Microsoft Research Podcast

Haiyan Zhang is a designer, technologist and maker of things (really cool technical things) who currently holds the unusual title of Innovation Director at the Microsoft Research lab in Cambridge, England. There, she applies her unusual skillset to a wide range of unusual solutions to real-life problems, many of which draw on novel applications of gaming technology in serious areas like healthcare. On today’s podcast, Haiyan talks about her unique “brain hack” approach to the human-centered design process, and discusses a wide range of projects, from the connected play experience of Zanzibar, to Fizzyo, which turns laborious breathing exercises for children with cystic fibrosis into a video game, to Project Emma, an application of haptic vibration technology that, somewhat curiously, offsets the effects of tremors caused by Parkinson’s disease.

From his deep technical roots as a principal researcher and founder of the Communications, Collaboration and Signal Processing group at MSR, through his tenure as Managing Director of the lab in Redmond, to his current role as Distinguished Engineer, Chief Scientist for Microsoft Research and manager of the MSR NExT Enable group, Dr. Rico Malvar has seen – and pretty well done – it all. Today, Dr. Malvar recalls his early years at a fledgling Microsoft Research, talks about the exciting work he oversees now, explains why designing with the user is as important as designing for the user, and tells us how a challenge from an ex-football player with ALS led to a prize winning hackathon project and produced the core technology that allows you to type on a keyboard without your hands and drive a wheelchair with your eyes.

You never know how an incident in your own life might inspire a breakthrough in science, but Dr. Cecily Morrison, a researcher in the Human Computer Interaction group at Microsoft Research Cambridge, can attest to how even unexpected events can cause us to see things through a different – more inclusive – lens and, ultimately, give rise to innovations in research that impact everyone. On today’s podcast, Dr. Morrison gives us an overview of what she calls the “pillars” of inclusive design, shares how her research is positively impacting people with health issues and disabilities, and tells us how having a child born with blindness put her in touch with a community of people she would otherwise never have met, and on the path to developing Project Torino, an inclusive physical programming language for children with visual impairments.

The entertainment industry has long offered us a vision of the perfect personal assistant: one that not only meets our stated needs but anticipates needs we didn’t even know we had. But these uber-assistants, from the preternaturally prescient Radar O’Reilly in the TV show M.A.S.H. to Tony Stark’s digital know-and-do-it-all Jarvis in Iron Man, have always lived in the realm of fiction or science fiction. That could all change, if Dr. Paul Bennett, Principal Researcher and Research Manager of the Information and Data Sciences group at Microsoft Research, has anything to say about it. He and his team are working to make machines “calendar and email aware,” moving intelligent assistance into the realm of science and onto your workstation. Today, Dr. Bennett brings us up to speed on the science of contextually intelligent assistants, explains how what we think our machines can do actually shapes what we expect them to do, and shares how current research in machine learning and data science is helping machines reason on our behalf in the quest to help us find the right information effortlessly.

When people first started making software, computers were relatively rare and there was no internet, so programming languages were designed to get the job done quickly and run efficiently, with little thought for security. But software is everywhere now, from our desktops to our cars, from the cloud to the internet of things. That’s why Dr. Jonathan Protzenko, a researcher in the RiSE – or Research in Software Engineering – group at Microsoft Research, is working on designing better software tools in order to make our growing software ecosystem safer and more secure. Today, Dr. Protzenko talks about what’s wrong with software (and why it’s vitally important to get it right), explains why there are so many programming languages (and tells us about a few he’s been working on), and finally, acts as our digital Sherpa for Project Everest, an assault on software integrity and confidentiality that aims to build and deploy a verified HTTPS stack.

The episode first aired in May, 2018.In the world of machine learning, there’s been a notable trade-off between accuracy and intelligibility. Either the models are accurate but difficult to make sense of, or easy to understand but prone to error. That’s why Dr. Rich Caruana, Principal Researcher at Microsoft Research, has spent a good part of his career working to make the simple more accurate and the accurate more intelligible.Today, Dr. Caruana talks about how the rise of deep neural networks has made understanding machine predictions more difficult for humans, and discusses an interesting class of smaller, more interpretable models that may help to make the black box nature of machine learning more transparent.

This episode first aired in January, 2018.When we look at a skyscraper or a suspension bridge, a simple search engine box on a screen looks tiny by comparison. But Dr. Simon Peyton Jones would like to remind us that computer programs, with hundreds of millions of lines of code, are actually among the largest structures human beings have ever built. A principle researcher at the Microsoft Research Lab in Cambridge, England, co-developer of the programming language Haskell, and a Fellow of Britain’s Royal Society, Simon Peyton Jones has dedicated his life to this very particular kind of construction work. Today, Dr. Peyton Jones shares his passion for functional programming research, reveals how a desire to help other researchers write and present better turned him into an unlikely YouTube star, and explains why, at least in the world of programming languages, purity is embarrassing, laziness is cool, and success should be avoided at all costs.

This episode first aired in March, 2018.Learning to read, think and communicate effectively is part of the curriculum for every young student. But Dr. Adam Trischler, Research Manager and leader of the Machine Comprehension team at Microsoft Research Montreal, would like to make it part of the curriculum for your computer as well. And he’s working on that, using methods from machine learning, deep neural networks, and other branches of AI to close the communication gap between humans and computers.Today, Dr. Trischler talks about his dream of making literate machines, his efforts to design meta-learning algorithms that can actually learn to learn, the importance of what he calls “few-shot learning” in that meta-learning process, and how, through a process of one-to-many mapping in machine learning, our computers not may not only be answering our questions, but asking them as well.

Amos Miller is a product strategist on the Microsoft Research NeXT Enable team, and he’s played a pivotal role in bringing some of MSR’s most innovative research to users with disabilities. He also happens to be blind, so he can appreciate, perhaps in ways others can’t, the value of the technologies he works on, like Soundscape, an app which enhances mobility independence through audio and sound. On today’s podcast, Amos Miller answers burning questions like how do you make a microwave accessible, what’s the cocktail party effect, and how do you hear a landmark? He also talks about how researchers are exploring the untapped potential of 3D audio in virtual and augmented reality applications, and explains how, in the end, his work is not so much about making technology more accessible, but using technology to make life more accessible.

Dr. Sebastien Bubeck is a mathematician and a senior researcher in the Machine Learning and Optimization group at Microsoft Research. He’s also a self-proclaimed “bandit” who claims that, despite all the buzz around AI, it’s still a science in its infancy. That’s why he’s devoted his career to advancing the mathematical foundations behind the machine learning algorithms behind AI. Today, Dr. Bubeck explains the difficulty of the multi-armed bandit problem in the context of a parameter- and data-rich online world. He also discusses a host of topics from randomness and convex optimization to metrical task systems and log n competitiveness to the surprising connection between Gaussian kernels and what he calls some of the most beautiful objects in mathematics.