GIDS Seminar Series Presents Amy Perfors7026d690ac19042a2871a629f2370854Meliora 366false14841540000002017-01-11T12:00:0014841576000002017-01-11T13:00:00Talks2017/01/seminar-perfors.htmltrueOnceGIDS Seminar Series Presents Liang Huangbd13579fac19042a2871a6297093cb81Meliora 203false14866722000002017-02-09T15:30:0014866758000002017-02-09T16:30:00Talks2017/02/seminar-huang.htmlfalseOnceGIDS/NRT Seminar Series Presents Margaret Mitchelle0acd624ac19042a22d9d651031d1855CSB 601false14873535000002017-02-17T12:45:0014873571000002017-02-17T13:45:00Talks2017/02/seminar-mitchell.htmltrueOnceGIDS/NRT Seminar Series Presents Stephanie Tellexe0afdf95ac19042a22d9d651159430feMeliora 366false14870943000002017-02-14T12:45:0014870979000002017-02-14T13:45:00Talks2017/02/seminar-tellex.htmltrueOnceGIDS Seminar Series Presents Leon Bergen42917f31ac19042a22d9d6511e85e0fcMeliora 203false14878818000002017-02-23T15:30:0014878854000002017-02-23T16:30:00Talks2017/02/seminar-Bergen.htmlfalseOnce2016-2017 Industry Series Presents Cheryl G. Howard61a458f9ac19042a22d9d651199077e3Goergen 108false14879538000002017-02-24T11:30:0014879574000002017-02-24T12:30:00Talks2017/02/industry-Howard.htmlfalseOnceGIDS Seminar Series Presents Zhou Yu4295e01aac19042a22d9d65120ff27b4Meliora 203false14909022000002017-03-30T15:30:0014909058000002017-03-30T16:30:00Talks2017/03/seminar-Yu.htmlfalseOnceGIDS Seminar Series Presents Aaron White42990943ac19042a22d9d651a517cd1eMeliora 203false14884866000002017-03-02T15:30:0014884902000002017-03-02T16:30:00Talks2017/03/seminar-White.htmlfalseOnce Physics Presents Dr. Natalia Connollyec141123ac19042a22d9d651756d1ffaB&L 208false14901972000002017-03-22T11:40:0014902002000002017-03-22T12:30:00Talks2017/03/Physics seminar-Connolly.htmlfalseOncePhysics Colloquiumec24fb69ac19042a22d9d651f42722bcB&L 106false14902110000002017-03-22T15:30:0014902146000002017-03-22T16:30:00Talks2017/03/Physics Colloquium - Connolly.htmlfalseOnceCenter for Biomedical Informatics Presents Matthew N. McCallec3fc8d2ac19042a22d9d651c5a58e85K-307 Auditorium (3-6408) at Medical Centerfalse14907168000002017-03-28T12:00:0014907204000002017-03-28T13:00:00Talks2017/03/Center for Biomedical Informatics Presents Matthew N. McCall, PhD..htmlfalseOnceCareers in Data Science and Data Engineering7c560fccac19042a7a343c0b27fbc85cGleason Hall, Room G318/418false14931378000002017-04-25T12:30:0014931414000002017-04-25T13:30:00Career Events2017/04/careers-in-data-science-and-data-engineering.htmlfalseOnceECE Seminar Series Presents Dr Paris Smaragdis869e3ab3ac19042a7a343c0b07ef8f7cComputer Studies Building (CSB) 209false14926176000002017-04-19T12:00:0014926212000002017-04-19T13:00:00Talks2017/04/ECE seminar-Smaragdis.htmlfalseOncePresentation on MINE and Review of University Data Science Projects in Health Careabcacf85ac19042a7a343c0b0ee7ed45Saunders, Room 1-1416false14939208000002017-05-04T14:00:0014939280000002017-05-04T16:00:00Talks2017/05/presentation-on-mine-and-review-of-data-science-projects-in-health-care.htmlfalseOnceConcert Debut of Prof. Zhiyao Duan's Chinese / English Lyric Tracking Systemc5ee58e6ac19042a7a343c0b388a3e7cPenfield High School Auditoriumfalse14947182000002017-05-13T19:30:0014946786000002017-05-13T08:30:00Other2017/05/concert-debut-of-prof.-zhiyao-duans-chinese--english-lyric-tracking-system.htmlfalseOnceKLA-Tencor Open Office Hours: Jobs and Internshipscab2b970ac19042a7a343c0b18d7abed4-200 Dewey Hallfalse14939172000002017-05-04T13:00:0014939244000002017-05-04T15:00:00Career Events2017/05/kla-tencor-open-office-hours-jobs-and-internships.htmlfalseOnceR-CHIVE Conference: Rochester Cultural Heritage Imaging, Visualization and Education9cc7960fac19042a7a343c0b74658fecRCHIVE Conference 2017 at the Rochester Institute of Technology and University of RochesterRochester Institute of Technology and University of Rochesterfalse14978448000002017-06-19T00:00:0014979312000002017-06-20T00:00:00Meetings2017/06/r-chive-conference-rochester-cultural-heritage-imaging,-visualization-and-education.htmltrueOnceInternational Workshop on Urban Data Sciencec650c09dac19042a7a343c0b2dd7fcedRochester Institute of Technologyfalse15008688000002017-07-24T00:00:0015009552000002017-07-25T00:00:00Meetings2017/07/international-workshop-on-urban-data-science.htmltrueOnceCIRC September Symposium - Gregory Heyworth71475e75ac19042a71a836ad5cb50438Wegmans 1400false15054894000002017-09-15T11:30:0015054948000002017-09-15T13:00:00Talks2017/09/circ-september-symposium---gregory-heyworth.htmlfalseOnceResume Drive-thru -- Hajim Societies Conference Prep I71608834ac19042a71a836adc8424178Career Center, 4-200 Dewey Hallfalse15054264000002017-09-14T18:00:0015054318000002017-09-14T19:30:00Career Events2017/09/resume-drive-thru----hajim-societies-conference-prep-i.htmlfalseOnceCareers in Data Science: What I Did on My Summer Internship!714d88c0ac19042a71a836ad92a9fd37Wegmans 1400false15042780000002017-09-01T11:00:0015042816000002017-09-01T12:00:00Career Events2017/09/careers-in-data-science--what-i-did-on-my-summer-internship.htmlfalseOnceData Science Town Hall716d1184ac19042a71a836ad50b5aa16Wegmans 1400false15042888000002017-09-01T14:00:0015042942000002017-09-01T15:30:00Meetings2017/09/data-science-town-hall.htmlfalseOnceFall 2017 Engineering, Tech and Data Science Career Expo7172a850ac19042a71a836ad0878f56e2nd Floor, Frederick Douglass Ballroom false15070428000002017-10-03T11:00:0015070536000002017-10-03T14:00:00Career Events2017/09/fall-2017-engineering-and-corporate-career-expo.htmlfalseOnceNSF NRT Graduate Training Program Mini-Conference76f2fa2dac19042a71a836ad179f654cMeliora Hall, Room 366false15064596000002017-09-26T17:00:0015064704000002017-09-26T20:00:00Talks2017/09/nsf-nrt-graduate-training-program-mini-conference.htmlfalseOnceData Science Undergraduate Council GIM772befa3ac19042a71a836addc6bf88eDewey Hall 2110-D false15055092000002017-09-15T17:00:0015055128000002017-09-15T18:00:00Meetings2017/09/data-science-undergraduate-council-gim.htmlfalseOnceCareers in Data Science: Ian Delbridge '16, Sikorsky Aircraft7c22277aac19042a71a836adf9069f70false15059664000002017-09-21T00:00:0015059664000002017-09-21T00:00:00Career Events2017/09/careers-in-data-science-ian-delbridge-16,-sikorsky-aircraft.htmltrueOnceCareers in Data Science: Jack Teitel '16, MS '17, URMC Health Lab95c8bbfaac19042a71a836ad12af1ae9Wegmans 1400false15066972000002017-09-29T11:00:0015067008000002017-09-29T12:00:00Career Events2017/09/careers-in-data-science-jack-tietel-16,-ms-17.htmlfalseOnceThe Eastman/UR/Cornell/Buffalo Music Cognition Symposiumba76b3b6ac19042a71a836ad4b41fec7Ciminelli Lounge, Eastman Student Living Center, 100 Gibbs St., Rochesterfalse15067944000002017-09-30T14:00:0015068052000002017-09-30T17:00:00Talks2017/09/the-eastmanurcornellbuffalo-music-cognition-symposium.htmlfalseOnceGoergen 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) 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) Host: Gourab Ghoshal, 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.htmlfalseOnceCIRC Symposium Series: Elliot Inman76e416afac19042a71a836ad75454e39Elliot Inman Manager of Software Development for SAS Every third Friday of the month, the Center for Integrated Research Computing hosts a research symposium (known as the CIRC Symposium), where faculty, staff, and student researchers convene to learn about research projects utilizing the center's resources, meet potential collaborators, and learn about new technologies and trends in research computing. This event is user-driven and features presentations by researchers using CIRC systems. CIRC Symposia are open to all members of the university community and a complimentary lunch is provided. TITLE: The Human Factor in the Age of Machine Learning ABSTRACT: The advent of machine learning algorithms has been described as everything from a "second industrial revolution" to a "national security threat." Alarming stories in the popular press describe a world in which human beings have become obsolete. Exceptionally high-speed algorithms that constantly adapt to streaming data will do everything from generating the art that entertains and inspires us to diagnosing cancer early enough to defeat it. Machine learning will provide an automated control system for a nation with hundreds of millions of self-driving cars and order our groceries based on a diet the machine has determined is best for us to eat. And all of these machines will do all of that without any human being needed to make it all work. But, as the poet e.e. cummings wrote, "pity this busy monster, mankind, not." There will still be a role for human beings. Machines feed on data. People will decide what data are collected, who owns those data, and how the data may be used. People will decide what and when to feed the machine. Data Scientists and Statisticians will decide the standard for a reliable system and determine which machine is consistent enough to be trusted. Researchers with expertise in physics, chemistry, biology, and botany from fields like health care, agriculture, and education, and so on will set the standard for a valid system. They will decide which machine has a true understanding of what science has already uncovered. They will be the first to know whether the machine is right or wrong. And finally, human beings will be the ones to make machine learning algorithms better and more useful to us all. In this talk, Elliot Inman, Ph.D., a manager of software development for SAS, will discuss his perspective on machine learning and the role of data scientists and other researchers in the future of computational science. This talk is a part of the Goergen Institute for Data Science’s Data Science Industry Speakers Series. In addition to his talk, Inman will also lead a series of workshops called "Microcontrollers for the Rest of Us” with the River Campus Libraries TinkerSpace program. BIO: Elliot Inman, Ph.D., is a Manager of Software Development for SAS® Solutions OnDemand. Over the past 25 years, he has analyzed a wide variety of data in areas as diverse as the effectiveness of print and digital advertising, social service outcomes analysis, healthcare claims analysis, employee safety, educational achievement, clinical trial monitoring, sales forecasting, risk-scoring and fraud analytics, general survey analysis, performance benchmarking, product pricing, text processing, and basic scientific research on human memory and cognitive processes. After completing his undergraduate degree at North Carolina State University, he went on to earn a Ph.D. in Experimental Psychology from the University of Kentucky in 1997. In 2005, he started at SAS as an Analytical Consultant. In 2010, he joined SAS Solutions OnDemand, SAS’ high performance cloud computing center. His current focus is on implementing Visual Analytics to provide non-statisticians deeper insight into the results of data mining and predictive models. In addition to his work at SAS, he has led makerspace workshops using microcontrollers to gather data for the Internet of Things and other applications.Wegmans Hall 1400 (auditorium), River Campusfalse15109362000002017-11-17T11:30:0015109416000002017-11-17T13:00:00Talks2017/11/1117_circ-symposium-series 1117.htmlfalseOnceTinkerSpace: Microcontrollers for the Rest of Us05ab2f87ac19042a71a836ad4a106ea2Carlson Library, 2nd Floorfalse15108426000002017-11-16T09:30:0015109524000002017-11-17T16:00:00Other2017/11/1116_tinkerspace-microcontrollers-for-the-rest-of-us.htmltrueOnceThe Arduino as an Audience Experience Meter Workshop0b91f120ac19042a71a836ad8c6d93ccCarlson Library, 2nd Floorfalse15108588000002017-11-16T14:00:0015108660000002017-11-16T16:00:00Other2017/11/1116_the-arduino-as-an-audience-experience-meter-workshop.htmlfalseOnceCTSI Analytics Colloquium956e2610ac19042a71a836adcdd26e12The first CTSI Informatics Analytics Cluster Colloquium will be held on Tuesday Nov. 7th from noon to 1pm with lunch provided. The theme of the colloquium is the historical roots of machine learning. The Historical Foundations of Machine Learning by Anthony Almudevar, PhD, Associate Professor of Biostatitics and Computational Biology, University of Rochester. Applications of Machine Learning in Precision Medicine in Machine by Varun Chandola, PhD, Assistant Professor, Department of Computer Science & Engineering, SUNY Buffalo. Includes a light lunch! For questions or comments, please contact Dongmei Li Lower Adolph Auditorium (1-7619) in the Medical CenterLower Adolph Auditorium (1-7619) in the Medical Centerfalse15100740000002017-11-07T12:00:0015100776000002017-11-07T13:00:00Talks2017/11/1107_ctsi-analytics-colloquium.htmlfalseOnceCareers in Data Science: Kirk Ocke, PARC3554024aac19042a71a836ad69222d70Kirk Ocke, Senior Data Scientist and Software Engineer at PARC, a Xerox Corp. company, will discuss his career path and trends in data science at a career lunch at 12 noon Friday Nov. 10 in 1400 Wegmans Hall (Auditorium. Kirk Ocke, formerly an adjunct professor of data science at the University of Rochester, will discuss his career path and trends in data science. Mr. Ocke develops innovative solutions in the areas of data science and workflow automation. PARC provides custom R&D services, best practices and intellectual property to Fortune 500 and Global 1000 companies, start-ups, government agencies and partners. The company creates new business options, accelerates time to market and augments internal capabilities for its clients. Mr. Ocke’s current areas of research include predicting the payment patterns, and other behaviors, of participants in child support cases, along with adapting lean and agile software development techniques for use in a research organization. His areas of expertise and interest include: applied data science, predictive analytics, lean/agile software development, workflow automation and automated reasoning. Prior to joining Conduent, Mr. Ocke worked at Xerox for 25 years; moving to PARC, a Xerox company in 2014. He started his career as a software engineer in product and development working on network and printing protocols and in 1998 moved to Xerox research where he spent the next several years helping develop industry wide printing and workflow technologies such as the Internet Printing Protocol (IPP) and the Job Definition Format (JDF). In recent years his work has focused on data analytics projects in the field of child support case management. Mr. Ocke earned his B.S. degree in Mathematics from St. John Fisher College and his M.S. degree in Computer Science from the Rochester Institute of Technology. He is also a certified scrum master, holds 17 patents, and is a co-author of 3 RFCs on the Internet Printing Protocol (IPP). 1400 Wegmans Hall (Auditorium), River Campusfalse15103332000002017-11-10T12:00:0015103368000002017-11-10T13:00:00Career Events2017/11/1110_careers-in-data-science-kirk-oche,-parc.htmlfalseOnceBiostatistics Fall Colloquium: Automated Model Building and Deep Learning49fce4c4ac19042a71a836ad279600b2Department of Biostatistics and Computational Biology University of Rochester School of Medicine and Dentistry 2017 Fall Colloquium Xiao Wang, Ph.D. Professor Department of Statistics Purdue University “Automated Model Building and Deep Learning” Thursday, November 9, 2017 3:30 p.m. – 5:00 p.m. Helen Wood Hall – Collins & Wilson Classroom 1W-502 Abstract: Analysis of big data demands computer aided or even automated model building. It becomes extremely difficult to analyze such data with traditional statistical models and model building methods. Deep learning has proved to be successful for a variety of challenging problems such as AlphaGo, driverless cars, and image classification. Understanding deep learning has however apparently been limited, which makes it difficult to be fully developed. In this talk, we focus on neural network models with one hidden layers. We provide an understanding of deep learning from an automated modeling perspective. This understanding leads to a sequential method of constructing deep learning models. This method is also adaptive to unknown underlying model structure. This is a joint work with Chuanhai Liu.Helen Wood Hall - Collins & Wilson Classroom 1W-502false15102594000002017-11-09T15:30:0015102648000002017-11-09T17:00:00Talks2017/11/1109_automated-model-building-and-deep-learning.htmlfalseOnceNailing the Technical Interview4f4f486aac19042a71a836ad9ff9675d“Nailing the Technical Interview” Hosted by Gwen M. Greene Career and Internship Center Monday, November 6, 2017 1400 Wegmans Hall, River Campus Have an upcoming technical interview? Come to this event to hear the employee and student perspective of how to succeed on your technical interview! We will review what to expect, along with tips and tricks for nailing the interview! RSVP on Handshake 1400 Wegmans Hall (Auditorium), River Campusfalse15099984000002017-11-06T15:00:0015073200000002017-10-06T16:00:00Career Events2017/11/1106_nailing-the-technical-interview.htmlfalseOnceInnovationQ Training by rep Michael Dermody is hosting 1.5-2 hour long demo training session for our new resource, InnovationQ. Find out what InnovationQ can do for you! for Students: *Promotes job-market awareness *See which entities are energetically patenting in various industries *Stimulates creativity and innovation *Exposure to actual patents, IEEE documents, landscape mapping *Deep awareness of the technology landscape – throughout the innovation cycle *Determine which whitespaces to pursue early in the process *Exposure to actual patents, preparation for a lifetime of understanding patents *Cross curricular collaboration *Business and STEM students alike can use the software, collaborate on ideas for creation, production, and bringing to market for Faculty: *The world’s finest semantic algorithms: elegant, easy to use, accurate and FAST *Easy-to-use, accurate, and fast access to the world’s innovations *No need to learn and employ complex Byzantine Boolean queries *Create a more collaborative, vibrant information system for patents and innovation *Not just usable by those trained in patent search – ANYONE can gain insight from this tool *Cross curricular collaboration with minimal fuss – connect business school with science & engineering to gain incredible insights without the need for complex Boolean queries For more information, contact Lauren Di Monte, Data & Research Impact Librarian, 585-276-3274.Gamble Room, 3rd Floor Rush Rhees Library, River Campusfalse15103404000002017-11-10T14:00:0015103476000002017-11-10T16:00:00Talks2017/11/ Student Workshop: Career Education and Explorationa264ffdeac19042a1507b5d7b42da1a0There is a great interactive workshop happening next week that will be especially helpful for first-year graduate students as you begin your program. All AS&E graduate students are welcome to attend so feel free to bring a friend! The workshop is being led by the new Director for Graduate Career Education and Professional Development, Kari Brick. Now is the time to start developing a plan for career success. Please bring a copy of your resume and join us on Thursday, November 16 at 3pm in the Feldman Ballroom (D) in Douglas Commons for refreshments and an interactive workshop to help students initiate the exploration of life beyond graduate school. This workshop will introduce different resources and tools available to graduate students as you begin to navigate your opportunities and options, and will also include a resume workshop. Sneak peek at the agenda: Intro: Learn what are employers looking for! Resources: Tools available to you as you navigate options and opportunities, etc. Resume Workshop: Bring a copy of your resume! We’ll discuss the components of a strong resume and evaluate your current resume. You can RSVP here: RSVPs are helpful for planning purposes, but not requiredFeldman Ballroom (D) in Douglas Commonsfalse15108624000002017-11-16T15:00:0015108660000002017-11-16T16:00:00Career Events2017/11/1116_graduate-student-workshop-career-education-and-exploration.htmlfalseOnceBiostatitstics & Computational Biology Fall Colloquium: Xing Quic5c35f7dac19042a1507b5d7bf1e000fDepartment of Biostatistics and Computational Biology University of Rochester School of Medicine and Dentistry 2017 Fall Colloquium Xing Qiu, Ph.D. Associate Professor Department of Biostatistics & Computational Biology University of Rochester “Toward the Era of “Large ¿¿¿¿, Medium ¿¿” Thursday, November 30, 2017 3:30 p.m. – 5:00 p.m. Helen Wood Hall – School of Nursing Auditorium 1W-304 Abstract: In the past two decades or so, the emergence of many different types of high-throughput data, such as whole transcriptome gene expression, genotyping, and microbiota abundance data, has revolutionized medical research. One common property shared by these “Omics” data is that typically they have much more features than the number of independent measurements (sample size). This property is also known as the “large p, small n” property in the research community, and has motivated many instrumental statistical innovations. A few of these examples include Benjamini-Hochberg’s FDR controlling multiple testing procedure; Fan and Lv’s sure independence screening; a host of advanced penalized regression methods; sparse matrix and tensor decomposition techniques; just to name a few. Due to the rapid advancing of biotechnology, the unit cost of generating high-throughput data has decreased significantly in recent years. Consequently, the sample size of those data in a respectful study is now about ¿¿=100~500, which I consider as “medium n”, and is certainly a huge improvement to the old “small n” studies in which ¿¿<10 is the norm. With the increased sample size, medical investigators are starting to ask more sophisticated questions – feature selection based on hypothesis testing and regression analysis is no longer the end, but the new starting point for secondary analyses such as network analysis, multi-modal data association, gene set analyses, etc. The overarching theme of these advanced analyses is that they all require statistical inference for models that involve ¿¿2 parameters. In my opinion, it takes a combination of proper data preprocessing, feature selection, dimension reduction, model building and selection, as well as domain knowledge and computational skills to do it right. Despite of the technical difficulties of designing and performing these avant-garde analyses, I believe that they will soon become mainstream, and inspire a generation of young statisticians and data scientists to invent the next big breakthroughs in statistical science. In this talk, I will share some of my recent methodology and collaborative research that involves “large ¿¿2” models, and list a few potential extensions of these methods that may be used in other areas of statistics.Helen Wood Hall – School of Nursing Auditorium 1W-304false15120738000002017-11-30T15:30:0015120810000002017-11-30T17:30:00Talks2017/11/1130_biostatitstics--computational-biology-fall-colloquium-xing-qui.htmlfalseOnceCIRC Symposium Series76e56639ac19042a71a836ad1806e08bWegmans 1400false15133554000002017-12-15T11:30:0015133608000002017-12-15T13:00:00Talks2017/12/1215_circ-symposium-series 1217.htmlfalseOnceGoergen Institute for Data Science Distinguished Speaker Series: Jeannette Wing72106b8dac19042a71a836ad04a102c5Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Consulting Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology. Professor Wing's general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM. She is currently Retiring Chair of the AAAS Section on Information, Computing and Communications, and a member of: the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba Technical Advisory Board. She has been a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE). Abstract: Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe. At the same time, such capabilities risk leading to biased, inappropriate, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk. The Data Science Institute at Columbia University promotes “Data for Good”: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. Started in 2012, the Institute is now a university-level institute representing over 250 affiliated faculty from 12 different schools across campus. Data science literally touches every corner of the university. In this talk, I will present the mission of the Institute, highlights of our educational and research activities, and plans for future initiatives.Wegmans Hall 1400 (auditorium), River Campusfalse15127488000002017-12-08T11:00:0015127524000002017-12-08T12:00:00Talks2017/12/1208_goergen-institute-for-data-science-distinguished-speaker-series-jeannette-wing.htmlfalseOnceCareers in Data Science: Jason Morrissette, Wegmans Food Marketsa7e272b7ac19042a1507b5d7e7673b23Alumnus Jason Morrissette is a Rochester native. He graduated magna cum laude with a double major in Statistics and Mathematics from the University of Rochester in 2011 and continued his education at the University of Rochester School of Medicine and Dentistry with an MA and PhD in Statistics. While going to school he worked part-time in customer service and intern roles for Wegmans Food Markets. Wegmans is a family-owned supermarket chain headquartered in Rochester, NY that has consistently been named one of FORTUNE magazine’s 100 Best Companies to Work For, topping the list at #1 in 2005 and most recently at #4 in 2016. He now has 10 years with the company and is currently an Analyst in the Customer Insights department. In his current role, he uses his knowledge of data mining and predictive analytics to produce actionable insights from large amounts of customer-related data. Join us to hear about his career path, his work projects and his tips for having a career in data science. Light lunch during the talk. RSVP recommended on Handshake to help us determine food count.Wegmans Hall 1400 (auditorium), River Campusfalse15121476000002017-12-01T12:00:0015121512000002017-12-01T13:00:00Talks2017/12/1201_careers-in-data-science-jason-morrissette,-wegmans.htmlfalseOnceDSUG presents MLH Hack Daye0063d9fac19042a1507b5d7c956406fRettner Atrium (1st floor)false15122268000002017-12-02T10:00:0015122700000002017-12-02T22:00:00Other2017/12/1202_dsug-presents-mlh-hack-day.htmlfalseOnceGraduate Student Workshop: Job Search Strategies e03a1a7fac19042a1507b5d754da827bSummary: This will be an interactive workshop geared towards graduate students where participants will learn about tips, tricks, and strategies that can be applied to most internship and job searches. This is the first workshop in a series of professional development events for AS&E graduate students. For more information and to RSVP click here. Career Center Conference Room, 4-200 Dewey Hall, River Campusfalse15121566000002017-12-01T14:30:0015121602000002017-12-01T15:30:00Career Events2017/12/1201_graduate-student-workshop-job-search-strategies-.htmlfalseOnceComputer Science Colloquim Series: Designing and Developing Cyber-Resilient Systems13cb970cac19042a1507b5d78a244dffWegmans Hall 1400 (auditorium), River Campusfalse15124068000002017-12-04T12:00:0015124104000002017-12-04T13:00:00Meetings2017/12/1204_computer-science-colloquim-series-designing-and-developing-cyber-resilient-systems.htmlfalseOnceMechanical Engineering Seminar: Ultrafast Large-scale Neural Network Processor on a Chip19b85db8ac19042a1507b5d776fdf866Hopeman 224false15127578000002017-12-08T13:30:0015127614000002017-12-08T14:30:00Talks2017/12/1208_mechanical-engineering-seminar-ultrafast-large-scale-neural-network-processor-on-a-chip.htmlfalseOnceComputer Science Colloquium Series: Towards Human-like Understanding of Visual Content: Facilitating Search and Decoding Visual Media4601b557ac19042a1507b5d79599a6d3TITLE: Towards Human-like Understanding of Visual Content: Facilitating Search and Decoding Visual Media ABSTRACT: In the first part of this talk, I will describe our work on interactive image search. We introduced a new form of interaction for search, where the user can give rich feedback to the system via semantic visual attributes (e.g., "metallic", "pointy", and "smiling"). The proposed WhittleSearch approach allows users to narrow down the pool of relevant images by comparing individual properties of the results to those of the desired target. Building on this idea, we develop a system-guided version of the method which engages the user in a 20-questions-like game where the answers are visual comparisons. To ensure that the system interprets the user's attribute-based feedback as intended, we further show how to efficiently adapt a generic model for an attribute to more closely align with the individual user's perception. Our work transforms the interaction between the image search system and its user from keywords and clicks to precise and natural language-based communication. We demonstrate the impact of this new search modality for effective retrieval on databases ranging from consumer products to human faces. This is an important step in making the output of vision systems more useful, by allowing users to both express their needs better and better interpret the system's predictions. In the second part of my talk, I will discuss two recent projects on using computer vision to analyze images in the media, which often have persuasive intents that lie beyond the physical content. As a first step in understanding persuasion in the visual media, we released a dataset of 64,832 image ads, and a video dataset of 3,477 ads, containing rich annotations about the subject, sentiment, and rhetoric of the ads. The key task we focus on is the ability of a computer vision system to answer questions about the actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer. To help perform this task, we focus on two challenges: decoding the symbolic references that ads make (e.g. a dove symbolizes peace), and recognizing objects in the severely non-photorealistic portrayals that some ads use. In a second media understanding project, we develop a method that captures photographers’ styles and predicts the authorship of artistic photographs. To explore the feasibility of current computer vision techniques to address photographer identification, we create a new dataset of over 180,000 images taken by 41 well-known photographers. We examine the effectiveness of a variety of features and convolutional neural networks for this task. We also use what our method has learned to generate new “pastiche” photographs in the style of an author. BIO: Adriana Kovashka is an Assistant Professor in Computer Science at the University of Pittsburgh. She received her PhD in 2014 from The University of Texas at Austin. Her research interests primarily lie in computer vision, with some overlap in machine learning, information retrieval, natural language processing, and human computation. Her work is funded by two NSF grants and a Google Faculty Research Award. Her research has been published in the top computer vision conferences, such as Computer Vision and Pattern Recognition (CVPR) and the International Conference on Computer Vision (ICCV), as well as the annual conference of the Association for Computational Linguistics (ACL). She has served as Area Chair for CVPR 2018, Tutorial Chair for WACV 2018, and Doctoral Consortium Chair for CVPR 2015-2017.Wegmans Hall 1400 (auditorium), River Campusfalse15130116000002017-12-11T12:00:0015130152000002017-12-11T13:00:00Talks2017/12/1211_computer-science-colloquium-series-towards-human-like-understanding-of-visual-content-facilitating-search-and-decoding-visual-media.htmltrueOnce