Year: 2017

The Jonathan Gross Prize is being awarded this year for the first time to honor students who graduate at the top of their class with a track record of promising innovative contributions to computer science. Five graduating computer science students—one senior from each of the four undergraduate schools and one student from the master’s program—were selected to receive the prize, which is named for Jonathan L. Gross, the popular professor emeritus who taught for 47 years at Columbia and cofounded the Computer Science Department. The prize is made possible by an endowment established by Yechiam (Y.Y.) Yemini, who for 31 years until his retirement in 2011, was a professor of computer science at Columbia.

Filipe de Avila Belbute Peres, Columbia College

“We are fortunate to live in a time when machines are powerful enough to let us study intelligence by trying to reproduce it.”

Fascinated by computers and computer science from a young age, Filipe de Avila Belbute Peres started building his own PCs and swapping out parts while still a kid; he was programming by his teens. Yet when it came time for college, he applied, and was accepted, to medical school in his native Brazil. The choice reflected his developing interest in studying the biological aspects of intelligence. Upon realizing that he could tackle the problems of neuroscience through a computational/algorithmic approach rather than a biological one, he applied to study computer science in the US. “I found a way to join my different interests, and study the relationships between machine intelligence and human intelligence. That’s where my interest in artificial intelligence comes from.”

This summer he will work at Facebook AI Research, applying neural networks to natural language problems. In the fall, he begins his PhD program in Computer Science at Carnegie Mellon, where he will continue studying artificial intelligence and machine learning with an emphasis in neural nets and deep learning.

Mitchell Gouzenko, School of Engineering and Applied Science

“My first criteria when picking a major was to choose something that I could do for hours on end without getting tired of it.”

Majoring in computer science wasn’t Mitchell Gouzenko’s first choice, or even second. He came to Columbia fully intending to pursue Financial Engineering before becoming convinced that he wanted to study physics; he declared, however, for Applied Math. “I was all over the place,” he says now. In his sophomore year, his computer science classes started growing on him. “While taking Operating Systems with professor Jae Woo Lee, I finally realized how much I enjoy programming and knew it would be a huge mistake not to major in computer science.” Because programming was fun—employability concerns were secondary—he changed his major to computer science. He hasn’t looked back. His main interests are in operating systems and more generally, software architecture. “I love reasoning about large codebases and analyzing how the various components interface with each other, with attention to security, performance, and clarity of design.”

This September he starts work at Google where he easily sees himself working for the next five years. A PhD may someday also be in store, but it will first require narrowing his interests.

Rudra Gopinath, General Studies

“The advantage of a computer science education is in seeing patterns the human eye can’t see because there is too much data.”

For Rudra Gopinath, that data will be financial in nature as he prepares to take a position at Citigroup in New York City. By his own admission, it is an interesting choice for someone most excited by theoretical computer science. Taking classes with professors Mihalis Yannakakis and Xi Chen, he was “that student always asking questions in class and always showing up for office hours.” He gravitated particularly to computational complexity—and not because it offered answers. “We don’t know anything about anything in this field. We don’t even know what we know, as opposed to knowing what we don’t know. If I eventually come back to school, this is the field I still study.” But for now there is a major diversion as he moves to apply the hard skill of computer science to business and the financial markets, two fields that have also long interested him. “All the banks I interviewed said the same thing: we can teach you the finance part. What we need from you is what Columbia teaches—the algorithms, the tools to work with data.” Getting to Columbia was not a straight path. He did his freshman and sophomore years in Hong Kong before enrolling in a dual-degree program that allowed him to transfer to Columbia. “Columbia is one of the few Ivy Leagues where you can come in with that sort of flexibility and plug in seamlessly. The professors, the administration, individual people, were highly supportive. I came in and started taking computer science. It was almost as if I had been here all along.”

Lanting He, Barnard College

“A wisely designed algorithm can be succinct but unbelievably powerful and quick.”

Lanting He arrived on campus expecting to major in business or economics. Shut out of an economics class in the second semester of her first year, she opted instead for Intro to Computer Science. Never having previously done any coding, she was amazed she could quickly write a simple program to do something practical in real life. It was after taking Data Structures, where she learned to love the logic behind the design of data structures, that she decided to major in computer science. “I find algorithms and data structures to be the most fascinating things to learn, and brainstorming algorithms is the thing I enjoy most.”

This summer, she will do an internship as a strategist analyst at Goldman Sachs. In the fall, she returns to Columbia to pursue a master’s degree at Columbia in Operations Research, exploring in depth how computer science connects with other interdisciplinary studies.

Gaurav Gite, Master’s student, School of Engineering and Applied Science

“I wish to develop computer systems to process, analyze and generate natural languages.”

Gaurav Gite’s interests lie in areas of natural language processing. After graduating from the Indian Institute of Technology (IIT), his desire to conduct research in the domain of artificial intelligence led him to pursue his master’s at Columbia University. He previously interned at Google, working with a research team to optimize the performance of neural networks. This helped in predicting the next words on keyboard inputs for Android devices. For his thesis, he worked with Kathleen McKeown in the Natural Language Processing (NLP) lab on the summarization of news articles, proposing new techniques based on the clustering of topics presented in the news article and salience of sentences. He also performed research at Columbia’s Center for Computational Learning System (CCLS) where he worked on automated methods to assess the content of written text and to grade student writing. Wise Crowd Content Assessment and Educational Rubrics, a research paper he coauthored while at the CCLS was published in the International Journal of Artificial Intelligence in Education in 2016.

After graduation, Gite will start working at Google, where he will continue working in the field of natural language processing.

A former student of Steven Feiner, Wilcox is now assistant professor in the School of Interactive Computing at Georgia Tech.

Zweben will speak at SEAS Class Day on May 15, when he will also be awarded the Illig Medal, the highest honor awarded an engineering undergraduate.

For work in Bayesian deep learning, Dustin Tran has been awarded a Google PhD Fellowship in Machine Learning. This two-year fellowship recognizes and supports outstanding graduate students doing exceptional research in computer science and related disciplines. It covers tuition and provides a $34K yearly stipend and an internship opportunity. Tran is one of 33 PhD students announced this month to receive the fellowship.

As a third-year computer science PhD student, Tran does research in the fields of Bayesian statistics, machine learning, and deep learning, with a particular focus on probabilistic modeling. He is advised by David Blei (of both the Computer Science Department and the Department of Statistics) and Andrew Gelman (of both the Department of Statistics and the Department of Political Science).

Like his advisors, Tran works at the intersection of two fields. After starting in Statistics at Harvard, he switched his PhD to Computer Science, at the same time transferring to Columbia. The decision was purely strategic; he chose the program with the fewest logistical constraints so he could maximize the time spent on research and learning. So far, he’s lived up to his side of the bargain, coauthoring seven published works in the 2016-2017 academic year, with another seven currently in review. Tran also regularly blogs on aspects of statistical modeling.

Learning is equally a focus. Machine learning is highly interdisciplinary—one reason his research is “literally the same” whether within statistics or within computer science—and liberally borrows techniques from other disciplines. Says Tran, “It’s hard to understand how to apply all these different techniques, how to discover what’s most useful about them, and how to advance them. I want to be able to distill these collections of techniques into their essential components and then apply them more broadly to tasks beyond image recognition and speech translation.”

One promising approach in his view is Bayesian deep learning, which incorporates complex models (neural networks) within a probabilistic paradigm. This enables one to incorporate parameter uncertainty, generalize the models for other tasks, and to criticize the model’s fit. “Rather than just connect components of neural nets together to make predictions, Bayesian deep learning encompasses neural nets within this richer, more scientific framework to reveal hidden structure,” says Tran. “It’s about the generative process. You’re building a model of the world and continually, iteratively refining it with new data; it’s the very definition of the scientific process.”

Merging Bayesian statistics and deep learning, which come from different traditions, is not straightforward. The first relies on mathematical principles investigated over a long history dating from Laplace in the 18th century, and it adheres to simple, small models to understand every component. The second uses many heuristics to train huge and highly complex models; though their inner workings cannot be known, they have achieved unprecedented success in large-scale experiments.

While new computing power and new techniques are making it easier to combine Bayesian statistics and machine learning, Tran is still within the minority doing so. A wider, more general acceptance will require software and tools to democratize the approach, better bridging the two disciplines. To that end, Tran leads the effort to develop Edward, a Python library for probabilistic modeling, inference, and criticism. He is also a contributor to both the open-source Stan, a probabilistic programming language featuring fast, highly optimized inference algorithms, and sgd, a suite of large-scale estimation tools in R via stochastic gradient descent.

For Tran, the fellowship’s most welcome benefit will be the expanded opportunity for collaboration, which is particularly important in machine learning, a highly interdisciplinary field that has benefited from many approaches.  “Collaborating with others has opened my perspective to understand the key open problems and what solutions exist to solve them. The funding provided by the fellowship will afford me additional opportunities to travel and collaborate directly with prominent researchers in the field, including Kevin Murphy and Matt Hoffman from Google Research, developers in OpenAI, Aki Vehtari from Aalto University, and Susan Athey from Stanford University. And for this I am deeply appreciative.”

– Linda Crane
Posted: April 21, 2017

Two Columbia computer science undergrads, Millie Yang and Spencer Yen, have been selected to participate in the Kleiner Perkins Caufield & Byers (KPCB) Fellows Program, a summer program that gives US college students the chance to intern inside Silicon Valley tech companies while being mentored by executives within those companies. The program also entails KPCB-arranged events that include talks by leading individuals from the tech industry and opportunities to meet and network with other fellows and KPCB partners.

Yang and Yen, among 54 students selected from 2000 applicants, will intern at DoorDash, a restaurant delivery service recently in the news for testing delivery robots.

Yang is a junior currently pursuing the computer science Applications Track. She came late to computer science, taking her first class in the subject her first year at Columbia. “I had thought that I would be disadvantaged, but the computer science community at Columbia welcomed me with open arms, and strongly encouraged me to try out computer science classes.” For her part, she embraces technology, and sees it as a way to empower underprivileged communities; it’s a lesson learned first hand after spending a summer volunteering in Peru. “I was astonished to find WiFi at a broadband greater than I had previously used. Watching the local Peruvians use their phones and second-hand tablets to check market prices and communicate in businesses, I realized technology is making a revolutionary impact and leveling the playing field in developing countries.”

For her, DoorDash stood out because it is a start-up facing rapid growth and is striving to revolutionize the delivery industry. The company’s mission statement of “streamlining the world’s cities” echoes her personal experiences. “Born in New York, and raised in Hong Kong and London, I embrace my multicultural background as a crucial part of my identity. My interviewers and recruiters at DoorDash have demonstrated their strong support of the company’s goals, and it made me feel very much at home.”

Yen is the rare first-year student to be selected as a KPCB fellow. Coming from the Silicon Valley (where he was a user of DoorDash), he is imbued with the self-starter ethos endemic to the area. He taught himself—with an assist from YouTube—how to code, and since early high school has been creating iPhone games and apps. “I love that I can come up with an idea and just make it, and seeing people enjoy using something I created makes it all the more rewarding.” Gaining confidence over time in his coding skills, he took the initiative, reaching out to companies in the area for summer work. Two summers ago, he finagled—via cold-emailing—an opportunity to work alongside Mark Pincus at Pincus’s Superlabs incubator where the other hires were all recent grads from MIT. “I learned a lot about how some of the best entrepreneurs and engineers in the industry operate. It was a great experience to see how they thought about design and product, and how they approached writing code.”

While he hesitates to call himself an entrepreneur—he is still in his first year at college after all—the early signs are all there, and he cops to “working on projects.” A startup last year made it into the Almaworks Startup Accelerator program before he and the others involved decided to go in another direction. While not actively looking to start a company, Yen is of course open to the possibility if the right idea comes along.

Neither Yang nor Yen lacked options for this summer, but both were looking for something more than just a job and work experience. The KPCB program came highly recommended; a former fellow from Columbia described the program to Yen as phenomenal. “It’s all about the other fellows in the program and the incredible people you meet through the program,” says Yen. “You learn a lot just by being around ambitious and talented people.”

Yang, who is studying economics in addition to computer science, would agree with that assessment. “KPCB’s Fellowship Program will allow me to be exposed to the venture capital network and educate myself on how startups succeed in the Silicon Valley.”

– Linda Crane
Posted 3/27/2017

His startup Parsegon also has an education focus, explains Pregasen in this Q&A by The Columbia Lion, helping students learn the language of math while enabling interactive math in the classroom and online.

Beginning this spring, five graduating computer science students—one senior from each of the four undergraduate schools and one student from the master’s program—will be selected to receive the Jonathan Gross Prize. The prize honors students who graduate at the top of their class with the highest overall GPA and a track record of promising innovative contributions to computer science. Students will be recommended for the prize by the department chair, with final recipients selected by a majority vote of the faculty.

The prize is being made possible by an endowment set up by Yechiam (Y.Y.) Yemini, who for 31 years until his retirement in 2011 was a professor of computer science at Columbia. “This university has amazing students, and they deserve to have their academic accomplishments recognized by faculty and their fellow students; in recognizing them, we inspire a culture of excellence.”

The prize recognizes students while honoring Jonathan L. Gross for his 47 years teaching at Columbia, first in the Mathematical Statistics Department and then as a cofounder of the Computer Science Department.

“Jonathan Gross built and led the CS department education program for several decades,” says Yemini. “His continuing contributions to developing CS education over several decades defined his role as the ‘father’ of CS education at Columbia.”

Though primarily a mathematician, Gross had an early interest in computers and believed computing was for everybody. He was Acting Chair of Mathematical Statistics when SEAS committed funds in 1978-1979 to found the Computer Science Department. Gross’s role in starting the new department was fundamental; among his first initiatives, Gross merged the computer science courses that were concurrently being offered by both the Mathematical Statistics and the Electrical Engineering departments. As the new Computer Science Department grew from just a few professors in its first year—it now numbers almost 50 professors and five teaching-oriented faculty—Gross organized department-wide efforts to keep the academic curriculum at the educational forefront.

“I am deeply appreciative that my dear friend YY has honored me by associating my name with this wonderful award he has endowed,” says Gross, now professor emeritus. “It is a wonderful way to recognize the academic excellence of our students.”

For his part, Yemini says he feels fortunate to have the opportunity to help ensure that the legacy of excellence begun by Gross continues. Yemini’s Columbia research lab pioneered a range of seminal network technologies, from autonomic self-managing networks, to microeconomic network control, to high-speed switches and (what became two decades later) software-defined networks. The coauthor of over 100 refereed publications and a holder of 30 patents, Yemini is also a serial entrepreneur who cofounded five high-tech startups. Three of those startups grew out of technologies developed in his Columbia Lab and employ Columbia graduates. His most recent startup, Turbonomic.com (2009), just celebrated its 24^th continuous quarter of record revenues growth, raising $50M investment to value the company at over $800M. Turbonomic’s R&D team includes scores of Columbia graduates in its NYC and Westchester offices.

Now professor emeritus and officially retired, Yemini continues to develop Turbonomic technologies while also teaching the popular Columbia Course “Principles of Innovation and Entrepreneurship (PIE)” and supporting philanthropic projects.

Julia Hirschberg, chair of the Computer Science Department, will announce and distribute the first Jonathan Gross Prizes this May at the graduation reception for computer science students. “This is a wonderful gift by a great emeritus faculty, YY Yemini, to honor another great emeritus, Jonathan Gross. The Department could not be more pleased.”

– Linda Crane
Posted 3/20/2017

EyeStyle—a visual platform for connecting shoppers with retailers—was recently accepted into both the NYC Media Lab Combine program and the Verizon Connected Futures Research and Prototyping Challenge. The two accelerator programs provide funding and mentoring to New York City university teams for help in developing, prototyping, and commercializing media-focused technologies. EyeStyle, which will receive $25K from the Combine and $15K from the Verizon Challenge, was the only Columbia team to advance to the final Combine round and is among three teams from Columbia to win the Verizon Challenge.

Formed by two computer vision researchers, Jie Feng and Svebor Karaman, EyeStyle employs computer vision and machine learning technology to analyze images and automatically identify and extract visual characteristics—color, shape, texture, pattern—from objects within the image. Among the possible applications are image search and automatic metadata generation.

An immediate commercial niche is online clothes shopping. Says Feng, “It’s very natural to get inspiration from seeing what other people are wearing. But finding an item of clothing is not easy. A Google search requires text, which works well when you know precisely what you’re looking for or the product is uniquely specified in some way. But using text to describe clothing, which is about style and visual impression, is hard. It’s much simpler take a photo or click on an image, and get back a list of similar products. Fashion by nature is communicated visually, why not discover it in the way it’s meant to be.”

With online shopping a major Internet activity, the demand for image search is growing (“LIKEtoKNOW.it” currently has 2M followers on Instagram). Feng and Karaman—one a computer science PhD student advised by Shih-Fu Chang and the other a postdoc in Columbia’s Digital Video | Multimedia Lab—are looking to technology to both streamline the image search workflow (having EyeStyle automatically select the search category, for instance) and to improve search capabilities, perhaps by using 3D models to more realistically represent how clothing is worn or to better train the models by taking advantage of the huge corpus of user-generated images on social media.

Image search, for which Feng and Karaman have created an app, is on the consumer side. On the retail or B2B end, Feng and Karaman are investigating how EyeStyle can best help retailers and media companies, both to draw customers to their stores or sites and to better organize their data and inventories.

Says Feng, “The combination of computer vision and machine learning will help shape the next generation of retail technology. With the funding and mentoring provided through the Combine and the Verizon Challenge, we’ll be able to explore and validate different business-use cases. We feel very excited.”

– Linda Crane
Posted 3/2/17

A former student of Mihalis Yannakakis, Diakonikolas is now Assistant Professor and the Andrew and Erna Viterbi Early Career Chair in the Department of Computer Science at the University of Southern California.

Cui’s research, which examined security risks of printers and other overlooked office devices, is the subject of a Fast Company article. Cui with his advisor, Salvatore Stolfo, founded Red Balloon Security to protect such devices.

Dingzeyu Li has been awarded an Adobe Research Fellowship, which recognizes outstanding graduate students doing exceptional research in areas of computer science important to Adobe.

A fourth-year PhD student advised by Changxi Zheng, Li was selected for his research in a relatively new area of computer graphics: simulating sound based on the physics of actions and motions occurring within an animation. Where sound for computer-generated animations has traditionally been created separately and then later combined and synchronized with the animation, Li is working to develop algorithms and tools that automatically generate sounds from the animation itself, thereby bridging the gap between simulation and fabrication.

Extending the concept into virtual reality environments, he is developing a real-time sound engine that responds to user interactions with realistic and synchronized 3D audio. The goal is to create more realistic virtual environments where the visible and audible components are natural extensions of one another, where changes or edits to one propagate automatically and naturally to the other. Provided with a virtual scene, the sound engine will compute the spatial sounds realistically and interactively as a user interacts with the environment.

Li’s research into simulated sound is also enabling new design tools for 3D printing. In a well-received paper from last year (one funded in part by Adobe), Li and his coauthors describe a computational approach for designing acoustic filters, or voxels, that fit within an arbitrary 3D shape. At a fundamental level, voxels demonstrate the connection between shape and sound; at a practical level, they allow for uniquely identifying 3D printed objects through each object’s acoustic properties

For this same work, which pushed the boundaries of 3D printing, Li was named a recipient of the Shapeways Fall 2016 EDU Grant Contest.

Besides computer graphics, the Adobe fellowships are being awarded to students in seven other fields: computer vision, human computer interaction, machine learning, visualization, audio, natural language processing, and programming languages. Fellows are selected based on their research (creative, impactful, important, and realistic in scope); their technical skills (ability to build complex computer programs); as well as personal skills (problem-solving ability, communication, leadership, organizational skills, ability to work in teams).

The Fellowship is for one year and includes a $10,000 award, an internship this summer at Adobe, and mentorship from an Adobe Research scientist for one year. Included also are a free year-long subscription and full access to software in Adobe’s Creative Cloud.

Li entered the PhD program at Columbia’s Computer Science Department in 2013 after graduating in the top 1% of his class at Hong Kong University of Science and Technology (HKUST), where he received a Bachelors of Engineering in Computer Engineering.

– Linda Crane
Posted: 2/8/2017

He received his PhD in 1999 under the supervision of Kathy McKeown. At Yale he will teach courses in Natural Language Processing and Artificial Intelligence and will lead the Language, Information, and Learning Lab.

The paper, The Social Dynamics of Language Change in Online Networks, used a data set of several million Twitter users to track language changes in progress.

His is the classic immigrant’s American success story. Once living a shelter, he made his way to Columbia to study computer science, and went on to found SmartSpot, a startup that last year raised $1.5M.