly influence us are the result of countless hours of collaborative and/or individual work, as well as creative inspiration and, at times, informed risk-taking. Each year, we look forward to welcoming some of the most outstanding individuals as fellows." Among the latest class of fellows is Vincent Conitzer, the Kimberly J. Jenkins University Professor of New Technologies at Duke University, who earned his master's and doctor's degrees in computer science at CMU; and Dawn Song, professor of electrical engineering and computer science at the University of California, Berkeley, who earned her master's degree in computer science at CMU and served on the faculty of CSD and the Electrical and Computer Engineering Department from 2002 to 2007. The 2019 fellows hail from Australia, Canada, China, Egypt, France, Germany, Israel, Italy, Switzerland, and the United States. The ACM will formally recognize its 2019 Fellows at the annual Awards Banquet, June 20, 2020, in San Francisco. Additional information is available through the ACM fellows site.

Deep Learning Helps Tease Out Gene Interactions

New Method Could Help Identify Disease-Related Genes, Pathways

Carnegie Mellon University computer scientists have taken a deep learning method that has revolutionized face recognition and other image-based applications in recent years and redirected its power to explore the relationship between genes. The trick, they say, is to transform massive amounts of gene expression data into something more image-like. Convolutional neural networks (CNNs), which are adept at analyzing visual imagery, can then infer which genes are interacting with each other. The CNNs outperform existing methods at this task. The researchers' report on how CNNs can help identify disease-related genes and developmental and genetic pathways that might be targets for drugs is being published today in the Proceedings of the National Academy of Science. But Ziv Bar-Joseph, professor of computational biology and machine learning, said the applications for the new method, called CNNC, could go far beyond gene interactions. The new insight described in the paper suggests that CNNC could be similarly deployed to investigate causality in a wide variety of phenomena, including financial data and social networking, said Bar-Joseph, who co-authored the paper with Ye Yuan, a post-doctoral researcher in CMU's Machine Learning Department. "CNNs, which were developed a decade ago, are revolutionary," Bar-Joseph said. "I'm still in awe of Google Photos, which uses them for facial recognition," he added as he scrolled through photos on his smartphone, showing how the app could identify his son at different ages, or identify his father based on an image of the rear right side of his head. "We sometimes take this technology for granted because we use it all the time. But it's incredibly powerful and is not restricted to images. It's all a matter of how you represent your data." In this case, he and Yuan were looking at gene relationships. The approximately 20,000 genes in humans work in concert, so it's necessary to know how genes work together in complexes or networks to understand human development or diseases. One way to infer these relationships is to look at gene expression — which represents the activity levels of genes in cells. Generally, if gene A is active at the same time gene B is active, that's a clue that the two are interacting, Yuan said. Still, it's possible that this is a coincidence or that both are activated by a third gene C. Several previous methods have been developed to tease out these relationships. To employ CNNs to help analyze gene relationships, Yuan and Bar-Joseph used single-cell expression data — experiments that can determine the level of every gene in a single cell. The results of hundreds of thousands of these single-cell analyses were then arranged in the form of a matrix or histogram so that each cell of the matrix represented a different level of co-expression for a pair of genes. Presenting the data in this way added a spatial aspect that made the data more image-like and, thus, more accessible to CNNs. By using data from genes whose interactions already had been established, the researchers were able to train the CNNs to recognize which genes were interacting and which weren't based on the visual patterns in the data matrix, Yuan said. "It's very, very hard to distinguish between causality and correlation," Yuan said, but the CNNC method proved statistically more accurate than existing methods. He and Bar-Joseph anticipate CNNC will be one of several techniques that researchers will eventually deploy in analyzing large datasets. "This is a very general method that could be applied to a number of analyses," Bar-Joseph said. The main limitation is data — the more data there is, the better CNNs work. Cell biology is well-suited for using CNNC, as a typical experiment can involve tens of thousands of cells and generate a massive amount of data. The National Institutes of Health, the National Science Foundation and the James S. McDonnell Foundation supported this research.

SCS Announces Dissertation Award Winners, ACM Nominees

The School of Computer Science has announced that Carlo Angiuli, who recently earned his Ph.D. from the Computer Science Department, has received the 2018-2019 SCS Dissertation Award. Presented annually, the award recognizes outstanding work by an SCS graduate student and includes a cash prize and distinguished lectures by the recipient. Angiuli, now a post-doctoral fellow in CSD, earned the award for his thesis, "Computational Semantics of Cartesian Cubical Type Theory," completed under the direction of Computer Science Professor Robert Harper. "About a decade ago, the late mathematician Vladimir Voevodsky discovered that dependent type theory, a programming language and logic, is compatible with a mathematically inspired principle that he called the univalence axiom," Angiuli said. "Unfortunately, the mathematics of univalence doesn't tell us what it means as a computer program. My thesis describes how to run univalence as a program, and, hence, how to view dependent type theory with univalence as a programming language." Honorable mentions for the SCS Dissertation Award include: Simon Shaolei Du, Machine Learning Department, "Gradient Descent for Non-Convex Problems in Modern Machine Learning," advised by Aarti Singh and Barnabas Poczos. Kenneth Holstein, Human-Computer Interaction Institute, "Designing Real-time Teacher Augmentation to Combine Strengths of Human and AI Instruction," advised by Vincent Aleven. Lerrel Joseph Pinto, Robotics Institute, "Data Centric Robotic Learning," advised by Abhinav Gupta. Sabrina Rashid, Computational Biology Department, "Distributed Computing in Nature," advised by Ziv Bar-Joseph. SCS can also nominate two students to compete for the Association for Computing Machinery's Doctoral Dissertation Award, which is presented annually for the best doctoral dissertation in computer science and engineering. This year's SCS nominees are Angiuli and Du. This year's award winners and ACM nominees were selected by Michael Erdmann, professor in the Robotics Institute; Nancy Pollard, a professor in the Computer Science Department and Robotics Institute; and Seyoung Kim, assistant professor in the Computational Biology Department.

Murphy Named 2020 IEEE Fellow

Robert F. Murphy, the Ray and Stephanie Lane Professor of Computational Biology and head of the Computational Biology Department at Carnegie Mellon University, has been elevated to fellow status in the Institute of Electrical and Electronics Engineers (IEEE), the world's largest technical professional organization. Murphy is also professor of Biological Sciences and holds courtesy appointments in the Biomedical Engineering and Machine Learning departments. Fellow status is a distinction reserved for select members who have demonstrated extraordinary accomplishments in an IEEE field of interest. The IEEE cited Murphy "for contributions to machine learning algorithms for biological images." Murphy's career has centered on combining fluorescence-based cell measurements with quantitative and computational methods. In the mid-1990s, his group pioneered the use of machine learning to analyze microscope images of subcellular structures. His group's work over the past 20 years led to the development of the first systems for automatically recognizing all major organelle patterns in 2D and 3D images of cells and tissues, and for building generative models of subcellular organization directly from images. At CMU, he developed the world's first formal undergraduate program in computational biology in 1987, created master's programs in computational biology and in automated science, and was a founding director of the joint Ph.D. program in computational biology offered by CMU and the University of Pittsburgh. He was the founding director of the Ray and Stephanie Lane Center for Computational Biology, which became the Computational Biology Department within the School of Computer Science. His honors include the Distinguished Service Award from the International Society for the Advancement of Cytometry, an honorary professorship at Albert Ludwig University of Freiburg, and the Alexander von Humboldt Foundation Senior Research Award. He is a fellow of the American Institute of Medical and Biological Engineering. The total number of fellows selected by IEEE in any one year cannot exceed one-tenth of one percent of the total voting IEEE membership. A complete list of the Class of 2020 fellows is available on the IEEE site.

Wehbe Works Machine Learning Magic To Understand How We Think

Readers around the world adore J.K. Rowling's Harry Potter series for the fascinating world of Hogwarts, its lively characters and vivid storytelling. And that's exactly why the first book of the series, "Harry Potter and the Sorcerer's Stone," was the perfect stimulus for Leila Wehbe to use in her studies to better understand how the brain interprets language. But first, a little background. Wehbe, currently an assistant professor in CMU's Machine Learning (ML) Department, has always been fascinated by the human brain. After studying electrical engineering at the American University of Beirut, she decided to combine her passions for neuroscience and computational studies at CMU, where she began a six-year journey to earn her Ph.D. in machine learning in 2009. "Coming to the ML Department was perfect for me, especially when I met my advisor, Tom Mitchell, because he was working on exactly what I was interested in — the intersection between machine learning and the neuroscience of language," Wehbe said. "I was not just in the Machine Learning Department, but could expand my interests through the Center for the Neural Basis of Cognition (CNBC). I could take more courses about the brain, get to know more researchers who share similar interdisciplinary research interests, and work on different aspects of research and analysis." Wehbe's research interests posed huge logistical challenges, though. "Fields like vision have been studied for a long time in animals, where one could perform different invasive procedures," she said. "But the challenge with studying language is that you can only study humans, and, for good reason, we can't just open up the brains of humans and record from there (except in rare medical situations). We're limited to noninvasive methods." To circumvent this challenge, Wehbe relies on two cutting-edge techniques that help her observe human brain activity: functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). The first method uses magnetic fields and radio waves to provide a detailed image of blood and oxygen flow through the brain. "fMRI is an indirect way to observe the changes in oxygen levels due to brain activity," Wehbe said. "The oxygen levels change slowly, however, so we can't track them quickly. The response takes a few seconds to rise to a peak, even though we think and use language at a much faster time scale." MEG is a more direct method than fMRI. While fMRI indirectly reflects brain activity through oxygen levels, MEG provides signals directly related to the electrical activity of neurons — specialized cells in the nervous system used to communicate and transmit information. But while these two methods differ in their mechanisms, they are both important in mapping the fast-paced activity of the brain, especially in the case of language processing. According to Wehbe, language is a complex process in that it interacts with many other cognitive domains, such as visual imagery, planning or social reasoning. So studying language could be a window to understanding the brain basis of these cognitive domains. "If you look at an individual word, like 'apple,' for instance, it has a specific meaning to us, and there are multiple theories about how that meaning is stored in our brain," she said. "One theory is that the meaning of the word 'apple' is based on how we interact with the apple. If you look at the apple, specific areas of your brain are activated that may be associated with the color red, or the taste of something sweet." "And from words to sentences, you start going broader and broader until you end up engaging the brain areas you use to interact with the world in everyday life. When you read, you start taking the perspective of story characters, or guessing the outcome of certain events. And it seems that you use the same brain areas when you perform these tasks while reading as you do when you perform them naturally in everyday life." Which brings us back to Harry Potter. In 2014, Wehbe and then-advisor Mitchell created a study to simulate this everyday life scenario through storytelling and observe how participants' brains were activated when they interacted with fictional characters. They performed fMRI scans of eight participants as they read a chapter of "Harry Potter and the Sorcerer's Stone." In the first iteration of the study, the researchers tried to observe all of the possible information the participants could have associated with the story, like the syntactic structure of the sentences, how many letters were in each word, and more complex associations like which characters were being mentioned and how they were feeling. They then used the corresponding brain activity to develop a computational model that expressed the features of the text at any given point as a function of what was going on in the brain. Wehbe and Mitchell could then use the model to predict what the brain activity would look like for passages the readers hadn't yet read. The model also reported other key features of the text like syntax, semantic properties, narrative text, and which brain regions modulate the activity of those features. The robustness of these findings was tested using statistical tools. "The model helps us detect where exactly natural language processing increases brain activity, and provides insight into what type of information is encoded in each one of the regions of the brain that respond to language," Wehbe said. Wehbe believes computer modeling and statistical analysis provide significant insight into the brain's robust ability to process language. She's especially grateful for her time at CMU for giving her an avenue to explore this interaction. "It's made me see more of a connection between the brain and machine learning," she said. "The ability to work with people from so many different departments and having the freedom of collaboration makes it a productive environment to explore these different ideas and interactions." And this is only the beginning of Wehbe's journey to unlock the inner workings of the human brain. More recently, she has been interested in exploring whether brain activity of people reading natural text could help design better artificial intelligence algorithms that can solve language tasks, such as translating text from one language to another. Artificial intelligence algorithms do not yet understand language, and the hope is that since the human brain is the only system that does understand language, brain activity recordings could provide insight into how to design better algorithms. Wehbe is also interested in using her approach for medical applications. She believes her research provides the groundwork to explore cognitive disorders and their treatments. "Based on our results across different subjects, we can still see that there's a high level of consistency between one subject and another. In general, this means that healthy brains are similarly organized," she said. "It might be possible to find hidden patterns in how our brains organize information, which can help diagnose and suggest a specific way to treat a disorder. Because the brain is a complex machine, you might not be able to just fix all of the parts of the machine, but vary one part at a time and understand how the machine works. A promising alternative approach is to perform rich, natural interactive tasks in the scanner and observe how the brain works in real-life simulations, which can offer a rich picture of the different parts of the brain working together."

Ding Earns 2019 Stehlik Scholarship

School of Computer Science senior Wenxin (Freda) Ding always dreamed of being a teacher. And while she's majoring in computer science and math — not teaching — her dedication to helping others ranks high among the reasons she's earned the 2019 Mark Stehlik SCS Alumni Undergraduate Impact Scholarship. Now in its fifth year, the Stehlik Scholarship recognizes undergraduate students near the end of their Carnegie Mellon careers whose reach for excellence extends beyond the classroom. Awardees are working to make a difference in SCS, the field of computer science and the world around them. In Ding's case, that reach for excellence includes a resume heavy on mentoring and teaching others. When she entered CMU as a math major, she had no programming experience. But during her first semester, she took 15-110: Principles of Computing and was surprised by the power coding offered. "I feel like when you're coding, it's like you're writing a story of your own," Ding said. "You have to make decisions about your structure and your code. The creativity that coding gives you goes way beyond what I had expected." But she was also impressed with how much help the course's teaching assistants (TAs) could offer, and immediately applied to be a 15:110 TA the next semester. Rejected, but not defeated, she reapplied for a position the summer before her sophomore year, and was accepted. She went on to TA for 15:110 during both her sophomore and junior years, rising to the rank of associate head TA during the latter. Ding's service to the larger community also includes volunteering for CMU's chapter of Strong Women Strong Girls, a mentoring organization dedicated to creating an outside support system for young girls and helping them develop skills for lifelong success. As a freshman, Ding regularly traveled to the Boys and Girls Club in Lawrenceville to share stories of successful women in various fields with elementary school girls. "That was a meaningful experience," Ding said. "When the girls talked about what they love and what they want to do in the future, their eyes still shone brightly." Beyond mentoring, TAing and carrying a full course load, Ding has also performed research since her first year at CMU. Her work has ranged from studying information entropy with Assistant Professor of Math Tomasz Tkocz to investigating differential privacy using convex optimization with Assistant Professor of Machine Learning and Computer Science Nihar Shah and Assistant Professor of Computer Science Weina Wang. Ding recently learned that she's been accepted into the SCS Fifth-Year Master's Program, and after that she hopes to pursue a Ph.D. in theoretical computer science — allowing her to perhaps fulfill that childhood dream of becoming a teacher. For now, she's grateful for what her time as a teaching assistant has taught her. "Being a TA was a really valuable and important experience for me," Ding said. "It enhanced my entire CMU experience. It gave it more meaning for me. I realized I have the power to help other people learn."