Eight computer scientists at Microsoft research labs around the world have been honored as Fellows of the Association of Computing Machinery, the world’s largest computing society. The organization also named five Microsoft researchers to their list of Distinguished Members.
The honors recognize the individuals’ significant contributions and impact to computer science across a range of disciplines and highlight the “tremendous respect, reputation and visibility of Microsoft researchers in the external scientific and engineering community,” said Jeannette Wing, corporate vice president, Microsoft Research.
Read on for snapshots of the eight newly named fellows and five distinguished scientists and engineers.
Ricardo Bianchini: In the early 2000s, Ricardo Bianchini pioneered techniques to increase the operational efficiency of datacenters, the warehouse-size buildings full of servers that make up what is known today as the cloud. His research focused on techniques for managing power and energy consumption as well as regulating temperature in datacenters, which reduce operating costs and impacts to society and the environment.
Bianchini performed much of his pioneering research on datacenter management at Rutgers University, including the development of an experimental solar-powered data center. Now at Microsoft’s research lab in Redmond, Washington, he leads the Cloud Efficiency group, where his research focuses more broadly on how to improve the overall cost efficiency of online services and datacenters. “I am looking at things like improving server utilization, being smarter about resource management,” he said. “Power and energy are nothing more than resources like CPUs and memories and disks.”
Xuedong Huang: When Xuedong Huang entered graduate school in China in the early 1980s, he realized a limitation of personal computers: the Chinese language is difficult to type on the traditional keyboard. His solution was to create a Chinese dictation engine, thus bypassing altogether the need to type. Today, as Microsoft’s chief speech scientist, he continues to work on versions of this problem. Along the way, he has advanced the field of speech recognition to the point that computers can now recognize words in a conversation as well as humans.
Steady improvements in data availability, computing power and a subfield of artificial intelligence called machine learning have fueled recent breakthroughs in speech recognition, noted Huang. In addition to human parity in speech recognition, people who speak different languages in different parts of the world can have a real-time conversation via technologies such as Skype Translator, which is based in part on Huang’s research, bringing the world closer to real-life manifestations of science fiction concepts such as the babel fish.
Ravi Kannan: These days, theoretical computer scientist Ravi Kannan in Microsoft’s research lab in Bangalore, India, is focused on developing general principles for the development of machine-learning algorithms, a branch of artificial intelligence that is transforming the computer industry in applications ranging from image and speech recognition to time and resource management. The research continues a decades-long push to integrate theory with the applied side of computer science, providing a solid foundation and guiding light for the computing industry.
About 20 years ago, for example, Kannan started worked on matrix algorithms, which enable efficient computations on large datasets such as millions of webpages or years of search results. “We developed, in theory, an algorithm that is based on sampling a part of the whole matrix and computing on that,” he explained. More than selecting a random sample, the theoretical algorithm quickly finds samples that yield robust results. “We gave a way of figuring out what to pick rather quickly,” said Kannan, adding that he is happiest working on mathematical computer science theory “but I certainly benefit a lot by listening to real-world problems and trying to see what I can do about them.”
K. Rustan M. Leino: When coders make a semantic error in the verification aware programming language Dafny, the mistake is flagged with a squiggly red line like the one Microsoft Word places under questionable spelling and grammar. And just as the word processing program has helped millions of people improve their writing, Dafny is widely used at universities to help computer science students learn to properly code. The language and automated verification tool is one of several designed and built by K. Rustan M. Leino, a principal researcher in Microsoft’s research lab in Redmond.
Beyond the classroom, Leino said Dafny was used at Microsoft for writing the Ironclad and IronFleet applications that allow the secure exchange of data between a user and a remote machine. Dafny, and other tools designed by Leino, is based on Boogie, a programming language he designed for building program verification tools. “Many other verifiers have been built on top of Boogie and then they reach other communities and other languages,” he said.
Venkat Padmanabhan: In the late 1990s, Venkat Padmanabhan, who was then working in Microsoft’s research lab in Redmond, and colleagues anticipated the proliferation of radio-frequency based wireless local area networks and designed a location service, RADAR, to ride on top of these to help people navigate indoor spaces such as office buildings and shopping malls where GPS signals are unavailable. The system is based on mapping a network’s radio frequency signal strength, which is known as RF fingerprinting. The technique continues to influence academic and industry research on location-based services such as how to serve ads to smartphones carried by shoppers walking around the mall.
In a similar vein, in 2001, Padmanabhan pioneered a suite of techniques to infer geographic location from IP addresses, known as IP2Geo, that were adopted by Microsoft to serve geo-targeted ads. In 2007, working out of Microsoft’s research lab in India, he and colleagues connected an accelerometer to an early smartphone, creating an influential sensor-enabled system, called Nericell, to monitor road and traffic conditions. Padmanabhan also invented ProbeGap, which estimates available bandwidth in wireless networks – a technique, he said, that solved “a pressing problem” and has become a standard feature in the Windows operating system.
Ganesan Ramalingam: About 20 years ago, anxiety was building in the computer science world over the pending calendar rollover to the year 2000. The so-called Y2K bug was a concern because most computer programs used two digits instead of four to represent calendar years. “The coding representation would no longer be adequate and programs would start failing,” recalled Ganesan Ramalingam, who works at Microsoft’s research lab in India. Ramalingam is an expert in static program analysis, a method to identify potential runtime errors in code – computer bugs – and other useful runtime properties of interest without executing the program. One of the many tools he developed in this field searched through programs for lines of code that required modification prior to Y2K, helping the computer industry avoid a feared disaster.
In the ensuing years, Ramalingam has applied his static program analysis expertise to programs running multiple computations simultaneously, as well as on methods to automatically generate correct code to make programming easier, more enjoyable and more reliable. An area of relevance today is the cloud, which spreads out computations across multiple machines. Since any machine in the cloud could fail at any time, programs need to work correctly in the presence of failures. “I have been working on languages that guarantee fault tolerance so the programmer doesn’t have to worry about that,” he said. “They can write the code largely assuming no failure and the language implementation takes care of dealing with failures.”
Abigail Sellen: As technology becomes enmeshed with every aspect of the human experience, Abigail Sellen, deputy director and a principal researcher at Microsoft Research Cambridge in the U.K., continues to think deeply about how we use “old-fashioned” technologies such as paper. The promise of the paperless office, which she and her co-author, Richard Harper, debunked in 2003, remains unfilled. “A lot of what we said back then still holds true. We can still learn a lot about our interactions with paper and how we can build tools that support people doing knowledge work, even as these tools become more intelligent,” she said. “We are not there yet. Our technologies need to be much more flexible, tailorable and seamless.”
Sellen has spent her career researching how people do seemingly ordinary tasks, such as mark up paper documents and speak to each other around conference tables, to better design technologies that extend human capabilities and take account of cultural and social norms. In recent years, her research has focused on how to apply computer vision to medical settings, such as manipulating data during vascular surgery and assessing disease progression in patients diagnosed with multiple sclerosis. The research, she noted, highlights that acceptance of artificial intelligence systems requires trust in them. “And to build trust, they have to be understandable by the people who are using them,” Sellen said.
Sudipta Sengupta: In the late 2000s, solid state drives (SSDs) built with flash technology began to emerge in the marketplace as a replacement for hard drives in desktops and servers. Sudipta Sengupta, a research scientist at Microsoft’s Redmond lab, recognized the potential for the technology to be more than simply a hard disk replacement. He initiated research that refashioned software for data management to exploit the unique properties of flash. Microsoft’s search engine Bing adopted the technology to more efficiently perform services such as serving advertisements alongside search results. The research also was deployed in Microsoft’s database service Azure DocumentDB. Today, the performance benefits of optimizing software for flash is widely recognized in the computing industry.
Sengupta also has led research efforts in the field of data deduplication, which avoids unnecessary duplication of data. The field was originally focused on backup data. Sengupta developed methods to apply deduplication to primary data, such as shared files on a cloud server. His methods are a standard feature in Windows Server software. Sengupta has also designed optimization techniques and routing algorithms to achieve predictable performance in networks with highly variable and unpredictable traffic patterns, such as the cloud. He has applied his ideas on so-called oblivious routing of network traffic in the design of VL2, a modern datacenter network that has been deployed in the Microsoft cloud via Azure.
ACM Distinguished Scientists
Gang Hua: Every face in the crowd belongs to someone. Facial recognition research led by Gang Hua at Microsoft Research Asia in Beijing, China, employs artificial intelligence and computer vision tools such as machine learning, pattern recognition and deep neural aggregation networks to discover each face’s owner. In 2008, he initiated the first facial recognition software development kit at Microsoft, which has been adopted by several Microsoft product groups and set industry standards. In recent years, Hua’s research has focused on solving problems in computer vision with limited or unsupervised data, including techniques to enable pattern discovery from a collection of images and videos jointly instead of handling them individually.
Suman Nath: Sensors are everywhere – traffic cameras, radar speed signs, thermometers, rain gauges, security cameras and microphones, to name a few. In the early 2000s, Suman Nath, a principal researcher in Microsoft’s research lab in Redmond, began designing systems and algorithms to aggregate data streams from sensors deployed throughout the world and incorporate them on searchable, online real-time mapping platforms. Deployments of the SenseWeb research help users make informed decisions about what route to take to work, for example, or whether to leave the rain boots at home. His research on data management for sensing and mobile systems has led to location inference techniques that reduce battery drain on smartphones from continuous context-aware applications such as jogging trackers, as well as apps that offer unique greetings when a user is driving.
Tao Mei: When users type queries into search engines, many engines will automatically suggest a list of terms that may better match the users’ intent, thus leading to improved results. Tao Mei, a senior researcher at Microsoft Research Asia, pioneered similar technology for video search, called visual query suggestion, that returns a joint result of suggested text and images, allowing users to more effectively find videos of interest. Mei’s research in in multimedia content analysis, computer vision and machine learning has also led to model-free techniques for automatic annotation of video, an innovative approach to predicting video popularity in social networks and online video advertising models that automatically discover the least obtrusive and most effective spot in an online video to insert an ad. A dozen of Mei’s inventions and technologies have shipped to a wide range of Microsoft products. The recently popular chatbot Xiaolce in China uses Mei’s technology for automatic video commenting.
Yu Zheng: Can computers improve city living? Research led by Yu Zheng, research manager in the Urban Computing Group at Microsoft Research Asia, connects sensing technologies, data management and analytics models, and visualization methods to create win-win-win solutions that improve the urban environment, quality of urban life and city operating systems. For example, he has pioneered cross-domain data fusion methods to tackle challenges such as predicting air quality by fusing data from traffic and meteorology. His method outperforms traditional air-quality forecasting models by 20 percent on accuracy. Other research projects have mined data from vehicles and people to create location-based social networks, algorithms that compute similarity between individuals based on location histories, and to improve urban driving through technologies such as ridesharing applications.
ACM Distinguished Engineer
Tie-Yan Liu: In a world where humans expect the most relevant information on everything from the best local bookstores and dive bars to the eating habits of Tyrannosaurus rex, search engines are trusted to make the ranked lists. How do they do it? Many, including Bing, employ machine-learning algorithms and theory for learning to rank developed by Tie-Yan Liu, principal research manager at Microsoft Research Asia. Liu also has led research on large-scale machine learning tools including, in 2005, development of the largest text classifier in the world with 250,000 categories and, in 2015, the fastest and largest topic model in the world with 1 million topics. Such models have improved targeted online advertising. In addition to the distinction from ACM, Liu was named a 2017 IEEE Fellow for contributions to machine learning for web search and online advertising.
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John Roach writes about Microsoft research and innovation. Follow him on Twitter.