Data Science Day @ Columbia University: Full Schedule

Columbia University’s Data Science Institute Presents:
DATA SCIENCE DAY

Authors/Collaborators are listed in alphabetical order.

2:00pm EDT

A 450mV Timing-Margin-Free Unsupervised Sorter based on Spiking Neural Network [P11]

In the on-going quest to enabling energy-efficient cognitive computing, specialized, often non-Von-Neumann, architecture in near/sub-VTH circuits emerges as a promising candidate. However, the complex and parallel nature of the architecture combined with the large delay variability from near/sub-VTH circuits impose prohibitive timing margin to cycle time and limit the achievable efficiency. The conventional error detection and correction (EDAC) techniques can remove margin, however, they can cause significant overhead since most of them target super-VTH in-order microprocessors. In this work, EDAC technique suitable for near/sub-VTH complex and parallel architecture is demonstrated in an unsupervised sorter based on spiking neural network for brain-computer-interface microsystems using 1) body boost correction, 2) fully-static error-detecting latch, 3) sparse error detection, and 4) local-buffer-less latches and clock distribution. The technique improves energy-efficiency by 49.3% and FCLK by 35.6% compared to the baseline with margin at the area overhead of 4.1%. No additional VDD is needed.

Demo/Poster Presenter

Josh Seongjong Kim

PhD Candidate in Electrical Engineering, Columbia Engineering

Josh Seongjong Kim received his B.S. degree in electrical engineering from Hanyang University, South Korea, in 2010, and his M.S. degree from University of Michigan in 2012. He is currently pursuing Ph.D. degree in electrical engineering at Columbia University, New York. His research... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

A Lightweight Early Arbitration Method for Low-Latency Asynchronous 2D-Mesh NoC’s [P12]

The thrust of massively-parallel computing has become the dominating trend in the micro-electronics industry over the last decade. An efficient on-chip communication infrastructure is a key to building such high-performance many-core processors. In recent years, networks-on-chip (NoC’s) are identified as a promising solution to resolve the on-chip-communication bottleneck. These on-chip networks replace traditional ad hoc bus-based communication with packet switching in a structured network architecture, and therefore inherently separate the design of communication from computing elements. A new low-latency interconnection network, "AEoLiAN," is proposed, using a 2D mesh topology. An asynchronous (i.e. clock-less) design methodology is used to obtain high-performance and ease-of-integration heterogeneous processors. The NoC contains a lightweight monitoring network to rapidly notify routers of incoming traffic in advance, thereby allowing early arbitration and channel allocation. Network-level simulations show significant improvements in system latency (34.4-37.9%) and moderate throughput gain (14.7-27.1%) over an existing optimized asynchronous baseline network.

Demo/Poster Presenter

Weiwei Jiang

PhD Candidate in Computer Science, Columbia Engineering

Weiwei Jiang is a Ph.D. candidate in the Department of Computer Science at Columbia University, working with Prof. Steven Nowick. He received the M.S. degree in computer science from Columbia University in 2010 and the B.S. degree from Tsinghua University, China in 2008, with a concentration... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Adaptive Quantification of Pulmonary Emphysema With a Hidden Markov Measure Field Model [P1]

Full-lung (FL) computed tomography (CT) has been widely used for pulmonary emphysema quantification. Cardiac CT scans include approximately 2/3 of the lung and can be obtained more rapidly and with lower radiation exposure than FL scans. This work presents a novel method for emphysema quantification on both FL and cardiac scans, based on parametric modeling of intensity distributions and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols and scanner types. Compared to standard approaches, the presented model provides 1) very high longitudinal correlation with 87 subjects and a total of 365 FL scans acquired with varying imaging protocols; and 2) more reproducible emphysema segmentation and higher longitudinal correlations from a diverse pool of scanner types and thousands of subjects with ten thousands of cardiac scans.

Demo/Poster Presenter

Jie Yang

PhD Candidate in Biomedical Engineering, Columbia Engineering

Jie Yang is a Ph.D. student in the Department of Biomedical Engineering at Columbia University, working with Dr. Andrew Laine. Her research interests are in quantitative medical image analysis and pattern recognition. She is currently working on adaptive pulmonary emphysema quantification... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Financial and Business Analytics

2:00pm EDT

Cardiorespiratory Events in the NICU: Continuous Monitoring, Data Repositories and Daily Automated Reporting Versus Intermittent Bedside Documentation [P2]

We compared hemoglobin oxygen saturation data (SpO2) from a continuous data (CD) repository called BedMaster with intermittent documentation (ID) by the bedside nurse in the first month of life in premature infants. BedMaster is an archive database that is interfacing with the bedside patient monitors to collect and archive vital signs, waveforms and clinical alarm data every 2 seconds. A novel algorithm is then applied to remove artifact based on heart rate signal. CD showed higher number of desaturation episodes per day compared to ID (35 vs 3; p<0.001). CD was also able to be manipulated to show the percent time infants spent in various categories of oxygenation levels, something not yet available at the bedside or in the literature. Using a CD repository and custom algorithms, we are able to categorize a natural history of oxygen burden in premature infants not previously available to us or in the literature.

Demo/Poster Presenter

Nimrod Goldshtrom

Postdoctoral Clinical Fellow in Pediatrics, College of Physicians and Surgeons

Nimrod is a third year clinical fellow in neonatology at the medical center. His research is currently on using non-invasive monitoring on preterm infants specifically looking at associations with feeding and whether near-infrared spectroscopy can be used to predict disease. He is... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Cardiovascular Disease Risk And Birth Month? Discovering and Replicating Novel Birth Month Associations [P3]

An individual’s birth month has a significant impact on their lifetime disease risk. Previous studies reveal relationships between birth month and several diseases including asthma, attention deficit hyperactivity disorder, and myopia, leaving most diseases completely unexplored. We developed a hypothesis-free method called SeaWAS (Season-Wide Association Study) that systematically investigates disease-birth month patterns across all conditions. Our algorithm was first applied at Columbia University Medical Center on a dataset containing 1,749,400. We found that 9 cardiovascular conditions were significantly associated with birth month (Jan-Apr), which had never been reported previously. External validation of these findings was performed at Mount Sinai Hospital (MSH, N=1,169,599) also located in New York City (same climate). Seven of the nine conditions revealed at CUMC had significantly correlated birth month – disease risk patterns at MSH. We also explore relationships between biological mechanisms underlying these novel relationships including maternal flu infection and serum vitamin D levels.

Demo/Poster Presenter

Mary Regina Boland

PhD Candidate in Biomedical Informatics, College of Physicians and Surgeons

As a PhD student in the laboratory of Dr. Nicholas Tatonetti, Boland aims to explore and understand the systems-level genetic interactions that occur in patients taking multiple drugs targeting diverse genes and gene pathways. In particular, her focus is to develop methods for... Read More →

Demo/Poster Collaborator

Joel Dudley

Assistant Professor of Genetics and Genomic Sciences; Director of Biomedical Informatics, Icahn School of Medicine at Mount Sinai

Dr. Dudley is currently Assistant Professor of Genetics and Genomic Sciences and Director of Biomedical Informatics at the Icahn School of Medicine at Mount Sinai. He also directs the newly formed Harris Center for Precision Wellness at Mount Sinai. Prior to Mount Sinai, he held... Read More →

Li Li

Assistant Professor, Columbia University

Li is an Assistant Professor with over 10 years experience in clinical research and translational bioinformatics involving genetic and clinical risk factor identification, diagnostic assay development, and new therapeutic targets discovery. Her expertise is in translational research... Read More →

Riccardo Miotto

Data Scientist, Icahn School of Medicine at Mount Sinai

Riccardo's research interests encompass the design of algorithms for information retrieval, machine learning, and data mining applied to real-world data collections.He is currently focusing on machine learning applied to clinical data for personalized medicine. His previous... Read More →

Nicholas Tatonetti

Assistant Professor, Biomedical Informatics

Assistant Professor of Biomedical Informatics Department of Biomedical Informatics, Columbia Initiative for Systems Biology, & Department of Medicine at Columbia University Research Specialty: Translational bioinformatics, machine learning, observational data mining, combinatorial... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Health Analytics

2:00pm EDT

Central Clearing: On the Regulation of Collateral Requirements [P25]

Clearinghouses operate in a highly regulated environment. Collateral requirements are subject to regulatory approval, and are critically reviewed, since collateral is viewed as the first line of protection for the systemi-cally important clearinghouse in the event of a client default. This does not, however, take away entirely the freedom of the clearinghouse to choose her preferred collat-eralization rules. Regulators often require portfolios to be “fully collateralized,” which effectively creates a floor on the level of collateral a clearinghouse can demand. Since collateral, essentially a contingent claim that pays off only when there is a default, protects clearinghouses from counterparty risk, one may argue that it may be incentive compatible for the clearinghouse to impose even higher requirements. Regulatory authorities thus need to understand the incentives of the clearinghouse, so to design policies that align the interests of the clearinghouse with those of the regulator. We design an extensive form game where the profit maximizing clearinghouse clears client trades submitted via a clearing member bank, in the absence of a regulator. We analytically characterize all subgame perfect Nash equilibria, and show that the resulting equilibrium can only involve very low or very high collateral levels, depending on the riskiness of the traded contract and the benefits that clients can capture from trading. When equilibrium collateral levels are high, the introduction of a regulatory constraint would have no effect since the clearinghouse would have chosen high collateral requirements anyway; when equilibrium collateral levels are low, regulations are necessary to protect the clearinghouse from default losses in bad states of nature.

Demo/Poster Presenter

W. Allen Cheng

PhD Candidate in Industrial Engineering and Operations Reasearch, Columbia Engineering

Wan-Schwin Allen Cheng (1988 --) is a Taiwanese-American born in Buffalo, New York. He graduated from National Taiwan University in 2010 with honors, majoring in Mathematics. After fulfilling his Taiwanese military duties, he worked as a research assistant at Academia Sinica. He then... Read More →

Demo/Poster Collaborator

Agostino Capponi

Assistant Professor of Industrial Engineering and Operations Research, Columbia Engineering

Agostino Capponi joined Columbia University's IEOR Department in August 2014, where he is also a member of the Data Science Institute. His main research interests are in the area of networks, with a special focus on systemic risk, contagion, and control. In the context of financial... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Financial and Business Analytics

2:00pm EDT

Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality [P4]

Synthetic lethality (SL) is an interaction where two nonessential genes lead to cellular inviability when knocked out simultaneously. Recent work suggests it may be useful in cancer therapy. It may also predict novel drug-drug interactions and suggest mechanisms of adverse drug reactions. Few SL interactions have been found in humans to date, but many are known in yeast. Here, we present Species INdependent TRAnslation (SINaTRA), an algorithm that predicts SL in translation between species using protein-protein interaction (PPI) networks and connectivity homology. We validate SINaTRA using S. cerevisiae and S. pombe and find that model performance improves significantly with the use of SINaTRA. In addition, SINaTRA outperforms other methods of predicting SL, including genetic homology. We then apply our model to the human PPI network. After genetic filtering using 1000 Genomes data, we provide 100 million human gene pairs with measures of their predicted synthetic lethality (SINaTRA scores)

Demo/Poster Presenter

Alexandra Jacunski

PhD Candidate in Systems Biology, College of Physicians and Surgeons

Alexandra is a PhD candidate in the Tatonetti Lab. Her research focuses on utilizing and understanding biological networks, especially in the contexts of drug-drug interactions (DDIs) and human disease. Part of her research pertains to the development of methods that allow for... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Crowd-Sourcing Data and Quality Control: OSM Roads Validation in Low-Income Countries [P13]

Launched in 2004, OpenStreetMap (OSM) is the largest collaborative project to date to create free and editable cartographic data of the world. This study aims to validate quality aspects of road features in OSM such as positional accuracy, completeness, and lineage in four West African countries. A series of test diagnostics integrates statistical and spatial analysis to measure positional accuracy at road intersections against satellite imagery from Google Earth; completeness via discrete classification, spatial regression, and inter-settlement connectivity analyses; and finally, lineage by comparing versioning of road features against positional accuracy results. All the analysis is conducted in R and ArcGIS. This work was completed by CIESIN, a unit of the Earth Institute at Columbia University, under the CODATA Task Group for Global Roads Data Development.

Demo/Poster Presenter

Paola Kim-Blanco

Senior Staff Associate, Earth Institute, Center for International Earth Science Information Network

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Describing High-Order Statistical Dependence Using "Concurrence Topology,” with Application to Functional MRI Brain Data [P21]

We propose a new nonparametric method, "Concurrence Topology (CT)", for describing dependence among dichotomous variables. CT starts by translating the data into a "filtration", i.e., a series of shapes. Holes in the filtration correspond to relatively weak or negative association among the variables. CT uses computational topology to describe the pattern of holes in the filtration. CT is able to describe high-order dependence while avoiding combinatorial explosion. We employed CT to investigate brain functional connectivity based on dichotomized functional MRI data. The data set includes subjects diagnosed with ADHD and healthy controls. In an exploratory analysis, working in both the time and Fourier domains, CT found a number of differences between ADHD subjects and controls in the topology of their filtrations. A paper based on this work has been published.

Demo/Poster Presenter

Steven Ellis

Associate Professor of Clinical Neurobiology in Psychiatry, College of Physicians and Surgeons

Steven P. Ellis, Ph.D. is Associate Prof. of Clinical Neurobiology (in Psychiatry). He is Director, Statistics and Computing Core, Conte Center: Neurobiological and Developmental Antecedents to Suicidal Behavior: The Neurobiology of Suicidal Behavior (CCNDASB). He is also PI on... Read More →

Demo/Poster Collaborator

Arno Klein

Director of Neuroimaging and of SIMPL(E) Principal Scientist of Systems Biology, Sage Bionetworks

Arno's current research focuses on the analysis of mobile health sensor data and brain image data. He was the scientific lead on the mPower study that tracks symptoms of Parkinson disease by collecting voice, accelerometer, and tapping data, and am completing work on the mhealth... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Foundations of Data Science

2:00pm EDT

Developing High Performance Green Infrastructure System to Sustain Coastal Cities: A RESPONSIVE WEBSITE FOR CITIZEN-BASED BIOSWALE MONITORING [P14]

BIOSMILES is a smart site that allows residents of NYC to monitor the conditions of green infrastructure in their neighborhood. This tool comprises two components: a physical installation that includes a QR code, and a responsive platform available for free to all interested stakeholders. By using mobile devices, users are asked to input images or data that become instantly visible to the community and scientific cohort related to the site, including academic researchers, local authorities and representatives, and maintenance crew staff members. City agencies can address problems related to vandalism, health of vegetation, and additional detriments due to the presence of garbage or other solid waste. Geocoded data inform researchers and planners for site specific improvements and further expansion of the system. Community benefits include increased knowledge of vital aspects of environmental sustainability and strengthened social cohesion through a shared network to improve new distributed infrastructure approaches.

Demo/Poster Presenter

Daniel Laimer do Carvalhal da Câmara Machado

Urban Design Lab Analysis Assistant, Earth Institute

Originally from Sao Miguel, Portugal, Daniel has an educational background in Landscape Architecture and Urban Design. He pursued undergraduate studies as a Landscape Architect at both the Pennsylvania State University and the Danish Institute for Studies Abroad, affording him opportunities... Read More →

Filiberto Viteri Chavez

Urban Design Lab Spatial Analysis Associate, Earth Institute

Filiberto Viteri is a licensed architect in Ecuador, having graduated with a professional Degree in Architecture from the Catholic University of Santiago de Guayaquil in 2006. He received his Master of Architecture at the University of Illinois at Urbana Champaign with a Fulbright... Read More →

Maria Paola Sutto

Program and Communication Manager, Earth Institute, Urban Design Lab

Maria Paola Sutto is a biologist and a journalist. Her research interests focus on environmental impacts at different scales, from molecular markers to the organized urban systems that allow human species to develop. She moved to the United States in 1992 as foreign correspondent... Read More →

Richard Plunz

Professor of Architecture, Planning and Preservation, Graduate School of Architecture, Planning and Preservation

Richard Plunz is a leading figure in urban design and one of the world’s leading authorities in urban housing: His Housing Studios, which he developed at Columbia are now an integral part of architectural curricula everywhere. Plunz moved to Columbia University in 1974 and in 1977... Read More →

Demo/Poster Collaborator

Linda Cox

Executive Director, Bronx River Alliance

Patricia Culligan

Associate Director and Professor of Civil Engineering and Mechanical Engineering, Data Science Institute

A leader in the field of water resources and urban sustainability, Culligan has worked extensively with The Earth Institute's Urban Design Lab at Columbia University to explore novel, interdisciplinary solutions to the modern day challenges of urbanization, with a particular emp... Read More →

Stuart Gaffin

Research Scientist, Earth Institute, Center for Climate Systems Research

Matthew Palmer

Senior Lecturer in Discipline of Ecology, Evolution and Environmental Biology, Columbia University

John Squires

Research Staff Assistant , Geospatial Applications Division, Columbia University

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Smart Cities

2:00pm EDT

Digital Matatus: Mapping Informal Mini-Bus Routes in African Cities [P15]

In many of the world's growing cities, semi-formal buses form the basis of public transit. However, little open and standardized data exist on these systems. The Digital Matatus project set out to test whether the geo-locative capabilities of mobile technology could be used to collect data on a semi-formal transit system in Nairobi, Kenya and whether that data could be translated into the General Transit Feed Specification (GTFS) standard for wider use. We were able to do this and get the first GTFS data for these systems upon Google Maps making the transit app available for Nairobi. Results of this work show that mobile technologies, which are increasingly prevalent in developing countries, can be used effectively to collect and deliver data in a modified GTFS format for semi-formal transit. Perhaps more importantly, through our work in Nairobi, we were able to identify the benefits and technical needs for developing data on semi-formal transit.

Demo/Poster Presenter

Jacqueline Klopp

Associate Research Scholar, Earth Institute, Center for Sustainable Urban Development

Jacqueline Klopp is an Associate Research Scholar at the Center for Sustainable Urban Development at Columbia University. She previously taught the politics of development at the School of International and Public Affairs for many years. A political scientist by training, her... Read More →

Demo/Poster Collaborator

Adam White

Sarah Williams

Assistant Professor, MIT Civic Data Design Lab

Sarah Williams is currently an Assistant Professor of Urban Planning and the Director of the Civic Data Design Lab at Massachusetts Institute of Technology’s (MIT) School of Architecture and Planning School. The Civic Data Design Lab works with data, maps, and mobile technologies... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Drone Based Preventive Surveillance Techniques for Vector-Borne Diseases with a Focus on the Zika Virus [P5]

With a particular focus on the Zika Virus, we propose the usage of drone-based preventive health surveillance techniques for vector-borne diseases. Using a combination of image streams (from a drone-mounted camera) and localized sensor information, this project aims to identify potential mosquito breeding grounds. Thermal images superimposed on RGB camera images are used in conjunction with geotagged information to help pinpoint a precise site. Using Image and Video Processing we intend to detect stagnant water bodies that contribute to the spread of mosquitoes. Quality assessment of images is done to reject images with noise and distortion. Ambient conditions are correlated with the results of Image Processing and a probability distribution is used to generate heat map visualization of the mosquito prevalent regions at a city level. An iOS App serves as a visualization tool.

Demo/Poster Presenter

Aditya Bagri

MS Student in Electrical Engineering, Columbia Engineering

Aditya Bagri received his B.E. degree in Electronics & Telecommunication engineering from the University of Mumbai, in 2015. His interests lie in the field of Internet of Things and Data Analytics, primarily employing Smarter Technology and using Big Data in the world of sports.

Doyun Kim

PhD Candidate in Electrical Engineering, Columbia Engineering

Doyun Kim received his B.S. degree in electrical engineering from POSTECH( Pohang University of Science and Technology), South Korea, in 2013. He is currently pursuing a Ph.D. degree in Electrical Engineering at Columbia University, New York. His research interests include low-power... Read More →

Maanit Mehra

MS Student in Electrical Engineering, Columbia Engineering

Maanit Mehra is a graduate student in the Electrical Engineering department at Columbia University, New York. Having worked in the space of embedded system design, he is currently pursuing his interests in the areas of robotics, smarter machines & the Internet-of-Things.

Palash Matey

MS Student in Electrical Engineering, Columbia Engineering

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

From Players to Teams: Basketball Match Simulation [P26]

Conventional approaches to simulate matches have ignored that in basketball the dynamics of ball movement is very sensitive to the lineups on the court and unique identities of players on both offense and defense sides. In this work, we propose the simulation infrastructure that can bridge the gap between player identity and team level network. We model the progression of a basketball match using a probabilistic graphical model. We model every touch and event in a game as a sequence of transitions between discrete states. We treat the progression of a match as a graph, where each node is a network structure of players on the court, their actions, events, etc., and edges denote possible moves in the game flow. Our results show that either changes in the team lineup or changes in the opponent team lineup significantly affects the dynamics of a match progression.

Demo/Poster Presenter

Min-hwan Oh

PhD Candidate, Columbia University

Suraj Keshri

PhD Candidate in Industrial Engineering and Operations Reasearch, Columbia Engineering

Demo/Poster Collaborator

Garud Iyengar

Professor of Industrial Engineering and Operations Research and Department Chair, Columbia Engineering

Professor Garud Iyengar joined Columbia University’s Industrial Engineering and Operations Research Department in 1998. Professor Iyengar teaches courses in simulation and optimization. Professor Garud Iyengar’s research interests include convex optimization, robust optimization... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Generation of Synthetic Spatially Embedded Power Grid Networks [P23]

The development of algorithms for enhancing the resilience and efficiency of the power grid requires evaluation with topologies of real transmission networks. However, due to security reasons, such topologies and particularly the locations of the substations and lines are usually not publicly available. Therefore, we study the structural properties of the North American grids and present an algorithm for generating synthetic spatially embedded networks with similar properties to a given grid. The algorithm uses the Gaussian Mixture Model (GMM) for node density estimation and generates nodes with similar spatial distribution to the nodes in a given network. Then, it uses two procedures, which are inspired by the historical evolution of the grids, to connect the nodes. The algorithm has several tunable parameters that allow generating grids similar to any given grid. We apply it to the Western Interconnection (WI) and to grids that operate under the SERC Reliability Corporation (SERC) and the Florida Reliability Coordinating Council (FRCC), and show that the generated grids have similar structural and spatial properties to these grids. To the best of our knowledge, this is the first attempt to consider the spatial distribution of the nodes and lines and its importance in generating synthetic grids.

Demo/Poster Presenter

Gil Zussman

Associate Professor of Electrical Engineering, Columbia Engineering

Gil Zussman received the B.Sc. degree in Industrial Engineering and Management and the B.A. degree in Economics (both summa cum laude) from the Technion – Israel Institute of Technology in 1995. He received the M.Sc. degree (summa cum laude) in Operations Research from Te... Read More →

Saleh Soltan

PhD Candidate in Electrical Engineering, Columbia Engineering

Saleh Soltan is a Ph.D. student in the department of Electrical Engineering at Columbia University. He received B.S. degrees in Electrical Engineering and Mathematics (double major) from Sharif University of Technology, Iran in 2011 and the M.S. degree in Electrical Engineering from... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Cybersecurity

2:00pm EDT

Geographic Patterns for Social Network Users [P31]

This paper studies social network users’ geographic patterns by comparing Twitter and Flickr’s user distributions through the New York City. Twitter users’ locations are more widely-spread across the city than Flickr photos’ locations. Cluster patterns are very obvious for Flickr photos by looking at specific famous landmark sites. By aggregating public available data from both social networks onto census tract level, this research analyzes the effects of the number of landmark sites and population on the number of Flickr photos and tweets. The point clusters and regression results suggest that population tends to have positive relationship with tweet counts and negative relationship with photo counts on Flickr, while the number of landmark sites have much larger positive effect on Flickr photo counts than on tweet counts. The study discusses both the similarities and differences between the distribution patterns of the two social networks based on several different methods.

Demo/Poster Presenter

Yaran Fan

MS Student in Data Science, Data Science Institute

With 3-years of industry experience focusing on data analysis and customer insights, Yaran Fan is currently pursuing a MS degree in Data Science at Data Science Institute, Columbia University.

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

GPU-Based Deep Learning Inference: An Application to Identyfing Diseases Based on Medical Images [P22]

The advent of deep learning has revolutionized the field of computer vision and machine perception. Deep learning technology combines artificial neural network architectures with massive computing power to perform training and inferences. On a high level, deep learning involves a two stage process: First a neural network is trained by tuning its numeric weights based on experience - i.e. determining its parameters using labeled examples of inputs and desired output. This makes neural nets adaptive to inputs and capable of learning. Second, the network is deployed to run inferences i.e. using its previously trained parameters to classify, recognize, and generally process unknown but similar inputs.[2] GPUs massively parallel capabilities and energy efficiency makes them the state of the art in training deep neural networks as opposed to more traditional CPU based platforms.

Demo/Poster Presenter

Jeet Raut

Co-founder and CTO, Behold.ai, Inc

Jeet is the Co-founder and CEO of Behold.ai Behold.ai’s medical software uses artiﬁcial intelligence to identify abnormalities in medical images, to help healthcare practitioners rapidly, accurately and consistently make better medical decisions. Behold.ai are proud graduates... Read More →

Demo/Poster Collaborator

Peter Njenga

Cofounder and CTO, Behold.ai, Inc

Peter is a founder of behold.ai. He's an alumnus of Columbia University and University of California at Berkeley. He Previously worked at Facebook on machine learning for place search ranking and on messaging infrastructure. He also worked at Intel and AMD on chip verification. He... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Grandet: A Unified, Economical Object Store for Web Applications [P27]

Web applications are getting ubiquitous every day because they offer many useful services to consumers and businesses. Many of these web applications are quite storage-intensive. Cloud computing offers attractive and economical choices for meeting their storage needs. Unfortunately, it remains challenging for developers to best leverage them to minimize cost. This poster presents Grandet, a storage system that greatly reduces storage cost for web applications deployed in the cloud. Grandet provides both a key-value interface and a file system interface, supporting a broad spectrum of web applications. Under the hood, it supports multiple heterogeneous stores, and unifies them by placing each data object at the store deemed most economical. We implemented Grandet on Amazon Web Services and evaluated Grandet on a diverse set of four popular open-source web applications. Our results show that Grandet reduces their cost dramatically, and it is fast, scalable, and easy to use.

Demo/Poster Presenter

Gang Hu

PhD Candidate in Computer Science, Columbia Engineering

Gang Hu is a fifth year PhD student in computer science. His research is focused on software reliability and privacy.

Yang Tang

PhD Candidate in Computer Science, Columbia Engineering

Yang Tang is a Ph.D. student in the Department of Computer Science at Columbia University, working with Prof. Junfeng Yang. He received his M.Phil. degree in Computer Science and Engineering from the Chinese University of Hong Kong in 2011, and his B.Eng. degree in Computer Science... Read More →

Demo/Poster Collaborator

Lingmei Weng

Computer Science Student, Columbia University

Junfeng Yang

Associate Professor of Computer Science, Columbia University

Xinhao Yuan

Computer Science Student, Columbia University

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Financial and Business Analytics

2:00pm EDT

Identifying Novel Drug Interactions Using Data Science: Drug Interaction Prediction Using Latent Signals and EHRs (DIPULSE) [P6]

Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia called Torsades de Pointes (TdP). Over 40 drugs with both cardiac and non-cardiac indications have been found to increase risk for TdP, but drug-drug interactions contributing to LQTS (QT-DDIs) remain poorly characterized. Traditional methods for mining observational healthcare data are poorly equipped to detect QT-DDI signals due to low reporting numbers and a lack of direct evidence for LQTS. We developed an integrative data science pipeline that effectively circumvents these limitations by identifying latent signals for QT-DDIs in the FDA’s Adverse Event Reporting System and retrospectively validating these predictions using electrocardiogram data in electronic health records. The method generated 8 novel QT-DDIs. We experimentally validated one of these pairs – lansoprazole (proton-pump inhibitor) and ceftriaxone (cephalosporin antibiotic) – using patch-clamp electrophysiology to assess block of the hERG channel (IKr current).

Demo/Poster Presenter

Tal Lorberbaum

PhD Candidate in Physiology and Cellular Biophysics, College of Physicians and Surgeons

Tal Lorberbaum is a 3rd year PhD candidate in the lab of Nicholas Tatonetti at Columbia University Medical Center. Tal's work focuses on developing computational approaches to analyze large clinical and biological datasets for the purpose of predicting and understanding adverse effects... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Intelligent Wireless Charging for Electric Buses in Smart City [P16]

The auto industry is amidst a technological transformation to identify alternative sources of energy to power vehicles due to environmental factors. We examine the Metropolitan Transit Authority (MTA) buses since they operate in the city on a continuous cycle with increased coverage during peak transit times. We focus on the B63 bus route and perform a feasibility study to determine primarily whether wireless charging at specifically designated bus stops throughout the city can help to increase the feasibility of electric buses for city use both from an operational standpoint. We propose a framework that consists of a probabilistic model to capture the nature of the data and formalizing the feasibility study as an optimization problem. Using this framework we utilize the history of the system and the properties of the technology to find suitable locations for electric chargers without disrupting the operation of the system.

Demo/Poster Presenter

Albert Boulanger

Senior Staff Associate, Center for Computational Learning Systems

Albert Boulanger (M.S. CS Univ. of Illinois 1983) has been at CU since 1994 and is Senior Staff Associate with the Center for Computational Learning Systems (CCLS) of Columbia University. Prior to that, Albert was a research scientist at Bolt, Beranek and Newman. Albert has played... Read More →

Antonius Dieker

Associate Professor of Industrial Engineering and Operations Research, Columbia Engineering

Ton Dieker earned a master's degree in Operations Research from the Vrije Universiteit Amsterdam in 2002 and a PhD degree in Mathematics from the University of Amsterdam in 2006. His research interests include stochastic models and computer simulation techniques. Honors include the... Read More →

Hooshmand Shokri Razaghi

PhD Candidate in Computer Science, Columbia Engineering

Promiti Dutta

Ph.D Student, Computer Science, Columbia University

Promiti Dutta holds a B.S. in chemical engineering, a master's degree in public health in molecular toxicology and epidemiology, and an M.S. in electrical engineering from Columbia University. She is currently a Ph.D. candidate in computer sciecne at Columbia. Her research interests... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Interactive Visualizations for Monoscopic Eyewear to Assist in Manually Orienting Objects in 3D [P17]

Assembly or repair tasks often require objects to be held in specific orientations to view or fit together. Research has addressed the use of AR to assist in these tasks, delivered as registered overlaid graphics on stereoscopic head-worn displays. In contrast, we are interested in using monoscopic head-worn displays, such as Google Glass. To accommodate their small monoscopic field of view, off center from the user's line of sight, we are exploring alternatives to registered overlays. We describe four interactive rotation guidance visualizations for tracked objects intended for these displays.

Demo/Poster Presenter

Barbara Tversky

Professor of Psychology and Education, Teachers College

Barbara Tversky is Professor of Psychology and Education at Columbia Teachers College and Professor Emerita of Psychology at Stanford University. She has done basic research on memory, categorization, spatial language and thinking, event perception and cognition with applications... Read More →

Carmine Elvezio

Staff Associate in Computer Science, Columbia Engineering

Carmine Elvezio is a staff associate in the Computer Graphics and User Interfaces Lab at Columbia University. His research interests include interaction and visualization techniques in augmented and virtual reality and system design of real-time interactive 3D graphics applications... Read More →

Mengu Sukan

PhD Candidate in Computer Science, Columbia Engineering

Mengu Sukan is a PhD student working with Steven Feiner in the Computer Graphics and User Interfaces Lab at Columbia University. In his research, he designs and evaluates novel 3D visualizations and interaction techniques focusing on augmented reality (AR) for task assistance... Read More →

Steven Feiner

Professor of Computer Science, Columbia Engineering

Steven Feiner is professor of computer science at Columbia Engineering, where he directs the Computer Graphics and User Interfaces Lab and co-directs the Columbia Vision and Graphics Center. His interests include human–computer interaction, augmented reality and virtual environments... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Mapping Cells and Their Connections in Networks of the Brain Master Clock [P7]

Understanding the neural networks associated with specific brain functions is a challenge. The brain’s master clock, located in the suprachiasmatic nucleus (SCN), and made up of ~20,000 “clock cells”, presents an ideal system for exploring the relationship of cellular/molecular events to inter-connections among cells, and how these networks sustain the body’s daily rhythms. At the intracellular level, rhythmicity is produced by feedback loops involving daily rhythms in expression of “clock” genes and associated proteins. To determine whether or not all SCN cells are identical in their intracellular/molecular clocks, we designed programs to assess protein expression levels over days, over time of day, and in location of individual cells within the nucleus, using Mathematica. Our novel approach to analyzing neural networks yields evidence of both regional and cellular specialization of clock protein expression. The findings point to a new mechanism for encoding information in SCN networks.

Demo/Poster Presenter

Erica Mezias

Research Assistant, Cognitive Development Center, Barnard College

Erica Mezias is a senior at Barnard College majoring in Psychology. She is currently working on a senior thesis in Neuroscience and Behavior in Dr. Rae Silver's Neurobiology lab where she investigates the molecular basis of circadian rhythms using novel computational methods... Read More →

Malini Riddle

Undergraduate Research Assistant, Neuroscience & Behavior, Barnard College

Malini is a junior at Barnard College majoring in cellular neuroscience. She works as a research assistant at Dr. Rae Silver's Neurobiology Lab at Barnard, where she studies circadian rhythms using quantitative computational methods that she helps to develop. Malini has experience... Read More →

Demo/Poster Collaborator

Duncan Foley

Department of Economics, New School for Social Research

Joseph Lesauter

Senior Research Scientist in Psychology, Barnard College

Rae Silver

Helene L. and Mark N. Kaplan Professor of Natural and Physical Sciences, Columbia University

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Mining the Performance History of the New York Philharmonic from 1842-2015: Programming Trends and Performer Networks [P32]

The recent release into the public domain of historical concert program data in XML format by the New York Philharmonic affords the opportunity of diachronic analysis of musical practice in the city, as reflected in the selected pieces performed and records of the participating performers themselves. First, time-series clustering of performance volume (number of performances per annum) over time factored on musical works (pieces) and their composers is performed to model how new music has been adopted by the ensemble. The orchestra's discovery and adoption of music can be characterized as a Fourier decomposition of the performance volume time-series. Then, nodes on a social network are given by the population of performers named in the database; edges between two nodes indicate a collaborative performance. The interactions between these adoption profiles and their performers are studied and general trends in the musical performance history of the New York Philharmonic are inferred.

Demo/Poster Presenter

Eamonn Bell

PhD Candiate in Music Theory, Graduate School of Arts and Sciences

Eamonn Bell graduated from Trinity College, Dublin with a B.A. (Mod.) Mathematics and Music in June 2013. He was awarded the University Gold Medal for performance in the degree examinations. He was awarded the Geoffrey Singleton Prize and the Mahaffy Memorial Prize for "an essay... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags New Media

2:00pm EDT

Model-Based Dashboards for Customer Analytics [P28]

Automating the customer analytics process is crucial for companies managing distinct customer bases. In dynamic spending environments, visualization plays a key role in understanding events of interest. These ideas have led to the real world popularity of analytics dashboards, a tool that has received scant attention from academics. In this work, we develop a nonparametric framework for understanding individual-level spending using Gaussian process priors over latent functions that describe customer spending along calendar, interpurchase, and customer lifetime dimensions. These curves form a dashboard that provides a visual representation of purchasing dynamics. The model flexibly and automatically captures the impact of events that influence spend propensity, even when these events are unknown a-priori. We illustrate the use of our model on spending data from two popular mobile games, and show that it generalizes common customer base analysis models, leading to better performance both in fitting and forecasting spending incidence.

Demo/Poster Presenter

Ryan Dew

PhD Candidate in Marketing, Columbia Business School

Ryan is a third year doctoral student in the Marketing Division of Columbia University's Graduate School of Business with a focus on Quantitative Marketing. His research focuses on developing and applying methods from Bayesian econometrics and machine learning to problems in marketing analytics. He graduated from the Universi... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Personal Energy Footprinting [P18]

Recent advancements in energy monitoring technologies have contributed hugely to developing smart and energy-efficient buildings. However, existing solutions do not provide enough granularity to generate sufficient information about the real-time effect of an occupant’s personal actions on the overall energy consumption of a building. Extending the functionality of currently existing indoor localization and building energy monitoring techniques, our platform tracks each individual’s energy consumption in a shared environment, thus providing the user with visibility into his or her real-time energy footprint. The system we have implemented facilitates accountability of energy usage in commercial buildings, while simultaneously utilizing that information to provide actionable feedback and historical understanding for the occupants as well as building managers, ensuring that they can act appropriately in a timely manner.

Demo/Poster Presenter

Richa Glenn Netto

MS Student in Electrical Engineering, Columbia Engineering

Richa Netto received her B.E. degree in Computer Engineering from the University of Mumbai in May 2015, and is currently pursuing a Masters in Electrical Engineering from Columbia University. Her primary areas of interest include Embedded Systems Design and the application of the... Read More →

Rishikanth Chandrasekaran

MS Student in Computer Engineering, Columbia Engineering

Rishikanth finished his Bachelors in Electrical and Electronics Engineering in India and is currently pursuing his Masters in Computer Engineering at Columbia University. He is currently part of the Intelligent and Connected Systems Laboratory at Columbia University as a researcher... Read More →

Demo/Poster Collaborator

Xiaofan (Fred) Jiang

Assistant Professor of Electrical Engineering, Columbia Engineering

Xiaofan (Fred) Jiang is an Assistant Professor in the Electrical Engineering Department at Columbia University. Fred received his B.Sc. (2004) and M.Sc. (2007) in Electrical Engineering and Computer Science, and his Ph.D. (2010) in Computer Science, all from UC Berkeley. Before... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Preventing Code-Reuse Attacks with Instruction-Set Randomization [P24]

Instruction Set Randomization (ISR) was proposed in the last decade as a countermeasure against code injection attacks, where attackers introduce new code into the system as a step towards gaining control. Conventional wisdom is that ISR has lost its relevance due to the prevalence of code-reuse attacks, , a newer and harder threat, wherein the attackers stitches together existing code towards the same end. Code-injection no longer remains a critical component in typical contemporary attacks. In this work, we show that ISR is ineffective even against code-injection but (surprisingly) can be relevant against code-reuse attacks. However, to provide this capability, ISR needs to satisfy additional properties, specifically strong encryption, not found in older ISR implementations. We implement a new ISR system, called Polyglot, on a SPARC32-based Leon3 FPGA system that runs Linux. We show that it incurs very low performance overhead (approx. 6% for SPEC CPU benchmarks), while defending against ROP attacks and allowing critical features like page-sharing. Additionally, we argue that for threat models used by previous work, our scheme incurs no overhead on modern systems.

Demo/Poster Presenter

Kanad Sinha

PhD Candidate in Computer Science, Columbia Engineering

Demo/Poster Collaborator

Vasileios Kemerlis

PhD Candidate in Computer Science, Columbia Engineering

Angelos Keromytis

Associate Professor of Computer Science, Columbia Engineering

Simha Sethumadhavan

Associate Professor of Computer Science, Columbia University

Simha Sethumadhavan is an Associate Professor of Computer Science at Columbia Engineering. He is the founding director of the Computer Architecture and Security Technologies Lab (CASTL) at Columbia University. Sethumadhavan’s research interests are in hardware security, hardware... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Real-Time Optical Spectroscopic Monitoring of Permanent Lesion Progression During Cardiac Radiofrequency Ablation [P8]

Despite considerable advances in guidance of radiofrequency ablation (RFA) therapies for atrial fibrillation, success rates have been hampered by an inability to intraprocedurally characterize the extent of permanent injury. Insufficient lesions can elusively create transient conduction blockages that eventually re-conduct. Prior studies suggest significantly greater met-myoglobin concentrations (ctMmb) in the lesion core than in the healthy myocardium and may serve as a marker for irreversible tissue damage. In this work, we present real-time monitoring of permanent injury through spectroscopic assessment of ctMmb at the catheter tip. A commercial RFA catheter was modified accept illumination and collection fiber pairs to acquire real-time optical measurements throughout RF energy delivery. Dynamic changes in ctMmb were recovered from near-infrared diffuse reflectance spectra using a two-step inversion routine. A robust correlation (r=0.88,P<1e-7) was observed between extracted tissue ctMmb and the extent of tissue injury. These results support the use of spectroscopy-facilitated guidance of RFA procedures.

Demo/Poster Presenter

Charles C. Marboe, MD

Professor of Pathology and Cell Biology, College of Physicians and Surgeons

Dr. Charles C. Marboe graduated from the Pennsylvania State University College of Medicine in 1976. He works in New York, NY and specializes in Anatomic Pathology. Dr. Marboe is affiliated with New York Presbyterian Hospital Columbia University Medical Center.

Christine P. Hendon

Assistant Professor of Electrical Engineering, Columbia Engineering

Christine Hendon is an assistant professor in the Department of Electrical Engineering. Her research interests are in developing optical imaging and spectroscopy instruments for applications in cardiac electrophysiology and interventional cardiology. Dr. Hendon received the B.S... Read More →

Rajinder Singh-Moon

PhD Candidate in Electrical Engineering, Columbia Engineering

Vivek Iyer, MD

Assistant Professor of Medicine, College of Physicians and Surgeons

Vivek Iyer, MD, MSE is a cardiac electrophysiologist, specializing in treating heart rhythm abnormalities. Dr. Iyer is boarded in Internal Medicine, Cardiovascular Disease and Clinical Cardiac Electrophysiology.

William Whang, MD

Associate Professor of Medicine, College of Physicians and Surgeons

Xinwen Yao

PhD Candidate in Electrical Engineering, Columbia Engineering

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Health

2:00pm EDT

SEUS - Audio Based Vehicle Localization [P19]

The rapid growth of commercial technologies made our environment and behavior change much faster than our body and natural skills. While thousands of years of evolution have trained us to cope with tasks that guaranteed our survival, our constant pursuit for a more confortable life introduced other unpredictable dangers in few decades. The increase in the number of automobiles together with the high demographic density of the modern cities, make the urban regions a hotspot for these hostile situations. As a solution we are introducing the SEUS (Sense Enhancement for Urban Safety), an embedded IoT system that uses multiple sensors, advance recognition algorithms, and cloud computing to scan the environment for potential treats and alert the user. The first SEUS prototype uses multiple MEMS microphones spread over the body to localize incoming vehicles.

Demo/Poster Presenter

Daniel de Godoy Peixoto

PhD Candidate in Electrical Engineering, Columbia Engineering

Daniel de Godoy Peixoto is a PhD Candidate at Columbia University . Daniel received his Electrical Engineering B.Sc. (2007) at the Universidade Federal de Pernambuco,Brazil and M.S. (2015) at Columbia University. Daniel has experience designing Analog Integrated Systems and is currently... Read More →

Peter Kinget

Professor of Electrical Engineering, Columbia Engineering

Peter R. Kinget received an engineering degree in electrical and mechanical engineering and the Ph.D. in electrical engineering from the Katholieke Universiteit Leuven, Belgium. He has worked in industrial research and development at Bell Laboratories, Broadcom, Celight and Multilink... Read More →

Xiaofan (Fred) Jiang

Assistant Professor of Electrical Engineering, Columbia Engineering

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Tax Laws and Tax Capacity: A Machine-Learning Approach [P30]

I use tools from natural language processing to construct a high-dimensional representation of tax code changes from the text of 1.6 million statutes enacted by state legislatures since 1963. A data-driven approach is taken to recover the effective tax code – the set of legal phrases in tax law that have the largest impact on revenues, holding major tax rates constant. Exogenous variation in tax legislation from judicial districts is used to capture revenue impacts that are solely due to changes in the tax code language, with the resulting phrases providing a robust out-of-sample predictor of tax collections. I then test whether political parties differ in patterns of effective tax code changes when they control state government. Relative to Republicans, Democrats use revenue-increasing language for income taxes but use revenue-decreasing language for sales taxes – consistent with a more redistributive fiscal policy – despite making no changes on average to statutory tax rates.

Demo/Poster Presenter

Elliott Ash

PhD Candidate in Economics, Graduate School of Arts and Sciences

Elliot Ash is a Ph.D. student in Economics. His research interests include Political Economy, Law and Economics, Public Economics, Applied Microeconomics, Natural Language Processing, Machine Learning.

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Financial and Business Analytics, New Media

2:00pm EDT

The Hidden Advantage of Lefties in Sports [P29]

In the modern era of professional tennis, there have been many great left-handed players. Although only 11% of the world's population is left-handed, left-handers comprise roughly 15% of professional tennis players. In sports as varied as boxing, baseball and fencing, the contrast is even more stark. Here we present a method for extracting the advantage of being left-handed in sports (as well as the inherent skill of each player) from match results, applying it to data from men’s professional tennis. Unlike previous point estimate approaches to this problem, our formulation is Bayesian and uses induced order statistics to address the truncated nature of the data set. We further demonstrate how the question can be approached in the absence of explicit match result data, outlining a methodology that can be used to determine the latent advantage of specific factors wherever there exists notions of ranking and competition.

Demo/Poster Presenter

Francois Fagan

PhD Candidate in Industrial Engineering and Operations Reasearch, Columbia Engineering

Francois Fagan started his PhD in the IEOR department in September 2013. He is currently working with Professors Martin Haugh and Garud Iyengar in the field of machine learning. Before coming to Columbia University, he attended the University of Cape Town in South Africa where he... Read More →

Hal Cooper

PhD Candidate in Industrial Engineering and Operations Reasearch, Columbia Engineering

Hal Cooper is an IEOR Ph.D. Candidate who began his Ph.D. in the fall of 2013 under adviser Garud Iyengar. Hal works on a variety of machine learning problems, particularly Bayesian inference and deep learning. His research focuses on the development of new modeling techniques for... Read More →

Demo/Poster Collaborator

Martin Haugh

Associate Professor of Professional Practice in Industrial Engineering and Operations Research, Columbia Engineering

Martin Haugh originally joined the Industrial Engineering and Operations Research Department in January 2002 after completing his PhD in Operations Research at MIT. He was a faculty member in the IEOR department until June 2005 and during this time his teaching and research focused... Read More →

Garud Iyengar

Professor of Industrial Engineering and Operations Research and Department Chair, Columbia Engineering

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

The Impact of Earthquakes on Economic Productivity [P20]

This project combines natural science data with social science data, applying an interdisciplinary approach to investigate the long-term economic impacts of earthquakes. USGS ShakeMap data is used to provide a measure for the exogenous natural hazard of earthquakes in terms of peak ground acceleration. In the past, social science research has predominantly employed inadequate measures for the natural hazard of earthquakes. Here, a data set that represents global relevant ground shaking since 1973 is constructed and used for the first time to analyze global economic impacts. This data set is combined with social productivity data on two different geographic levels. First, conventional country-level World Bank GDP data is considered and then compared with an approach that looks at a 1x1 degree grid level, by using gridded G-Econ output data. Different regression models are applied for the quantitative analysis with particular focus on the spatial nature of the data.

Demo/Poster Presenter

Stephanie Lackner

PhD Candidate in Sustainable Development, School of International and Public Affairs

Stephanie Lackner is a PhD candidate in the Sustainable Development PhD program at SIPA. Her research interest are disasters and their short and long term socio-economic impacts, particularly earthquakes and other natural disasters. She holds a Master degree in Mathematics from the... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Towards the Automatic Classification of Endomyocardial Tissues for Intracardiac OCT [P9]

Aiming to establish the relationship between ultrastructure and endomyocardial tissue types, especially diseased tissue, we develop an automated algorithm to segment and classify tissue types from intracardiac OCT images. We segmented the OCT image volumes using a graph searching method. Features are extracted and compared in each segmented region. A probabilistic model of relevance vector machine is developed to classify multiple tissue types such as scar, fibrotic myocardium, normal myocardium, endocardium, and adipose tissue. The algorithm is validated from OCT images obtaining from human heart. The tissue types are classified with a good accuracy and are visualized in three dimensions.

Demo/Poster Presenter

Yu Gan

PhD Candidate in Electrical Engineering, Columbia Engineering

Yu Gan is a Ph.D. candidate in the Department of Electrical Engineering at Columbia University. He is a research assistant in the Structure Function Imaging Laboratory under supervision of Dr. Christine Hendon. His research interest involves image processing. He is working towards... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

2:00pm EDT

Using Social Media to Detect Foodborne Disease Outbreaks [P10]

Foodborne diseases are estimated to cause 3,000 deaths per year in the United States and many of these cases are caused by restaurants. Identifying and acting on outbreaks of these foodborne diseases quickly is essential to mitigating their negative effects on public health. Although there are official channels to report incidents of food poisoning to the local health departments, often restaurant patrons instead post informal complaints about their negative experiences on social media sites, such as Yelp and Twitter. In this work we construct a social media analysis system, targeted at identifying food poisoning-related documents from multiple online noisy text sources. By tracking social media posts from NYC that contain information about food poisoning incidents, we can successfully identify foodborne illness outbreaks that would otherwise go uninvestigated. These actionable inferences are then utilized by the NYC Department of Health to alleviate the consequences of outbreaks in NYC restaurants more effectively.

Demo/Poster Presenter

Tom Effland

PhD Candidate in Computer Science, Columbia Engineering

Effland is an NSF IGERT "Data to Solutions" fellow working with Prof. Luis Gravano. He researches novel techniques for extracting actionable information on rare events from social media. He is currently working on a project finding evidence of point-source food poisoning outbreaks... Read More →

Demo/Poster Collaborator

Fotis Psallidas

PhD, Computer Science, Columbia University

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags Health Analytics

2:00pm EDT

Virtual Replicas for Remote Assistance in Virtual and Augmented Reality [P33]

In many complex tasks, a remote subject-matter expert may need to assist a local user to guide actions on objects in the local user's environment. However, effective spatial referencing and action demonstration in a remote physical environment can be challenging. We introduce two approaches that use Virtual Reality (VR) or Augmented Reality (AR) for the remote expert, and AR for the local user, each wearing a stereo head-worn display. Both approaches allow the expert to create and manipulate virtual replicas of physical objects in the local environment to refer to parts of those physical objects and to indicate actions on them. This can be especially useful for parts that are occluded or difficult to access.

Demo/Poster Presenter

Barbara Tversky

Professor of Psychology and Education, Teachers College

Carmine Elvezio

Staff Associate in Computer Science, Columbia Engineering

Mengu Sukan

PhD Candidate in Computer Science, Columbia Engineering

Steven Feiner

Professor of Computer Science, Columbia Engineering

Demo/Poster Collaborator

Ohan Oda

PhD, Computer Science, Columbia University

Ohan Oda is a Ph.D candidate in Computer Science and a member of the Computer Graphics and User Interface Lab. He is currently developing the Goblin XNA infrastructure, which is a platform for research on 3D user interfaces, including augmented reality and virtual reality, with an emphasis on games. He is broadly interested in applications of mobile augmented reality systems in the field of recreation and entertainment, including virtua... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Poster

Tags New Media