Scholarship Awardees

Google Tech Talks
December 8, 2008

ABSTRACT

User search query logs have proven to be very useful, but have vast potential for misuse. Several incidents have shown that simple removal of identifiers is insufficient to protect the identity of users. Publishing such inadequately anonymized data can cause severe breach of privacy. While significant effort has been expended on coming up with anonymity models and techniques for microdata/relational data, there is little corresponding work for query log data — which is different in several important aspects. In this work, we take a first cut at tackling this problem. Our main contribution is to define effective anonymization models for query log data, along with techniques to achieve such anonymization.

Speaker: Dr. Jaideep Vaidya
Dr. Jaideep Vaidya is an Assistant Professor at Rutgers University. He received his Masters and Ph.D. at Purdue University and his Bachelors degree at the University of Mumbai. His research interests are in Data Mining, Privacy, Security, and Information Sharing. He has published over 30 papers in international conferences and archival journals, and has received two best paper awards from the premier conferences in data mining and databases. He is also the recipient of a NSF Career Award and is a member of the ACM, and the IEEE Computer Society.

Duration : 0:56:14

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Google Tech Talks
January, 17 2008

ABSTRACT

Team Cornell was one of six teams to complete the 2007 DARPA Urban Challenge, completing over 55 miles of autonomous driving in an urban environment in approximately seven hours, including competition stops. The competition included many urban driving scenarios such as staying in a lane, merging into traffic, passing, intersections, parking, and even robot-robot interaction. Team Cornell designed and built a vehicle around technological innovations in vehicle automation, a real time UDP based data distribution system, tightly coupled pose estimation, scene estimation including localization within an urban environment and tracking all obstacles with a fusion of laser, radar and vision sensors, and hierarchical intelligent planning. Team Cornell’s vehicle was designed to drive “human-like” with smooth, intelligent behaviors, even in the presence of a vast array of uncertainties. The systematic approach taken by Team Cornell led to an innovative, robust solution to the complex problem proposed in the 2007 DARPA Urban Challenge. This seminar will present the key technologies, semi-final and final results, and plans for future research.

Speaker: Dan Huttenlocher
Dan Huttenlocher is the John P. and Rilla Neafsey Professor of Computing, Information Science and Business at Cornell University, where he holds a joint appointment in the Computer Science Department and the Johnson Graduate School of Management. His research interests are in computer vision, social and information networks, collaboration tools, geometric algorithms and financial trading systems. He has been recognized for his research and teaching contributions on several occasions, including being named an NSF Presidential Young Investigator, New York State Professor of the Year and a Fellow of the ACM. In addition to academic posts he has been chief technical officer of Intelligent Markets, a provider of advanced trading systems on Wall Street, and spent more than ten years at Xerox PARC directing work that led to the ISO JBIG2 image-compression standard.

Speaker: Mark Campbell
Mark Campbell is an Associate Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University. His research interests are in the areas of autonomous systems, probabilistic models of human decision making, nonlinear estimation theory, cooperative vehicle control and estimation, and sensor fusion. He has been recognized from NASA for his modeling and control work on the Middeck Active Control Experiment, flown on STS-67 in 1995. He received best paper awards from the AIAA and Frontiers in Education conference, and teaching awards Cornell, University of Washington, and the ASEE. He was also an Australian Research Council International Fellowship in 2006 while on sabbatical at the University of Sydney. He is an Associate Fellow of the AIAA, an Associate Director of the AACC board, and member of the AIAA GNC Technical Committee, and is active in both IEEE and ASEE.

Duration : 1:6:12

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Google Tech Talks
December 15, 2008

ABSTRACT

We explore a formal and computational characterization of real world regularity using discrete symmetry groups (hierarchy) as a theoretical basis, embedded in a well-defined Bayesian framework. Our existing work on “A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups” (TPAMI 2004), ‘Near-regular texture analysis and manipulation’ (SGIGRAPH 2004), and “A Lattice-based MRF Model for Dynamic Near-regular Texture Tracking” (PAMI 2007) already demonstrate the power of such a formalization on a diverse set of real problems, such as texture analysis, synthesis, tracking, perception and manipulation in terms of regularity. Symmetry and symmetry group detection from real world data turns out to be a very challenging problem that has been puzzling computer vision researchers for the past 40 years. Our novel formalization will lead the way to a more robust and comprehensive algorithmic treatment of the whole regularity spectrum, from regular (perfect symmetry), near-regular (deviations from symmetry), to various types of irregularities. The recent results of the proposed methodology will be illustrated in this talk by several real world applications such as deformed lattice detection, rotation and glide-reflection detection, gait recognition, grid-cell clustering, symmetry of dance, automatic geo-tagging and image de-fencing.

Speaker: Yanxi Liu
Yanxi Liu received her B.S. degree in physics/electrical engineering and her Ph.D. degree in computer science for group theory applications in robotics (UMass Amherst). Her postdoctoral training was performed in LIFIA/IMAG, Grenoble, France. She spent one year at DIMACS (NSF center for Discrete Mathematics and Theoretical Computer Science) with an NSF research-education fellowship award. Before joining the Departments of Computer Science and Engineering and Electrical Engineering at Penn State in Fall 2006 as a tenured faculty member, Dr. Liu had been with the faculty of the Robotics Institute of Carnegie Mellon University, and affiliated with the Machine Learning Department of CMU. She is also an adjunct associate professor in the Radiology Department of University of Pittsburgh. Dr. Liu is the co-director of the Laboratory for Perception, Action, and Cognition (LPAC) at Penn State (http://vision.cse.psu.edu/). Dr. Liu’s research interests span a wide range of applications in computer vision and pattern recognition, computer graphics, medical image analysis and robotics, with two main research themes: computational (a)symmetry and discriminative subspace learning. With her colleagues, Dr. Liu won first place in the clinical science category and the best paper overall at the Annual Conference of Plastic and Reconstructive Surgeons for their work on “Measurement of Asymmetry in Persons with Facial Paralysis.” Dr. Liu chaired the First International Workshop on Computer Vision for Biomedical Image Applications (CVBIA) in conjunction with ICCV 2005 in Beijing, and co-edited the book: “CVBIA: Current Techniques and Future Trends,” Springer-Verlag LNCS 3765. Dr. Liu serves as an area chair/reviewer/committee member/panelist for all major journals, conferences, and NIH/NSF panels in computer vision, computer graphics, pattern recognition, biomedical image analysis, and machine learning. She has served as a chartered NIH study section member. She is a senior member of IEEE and the IEEE Computer Society.

Duration : 1:14:13

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Google Tech Talks
November, 15 2007

ABSTRACT

Neurocomputational models provide fundamental insights towards
understanding the human brain circuits for learning new associations
and organizing our world into appropriate categories. In this talk I
will review the information-processing functions of four interacting
brain systems for learning and categorization:

(1) the basal ganglia which incrementally adjusts choice behaviors using environmental
feedback about the consequences of our actions,

(2) the hippocampus which supports learning in other brain regions through the creation of
new stimulus representations (and, hence, new similarity
relationships) that reflect important statistical regularities in the
environment,

(3) the medial septum which works in a feedback-loop with
the hippocampus, using novelty-detection to alter the rate at which
stimulus representations are updated through experience,

(4) the frontal lobes which provide for selective attention and executive
control of learning and memory.

The computational models to be described have been evaluated through a variety of empirical
methodoligies including human functional brain imaging, studies of
patients with localized brain damage due to injury or early-stage
neurodegenerative diseases, behavioral genetic studies of
naturally-occuring individual variability, as well as comparative
lesion and genetic studies with rodents. Our applications of these
models to engineering and computer science including automated anomaly
detection systems for mechanical fault diagnosis on US Navy
helicopters and submarines as well more recent contributions to the
DoD’s DARPA program for Biologically Inspired Cognitive Architectures
(BICA).

Speaker: Dr. Mark Gluck
Mark Gluck is a Professor of Neuroscience at Rutgers University – Newark, co-director of the Rutgers Memory Disorders Project, and publisher of the public health newsletter, Memory Loss and the Brain. He works at the interface between neuroscience, psychology, and computer science, where his research focuses on the neural bases of learning and memory, and the consequences of memory loss due to aging, trauma, and disease. He is the co-author of “Gateway to Memory: An Introduction to Neural Network Models of the Hippocampus and Memory ” (MIT Press, 2001) and a forthcoming undergraduate textbook, “Learning and Memory: From Brain to Behavior.” He has edited several other books and has published over 60 scientific journal articles. His awards include the Distinguished Scientific Award for Early Career Contributions from the American Psychological Society and the Young Investigator Award for Cognitive and Neural Sciences from the Office of Naval Research. In 1996, he was awarded a NSF Presidential Early Career Award for Scientists and Engineers by President Bill Clinton. For more information, see http://www.gluck.edu.

Duration : 1:2:13

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Google Tech Talks
December 19, 2008

ABSTRACT

For the past 10 years, Tufts Center for Engineering Education and Outreach has been working with the LEGO Group to bring engineering into every classroom as a way to teach creativity, teamwork, and systems engineering as well as math, science, and literacy. We believe that as the world becomes more technical, and more dependent on technology, it is imperative that those who vote and who make policy understand the fundamentals of science and engineering so that they will make informed decisions on policies like developing a sustainable energy plan or reducing global warming. We do this by bringing engineering into the pre-college classroom and challenging students to design and build solutions to open-ended problems. Chris Rogers will show a number of examples from around the world of how teachers have used LEGO Robots to teach everything from how to graph to how to problem-solve. From LEGO snowplows (made by 1st graders) to automated hamburger makers (made by 13 year olds) to a LEGO robot driven by a fruit fly (made by a doctoral student), students have been excited, innovative, and very enthusiastic to learn. He will conclude by explaining how you can help affect your local school and classroom. Kids (of all ages) welcome.

Speaker: Chris Rogers
Chris got all three of his degrees at Stanford Univ., where he worked with John Eaton on his thesis looking at particle motion in a boundary layer flow. From Stanford, he went to Tufts as a faculty member, where he has been for the last million years, with a few exceptions. His first sabbatical was spent at Harvard and a local kindergarten looking at methods of teaching engineering. He spent half a year in New Zealand on a Fulbright Scholarship looking at 3D reconstruction of flame fronts to estimate heat fluxes. In 2002-3 he was at Princeton as the Kenan Professor of Distinguished Teaching where he played with underwater robots, wind tunnels, and LEGO bricks. In 2006-7, he spent the year at ETH in Zurich playing with very very small robots and measuring the lift force on a fruit fly. He received the 2003 NSF Directors Distinguished Teaching Scholar Award for excellence in both teaching and research. Chris is involved in several different research areas: particle-laden flows (a continuation of his thesis), telerobotics and controls, slurry flows in chemical-mechanical planarization, the engineering of musical instruments, measuring flame shapes of couch fires, measuring fruit-fly locomotion, and in elementary school engineering education. His work has been funded by numerous government organizations and corporations, including the NSF, NASA, Intel, Boeing, Cabot, Steinway, Selmer, National Instruments, Raytheon, Fulbright, and the LEGO Corporation. His work in particle-laden flows led to the opportunity to fly aboard the NASA 0g experimental aircraft. He has flown over 700 parabolas without getting sick.

Chris also has a strong commitment to teaching, and at Tufts has started a number of new directions, including learning robotics with LEGO bricks and learning manufacturing by building musical instruments. He was awarded the Carnegie Professor of the Year in Massachusetts in 1998 and is currently the director of the Center for Engineering Education Outreach (www.ceeo.tufts.edu). His teaching work extends to the elementary school, where he talks with over 1000 teachers around the world every year on ways of bringing engineering into the younger grades. He has worked with LEGO to develop ROBOLAB, a robotic approach to learning science and math. ROBOLAB has already gone into over 50,000 schools worldwide and has been translated into 15 languages. He has been invited to speak on engineering education in Singapore, Hong Kong, Australia, New Zealand, Denmark, Sweden, Norway, Luxembourg, Switzerland, the UK, and in the US. He works in various classrooms once a week, although he has been banned from recess for making too much noise.

Most importantly, he has three kids – all brilliant – who are responsible for most of his research interests and efforts.

Duration : 0:52:55

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Google Tech Talks
December 16, 2008

ABSTRACT

Graph identification refers to methods that transform observational data described as a noisy, input graph into an inferred “clean” output graph. Examples include inferring social networks from communication data, identifying gene regulatory networks from protein-protein interactions, etc. On the flip-side, there is a growing interest in anonymizing social network data, and understanding the different types of privacy threats inherent in relational data. In this talk, I will discuss some of the key processes involved in identification (entity resolution, link prediction, collective classification and group detection) and I will overview results showing that on several well-known social media sites, we can easily and accurately recover information that users may wish to remain private.

Speaker: Lise Getoor
Lise Getoor is an associate professor in the Computer Science Department at the University of Maryland, College Park. She received her PhD from Stanford University in 2001. Her current work includes research on link mining, statistical relational learning and representing uncertainty in structured and semi-structured data. She has published numerous articles in machine learning, data mining, database, and artificial intelligence forums. She was awarded an NSF Career Award, is an action editor for the Machine Learning Journal, a JAIR associate editor, has been a member of AAAI Executive council, and has served on a variety of program committees including AAAI, ICML, IJCAI, KDD, SIGMOD, UAI, VLDB, and WWW. More information can be found at www.cs.umd.edu/~getoor

Duration : 0:56:31

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Google Tech Talks
November 21, 2008

ABSTRACT

Personal Growth Series: Cracking the Neural Code: Speaking the Language of the Brain with Optics

The technological seeds of a Manhattan project-style scientific enterprise, the optical reverse-engineering of brain circuits to crack the neural code, have recently been planted at Stanford.

The brain is a high-speed dynamical system consisting of different players that are intertwined and that cannot be separately controlled using conventional methods. For this reason, until recently we have not been able to speak the language of the brain (with millisecond timescale and cell-specific resolution), and in 1979 Francis Crick called for a technology by which all neurons of just one type could be controlled, “leaving the others more or less unaltered”.

Tools from the Deisseroth laboratory at Stanford over the past four years have responded to this challenge. These include optical technologies for controlling neural circuits, using precisely-targeted delivery of light energy of different colors that is captured by neurons using nanoscale protein-based antennae, resulting in controlled activity of just the targeted cell types with millisecond precision. Light is delivered by fiberoptics; while light encounters all cell types, only the desired cell type is light-sensitive and responds. Using different optogenetic probes, cells can be turned on or off with millisecond precision and in different combinations.

These tools have now been used to optically deconstruct Parkinsonian neural circuitry, setting the stage both for cracking the neural codes of normal brain function, and for re-engineering neural circuits in disease.

Speaker: Karl Deisseroth
Professor Deisseroth received his bachelor’s degree from Harvard in 1992, his PhD from Stanford in 1998, and his MD from Stanford in 2000. He completed medical internship and adult psychiatry residency at Stanford, and he was board-certified by the American Board of Psychiatry and Neurology in 2006. He joined the faculty on January 1, 2005. He is the first, and so far only, practicing psychiatrist in the nation with a primary appointment in a bioengineering department.

As a bioengineer focused on neuroengineering, he has launched an effort to map neural circuit dynamics in neuropsychiatric disease, including depression and Parkinson’s Disease, on the millisecond timescale. His group at Stanford has developed optical and stem-cell based neuroengineering technologies for noninvasive imaging and control of brain circuits, as they operate within living intact tissue. His work on optical control of neural circuits has launched a new field called “optogenetics”, and he has published major papers in Nature and Science that have been termed “stunning” and “revolutionary” by his scientific colleagues.

Professor Deisseroth has received many major awards including the NIH Director’s Pioneer Award, the Presidential Early Career Award for Science and Engineering (PECASE), the McKnight Foundation Technological Innovations in Neuroscience Award, the Larry Katz Prize in Neurobiology, the Schuetze Award in Neuroscience, the Whitehall Foundation Award, the Charles E. Culpeper Scholarship in Medical Science Award, the Klingenstein Fellowship Award and the Robert H. Ebert Clinical Scholar Award.

Duration : 0:54:35

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Google Tech Talks
January, 8 2008

NIH awards more than $500M/yr for grants to researchers pursuing research in biomedical informatics. This includes computation, simulation, modeling, and, increasingly, work on storage, retrieval, curation, and analysis of massive amounts of data needed for biomedical research. Dr. Marron will outline the investment strategy for making awarding funds in this broad area of research.

Speaker: Dr. Michael Marron
Mike Marron is the director of the Biomedical Technology Division of the National Center for Research Resources (NCRR) at the National Institutes of Health (NIH). His group focuses on funding development and collaborative sharing of cutting-edge technologies ranging from new imaging techniques using advanced light sources (e.g. synchrotrons) to computational and networking infrastructure for integrative, multi-disciplinary science (e.g. http://www.nbirn.net/).

Duration : 0:46:38

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