Goergen Institute for Data Science Seminar Series: Satinder Singh71fef693ac19042a71a836ad07011530Wegmans 1400false15073020000002017-10-06T11:00:0015073056000002017-10-06T12:00:00Talks2017/10/1006_goergen-institute-for-data-science-seminar-series-satinder-singh.htmlfalseOnceCIRC Symposium Series76e2ddadac19042a71a836ad2e1ce172The Center for Integrated Research Computing (CIRC) will host its next symposium on Friday, October 20th from 11:30 a.m. to 1 p.m. in Wegmans Hall 1400. This month’s featured speaker is John Ashton from the Department of Microbiology and Immunology and the Genomics Research Center. He will discuss sequencing technologies for single cell RNA. Our on-going research talk will be provided by Binshuang Li from the Department of Biology. Binshuang’s presentation will demonstrate the use of second and third generation sequencing techniques to identify a master genetic switch. Please see the webpage for details of the titles and abstracts. Lunch will be provided. Wegmans 1400false15085134000002017-10-20T11:30:0015085188000002017-10-20T13:00:00Talks2017/10/1020_circ-symposium-series.htmlfalseOnceFacebook Info Session9bc1610cac19042a71a836ad64d269abfalse15072192000002017-10-05T12:00:0015072228000002017-10-05T13:00:00Career Events2017/10/1005_facebook-info-session.htmlfalseOnceCareers in Data Science: David L. Ennist '78, PhD, MBA9bd900bcac19042a71a836ad5254ade51400 Wegmans Hall (auditorium)false15077448000002017-10-11T14:00:0015077484000002017-10-11T15:00:00Career Events2017/10/1011_careers-in-data-science--david-l.--ennist-78,-mba,-phd.htmlfalseOnceWing Lecture Series: Sprawl and other dispersion metricsba5024e4ac19042a71a836adafc6f566116 Wilmot Hallfalse15070428000002017-10-03T11:00:0015070464000002017-10-03T12:00:00Talks2017/10/1003_wing-lecture-series-sprawl.htmlfalseOnceWing Lecture Series: Political Geometry: What do shapes have to do with fair voting?ba4ef620ac19042a71a836ad3decadb8Hutch 140 (Lander Auditorium)false15069780000002017-10-02T17:00:0015069816000002017-10-02T18:00:00Talks2017/10/1002_wing-lecture-series-gerrymandering.htmlfalseOnceMachine-Reading and Crowdsourcing Medieval Music Manuscriptsddca6fbbac19042a71a836adc9631b7fAn international group of scholars will provide the state of digital humanities research in a half-day symposium as it relates to studies of medieval music manuscripts, including machine-reading of early music notation and collaborative techniques for indexing manuscripts of medieval chant. An evening performance by the women of Chicago-based early music ensemble Schola Antiqua features a pre-modern convent program, including music associated with a 13th-century Italian convent, which will be discussed in the morning sessions. The concert also includes keyboard pieces and some of the earliest known polyphony associated with nuns. FREE and open to the public. Registration recommended. Presentations on issues in machine-reading and crowdsourcing of medieval music manuscripts by researchers involved with the international database Latin plainchant known as CANTUS. Conference Organizers: Michael Alan Anderson (Eastman School of Music), Jennifer Bain (Dalhousie University), and Debra Lacoste (University of Waterloo) Presenters: Kate Helsen (University of Western Ontario); Sarah Long (Michigan State University); Rebecca Shaw (Dalhousie University); Barbara Swanson (York University) Respondent: Tamsyn Rose-Steel (Johns Hopkins University) https://www.esm.rochester.edu/machine-reading/ Hatch Recital Hall | Eastman East Wingfalse15090228000002017-10-26T09:00:0015090354000002017-10-26T12:30:00Talks2017/10/1026_machine-reading-and-crowdsourcing-medieval-music-manuscripts.htmlfalseOnceVinko Zlatic: Unexpected properties of color-avoiding-percolation (CAP)01753891ac19042a71a836ad4654fd51Title: Unexpected properties of color-avoiding-percolation (CAP) Speaker: Rudjer Boskovic (Institute for Theoretical Physics, Zagreb) http://www.irb.hr/eng/People/Vinko-Zlatic Host: Gourab Ghoshal, gghoshal@pas.rochester.edu Abstract: Many real world networks have groups of similar nodes which are vulnerable to the same failure or adversary. Nodes can be colored in such a way that colors encode the shared vulnerabilities. Using multiple paths to avoid these vulnerabilities can greatly improve network robustness. Color-avoiding percolation provides a theoretical framework for analyzing this scenario, focusing on the maximal set of nodes which can be connected via multiple color-avoiding paths. We explicitly account for the fact that the same particular link can be part of different paths avoiding different colors. This fact was previously accounted for with a heuristic approximation. We compare this approximation with a new, more exact theory and show that the new theory is substantially more accurate for many avoided colors. Further, we formulate our new theory with differentiated node functions, as senders/receivers or as transmitters. In both functions, nodes can be explicitly trusted or avoided. With only one avoided color we obtain standard percolation. With one by one avoiding additional colors, we can understand the critical behavior of color avoiding percolation. For heterogeneous color frequencies, we find that the colors with the largest frequencies control the critical threshold and exponent. Colors of small frequencies have only a minor influence on color avoiding connectivity, thus allowing for approximations. We show that modularity of network provides an interesting analogue in the study of networks to the Ising model in the presence of a magnetic field. Lastly, we find from the perspective of statistical physics, that the measured critical exponents define new universality classes characterizing higher-order transitions with corresponding higher-order derivative analogues of \gamma. To the best of our knowledge the present study represents the first time that higher-order transitions are observed in percolation type systems with their higher-order scaling exponents defined and measured.Bauch & Lomb 109false15078384000002017-10-12T16:00:0015078420000002017-10-12T17:00:00Talks2017/10/1012_vinko-zlatic-unexpected-properties-of-color-avoiding-percolation-cap.htmlfalseOnceROC Data Science Meetup067c87a3ac19042a71a836ad01abeed1The University of Rochester’s Goergen Institute of Data Science (GIDS) is hosting the kick-off for the 2017-18 ROC Data Science Meetup. Meet faculty, students, and staff from GIDS as well as area professionals for refreshments from 5:30-6:15pm for a reception on the 2nd Floor of the new Wegmans Hall. Then we will move to the auditorium to hear about the data science program and research at the University of Rochester from Professor Henry Kautz, the Tim and Robin Wentworth Director of GIDS. Meetup guests to park in the Intercampus Drive Lot. Upon arrival, look for signage from parking to Wegmans Hall.Wegmans Hall 2nd floor and auditorium room 1400false15088806000002017-10-24T17:30:0015088878000002017-10-24T19:30:00Meetings2017/10/1024_roc-data-science-meetup.htmlfalseOnceDean Follman, PhD - Sieve Analysis using the Number of Infecting Pathogens111d26a5ac19042a71a836ad89374097Department of Biostatistics and Computational Biology University of Rochester School of Medicine and Dentistry 2017 Fall Colloquium Dean Follman, Ph.D. Chief, Biostatistics Research Branch National Institute of Allergy & Infectious Disease “Sieve Analysis using the Number of Infecting Pathogens” Thursday, October 26, 2017 3:30 p.m. – 5:00 p.m. SRB- First Floor- Room 1416 Abstract: Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogen strains for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject. The similarity between this single entity and the vaccine immunogen is quantified, for example, by exact match or number of mismatched amino acids. With new technology we can now obtain the actual count of genetically distinct pathogens that infect an individual. Let F be the number of distinct features of a species of pathogen. We assume a log-linear model for the expected number of infecting pathogens with feature ``f", f=1,…, F. The model can be used directly in studies with passive surveillance of infections where the count of each type of pathogen is recorded at the end of some interval, or active surveillance where the time of infection is known. For active surveillance we additionally assume that a proportional intensity model applies to the time of potentially infectious exposures and derive product and weighted estimating equation (WEE) estimators for the regression parameters in the log-linear model. The WEE estimator explicitly allows for waning vaccine efficacy and time-varying distributions of pathogens. We give conditions where sieve parameters have a per-exposure interpretation under passive surveillance. We evaluate the methods by simulation and analyze a phase III trial of a malaria vaccine.Saunders Research Building (SRB) First Floor- Room 1416false15090462000002017-10-26T15:30:0015090516000002017-10-26T17:00:00Talks2017/10/1026_dean_follman.htmlfalseOnceComputer Science Colloquium Series: Decoding the Brain to Help Build Machines164b78a4ac19042a71a836ad045b1a7fTITLE: Decoding the Brain to Help Build Machines ABSTRACT: Humans can describe observations and act upon requests. This requires that language be grounded in perception and motor control. I will present several components of my long-term research program to understand the vision-language-motor interface in the human brain and emulate such on computers. In the first half of the talk, I will present fMRI investigation of the vision-language interface in the human brain. Subjects were presented with stimuli in different modalities—spoken sentences, textual presentation of sentences, and video clips depicting activity that can be described by sentences—while undergoing fMRI. The scan data is analyzed to allow readout of individual constituent concepts and words—people/names, objects/nouns, actions/verbs, and spatial-relations/prepositions—as well as phrases and entire sentences. This can be done across subjects and across modality; we use classifiers trained on scan data for one subject to read out from another subject and use classifiers trained on scan data for one modality, say text, to read out from scans of another modality, say video or speech. Analysis of this indicates that the brain regions involved in processing the different kinds of constituents are largely disjoint but also largely shared across subjects and modality. Further, we can determine the predication relations; when the stimuli depict multiple people, objects, and actions, we can read out which people are performing which actions with which objects. This points to a compositional mental semantic representation common across subjects and modalities. In the second half of the talk, I will use this work to motivate the development of three computational systems. First, I will present a system that can use sentential description of human interaction with previously unseen objects in video to automatically find and track those objects. This is done without any annotation of the objects and without any pretrained object detectors. Second, I will present a system that learns the meanings of nouns and prepositions from video and tracks of a mobile robot navigating through its environment paired with sentential descriptions of such activity. Such a learned language model then supports both generation of sentential description of new paths driven in new environments as well as automatic driving of paths to satisfy navigational instructions specified with new sentences in new environments. Third, I will present a system that can play a physically grounded game of checkers using vision to determine game state and robotic arms to change the game state by reading the game rules from natural-language instructions. Joint work with Andrei Barbu, Daniel Paul Barrett, Charles Roger Bradley, Seth Benjamin Scott Alan Bronikowski, Zachary Burchill, Wei Chen, N. Siddharth, Caiming Xiong, Haonan Yu, Jason J. Corso, Christiane D. Fellbaum, Catherine Hanson, Stephen Jose Hanson, Sebastien Helie, Evguenia Malaia, Barak A. Pearlmutter, Thomas Michael Talavage, and Ronnie B. Wilbur. BIO: Jeffrey M. Siskind received the B.A. degree in computer science from the Technion, Israel Institute of Technology, Haifa, in 1979, the S.M. degree in computer science from the Massachusetts Institute of Technology (M.I.T.), Cambridge, in 1989, and the Ph.D. degree in computer science from M.I.T. in 1992. He did a postdoctoral fellowship at the University of Pennsylvania Institute for Research in Cognitive Science from 1992 to 1993. He was an assistant professor at the University of Toronto Department of Computer Science from 1993 to 1995, a senior lecturer at the Technion Department of Electrical Engineering in 1996, a visiting assistant professor at the University of Vermont Department of Computer Science and Electrical Engineering from 1996 to 1997, and a research scientist at NEC Research Institute, Inc. from 1997 to 2001. He joined the Purdue University School of Electrical and Computer Engineering in 2002 where he is currently an associate professor. His research interests include computer vision, robotics, artificial intelligence, neuroscience, cognitive science, computational linguistics, child language acquisition, automatic differentiation, and programming languages and compilers.1400 Wegmans Hall (auditorium)false15093792000002017-10-30T12:00:0015093828000002017-10-30T13:00:00Talks2017/10/1030_computer-science-colloquium-series-decoding-the-brain-to-help-build-machines.htmlfalseOnceComputer Science Colloquium Series: Practical Formal Methods for Mainstream Compiler Developers16543eabac19042a71a836ad09366f9aTITLE: Practical Formal Methods for Mainstream Compiler Developers ABSTRACT: Compilers perform semantics-preserving optimizations to improve the efficiency of the input code. Among them, peephole optimizations that perform local algebraic simplifications are a persistent source of bugs. This talk will present domain specific languages (DSL) in the Alive-NJ tool kit to verify both integer and floating point based peephole optimizations in the LLVM compiler. The talk will briefly highlight the challenges in handling the ambiguities in the LLVM's semantics. A transformation expressed in these DSLs is shown to be correct by automatically encoding it as constraints whose validity is checked with help of a satisfiability modulo theories (SMT) solver. Furthermore, an optimization expressed in the DSL can be automatically translated into C++ code that is suitable for inclusion in an LLVM optimization pass. The talk will also highlight our recent results and future directions on data driven precondition inference for these optimizations, which will be useful while debugging an incorrect optimization. I will conclude the talk by briefly describing other active projects on profiling task parallel programs, approximations with Hadoop/ Spark frameworks, and reasoning about cryptography software. BIO: Santosh Nagarakatte is an Assistant Professor of Computer Science at Rutgers University. He obtained his PhD from the University of Pennsylvania in 2012. His research interests are in Hardware-Software Interfaces spanning Programming Languages, Compilers, Software Engineering, and Computer Architecture. His papers have been selected as IEEE MICRO TOP Picks papers of computer architecture conferences in 2010 and 2013. He has received the NSF CAREER Award in 2015, ACM SIGPLAN PLDI 2015 Distinguished Paper Award, and ACM SIGSOFT ICSE 2016 Distinguished Paper Award for his research on LLVM compiler verification. His papers have been selected as the 2016 SIGPLAN Research Highlights Paper and 2017 Communication of the ACM Research Highlights Paper.CSB 209false15091182000002017-10-27T11:30:0015091236000002017-10-27T13:00:00Talks2017/10/1027_computer-science-colloquium-series-practical-formal-methods-for-mainstream-compiler-developers.htmlfalseOnceURCSSA 2017 2nd Alumni Summit – Cloud, Big Data, and AI2b65a38fac19042a71a836ad777054dcTopic: Cloud, Big Data and AI Time: 19:00-21:00 Oct.26 Thursday Location: Wegmans Hall: 1400 lecture hall Join the Chinese Student and Scholar Association at the University of Rochester (URCSSA) for its second Annual Alumni Summit. URCSSA has invited several famous alumni and professors from the computer science department who will discuss and share their thoughts on popular topics in the current computer science industry. The topic of this year's alumni summit is "Cloud, Big Data, and AI." The purpose of this event is to build a bridge between current students and the alumni, and give them a chance to communicate. Alumni will also share their experiences with students and give them advice regarding their student life and career at UR. Free snacks will be offered at the event. All at UR are welcome to attend, and a student ID is not required. Speakers include: Henry Kautz, Ph.D.,Computer Science, University of Rochester; The Robin & Tim Wentworth Director of the Goergen Institute for Data Science; Professor in the Department of Computer Science; President of the Association for Advancement of Artificial Intelligence (AAAI) Wendi Heinzelman, Ph.D.,Electrical Engineering and Computer Science, MIT; Dean of the Edmund A. Hajim School of Engineering and Applied Sciences; Professor of Electrical and Computer Engineering; Professor in the Department of Computer Science Chengliang Zhang PhD., Alumnus in Computer Science Department, staff engineer at Google, Seattle Hui Zhang Ph.D., Alumnus in Biostatistics Department, working at St. Jude Children's Research Hospital, Memphis Dongmei Li, Ph.D., Associate Professor in the Department of Clinical & Translational Research (SMD) Orna Intrator, Ph.D., Professor in the Department of Public Health Sciences (SMD)1400 Wegmans Hall, River Campusfalse15090588000002017-10-26T19:00:0015090660000002017-10-26T21:00:00Talks2017/10/1026_urcssa-2017-2nd-alumni-summit--cloud,-big-data,-and-ai.htmlfalseOnceCenter For Biomedical Informatics Seminar499ab278ac19042a71a836ad912a7edfTITLE: Reproducibility and Statistical Methodology In recent years, the issue of reproducibility has become especially prominent, driven both by an increased emphasis on data driven science, as well as a number of initiatives aimed at assessing the frequency with which published findings can be duplicated using new experimental data. Some of these studies report distressingly low reproducibility rates, most notably, the Reproducibility Project undertaken by the Center for Open Science. This seminar will review these findings, placing them in the context of traditional statistical methodology. In particular, we will show how a careful study of the common practice of using preliminary data to estimate sample sizes for future studies can shed considerable light on this issue. Lunch will be provided. Tuesday, October 24, 12:00-1:00pm Ryan Case Method Room, 1-9576Ryan Case Method Room, 1-9576false15088608000002017-10-24T12:00:0015088644000002017-10-24T13:00:00Talks2017/10/1024_center-for-biomedical-informatics-seminar.htmlfalseOnce