- talks - lists.cs.princeton.edu

Colloquium Speaker: Shivaram Venkataraman, TODAY- 12:30pm
by Nicole E. Wagenblast 13 Mar '17

13 Mar '17

Colloquium Speaker Shivaram Venkataraman, University of California, Berkeley Monday, March 13- 12:30pm Computer Science 105 Host: Prof. Andrew Appel Scalable Systems for Fast and Easy Machine Learning Machine learning models trained on massive datasets power a number of applications; from machine translation to detecting supernovae in astrophysics. However the end of Moore’s law and the shift towards distributed computing architectures presents many new challenges for building and executing such applications in a scalable fashion. In this talk I will present my research on systems that make it easier to develop new machine learning applications and scale them while achieving high performance. I will first present programming models that let users easily build distributed machine learning applications. Next, I will show how we can exploit the structure of machine learning workloads to build low-overhead performance models that can help users understand scalability and simplify large scale deployments. Finally, I will describe scheduling techniques that can improve scalability and achieve high performance when using distributed data processing frameworks. Shivaram Venkataraman is a PhD Candidate at the University of California, Berkeley and is advised by Mike Franklin and Ion Stoica. His research interests are in designing systems and algorithms for large scale data processing and machine-learning. He is a recipient of the Siebel Scholarship and best-of-conference citations at VLDB and KDD. Before coming to Berkeley, he completed his M.S at the University of Illinois, Urbana-Champaign.

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Colloquium Speaker: Xiang 'Anthony' Chen Today-12:30pm
by Nicole E. Wagenblast 13 Mar '17

13 Mar '17

Colloquium Speaker Xiang 'Anthony' Chen, Carnegie Mellon University Thursday, March 9, 2017 - 12:30pm Computer Science 105 Host: Prof. Szymon Rusinkiewicz Computational Design and Fabrication to Augment the Physical World We are looking towards a future where a wide range of fabrication machines--from desktop 3D printers to industrial robotic arms--will allow end-users to create physical objects of their own designs. I develop computational design tools that enable end-users to express and convey their ideas into fabrication-ready 3D models. For example, Encore lets users print new parts directly onto, around or through existing objects; Reprise helps users generate and customize adaptations to mechanically enhance existing objects; Façade creates Braille overlays from user-taken photos to make appliances visually accessible; Forté turns a user's design into structures that can robustly support existing objects. In all these tools, the ultimate goal of fabrication is to augment the physical world, extending, adapting, annotating, or supporting existing objects to improve the quality of users' everyday lives. Xiang 'Anthony' Chen is a PhD student working with Scott Hudson and Stelian Coros from the School of Computer Science, Carnegie Mellon University. His research develops technical and design approaches to build novel human-computer interfaces that enhance users' physical interactivity with ubiquitous computers, or enable their creativity in fabricating physical objects of their design (e.g., using 3D printing). Anthony is an Adobe Research Fellow in Human-Computer Interaction. Frequently collaborating with industrial research labs (Microsoft, Autodesk, and Google), he has published 13 papers in top-tier HCI conferences and journal (CHI, UIST, and TOCHI) and has received two best paper awards.

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Colloquium Speaker: Shivaram Venkataraman, Monday, March 13- 12:30pm-reminder
by Nicole E. Wagenblast 12 Mar '17

12 Mar '17

Colloquium Speaker Shivaram Venkataraman, University of California, Berkeley Monday, March 13- 12:30pm Computer Science 105 Host: Prof. Andrew Appel Scalable Systems for Fast and Easy Machine Learning Machine learning models trained on massive datasets power a number of applications; from machine translation to detecting supernovae in astrophysics. However the end of Moore’s law and the shift towards distributed computing architectures presents many new challenges for building and executing such applications in a scalable fashion. In this talk I will present my research on systems that make it easier to develop new machine learning applications and scale them while achieving high performance. I will first present programming models that let users easily build distributed machine learning applications. Next, I will show how we can exploit the structure of machine learning workloads to build low-overhead performance models that can help users understand scalability and simplify large scale deployments. Finally, I will describe scheduling techniques that can improve scalability and achieve high performance when using distributed data processing frameworks. Shivaram Venkataraman is a PhD Candidate at the University of California, Berkeley and is advised by Mike Franklin and Ion Stoica. His research interests are in designing systems and algorithms for large scale data processing and machine-learning. He is a recipient of the Siebel Scholarship and best-of-conference citations at VLDB and KDD. Before coming to Berkeley, he completed his M.S at the University of Illinois, Urbana-Champaign.

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Colloquium Speaker: Sauvik Das, Thursday, March 16- 12:30pm
by Nicole E. Wagenblast 10 Mar '17

10 Mar '17

Colloquium Speaker Sauvik Das, Carnegie Mellon University Thursday, March 16- 12:30pm Computer Science 105 Host: Prof. Nick Feamster Social Cybersecurity: Reshaping Security Through An Empirical Understanding of Human Social Behavior Entering an era of pervasive, connected smart things, our cybersecurity decisions increasingly interfere with our social lives. Yet, little is known of the complex social consequences of our security behaviors, and vice versa. Absent this knowledge, it is difficult to develop better, more socially intelligent security systems that are intuitive for the layperson. My work on social cybersecurity bridges this gap. First, I will highlight some data science work on how social factors affect security behaviors through two empirical analyses: (i) an exploratory analysis of how optional-use security tools diffused through the social networks of 1.5 million Facebook users and (ii) a randomized, controlled experiment with 50,000 people. I will then discuss Thumprint, an inclusive authentication system I created based on the results of the prior empirical analyses. Using techniques from both supervised and unsupervised machine learning, Thumprint authenticates and identifies individual members of small, local groups (e.g., families or small work teams) through the acoustic and acceleration profiles of a single, shared secret knock. Taken together, my work points towards a future of socially intelligent security systems that understand and accommodate basic human behaviors, desires and capabilities. Sauvik Das is a PhD candidate at Carnegie Mellon University’s School of Computer Science. His research, which intersects HCI, data science and cybersecurity, aims to empower people with novel security systems that mitigate costs of time, effort and social capital. His work has won three best paper or honorable mention awards at premier venues (UbiComp 2013, CHI 2016 and 2017) as well as an Honorable Mention for the NSA’s Best Scientific Cybersecurity Paper Award in 2014. His work has also been covered by the popular press, including features on the Financial Times, Slate, Ars Technica and The Atlantic. In addition, he is a NDSEG Fellow, a Qualcomm Innovation Fellow, a Stu Card Graduate Fellow, and a NSF EAPSI Fellow. Prior to CMU, Sauvik earned a Bachelor’s in Computer Science at Georgia Tech.

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Colloquium Speaker: Karthik Narasimhan, Today- 12:30pm
by Nicole E. Wagenblast 10 Mar '17

10 Mar '17

Colloquium Speaker Karthik Narasimhan, Massachusetts Institute of Technology Friday, March 10- 12:30pm Computer Science 105 Host: Barbara Engelhardt Grounding natural language with autonomous interaction The resurgence of deep neural networks has resulted in impressive advances in natural language processing (NLP). However, this success is dependent on access to large amounts of structured supervision, often manually constructed and unavailable for many applications and domains. In this talk, I will present novel computational models that integrate reinforcement learning with language understanding to induce grounded representations of semantics using unstructured feedback. These techniques not only enable task-optimized representations which reduce dependence on high quality annotations, but also exploit language in adapting control policies across different environments. First, I will describe an approach for learning to play text-based games, where all interaction is through natural language and the only source of feedback is in-game rewards. Second, I will exhibit a framework for utilizing textual descriptions to assist cross-domain policy transfer for reinforcement learning. Finally, I will demonstrate how reinforcement learning can enhance traditional NLP systems in low resource scenarios. In particular, I describe an autonomous agent that can learn to acquire and integrate external information to improve information extraction. Karthik Narasimhan is a PhD candidate working with Prof. Regina Barzilay at CSAIL, MIT. His research interests are in natural language understanding and deep reinforcement learning. His current focus is on developing autonomous systems that can acquire language understanding through interaction with their environment while also utilizing textual knowledge to drive their decision making. His work has received a best paper award at EMNLP 2016 and an honorable mention for best paper at EMNLP 2015. Karthik received a B.Tech in Computer Science and Engineering from IIT Madras in 2012 and an S.M in Computer Science from MIT in 2014.

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Colloquium Speaker: Karthik Narasimhan, Friday March 10- 12:30pm- reminder
by Nicole E. Wagenblast 09 Mar '17

09 Mar '17

Colloquium Speaker Karthik Narasimhan, Massachusetts Institute of Technology Friday, March 10- 12:30pm Computer Science 105 Host: Barbara Engelhardt Grounding natural language with autonomous interaction The resurgence of deep neural networks has resulted in impressive advances in natural language processing (NLP). However, this success is dependent on access to large amounts of structured supervision, often manually constructed and unavailable for many applications and domains. In this talk, I will present novel computational models that integrate reinforcement learning with language understanding to induce grounded representations of semantics using unstructured feedback. These techniques not only enable task-optimized representations which reduce dependence on high quality annotations, but also exploit language in adapting control policies across different environments. First, I will describe an approach for learning to play text-based games, where all interaction is through natural language and the only source of feedback is in-game rewards. Second, I will exhibit a framework for utilizing textual descriptions to assist cross-domain policy transfer for reinforcement learning. Finally, I will demonstrate how reinforcement learning can enhance traditional NLP systems in low resource scenarios. In particular, I describe an autonomous agent that can learn to acquire and integrate external information to improve information extraction. Karthik Narasimhan is a PhD candidate working with Prof. Regina Barzilay at CSAIL, MIT. His research interests are in natural language understanding and deep reinforcement learning. His current focus is on developing autonomous systems that can acquire language understanding through interaction with their environment while also utilizing textual knowledge to drive their decision making. His work has received a best paper award at EMNLP 2016 and an honorable mention for best paper at EMNLP 2015. Karthik received a B.Tech in Computer Science and Engineering from IIT Madras in 2012 and an S.M in Computer Science from MIT in 2014.

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Colloquium Speaker: Xiang 'Anthony' Chen Thursday, March 9-12:30pm- Reminder
by Nicole E. Wagenblast 08 Mar '17

08 Mar '17

Colloquium Speaker Xiang 'Anthony' Chen, Carnegie Mellon University Thursday, March 9, 2017 - 12:30pm Computer Science 105 Host: Prof. Szymon Rusinkiewicz Computational Design and Fabrication to Augment the Physical World We are looking towards a future where a wide range of fabrication machines--from desktop 3D printers to industrial robotic arms--will allow end-users to create physical objects of their own designs. I develop computational design tools that enable end-users to express and convey their ideas into fabrication-ready 3D models. For example, Encore lets users print new parts directly onto, around or through existing objects; Reprise helps users generate and customize adaptations to mechanically enhance existing objects; Façade creates Braille overlays from user-taken photos to make appliances visually accessible; Forté turns a user's design into structures that can robustly support existing objects. In all these tools, the ultimate goal of fabrication is to augment the physical world, extending, adapting, annotating, or supporting existing objects to improve the quality of users' everyday lives. Xiang 'Anthony' Chen is a PhD student working with Scott Hudson and Stelian Coros from the School of Computer Science, Carnegie Mellon University. His research develops technical and design approaches to build novel human-computer interfaces that enhance users' physical interactivity with ubiquitous computers, or enable their creativity in fabricating physical objects of their design (e.g., using 3D printing). Anthony is an Adobe Research Fellow in Human-Computer Interaction. Frequently collaborating with industrial research labs (Microsoft, Autodesk, and Google), he has published 13 papers in top-tier HCI conferences and journal (CHI, UIST, and TOCHI) and has received two best paper awards.

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Colloquium Speaker: Shivaram Venkataraman, Monday, March 13- 12:30pm
by Nicole E. Wagenblast 07 Mar '17

07 Mar '17

Colloquium Speaker Shivaram Venkataraman, University of California, Berkeley Monday, March 13- 12:30pm Computer Science 105 Host: Prof. Andrew Appel Scalable Systems for Fast and Easy Machine Learning Machine learning models trained on massive datasets power a number of applications; from machine translation to detecting supernovae in astrophysics. However the end of Moore’s law and the shift towards distributed computing architectures presents many new challenges for building and executing such applications in a scalable fashion. In this talk I will present my research on systems that make it easier to develop new machine learning applications and scale them while achieving high performance. I will first present programming models that let users easily build distributed machine learning applications. Next, I will show how we can exploit the structure of machine learning workloads to build low-overhead performance models that can help users understand scalability and simplify large scale deployments. Finally, I will describe scheduling techniques that can improve scalability and achieve high performance when using distributed data processing frameworks. Shivaram Venkataraman is a PhD Candidate at the University of California, Berkeley and is advised by Mike Franklin and Ion Stoica. His research interests are in designing systems and algorithms for large scale data processing and machine-learning. He is a recipient of the Siebel Scholarship and best-of-conference citations at VLDB and KDD. Before coming to Berkeley, he completed his M.S at the University of Illinois, Urbana-Champaign.

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Tengyu Ma will present his Pre FPO "Better understanding of non-convex methods in machine learning" on Monday, March 13, 2017 at 2pm in CS 302
by Nicki Gotsis 07 Mar '17

07 Mar '17

Tengyu Ma will present his Pre FPO on Monday, March 13, 2017 at 2pm in CS 302. The members of his committee are as follows: Sanjeev Arora (Advisor, reader), Elad Hazan (non-reader), Mark Braverman (non-reader), Rong Ge (reader, Duke Univ), David Steurer (reader, IAS/Cornell). Everyone is invited to attend his talk. The talk title and abstract follow below: Title: Better understanding of non-convex methods in machine learning Abstract: Recent breakthroughs in machine learning, especially deep learning, often involve learning complex and high-dimensional models on massive datasets with non-convex optimization. Though empirically successful, the formal study of such non-convex methods is much less developed. My research aims to develop new algorithmic approaches and analysis tools in these settings. The talk will showcase a few results. First, we show that matrix completion — a famous problem in machine learning — can be solved by stochastic gradient descent on the straightforward non-convex objective function in polynomial time. (Formally, we show that all local minima of the objective are also global minima.) Then, we will analyze the landscape of the objective functions for linearized recurrent neural nets and residual nets, and demonstrate that over-parameterization and re-parameterization of the models can make the optimization easier.

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Colloquium Speaker: Dan Ports, Today- 12:30pm
by Nicole E. Wagenblast 06 Mar '17

06 Mar '17

Colloquium Speaker Dan Ports, University of Washington Monday, March 6 - 12:30pm Computer Science 105 Host: Prof. Michael Freedman Rethinking Distributed Systems for the Datacenter Today's most popular applications are deployed as massive-scale distributed systems in the datacenter. Keeping data consistent and available despite server failures and concurrent updates is a formidable challenge. Two well-known abstractions, strongly consistent replication and serializable transactions, can free developers from these challenges by transparently masking failures and treating complex updates as atomic units. Yet the conventional wisdom is that these techniques are too expensive to deploy in high-performance systems. I will demonstrate a new approach to designing distributed systems that allows strongly consistent distributed systems to be built with little to no performance cost. Taking advantage of the properties and capabilities of the datacenter environment, we can co-design distributed protocols and the network layer. Specifically, I will describe two systems for state machine replication, Speculative Paxos and Network-Ordered Paxos, and one for distributed transaction processing, Eris, built using this approach. They are able to achieve 5-17x performance improvements over conventional designs. Moreover, they achieve performance within 2% of their weakly consistent alternatives, demonstrating that strong consistency and high performance are not incompatible. Dan Ports is Research Assistant Professor in Computer Science and Engineering at the University of Washington, where he leads the distributed systems research group. His group's research focuses on building practical distributed systems with strong theoretical underpinnings. Prior to joining the faculty at UW in 2015, Dan received the Ph.D. from MIT (2012), where he was advised by Barbara Liskov, and completed a postdoc at UW CSE. His research has been recognized with best paper awards at NSDI and OSDI.

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