Hackerman Hall, B-17
Companies such as Google or Lyft collect a substantial amount of location data about their users to provide useful services. The release of these datasets for general use can enable numerous innovative applications and research. However, such data contains sensitive information about the users, and simple clocking-based techniques have been shown to be ineffective to ensure users’ privacy. These privacy concerns have motivated many leading technology companies and researchers to develop algorithms that collect and analyze location data with formal provable privacy guarantees. I will show a unified framework that can (a) enhance a better understanding about the many existing provable privacy guarantees for location data; (b) allow flexible trade-offs between privacy, accuracy, and performance, based on the application’s requirements. I will also describe some exciting new research about provable privacy guarantees for handling advanced settings involving complex queries or datasets and emerging data-driven applications, and conclude with directions for future privacy research in big-data management and analysis.
Xi He is a Ph.D. student at Computer Science Department, Duke University. Her research interests lie in privacy-preserving data analysis and security. She has also received a double degree in Applied Mathematics and Computer Science from the University of Singapore. Xi has been working with Prof. Machanavajjhala on privacy since 2012. She has published in SIGMOD, VLDB, and CCS, and has given tutorials on privacy at VLDB 2016 and SIGMOD 2017. She received best demo award on differential privacy at VLDB 2016 and was awarded a 2017 Google Ph.D. Fellowship in Privacy and Security.
Evaluating anatomical variations in structures like the nasal passage and sinuses is challenging because their complexity can often make it difficult to differentiate normal and abnormal anatomy. By statistically modeling these variations and estimating individual patient anatomy using these models, quantitative estimates of similarity or dissimilarity between the patient and the sample population can be made. In order to do this, a spatial alignment, or registration, between patient anatomy and the model must first be computed.
In this dissertation, a deformable most likely point paradigm is introduced that incorporates statistical variations into feature-based registration algorithms. This paradigm is a variant of the most likely point paradigm, which incorporates feature uncertainty into the registration process. Our deformable registration algorithms optimize the probability of feature alignment as well as the probability of model deformation allowing statistical models of anatomy to estimate, for instance, structures seen in endoscopic video without the need for patient specific computed tomography (CT) scans. The probabilistic framework also enables the algorithms to assess the quality of registrations produced, allowing users to know when an alignment can be trusted. This talk will cover 3 algorithms built within this paradigm and evaluated in simulation and in-vivo experiments.
Ayushi is a PhD candidate in Computer Science at JHU under the supervision of Dr. Russ Taylor and Dr. Greg Hager. She received her B.S in Computer Science and B.A. in Mathematics from Providence College, RI in 2011. During the course of her PhD, she worked on improving statistical shape models of anatomy and on using these models in deformable registration techniques. After finishing her PhD, Ayushi plans to continue developing these ideas further as a Provost Postdoctoral Fellow at JHU.
Hackerman Hall, B-17
Over the next few decades, we are going to transition to a new economy where highly complex, customizable products are manufactured on demand by flexible robotic systems. In many fields, this shift has already begun. 3D printers are revolutionizing production of metal parts in the aerospace, automotive, and medical industries. Whole-garment knitting machines allow automated production of complex apparel and shoes. Manufacturing electronics on flexible substrates makes it possible to build a whole new range of products for consumer electronics and medical diagnostics. Collaborative robots, such as Baxter from Rethink Robotics, allow flexible and automated assembly of complex objects. Overall, these new machines enable batch-one manufacturing of products that have unprecedented complexity.
In my talk, I argue that the field of computational design is essential for the next revolution in manufacturing. To build increasingly functional, complex and integrated products, we need to create design tools that allow their users to efficiently explore high-dimensional design spaces by optimizing over a set of performance objectives that can be measured only by expensive computations. I will discuss how to overcome these challenges by 1) developing data-driven methods for efficient exploration of these large spaces and 2) performance-driven algorithms for automated design optimization based on high-level functional specifications. I will showcase how these two concepts are applied by developing new systems for designing robots, drones, and furniture. I will conclude my talk by discussing open problems and challenges for this emerging research field.
Adriana Schulz is a Ph.D. student in the Department of Electrical Engineering and Computer Science at MIT where she works at the Computer Science and Artificial Intelligence Laboratory. She is advised by Professor Wojciech Matusik and her research spans computational design, digital manufacturing, interactive methods, and robotics. Before coming to MIT, she obtained a M.S. in mathematics from IMPA, Brazil and a B.S. in electronics engineering from UFRJ, Brazil.
Hackerman Hall, B-17
For the past six years, the Google Brain team (g.co/brain) has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has open-sourced the TensorFlow system (tensorflow.org), a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I’ll highlight some of the design decisions we made in building TensorFlow, discuss research results produced within our group in areas such as computer vision, language understanding, translation, healthcare, and robotics, and describe ways in which these ideas have been applied to a variety of problems in Google’s products, usually in close collaboration with other teams. I will also touch on some exciting areas of research that we are currently pursuing within our group.
This talk describes joint work with many people at Google.
Jeff Dean (research.google.com/people/jeff) joined Google in 1999 and is currently a Google Senior Fellow in Google’s Research Group, where he co-founded and leads the Google Brain team, Google’s deep learning and artificial intelligence research team. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools.
Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing.