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Fwd: EE Seminar - December 14, 2017, 4:30pm, E-Quad B205 - Christopher Brinton, Zoomi Inc
by Jennifer Rexford 05 Dec '17

05 Dec '17

> From: "Lisa R. Lewis" <ll2(a)Princeton.EDU> > Subject: EE Seminar - December 14, 2017, 4:30pm, E-Quad B205 - Christopher Brinton, Zoomi Inc > Date: December 5, 2017 at 3:36:16 PM EST > To: ee-seminar(a)Princeton.EDU > Reply-To: "Lisa R. Lewis" <ll2(a)Princeton.EDU> > > > Department of Electrical Engineering > Seminar > > Speaker: Christopher Brinton > Zoomi Inc > Title: Advancing eLearning with Data > Date: Thursday, December 14, 2017 > Time: 4:30pm > Room: E-Quad, Room B205 > Hosts: Profs. Prateek Mittal & H. Vincent Poor > > Abstract: Electronic learning (eLearning) has witnessed explosive growth over the past decade, with learning scenarios from corporate training to higher education now supporting full course delivery online. Despite the conveniences offered to both learners and instructors, eLearning has been widely criticized for poorer learning outcomes compared with those obtained by traditional classrooms, in terms of metrics such as engagement and knowledge transfer. Today’s learning technology systems, however, can be equipped with infrastructure to capture a plethora of data about learners as they take courses online, such as their multimedia clickstream measurements, assessment responses, and social interactions with one another. The availability of this data presents the opportunity to develop fine-granular models of learners, which can in turn drive methodologies at the intersection of machine learning, data science, and the science of learning aimed at improving learning outcomes. > > In this talk, I will describe the research I have undertaken to improve the quality of eLearning, while at my company Zoomi Inc., during my graduate studies, and as an instructor. Broadly speaking, my team’s investigations have followed three, complementary thrusts: Predictive Learning Analytics (PLA), Social Learning Networks (SLN), and Deep Learning Personalization (DLP). PLA involves developing algorithms for early detection of learning outcomes that model from behavioral data and can provide actionable analytics to instructors. SLN involves quantifying how learners seek and disseminate knowledge socially, and designing algorithms to predict and optimize these processes. DLP involves systematically dividing course content into micro-segments, and combining topic, learner, and reinforcement learning models to adapt the material presented to individual learners in real time. In describing these thrusts, I will point out results Zoomi has witnessed in deployments to different learning scenarios, and will also discuss open research questions. > > Bio: Dr. Christopher G. Brinton is the Head of Advanced Research at Zoomi Inc, an AI-driven learning technology company he co-founded in 2013, and a Lecturer in Electrical Engineering at Princeton University. His research focus is developing systems and methods to improve the quality of student learning, through predictive learning analytics, social learning networks, and individualization. Chris co-authored the book The Power of Networks: Six Principles that Connect our Lives, and has reached over 250,000 students through MOOCs based on his book. A recipient of the 2016 Bede Liu Best Dissertation Award in Electrical Engineering, Chris received his PhD from Princeton in 2016, his Master’s from Princeton in 2013, and his BSEE from The College of New Jersey (valedictorian and summa cum laude) in 2011, all in Electrical Engineering.

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Colloquium Speaker Duncan Watts, Dec 12
by Emily Lawrence 05 Dec '17

05 Dec '17

Colloquium Speaker Duncan Watts, Microsoft Research Tuesday, December 12, 2017 - 12:30pm Computer Science - Room 105 Hosts: Jennifer Rexford / Mitchell Duneier (Sociology) Title: "Computational Social Science: Exciting Progress and Future Challenges" Abstract: The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: "computational social science." In this talk I highlight two areas of research that would not have been possible just a handful of years ago: first, using "big data" to study social contagion on networks; and second, using virtual labs to extend the scale, duration, and complexity of traditional lab experiments. Although these examples were all motivated by substantive problems of longstanding interest to social science, they also illustrate how new classes of data can cast these problems in new light. At the same, they illustrate some important limitations faced by our existing data generating platforms. I then conclude with some thoughts on how CSS might overcome some of these obstacles to progress. Bio: Duncan Watts is a principal researcher at Microsoft Research and a founding member of the MSR-NYC lab. He is also an AD White Professor at Large at Cornell University. Prior to joining MSR in 2012, he was from 2000-2007 a professor of Sociology at Columbia University, and then a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group. His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review, and has been recognized by the 2009 German Physical Society Young Scientist Award for Socio and Econophysics, the 2013 Lagrange-CRT Foundation Prize for Complexity Science, and the 2014 Everett Rogers M. Rogers Award. He is also the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton, 2003) and Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press, 1999), and most recently Everything is Obvious: Once You Know The Answer (Crown Business, 2011). Watts holds a B.Sc. in Physics from the Australian Defense Force Academy, from which he also received his officer's commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.

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Distinguished Colloquium Speaker James Landay TODAY
by Emily Lawrence 01 Dec '17

01 Dec '17

Distinguished Colloquium Speaker Prof. James A. Landay, Stanford University Friday, December 1, 2017 - 12:30pm Computer Science - Room 105 Host: Prof. Olga Russakovsky "From On Body to Out of Body User Experience" Today's most common user interfaces represent an incremental change from the GUI popularized by the Apple Macintosh in 1984. Over the last 30 years the dominant hardware has changed drastically while the user interface has barely moved: from one hand on a mouse to two fingers on a panel of glass. I will illustrate how we are building on-body interfaces of the future that further engage our bodies by using muscle sensing for input and vibrotactile output, offering discrete and natural interaction on the go. I will also show how other interfaces we are designing take an even more radical approach, moving the interface off the human body altogether and onto drones that project into the space around them. Finally, I will introduce a new project where we envision buildings as hybrid physical-digital spaces that both sense and actuate to improve human wellbeing. Bio: James Landay is a Professor of Computer Science and the Anand Rajaraman and Venky Harinarayan Professor in the School of Engineering at Stanford University. He specializes in human-computer interaction. He is the founder and co-director of the World Lab, a joint research and educational effort with Tsinghua University in Beijing. Previously, Landay was a Professor of Information Science at Cornell Tech in New York City and prior to that he was a Professor of Computer Science & Engineering at the University of Washington and a Professor in EECS at UC Berkeley. From 2003 through 2006 he was the Laboratory Director of Intel Labs Seattle, a university affiliated research lab that explored the new usage models, applications, and technology for ubiquitous computing. He was also the chief scientist and co-founder of NetRaker, which was acquired by KeyNote Systems in 2004. Landay received his BS in EECS from UC Berkeley in 1990, and MS and PhD in Computer Science from Carnegie Mellon University in 1993 and 1996, respectively. He is a member of the ACM SIGCHI Academy and he is an ACM Fellow.

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Zeyu Jin will present his Pre FPO, "Speech Synthesis for Text-Based Editing of Audio Narrations" on Tuesday, December 5th, 2017 at 2pm in CS 302.
by Nicki Gotsis 30 Nov '17

30 Nov '17

Zeyu Jin will present his Pre FPO, "Speech Synthesis for Text-Based Editing of Audio Narrations" on Tuesday, December 5th, 2017 at 2pm in CS 302. The members of his committee are as follows: Advisor: Adam Finkelstein Readers: Thomas Funkhouser, Gautham Mysore (Stanford) Non-readers: Marshini Chetty, Szymon Rusinkiewicz Everyone is invited to attend his talk. The talk title and abstract follow below. Title: Speech Synthesis for Text-Based Editing of Audio Narrations Abstract: Editing audio narration using conventional software typically involves many painstaking low-level manipulations. Some state of the art systems allow the editor to work in a text transcript of the narration, and perform select, cut, copy and paste operations directly in the transcript; these operations are then automatically applied to the waveform in a straightforward manner. However, an obvious gap in the text-based interface is the ability to type new words not appearing in the transcript, for example inserting a new word for emphasis or replacing a misspoken word. While high-quality voice synthesizers exist today, the challenge is to synthesize the new word in a voice that matches the rest of the narration. This work focuses on synthesizing a new word or short phrase such that it blends seamlessly in the context of the existing narration. My thesis explores two strategies for voice synthesis in context. Our initial approach is to use a text to speech synthesizer to say the word in a generic voice, and then use data-driven voice conversion to convert it into a voice that matches the narration. Offering a range of degrees of control to the editor, our interface supports fully automatic synthesis, selection among a candidate set of alternative pronunciations, fine control over edit placements and pitch profiles, and even guidance by the editors own voice. Our experiments show that 60% of the time, the synthesized words are indistinguishable from real recordings. The goal of our second approach is to improve that success rate using a deep learning model to synthesize the waveform directly based on low level acoustic features.

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Distinguished Colloquium Speaker James Landay tomorrow at 12:30
by Emily Lawrence 30 Nov '17

30 Nov '17

Distinguished Colloquium Speaker Prof. James A. Landay, Stanford University Friday, December 1, 2017 - 12:30pm Computer Science - Room 105 Host: Prof. Olga Russakovsky "From On Body to Out of Body User Experience" Today's most common user interfaces represent an incremental change from the GUI popularized by the Apple Macintosh in 1984. Over the last 30 years the dominant hardware has changed drastically while the user interface has barely moved: from one hand on a mouse to two fingers on a panel of glass. I will illustrate how we are building on-body interfaces of the future that further engage our bodies by using muscle sensing for input and vibrotactile output, offering discrete and natural interaction on the go. I will also show how other interfaces we are designing take an even more radical approach, moving the interface off the human body altogether and onto drones that project into the space around them. Finally, I will introduce a new project where we envision buildings as hybrid physical-digital spaces that both sense and actuate to improve human wellbeing. Bio: James Landay is a Professor of Computer Science and the Anand Rajaraman and Venky Harinarayan Professor in the School of Engineering at Stanford University. He specializes in human-computer interaction. He is the founder and co-director of the World Lab, a joint research and educational effort with Tsinghua University in Beijing. Previously, Landay was a Professor of Information Science at Cornell Tech in New York City and prior to that he was a Professor of Computer Science & Engineering at the University of Washington and a Professor in EECS at UC Berkeley. From 2003 through 2006 he was the Laboratory Director of Intel Labs Seattle, a university affiliated research lab that explored the new usage models, applications, and technology for ubiquitous computing. He was also the chief scientist and co-founder of NetRaker, which was acquired by KeyNote Systems in 2004. Landay received his BS in EECS from UC Berkeley in 1990, and MS and PhD in Computer Science from Carnegie Mellon University in 1993 and 1996, respectively. He is a member of the ACM SIGCHI Academy and he is an ACM Fellow.

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Anne Edmundson will present her PreFPO on December 7th, 2017 at 3pm in CS 402.
by Nicki Gotsis 29 Nov '17

29 Nov '17

Anne Edmundson will present her PreFPO on December 7th, 2017 at 3pm in CS 402. The members of her committee are as follows: Advisor: Nick Feamster Readers: Ed Felten, Jen Rexford Non-readers: Prateek Mittal, Arvind Narayanan Everyone is invited to attend her talk. The talk title and abstract follow below. Title: Overhauling the Internet Infrastructure for Private Content Delivery and Communications Abstract: Citizens' privacy is coming under greater threat as an increasing number of entities can access user data. A powerful adversary, such as a nation-state, can gain access to user data using a broad range of techniques, from privately tapping wires and collecting traffic to serving warrants or subpoenas for user data. Protecting user privacy in the face of these types of activities is challenging. Existing protocol encryption such as TLS is not sufficient, since a wide range of data, from DNS lookups to server access logs, may be visible to eavesdroppers or subject to data requests. In this dissertation, I develop new techniques that demonstrate that three aspects of the existing Internet infrastructure, specifically routing, hosting, and naming, can be used to counter surveillance. First, I study the current state of routing by measuring which countries are on the paths between users and popular websites. I then evaluate different methods for routing Internet traffic around unfavorable countries based on these findings, I design and implement RAN, a lightweight system that routes a client's web traffic around specified countries with no modifications to client software (and in many cases with little performance overhead). Second, I describe modifications to content hosting that prevent a powerful adversary such as a nation-state from gaining access to a user’s requests for certain Web content. In today’s Internet, Content Distribution Networks (CDNs) have rich information both about the content they are serving and the users who are requesting that content. Access to this type of information makes CDNs a target for requests for data about users’ browsing activities. To counter this threat, I developed Oblivious CDN (OCDN), which hides from the CDN both the content it is serving and the users who are requesting that content. OCDN is compatible with the existing Web ecosystem of publishers and clients. In the last part of this talk, I will touch on how naming currently compromises client privacy, and present design suggestions for naming techniques that protect clients from surveillance activities.

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Qinxiang Cao will present his Pre FPO, "Separation-logic-based program verification in Coq" on Thursday, December 14th, 2017 at 8am in CS 402.
by Nicki Gotsis 27 Nov '17

27 Nov '17

Qinxiang Cao will present his Pre FPO, "Separation-logic-based program verification in Coq" on Thursday, December 14th, 2017 at 8am in CS 402. The members of his committee are as follows: Andrew W. Appel (advisor), Lars Birkedal (Aarhus University), Zachary Kincaid (reader), Aarti Gupta (non-reader) and Lennart Beringer (non-reader). Everyone is welcome to join. Talk title and abstract follow below. "Separation-logic-based program verification in Coq" Abstract In our interconnected world, software bugs can seriously compromise our safety and security. To provide adequate safety or protection, security-critical kernels (of large systems) must be functionally correct. To ensure functional correctness of C programs, we can use Hoare logic and its extensions such as separation logic. But such correctness proofs are large and complex enough that we cannot trust them unless they are machine-checked. This dissertation shows how we can construct mechanized proofs of program correctness using separation logic. I answer three questions: How shall we specify our programs? How shall we prove our programs’ correctness w.r.t. their specifications? How shall we mechanize such correctness proofs? In this dissertation, I present practical techniques for separation-logic-based program verification and demonstrate them on several examples. I introduce VST-Floyd, a tool for users to build formal C program correctness proofs in Coq using separation logic. VST-Floyd is built based on Verifiable C, a proved sound separation logic; I show how to reformulate its rules to make them practical for use in verification. Finally, I introduce a unifying semantics in separation logic so that research results of one separation logic can be generalized to all separation logics. Thus the techniques used in VST-Floyd can be used in other separation logics in the future.

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Distinguished Colloquium Speaker James Landay Friday Dec 1
by Emily Lawrence 27 Nov '17

27 Nov '17

Distinguished Colloquium Speaker Prof. James A. Landay, Stanford University Friday, December 1, 2017 - 12:30pm Computer Science - Room 105 Host: Prof. Olga Russakovsky "From On Body to Out of Body User Experience" Today's most common user interfaces represent an incremental change from the GUI popularized by the Apple Macintosh in 1984. Over the last 30 years the dominant hardware has changed drastically while the user interface has barely moved: from one hand on a mouse to two fingers on a panel of glass. I will illustrate how we are building on-body interfaces of the future that further engage our bodies by using muscle sensing for input and vibrotactile output, offering discrete and natural interaction on the go. I will also show how other interfaces we are designing take an even more radical approach, moving the interface off the human body altogether and onto drones that project into the space around them. Finally, I will introduce a new project where we envision buildings as hybrid physical-digital spaces that both sense and actuate to improve human wellbeing. Bio: James Landay is a Professor of Computer Science and the Anand Rajaraman and Venky Harinarayan Professor in the School of Engineering at Stanford University. He specializes in human-computer interaction. He is the founder and co-director of the World Lab, a joint research and educational effort with Tsinghua University in Beijing. Previously, Landay was a Professor of Information Science at Cornell Tech in New York City and prior to that he was a Professor of Computer Science & Engineering at the University of Washington and a Professor in EECS at UC Berkeley. From 2003 through 2006 he was the Laboratory Director of Intel Labs Seattle, a university affiliated research lab that explored the new usage models, applications, and technology for ubiquitous computing. He was also the chief scientist and co-founder of NetRaker, which was acquired by KeyNote Systems in 2004. Landay received his BS in EECS from UC Berkeley in 1990, and MS and PhD in Computer Science from Carnegie Mellon University in 1993 and 1996, respectively. He is a member of the ACM SIGCHI Academy and he is an ACM Fellow.

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Reminder: Distinguished Colloquium Speaker Mary Czerwinski TODAY at 12:30
by Emily Lawrence 20 Nov '17

20 Nov '17

Distinguished Colloquium Speaker Dr. Mary Czerwinski from Microsoft Research Monday, November 20, 2017 - 12:30pm Computer Science - Room 105 Host: Szymon Rusinkiewicz Abstract: Affective computing is emerging as an important field in the design of emotional, intelligent, conversational agents that can be used in the healthcare arena, but also in everyday life. In addition, ubiquitous recording, both in the field and in the doctor's office/patient's home, has influenced how we think about wellbeing in the future. In our research, we use sensing technologies to develop contextualized and precise delivery of interventions, both in terms of the content and in the timing the intervention delivery, using machine learning algorithms. I will discuss how we use affective computing technologies to deliver just in time health interventions for improved health and for personal, behavioral reflection. For example, I will describe the Entendre project, which has implications for the design of visual feedback to encourage empathic patient-centered communication. I will also talk about ParentGuardian, a wearable sensing system that delivers just in time interventions to parents with ADHD children. In addition, I'll present our findings from two applications that deliver interventions and skills from psychology for coping with conditions ranging from general stress and depression to serious mental illness, like the intent to commit suicide, using conversational agents that users trust. Finally, I'll briefly touch on some of our designs for helping users to reflect on their daily behaviors in order to improve general well-being. Bio: Dr. Mary Czerwinski is a Principle Researcher and Research Manager of the Visualization and Interaction (VIBE) Research Group. Mary's latest research focuses primarily on emotion tracking and intervention design and delivery, information worker task management and health and wellness for individuals and groups. Her research background is in visual attention and multitasking. She holds a Ph.D. in Cognitive Psychology from Indiana University in Bloomington. Mary was awarded the ACM SIGCHI Lifetime Service Award, was inducted into the CHI Academy, and became an ACM Distinguished Scientist in 2010. She also received the Distinguished Alumni award from Indiana University's Brain and Psychological Sciences department in 2014. Mary became a Fellow of the ACM in 2016.

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Colloquium Speaker Christopher Manning,TODAY at 12:30
by Emily Lawrence 16 Nov '17

16 Nov '17

Colloquium Speaker Prof. Christopher Manning, from Stanford University Thursday, November 16, 2017 - 12:30pm Computer Science - Room 105 Host: Dr. Sanjeev Arora *Please note, arrive early as seating and lunch may run out. Deep learning has had enormous success on perceptual tasks but still struggles in providing a model for inference. To address this gap, we have been developing Compositional Attention Networks (CANs). The CAN design provides a strong prior for explicitly iterative reasoning, enabling it to support explainable and structured learning, as well as generalization from a modest amount of data. The model builds on the great success of existing recurrent cells such as LSTMs: A CAN is a sequence of a single recurrent Memory, Attention, and Control (MAC) cell, and by careful design imposes structural constraints on the operation of each cell and the interactions between them, incorporating explicit control and soft attention mechanisms into their interfaces. We demonstrate the model's strength and robustness on the challenging CLEVR dataset for visual reasoning (Johnson et al. 2016), achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the new model is more computationally efficient and data-efficient, requiring an order of magnitude less time and/or data to achieve good results. Joint work with Drew Arad. Bio: Christopher Manning is the Thomas M. Siebel Professor in Machine Learning, Linguistics and Computer Science at Stanford University. He works on software that can intelligently process, understand, and generate human language material. He is a leader in applying Deep Learning to Natural Language Processing, including exploring Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, and deep language understanding. He also focuses on computational linguistic approaches to parsing, robust textual inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning is an ACM Fellow, a AAAI Fellow, an ACL Fellow, and a Past President of ACL. He has coauthored leading textbooks on statistical natural language processing and information retrieval. He is a member of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP software.

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