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enIQI Weekly Seminar: Using redundant ancilla encoding and flags for low overhead magic state preparationbjleung@caltech.edu (Bonnie Leung)IQI Weekly Seminar<strong>Speaker(s):</strong> Christopher Chamberland (Amazon Quantum Group)<br><strong>Location:</strong> Annenberg 107<br><p><b>Abstract</b>: The overhead cost of performing universal fault-tolerant quantum computation for large scale algorithms is very high. Despite several attempts at alternative schemes, magic state distillation remains one of the most efficient schemes for simulating non-Clifford gates fault-tolerantly. However, since magic state distillation circuits are not fault-tolerant, all Clifford operations must be encoded in a large distance code in order to have comparable failure rates with the magic states being distilled. In this work, we introduce a new concept which we call redundant ancilla encoding. The latter combined with flag qubits allows for circuits to both measure stabilizer generators of some code, while also being able to measure global operators to fault-tolerantly prepare magic states, all using nearest neighbor interactions. In particular, we apply such schemes to the triangular color code family. Such schemes are suitable for experimental implementations and are expected to significantly reduce the overhead for preparing high-fidelity magic states. Extensive numerical study to substantiate the resource reduction is underway.</p><p></p><p></p><p></p><p></p><p></p><p></p><p></p>Tue, 21 Jan 2020 15:00:00 -0800http://cms.caltech.edu/events/87468KNI Distinguished Seminar: From inverse design to implementation of practical (quantum) photonicstkimoto@caltech.edu (Tiffany Kimoto)KNI Distinguished Seminar<strong>Speaker(s):</strong> Jelena Vučković (Stanford University)<br><strong>Location:</strong> Noyes 153 (J. Holmes Sturdivant Lecture Hall)<br><h4>Speaker: Jelena Vučković, Stanford University</h4><h4>Talk Title: "From inverse design to implementation of practical (quantum) photonics"</h4><p></p><p><b>Abstract</b></p><p></p><p>Combining state of the art optimization and machine learning techniques with high speed electromagnetic solvers offers a new approach to "inverse" design and implement classical and quantum photonic circuits with superior properties, including robustness to errors in fabrication and environment, compact footprints, novel functionalities, and high efficiencies. We illustrate this with a number of demonstrated devices in silicon, diamond, and silicon carbide, including wavelength and polarization splitters and converters, power splitters, couplers, nonreciprocal switches and routers, on chip laser driven particle accelerators, and efficient quantum emitter-photon interfaces.</p><p></p><p><b>Biography</b></p><p>Jelena Vučković is the Jensen Huang Professor in Global Leadership in the School of Engineering, a Professor of Electrical Engineering and by courtesy of Applied Physics at Stanford, where she leads the Nanoscale and Quantum Photonics Lab. She is also a director of Q-FARM, Stanford-SLAC Quantum Science and Engineering Initiative, and is affiliated with Ginzton Lab, PULSE Institute, SIMES Institute, Stanford Photonics Research Center (SPRC), SystemX Alliance, and Bio-X at Stanford.</p><p></p><p>Upon receiving her PhD degree from Caltech in 2002, she worked as a postdoctoral scholar at Stanford. In 2003, she joined the Stanford Electrical Engineering Faculty, first as an assistant professor, then an associate professor, and finally as a professor of electrical engineering (since 2013). She has also held visiting positions at the Max Planck Institute for Quantum Optics (MPQ) in Munich, Germany (2019), at the Institute for Advanced Studies of the Technical University in Munich, Germany (2013-2018), and at the Institute for Physics of the Humboldt University in Berlin, Germany (2010-2013).</p><p></p><p>Vučković has received many awards including the IET A. F. Harvey Engineering Research Prize (2019), Distinguished Scholar of the Max Planck Institute for Quantum Optics - MPQ (2019), Hans Fischer Senior Fellowship from the Institute for Advanced Studies in Munich (2013), Humboldt Prize (2010), Marko V. Jaric award for outstanding achievements in physics (2012), DARPA Young Faculty Award (2008), Chambers Faculty Scholarship at Stanford (2008), Presidential Early Career Award for Scientists and Engineers (PECASE in 2007), Office of Naval Research Young Investigator Award (2006), Okawa Foundation Research Grant (2006), and Frederic E. Terman Fellowship at Stanford (2003). She is a Fellow of the American Physical Society (APS), of the Optical Society of America (OSA), and of the Institute of Electronics and Electrical Engineers (IEEE).</p><p></p><p>Vučković is a member of the scientific advisory board of the Max Planck Institute for Quantum Optics - MPQ (in Munich, Germany), of the Ferdinand Braun Institute (in Berlin, Germany), an advisory board member of the National Science Foundation (NSF) - Engineering Directorate, and a board member of SystemX at Stanford. Currently, she is also an Associate Editor of ACS Photonics, and a member of the editorial advisory board of Nature Quantum Information and APL Photonics.</p><p></p><p><a href="http://kni.caltech.edu/programs/kni-distinguished-seminar"><i>The KNI Distinguished Seminar Series</i></a> <i>is a new monthly series hosted by The Kavli Nanoscience Institute where eminent scientists and thinkers with strong yet varied backgrounds in nanoscience and nanotechnology share their expertise with the Caltech community. Seminars consist of a one-hour presentation, followed by a Q&A and light reception. The scopes of presentations may range from: recent outstanding scientific highlights/technological advancements, to innovative early-stage research developments, to broader cross-disciplinary topics that are relevant to nanoscience. Each seminar will be recorded and made available to the public via the</i> <a href="https://www.youtube.com/channel/UCWbGhTKjT6wv7T4sZ23E7WQ"><i>KNI's YouTube channel</i></a><i>.</i></p><p></p>Tue, 21 Jan 2020 16:00:00 -0800http://cms.caltech.edu/events/87445CMX Lunch Seminar: Binary Component Decomposition of Matricesjbrink@caltech.edu (Jolene Brink)CMX Lunch Seminar<strong>Speaker(s):</strong> Richard Kueng (Caltech)<br><strong>Location:</strong> Annenberg 213<br><p> </p><p>We study the problem of decomposing a low-rank matrix into a factor with binary entries, either from {±1} or from {0,1}, and an unconstrained factor. This research answers fundamental questions about the existence and uniqueness of these decompositions. It also leads to tractable factorization algorithms that succeed under a mild deterministic condition. </p><p><i>This is joint work with Joel Tropp (Caltech)</i></p>Wed, 22 Jan 2020 12:00:00 -0800http://cms.caltech.edu/events/86830IQIM Postdoctoral and Graduate Student Seminar: TBDmarciab@caltech.edu (Marcia Brown)IQIM Postdoctoral and Graduate Student Seminar<strong>Speaker(s):</strong> Xinwei Li ()<br><strong>Location:</strong> East Bridge 114<br><p><b>Abstract:</b> TBD</p>Fri, 24 Jan 2020 12:00:00 -0800http://cms.caltech.edu/events/87455CMS-EE Partners Tech Talk: TBDclairer@caltech.edu (Claire Ralph)CMS-EE Partners Tech Talk<strong>Speaker(s):</strong> <br><strong>Location:</strong> Annenberg 105<br><p></p>Mon, 27 Jan 2020 12:00:00 -0800http://cms.caltech.edu/events/87001H.B. Keller Colloquium: Challenges in Reliable Machine Learningdbohler@caltech.edu (Diana Bohler)H.B. Keller Colloquium<strong>Speaker(s):</strong> Kamalika Chaudhuri (University of California, San Diego)<br><strong>Location:</strong> Annenberg 105<br><p></p><p>As machine learning is increasingly used in real applications, there is a need for reliable and robust methods. In this talk, we will discuss two such challenges that arise in reliable machine learning. The first is sample selection bias, where training data is available from a distribution conditioned on a sample selection policy, but the resultant classifier needs to be evaluated on the entire population. We will show how we can use active learning to get a small amount of labeled data from the entire population that can be used to correct this kind of sample selection bias. The second is robustness to adversarial examples -- slight strategic perturbations of legitimate test inputs that cause misclassification. We next look at adversarial examples in the context of a simple non-parametric classifier -- the k-nearest neighbor classifier, and look at its robustness properties. We provide bounds on its robustness as a function of k, and propose a more robust 1-nearest neighbor classifier.</p><p>Joint work with Songbai Yan, Tara Javidi, Yaoyuan Yang, Cyrus Rastchian, Yizhen Wang and Somesh Jha</p>Mon, 27 Jan 2020 16:00:00 -0800http://cms.caltech.edu/events/87559IST Lunch Bunch: "Does This Vehicle Belong to You?": Extracting Social Meaning from Language by Computerdiane@cms.caltech.edu (Diane Goodfellow)IST Lunch Bunch<strong>Speaker(s):</strong> Dan Jurafsky (Stanford University)<br><strong>Location:</strong> Annenberg 105<br><p>Police body-worn cameras have the potential to play an important role in understanding and improving police-community relations. In this talk I describe a series of studies conducted by our large interdisciplinary team at Stanford that use speech and natural language processing on body-camera recordings to model the interactions between police officers and community members in traffic stops.</p><p>We draw on linguistic models of dialogue structure and of interpersonal relations like respect to automatically quantify aspects of the interaction from the text and audio. I describe the differences we find in the language directed toward black versus white community members, and offer suggestions for how these findings can be used to help improve the relations between police officers and the communities they serve. I'll also cover a number of our results on using computational methods to uncover historical societal biases, and detect framing, agenda-setting and political polarization in the media.</p><p>Together, these studies highlight how natural language processing can help us interpret latent social content behind the words we use.</p>Tue, 28 Jan 2020 12:00:00 -0800http://cms.caltech.edu/events/87415CMX Lunch Seminar: Statistical Guarantees for MAP Estimators in PDE-Constrained Regression Problemsjbrink@caltech.edu (Jolene Brink)CMX Lunch Seminar<strong>Speaker(s):</strong> Sven Wang (University of Cambridge)<br><strong>Location:</strong> Annenberg 213<br><p> </p><p>The main topic of the talk are convergence rates for penalised least squares (PLS) estimators in non-linear statistical inverse problems, which can also be interpreted as Maximum a Posteriori (MAP) estimators for certain Gaussian Priors. Under general conditions on the forward map, we prove convergence rates for PLS estimators.</p><p>In our main example, the parameter f is an unknown heat conductivity function in a steady state heat equation [a second order elliptic PDE]. The observations consist of a noisy version of the solution u[f] to the boundary value corresponding to f. The PDE-constrained regression problem is shown to be solved a minimax-optimal way.</p><p>This is joint work with S. van de Geer and R. Nickl. If time permits, we will mention some related work on the non-parametric Bayesian approach, as well as computational questions for the Bayesian posterior.</p>Wed, 29 Jan 2020 12:00:00 -0800http://cms.caltech.edu/events/86831Caltech + Finance Symposium 2020: TBDsabrina@hss.caltech.edu (Sabrina Hameister)Caltech + Finance Symposium 2020<strong>Speaker(s):</strong> <br><strong>Location:</strong> Dabney Hall, Lounge<br><p><b><i>Featuring distinguished Caltech faculty<br></i></b><a href="http://www.hss.caltech.edu/people/federico-m-echenique"><b>Federico Echenique</b></a>, Allen and Lenabelle Davis Professor of Economics<br><a href="http://www.hss.caltech.edu/people/michael-j-ewens"><b>Michael Ewens</b></a>, Professor of Finance and Entrepreneurship<br><a href="http://www.hss.caltech.edu/people/lawrence-j-jin"><b>Lawrence Jin</b></a>, Assistant Professor of Finance<br><b><i>and Keynote Speaker<br></i></b><a href="https://som.yale.edu/faculty/nicholas-c-barberis"><b>Nicholas C. Barberis</b></a>, Stephen and Camille Schramm Professor of Finance, Yale School of Management<br></p><p><b>Schedule<br></b>1:30 PM <b>||</b> Opening Remarks<br>1:40 PM <b>||</b> "Markets for Centralized Allocation Problems: Fairness, Efficiency, and Property Rights," Federico Echenique<br>2:20 PM <b>||</b> "Governance and Compensation in Startups," Michael Ewens<br>3:00 PM <b>||</b> Break<br>3:15 PM <b>||</b> "Prospect Theory and Stock Market Anomalies," Lawrence Jin<br>4:05 PM <b>||</b> Keynote Address, "What's Going on in Behavioral Finance? A Survey of the Latest Ideas," Nicholas Barberis<br>5:30 PM <b>||</b> Reception</p>Fri, 31 Jan 2020 13:30:00 -0800http://cms.caltech.edu/events/87557CMS-EE Partners Tech Talk: TBDclairer@caltech.edu (Claire Ralph)CMS-EE Partners Tech Talk<strong>Speaker(s):</strong> <br><strong>Location:</strong> Annenberg 105<br><p></p>Mon, 03 Feb 2020 12:00:00 -0800http://cms.caltech.edu/events/87002H.B. Keller Colloquium: Algorithms for Eliciting Machine Learning Metricsdbohler@caltech.edu (Diana Bohler)H.B. Keller Colloquium<strong>Speaker(s):</strong> Sanmi Koyejo (University of Illinois at Urbana-Champaign)<br><strong>Location:</strong> Annenberg 105<br><p> </p><p>Given a prediction problem with real-world tradeoffs, which cost function should the machine learning model be trained to optimize? Unfortunately, typical default metrics in machine learning, such as accuracy applied to binary classifiers, may not capture tradeoffs relevant to the problem at hand. This talk proposes metric elicitation as a formal strategy to address the metric selection problem, specifically by automatically discovering implicit preferences from an expert or an expert panel via interactive feedback. I will primarily focus on algorithms for eliciting classification metrics, showing that simple algorithms are efficient for metric elicitation under broad assumptions. Finally, I will briefly outline early work on metric selection for measuring group fairness in classification problems with sensitive groups.</p>Mon, 03 Feb 2020 16:00:00 -0800http://cms.caltech.edu/events/87570CMS-EE Partners Tech Talk: TBDclairer@caltech.edu (Claire Ralph)CMS-EE Partners Tech Talk<strong>Speaker(s):</strong> <br><strong>Location:</strong> Annenberg 105<br><p></p>Mon, 10 Feb 2020 12:00:00 -0800http://cms.caltech.edu/events/87003Special CMX Seminar: TBAjbrink@caltech.edu (Jolene Brink)Special CMX Seminar<strong>Speaker(s):</strong> Carolina Osorio (Massachusetts Institute of Technology)<br><strong>Location:</strong> Annenberg 213<br><p></p>Thu, 13 Feb 2020 16:30:00 -0800http://cms.caltech.edu/events/87416EE Systems Seminar: What is the role of curvature in complexity of optimization on manifolds?lchavarr@caltech.edu (Liliana Chavarria)EE Systems Seminar<strong>Speaker(s):</strong> Nicolas Boumal (Princeton University)<br><strong>Location:</strong> Moore B280<br><p><b>ABSTRACT</b> The talk is about solving optimization problems of the form: min f(x), where x lives on a (known) smooth manifold. For example, the manifold could be a sphere, a set of orthonormal matrices, complex phases, fixed-rank matrices or tensors, rigid motions, or a more abstract quotient space owing to symmetry. This comes up in signal and image processing, computer vision, machine learning, inverse problems etc.<br></p><p>After a brief description of how Riemannian geometry enables efficient algorithms, I'll discuss which properties of the cost function and of the manifold affect the worst-case complexity of computing approximate stationary points (both first and second order). In particular, I'll share some thoughts about the role of Riemannian curvature on that front.</p><p><b>BIO</b> Nicolas Boumal is an assistant professor in the mathematics department at Princeton University, where he was also an instructor with the Program in Applied and Computational Mathematics 2016-2018. He studies non-convex optimization, numerical analysis and statistical estimation, exploiting mathematical structures such as smooth geometry, convex geometry and low rank. He is the author of a popular Riemannian optimization toolbox called Manopt.<br></p><p>He obtained his PhD in mathematical engineering from the Université catholique de Louvain in Belgium in 2014, and was a postdoc with the computer science department of the Ecole Normale Supérieure de Paris in 2015. His research is partially supported by a grant of the National Science Foundation.</p>Fri, 21 Feb 2020 16:00:00 -0800http://cms.caltech.edu/events/87469CMS-EE Partners Tech Talk: TBDclairer@caltech.edu (Claire Ralph)CMS-EE Partners Tech Talk<strong>Speaker(s):</strong> <br><strong>Location:</strong> Annenberg 105<br><p></p>Mon, 24 Feb 2020 12:00:00 -0800http://cms.caltech.edu/events/87004CMX Lunch Seminar: An Optimal Transport Perspective on Uncertainty Propagationjbrink@caltech.edu (Jolene Brink)CMX Lunch Seminar<strong>Speaker(s):</strong> Amir Sagiv (Columbia University)<br><strong>Location:</strong> Annenberg 213<br><p> In many scientific areas, a deterministic model (e.g., a differential equation) is equipped with parameters. In practice, these parameters might be uncertain or noisy, and so an honest model should account for these uncertainties and provide a statistical description of the quantity of interest. Underlying this computational problem is a fundamental question - If two "similar" functions push-forward the same measure, are the new resulting measures close, and if so, in what sense? In this talk, I will first show how the probability density function (PDF) can be approximated, and present applications to nonlinear optics. We will then discuss the limitations of PDF approximation, and present an alternative Wasserstein-distance formulation of this problem, which through optimal-transport theory yields a simpler theory. </p>Wed, 26 Feb 2020 12:00:00 -0800http://cms.caltech.edu/events/86832Center for Social Information Sciences (CSIS) Seminar: Multiple Imputation for Large Multiscale Data with Linear Constraintsmmartin@caltech.edu (Mary Martin)Center for Social Information Sciences (CSIS) Seminar<strong>Speaker(s):</strong> Jian Cao (Caltech)<br><strong>Location:</strong> Baxter B125<br><p>Abstract: We present a new method that is capable of handling both missing and suppressed value problems for large multiscale data sets, such as the Quarterly Census of Employment and Wages (QCEW) from the U.S. Bureau of Labor Statistics. Existing multiple imputation methods are hard to scale for such data sets. This particularly acute in the case of QCEW, with as many as 1.5 billion observations aggregated along three different scales (industry structure, geographic levels, and time). Our method incorporates three innovations. First, we improve the accuracy of the Bootstrapping-based Expectation Maximization method (King et al. 2010), a state-of-the-art multiple imputation method, by utilizing the extra information from the singular covariance matrix and taking into account of the multiscale data structure. Second, we introduce a quasi-Monte Carlo technique to accelerate convergence. Third, we develop a parallel sequential approach that partitions the large data set into quasi-independent small data sets according to the data structure and patterns of suppressed and missing observations. We demonstrate that our new method improves speed and accuracy. Moreover, it can be applied to large data sets with complicated multiscale structures.</p>Fri, 28 Feb 2020 12:00:00 -0800http://cms.caltech.edu/events/87078CMS-EE Partners Tech Talk: TBDclairer@caltech.edu (Claire Ralph)CMS-EE Partners Tech Talk<strong>Speaker(s):</strong> <br><strong>Location:</strong> Annenberg 105<br><p></p>Mon, 02 Mar 2020 12:00:00 -0800http://cms.caltech.edu/events/87005Special CMX Seminar: TBAjbrink@caltech.edu (Jolene Brink)Special CMX Seminar<strong>Speaker(s):</strong> Liliana Borcea (University of Michigan)<br><strong>Location:</strong> Annenberg 213<br><p></p>Tue, 03 Mar 2020 16:30:00 -0800http://cms.caltech.edu/events/87511Finance Seminar: Topic to be announcedsabrina@hss.caltech.edu (Sabrina Hameister)Finance Seminar<strong>Speaker(s):</strong> Eric Zwick (University of Chicago)<br><strong>Location:</strong> Baxter B125<br><p>Please check later for additional details</p><p><i>Finance Seminars at Caltech are funded through the generous support of The Ronald and Maxine Linde Institute of Economic and Management Sciences (lindeinstitute.caltech.edu).</i></p>Thu, 05 Mar 2020 16:00:00 -0800http://cms.caltech.edu/events/86147