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."

CMU Algorithm Rapidly Finds Anomalies in Gene Expression Data

Algorithm Also Works to Identify and Correct Mistakes It Might Have Made

Computational biologists at Carnegie Mellon University have devised an algorithm to rapidly sort through mountains of gene expression data to find unexpected phenomena that might merit further study. What's more, the algorithm then re-examines its own output, looking for mistakes it has made and then correcting them. This work by Carl Kingsford, a professor in CMU's Computational Biology Department, and Cong Ma, a Ph.D. student in computational biology, is the first attempt at automating the search for these anomalies in gene expression inferred by RNA sequencing, or RNA-seq, the leading method for inferring the activity level of genes. As they report today in the journal Cell Systems, the researchers already have detected 88 anomalies — unexpectedly high or low levels of expression of regions within genes — in two widely used RNA-seq libraries that are both common and not previously known. "We don't yet know why we're seeing those 88 weird patterns," Kingsford said, noting that they could be a subject of further investigation. Though an organism's genetic makeup is static, the activity level, or expression, of genes varies greatly over time. Gene expression analysis has thus become a major tool for biological research, as well as for diagnosing and monitoring cancers. Anomalies can be important clues for researchers, but until now finding them has been a painstaking, manual process, sometimes called "sequence gazing." Finding one anomaly might require examining 200,000 transcript sequences — sequences of RNA that encode information from the gene's DNA, Kingsford said. Most researchers therefore zero in on regions of genes that they think are important, largely ignoring the vast majority of potential anomalies. The algorithm developed by Ma and Kingsford automates the search for anomalies, enabling researchers to consider all of the transcript sequences, not just those regions where they expect to see anomalies. This technology could uncover many new phenomena, such as the 88 previously unknown common anomalies found in the multi-tissue RNA-seq libraries. But Ma noted that identifying anomalies is often not clear cut. Some RNA-seq "reads," for instance, are common to multiple genes and transcripts and sometimes get mapped to the wrong one. If that occurs, a genetic region might appear more or less active than expected. So the algorithm re-examines any anomalies it detects and sees if they disappear when the RNA-seq reads are redistributed between the genes. "By correcting anomalies when possible, we reduce the number of falsely predicted instances of differential expression," Ma said. The Gordon and Betty Moore Foundation, the National Science Foundation, the National Institutes of Health, the Shurl and Kay Curci Foundation, and the Pennsylvania Department of Health supported this research.

New Technology Makes Internet Memes Accessible for People With Visual Impairments

CMU Researchers Develop System to Identify and Translate Memes

People with visual impairments use social media like everyone else, often with the help of screen reader software. But that technology falls short when it encounters memes, which don't include alternate text, or alt text, to describe what's depicted in the image. To counter this, researchers at Carnegie Mellon University have developed a method to automatically identify memes and apply prewritten templates to add descriptive alt text, making them intelligible via existing assistive technologies. Memes are images that are copied and then overlaid with slight variations of text. They are often humorous and convey a shared experience, but "if you're blind, you miss that part of the conversation," said Cole Gleason, a Ph.D. student in CMU's Human-Computer Interaction Institute (HCII.) "Memes may not seem like the most important problem, but a vital part of accessibility is not choosing for people what deserves their attention," said Jeff Bigham, an associate professor in the HCII. "Many people use memes, and so they should be made accessible." Memes largely live within social media platforms that have barriers to adding alt text. Twitter, for example, allows people to add alt text to their images, but that feature isn't always easy to find. Of 9 million tweets the CMU researchers examined, one million included images and, of those, just 0.1 percent included alt text. Gleason said basic computer vision techniques make it possible to describe the images underlying each meme, whether it be a celebrity, a crying baby, a cartoon character or a scene such as a bus upended in a sinkhole. Optical character recognition techniques are used to decipher the overlaid text, which can change with each iteration of the meme. For each meme type, it's only necessary to make one template describing the image, and the overlaid text can be added for each iteration of that meme. But writing out what the meme is intended to convey proved difficult. "It depended on the meme if the humor translated. Some of the visuals are more nuanced," Gleason said. "And sometimes it's explicit and you can just describe it." For example, the complete alt text for the so-called "success kid" meme states "Toddler clenching fist in front of smug face. Overlaid text on top: Was a bad boy all year. Overlaid text on bottom: Still got awesome presents from Santa." The team also created a platform to translate memes into sound rather than text. Users search through a sound library and drag and drop elements into a template. This system was made to translate existing memes and convey the sentiment through music and sound effects. "One of the reasons we tried the audio memes was because we thought alt text would kill the joke, but people still preferred the text because they're so used to it," Gleason said. Deploying the technology will be a challenge. Even if it was integrated into a meme generator website, that alt text wouldn't be automatically copied when the image was shared on social media. "We'd have to convince Twitter to add a new feature," Gleason said. It could be something added to a personal smartphone, but he noted that would put the burden on the user. CMU researchers are currently working on related projects, including a browser extension for Twitter that attempts to add alt text for every image and could include a meme system. Another project seeks to integrate alt text into the metadata of images that would stay with the image wherever it was posted. This work was presented earlier this year at the ACCESS conference in Pittsburgh. Other researchers involved in the project include HCII postdoctoral fellow Amy Pavel, CMU undergraduate Xingyu Liu, HCII assistant professor Patrick Carrington, and Lydia Chilton of Columbia University.

Carnegie Mellon System Locates Shooters Using Smartphone Video

New Analytical Tool Could Aid Human Rights, Public Safety Workers

Researchers at Carnegie Mellon University have developed a system that can accurately locate a shooter based on video recordings from as few as three smartphones. When demonstrated using three video recordings from the 2017 mass shooting in Las Vegas that left 58 people dead and hundreds wounded, the system correctly estimated the shooter’s actual location — the north wing of the Mandalay Bay hotel. The estimate was based on three gunshots fired within the first minute of what would be a prolonged massacre. Alexander Hauptmann, research professor in CMU’s Language Technologies Institute, said the system, called Video Event Reconstruction and Analysis (VERA), won’t necessarily replace the commercial microphone arrays for locating shooters that public safety officials already use, although it may be a useful supplement for public safety when commercial arrays aren’t available. One key motivation for assembling VERA was to create a tool that could be used by human rights workers and journalists who investigate war crimes, terrorist acts and human rights violations, Hauptmann said. “Military and intelligence agencies are already developing these types of technologies,” said fellow researcher Jay D. Aronson, a professor of history at CMU and director of the Center for Human Rights Science. “We think it’s crucial for the human rights community to have the same types of tools. It provides a necessary check on state power.” The researchers presented VERA and released it as open-source code last month at the Association for Computing Machinery’s International Conference on Multimedia in Nice, France. Hauptmann said he has used his expertise in video analysis to help investigators analyze events such as the 2014 Maidan massacre in Ukraine, which left at least 50 antigovernment protesters dead. Inspired by that work — and the insight of ballistics experts and architecture colleagues from the firm SITU Research — Hauptmann, Aronson and Junwei Liang, a Ph.D. student in language and information technology, have pulled together several technologies for processing video, while automating their use as much as possible. VERA uses machine learning techniques to synchronize the video feeds and calculate the position of each camera based on what that camera is seeing. But it’s the audio from the video feeds that's pivotal in localizing the source of the gunshots, Hauptmann said. Specifically, the system looks at the time delay between the crack caused by a supersonic bullet’s shock wave and the muzzle blast, which travels at the speed of sound. It also uses audio to identify the type of gun used, which determines bullet speed. VERA can then calculate the shooter's distance from the smartphone. “When we began, we didn’t think you could detect the crack with a smartphone because it’s really short,” Hauptmann said. “But it turns out today’s cell phone microphones are pretty good.” By using video from three or more smartphones, the direction from which the shots were fired — and the shooter’s location — can be calculated based on the differences in how long it takes the muzzle blast to reach each camera. With the proliferation of mass protests occurring in places such as Hong Kong, Egypt and Iraq, identifying where a shot originated can be critical to determining whether protesters, police or other groups might be responsible when a shooting takes place, Aronson said. But VERA is not limited to detecting gunshots. It is an event analysis system that can be used to locate a variety of other sounds relevant to human rights and war crimes investigations, he said. He and Hauptmann hope that other groups will add functionalities to the open-source software. “Once it’s open source, the journalism and human rights communities can build on it in ways we don’t have the imagination for or time to do,” Aronson added. The National Institute of Standards and Technology provided partial support for this work. The MacArthur Foundation and the Oak Foundation also have supported this work.

Trash Talk Hurts, Even When It Comes From a Robot

Discouraging Words From Machines Impair Human Game Play

Trash talking has a long and colorful history of flustering game opponents, and now researchers at Carnegie Mellon University have demonstrated that discouraging words can be perturbing even when uttered by a robot. The trash talk in the study was decidedly mild, with utterances such as "I have to say you are a terrible player," and "Over the course of the game your playing has become confused." Even so, people who played a game with the robot — a commercially available humanoid robot known as Pepper — performed worse when the robot discouraged them and better when the robot encouraged them. Lead author Aaron M. Roth said some of the 40 study participants were technically sophisticated and fully understood that a machine was the source of their discomfort. "One participant said, 'I don't like what the robot is saying, but that's the way it was programmed so I can't blame it,'" said Roth, who conducted the study while he was a master's student in the CMU Robotics Institute. But the researchers found that, overall, human performance ebbed regardless of technical sophistication. The study, presented last month at the IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) in New Delhi, India, is a departure from typical human-robot interaction studies, which tend to focus on how humans and robots can best work together. "This is one of the first studies of human-robot interaction in an environment where they are not cooperating," said co-author Fei Fang, an assistant professor in the Institute for Software Research. It has enormous implications for a world where the number of robots and internet of things (IoT) devices with artificial intelligence capabilities is expected to grow exponentially. "We can expect home assistants to be cooperative," she said, "but in situations such as online shopping, they may not have the same goals as we do." The study was an outgrowth of a student project in AI Methods for Social Good, a course that Fang teaches. The students wanted to explore the uses of game theory and bounded rationality in the context of robots, so they designed a study in which humans would compete against a robot in a game called "Guards and Treasures." A so-called Stackelberg game, researchers use it to study rationality. This is a typical game used to study defender-attacker interaction in research on security games, an area in which Fang has done extensive work. Each participant played the game 35 times with the robot, while either soaking in encouraging words from the robot or getting their ears singed with dismissive remarks. Although the human players' rationality improved as the number of games played increased, those who were criticized by the robot didn't score as well as those who were praised. It's well established that an individual's performance is affected by what other people say, but the study shows that humans also respond to what machines say, said Afsaneh Doryab, a systems scientist at CMU's Human-Computer Interaction Institute (HCII) during the study and now an assistant professor in Engineering Systems and Environment at the University of Virginia. This machine's ability to prompt responses could have implications for automated learning, mental health treatment and even the use of robots as companions, she said. Future work might focus on nonverbal expression between robot and humans, said Roth, now a Ph.D. student at the University of Maryland. Fang suggests that more needs to be learned about how different types of machines — say, a humanoid robot as compared to a computer box — might invoke different responses in humans. In addition to Roth, Fang and Doryab, the research team included Manuela Veloso, professor of computer science; Samantha Reig, a Ph.D. student in the HCII; Umang Bhatt, who recently completed a joint bachelor's-master's degree program in electrical and computer engineering; Jonathan Shulgach, a master's student in biomedical engineering; and Tamara Amin, who recently finished her master's degree in civil and environmental engineering. The National Science Foundation provided some support for this work.

CMU Researchers Propose New Rules for Internet Fairness

Just weeks after a team of Carnegie Mellon researchers demonstrated that Google's new congestion control algorithm (CCA) gives an unfair advantage to its own traffic, the same team has proposed new guidelines for how future algorithms should be developed. "Our work shows that it is not always the case that new CCAs will be fair to the old ones," said Justine Sherry, an assistant professor in CMU's Computer Science Department (CSD) and a co-author of the proposal. "Google is not the only company deploying new algorithms. Moving forward, we need guidelines." Those guidelines, offered in their study, "Beyond Jain's Fairness Index: Setting the Bar for the Deployment of Congestion Control Algorithms," were presented last week at the 18th ACM Workshop on Hot Topics in Networks (HotNets-2019) in Princeton, New Jersey. Despite the team's focus on internet fairness, their proposed guidelines don't focus on fairness itself. That's because perfect fairness, the authors argue, is actually difficult to achieve and few (if any) existing CCAs today are perfectly fair. "We need to stop making excuses for why our new algorithms are not meeting an unrealistic goal," said Ranysha Ware, a CSD Ph.D. student and lead author on the study. So instead of focusing on developing CCAs that are fair, Ware and her co-authors say that developers need to ensure that new CCAs would not inflict harm on the existing ecosystem of CCAs. Put simply: if a new CCA is more unfair than existing CCAs, it is not okay to deploy. "What makes Google's new algorithm special is not that it's unfair, it's that it is more unfair and causes more harm to the internet than existing CCAs," said Sherry, who is also a member of the university's CyLab Security and Privacy Institute. "You can only be as unfair as things already are. You can't be more unfair than things already are." Sherry likens the issue of CCA fairness to splitting a cookie between two children. "Ideally, we would cut the cookie perfectly in half, but no one can ever perfectly cut a cookie in half. One side always ends up uneven," Sherry said. "The trick is doing something that is reasonable, even if it's not perfectly fair: having one child split the cookie, and the other child choose which half they get." In the case of CCAs, the trick is ensuring that the status quo is left unperturbed. Other authors on the study included CSD Department Head Srinivasan Seshan and Nefeli Networks software engineer and CSD alumnus Matthew Mukerjee.

Two Endowed Professorships Created for Computer Science and Electrical and Computer Engineering Faculty

Gifts Totaling $6 Million by Cadence Design Systems and CEO Lip-Bu Tan Will Advance Research and Teaching

Cadence Design Systems Inc. and its CEO, Lip-Bu Tan, have made significant gifts of $3 million each to support Carnegie Mellon University faculty members working in computer-related fields. Cadence, a leading multinational company in the electronic design automation industry, has created the Cadence Design Systems Endowed Chair in Computer Science. Tan and his wife, Ysa Loo, have created the Tan Family Endowed Chair in Electrical and Computer Engineering (ECE). Together, the gifts total $6 million, which will provide funding to advance faculty members' activities, including research and teaching. "Exceptional people with pioneering ideas have fueled Carnegie Mellon’s game-changing research and education from the very beginning, so investing in human capital development is one of the most important ways that we can retain our global leadership," CMU President Farnam Jahanian said. "Endowed professorships provide a singularly powerful tool to support these bright minds, and we are grateful to Cadence, Lip-Bu and Ysa for their exceptional generosity toward this critical priority." Cadence's products are used by electronic systems and semiconductor companies to create innovative and transformational end products. Cadence's Academic Network Program, of which CMU is a member, promotes the proliferation of technology expertise among selected universities, research institutes and industry advisors in the area of microelectronic systems development. "Cadence is privileged to institute an endowed chair in the School of Computer Science," said John Shoven, chairman of the Board of Directors of Cadence Design Systems. "We are fortunate to have many CMU CS graduates on our Cadence team and look forward to enabling the advancement of faculty members' research priorities." "The connection between Cadence's work and computer science cannot be overstated," said Martial Hebert, dean of the School of Computer Science. "This new professorship is another indication of the deepening connections among computer science, electronic design automation and related areas." Tan has been the CEO of Cadence since 2009 and joined the company's board of directors in 2004. He is also the founder and chairman of Walden International, a venture capital firm that he launched in 1987. He is a member of The Business Council and serves on the board of directors of Hewlett Packard Enterprise Company and Schneider Electric SE. Tan also serves on CMU's Board of Trustees and is a member of the College of Engineering's Dean's Advisory Council. The couple's two sons, Andrew and Elliott, both received their master's degrees from CMU's College of Engineering. "Carnegie Mellon's ECE department has provided world-class education and an incredible learning experience to our two sons," Tan said. "Ysa and I are delighted to support the ECE department as it continues pushing the frontiers of cutting-edge, innovative research." Tan and Loo previously endowed a graduate student fellowship in the Department of Electrical and Computer Engineering. "I'm excited by the opportunity to recognize and support one of our star faculty as the Tan Family Professor in ECE," said Jon Cagan, interim dean of the College of Engineering. "We deeply value the additional support of research in the college by Lip-Bu Tan and his family."  

CMU Women Prominent Among Rising Stars 2019

Annual Workshop Boosts Women in Computer Science, Electrical and Computer Engineering

Women from Carnegie Mellon University outnumbered those from every other institution at Rising Stars 2019, an annual workshop for early career women in computer science and electrical and computer engineering. They also won two of the four prizes in the workshop's Research Pitch Competition. The intensive workshop, designed for women pursuing academic careers, was hosted this year by the University of Illinois at Urbana-Champaign Oct. 29-Nov. 1. It included the largest class of participants to date, with 90 participants from almost 40 institutions represented. Twelve CMU women attended the workshop. The University of California, Berkeley, with nine participants, was the only other institution that came close to that total. Participants were selected from about 300 applicants Pardis Emami Naeini, a Ph.D. student in CMU's CyLab and the School of Computer Science's Institute for Software Research (ISR), and Elahe Soltanaghaei, a post-doctoral researcher who joined CyLab last month, won the Research Pitch Competition. They and the other two winners will be invited back to Illinois to present their talks. Emami Naeini's talk was "Privacy and Security Label for IoT Devices," and Soltanaghaei discussed "Sensing the Physical World Using Pervasive Wireless Infrastructure." Rising Stars was launched at MIT in 2012 and has been hosted at different campuses each year since, including CMU. This year's workshop included opportunities for one-on-one mentoring and feedback on the first eight minutes of each participant's job talks. In addition to Emami Naeini and Soltanaghaei, the CMU contingent included Forough Arabshahi, a post-doctoral associate in the Machine Learning Department (MLD); Naama Ben-David, a Ph.D. student in the Computer Science Department; Maria De-Arteaga, a Ph.D. student in MLD and the Heinz College; Hana Habib, a Ph.D. student in the ISR and CyLab; and Guyue Liu, a post-doctoral researcher in CyLab. Other members of the contingent were Soo-Jin Moon, a Ph.D. student in the Electrical and Computer Engineering Department and CyLab; Swabha Swayamdipta, a recent Ph.D. graduate of the Language Technologies Institute and now a post-doctoral researcher at the Allen Institute of Artificial Intelligence; Hsia-Yu Tung, a Ph.D. student in MLD; Xu Wang, a Ph.D. student in the Human-Computer Interaction Institute; and Yang Yang, a Ph.D. student in the Computational Biology Department.

Neural Network Fills In Data Gaps for Spatial Analysis of Chromosomes

Machine Learning Enhances Study of 3D Genome Structure in Cell Nucleus

Computational methods used to fill in missing pixels in low-quality images or video also can help scientists provide missing information for how DNA is organized in the cell, computational biologists at Carnegie Mellon University have shown. Filling in this missing information will make it possible to more readily study the 3D structure of chromosomes and, in particular, subcompartments that may play a crucial role in both disease formation and determining cell functions, said Jian Ma, associate professor in CMU's Computational Biology Department. In a research paper published today by the journal Nature Communications, Ma and Kyle Xiong, a CMU Ph.D. student in the CMU-University of Pittsburgh Joint Ph.D. Program in Computational Biology, report that they successfully applied their machine learning method to nine cell lines. This enabled them, for the first time, to study differences in spatial organization related to subcompartments across those lines. Previously, subcompartments could be revealed in only a single cell type of lymphoblastoid cells — a cell line known as GM12878 — that has been exhaustively sequenced at great expense using Hi-C technology, which measures spatial interactivity among all regions of the genome. "We now know a lot about the linear composition of DNA in chromosomes, but in the nuclei of human cells, DNA isn't linear," Xiong said. "Chromosomes in the cell nucleus are folded and packaged into 3D shapes. That 3D structure is critical to understanding the cellular functions in development and diseases." Subcompartments are of particular interest because they reflect spatial segregation of chromosome regions with high interactivity. Scientists are eager to learn more about the juxtaposition of subcompartments and how it affects cell function, Ma said. But until now researchers could calculate the patterns of subcompartments only if they had an extremely high coverage Hi-C dataset — that is, the DNA had been sequenced in great detail to capture more interactions. That level of detail is missing in the datasets for cell lines other than GM12878. Working with Ma, Xiong used an artificial neural network called a denoising autoencoder to help fill in the gaps in less-than-complete Hi-C datasets. In computer vision applications, the autoencoder can supply missing pixels by learning what types of pixels typically are found together and making its best guess. Xiong adapted the autoencoder to high-throughput genomics, using the dataset for GM12878 to train it to recognize what sequences of DNA pairs from different chromosomes typically might be interacting with each other in 3D space in the cell nucleus. This computational method, which Ma and Xiong have dubbed SNIPER, proved successful in identifying subcompartments in eight cell lines whose interchromosomal interactions based on Hi-C data were only partially known. They also applied SNIPER to the GM12878 data as a control. But Xiong noted that it is not yet known how widely this tool can be used on all other cell types. He and Ma are continuing to enhance the method, however, so it can be used on a variety of cellular conditions and even in different organisms. "We need to understand how subcompartment patterns are involved in the basic functions of cells, as well as how mutations can affect these 3D structures," Ma said. "Thus far, in the few cell lines we've been able to study, we see that some subcompartments are consistent across cell types, while others vary. Much remains to be learned." The National Institutes of Health and the National Science Foundation supported this work.

EduSense: Like a FitBit for Your Teaching Skills

CMU Researchers Develop Comprehensive Classroom Sensing System

While training and feedback opportunities abound for K-12 educators, the same can't be said for instructors in higher education. Currently, the most effective mechanism for professional development is for an expert to observe a lecture and provide personalized feedback. But a new system developed by Carnegie Mellon University researchers offers a comprehensive real-time sensing system that is inexpensive and scalable to create a continuous feedback loop for the instructor. The system, called EduSense, analyzes a variety of visual and audio features that correlate with effective instruction. "Today, the teacher acts as the sensor in the classroom, but that's not scalable," said Chris Harrison, assistant professor in CMU's Human-Computer Interaction Institute (HCII). Harrison said classroom sizes have ballooned in recent decades, and it's difficult to lecture and be effective in large or auditorium-style classes. EduSense is minimally obtrusive. It uses two wall-mounted cameras — one facing students and one facing the instructor. It senses things such as students' posture to determine their engagement, and how much time instructors pause before calling on a student. "These are codified things that educational practitioners have known as best practices for decades," Harrison said. A single off-the-shelf camera can view everyone in the classroom and automatically identify information such as where students are looking, how often they're raising their hands and if the instructor moves through the space instead of staying behind a podium. The system uses OpenPose, another CMU project, to determine body position. With advances in computer vision and machine learning, it’s possible to provide insights that would take days if not months to get with manual observation said the HCII's Karan Ahuja and Dohyun Kim of CMU’s Institute for Software Research, the two lead Ph.D. students working on the EduSense project.  Harrison said learning scientists are interested in the instructional data. "Because we can track the body, it's like wearing a suit of accelerometers. We know how much you're turning your head and moving your hands. It's like you're wearing a virtual motion-capture system while you're teaching." Using high-resolution cameras steaming 4K video for many classes at once is a "computational nightmare," Harrison said. To keep up, resources are elastically assigned to provide the best possible frame rate for real-time data. The project also has a strong focus on privacy protection, guided by Yuvraj Agarwal, an associate professor in the university's Institute for Software Research (ISR). The team didn't want to identify individual students, and EduSense can't. No names or identifying information are used, and since camera data is processed in real time, the information is discarded quickly. Now that the team has demonstrated that they can capture the data, HCII faculty member Amy Ogan said their current challenge is wrapping it up and presenting it in a way that's educationally effective. The team will continue working on instructor-facing apps to see if professors can integrate the feedback into practice. "We have been focused on understanding how, when and where to best present feedback based on this data so that it is meaningful and useful to instructors to help them improve their practice," she said. This research has been presented at Ubicomp, the International Conference of the Learning Sciences, and will be presented this coming April at the American Educational Research Association annual meeting. Other researchers involved in EduSense include HCII Ph.D. student Franceska Xhakaj; Annie Xie, a project manager in the HCII; Jay Eric Townsend, former senior engineer in the HCII; Stanley Zhang, a student in CMU’s Electrical and Computer Engineering Department; and Virag Varga, from ETH Zurich.

ACM Names Tom Cortina as Distinguished Member

The Association for Computing Machinery (ACM) has named Thomas Cortina, assistant dean for undergraduate education in the School of Computer Science, one of 62 computer scientists worldwide to be recognized this year as Distinguished Members for their outstanding contributions. All 2019 inductees are longstanding ACM members and were selected as Distinguished Members by their peers for a range of accomplishments that have contributed to technologies that underpin how we live, work and play. Cortina is one of nine members selected for their educational contributions to computing. A faculty member since 2004, Cortina became assistant dean in 2012, overseeing a rapid expansion of the undergraduate program. He helped launch the popular CS4HS workshop for computer science high school teachers, and ACTIVATE workshops for science, technology, engineering and math teachers in the Pittsburgh region. Prior to joining CMU, Cortina taught for a combined 16 years at Polytechnic University in Brooklyn, New York, and at Stony Brook University. He has been active in ACM's Special Interest Group in Computer Science Education (SIGCSE) and currently serves on the ACM's Education Advisory Committee. He served on the National Science Foundation's Computer and Information Science and Engineering advisory committee for four years, and was on the advisory board of a joint NSF-College Board project to develop the latest Advanced Placement Computer Science Principles course. "Each year it is our honor to select a new class of Distinguished Members," said ACM President Cherri M. Pancake. "Our overarching goal is to build a community wherein computing professionals can grow professionally and, in turn, contribute to the field and the broader society. We are delighted to recognize these individuals for their contributions to computing, and we hope that the careers of the 2019 ACM Distinguished Members will continue to prosper through their participation with ACM."