AT&T Labs Research - Loh, Ji M.

Research Interests

Spatial statistics
Anomaly detection
Spatial bootstrap
Astro-statistics
Applications in fMRI and public health

Experience

AT&T Labs Research, Principal Member of Technical Staff, 2009-Present
Columbia University, Associate Professor, 2006-2009
Statistical and Applied Mathematical Sciences Institute (SAMSI), Research Fellow, Jan - Jun 2006
Columbia University, Assistant Professor, 2001-2006
University of Chicago, Ph D, Statistics, 1997-2001
Anderson Junior College, Singapore, Teacher, 1994-1997
National Institute of Education, Singapore, Postgraduate Diploma in Education, 1993-1994
Victoria University of Wellington, New Zealand, BSc (Hons), Mathematics/Physics, 1988-1991

Publications

2011

Yau and Loh (2011), A Generalization of the Neyman-Scott Process, submitted to Statistica Sinica.

Yue and Loh (2011), Bayesian Nonparametric Intensity Estimation for Inhomogeneous Spatial Point Processes, Biometrics (to appear).

2010

Hariharan, Loh, Shanahan and Yamada (2010), Spatial Probabilistic Modeling of Calls to Businesses, ACM SIGSPATIAL 2010.

Kwate and Loh (2010), Separate and Unequal: The Influence of Neighborhood and School Characteristics on Spatial Proximity between Fast Foods and Schools, Preventive Medicine, 51, 153-156.

Loh and Jang (2010), Estimating a Cosmological Mass Bias Parameter with Semi-Parametric Bootstrap Bandwidth Selection, Journal of the Royal Statistical Society, Series C, 59, 761-779.

Loh (2010), K-Scan for Anomaly Detection in Disease Surveillance, Environmetrics (published online; to appear in print)

Yue, Loh and Lindquist (2010), Adaptive Spatial Smoothing of fMRI Images, Statistics and Its Interface, 3, 3-14.

Jang and Loh (2010), Density Estimation for Grouped Data with Application to Line Transect Sampling, Annals of Applied Statistics,

Loh (2010), Bootstrapping an Inhomogeneous Point Process, Journal of Statistical Planning and Inference, 140, 734-749.

2000-2009

Kwate, Yip, Loh and Williams (2009), Inequality in Obesigenic Environments: Fast Food Density in New York City, Health and Place, 15, 364-373.

Lindquist, Loh, Atlas and Wager (2008), Modeling the Hemodynamic Response Function in fMRI: Efficiency, Bias and Mis-modeling, NeuroImage, 45, S187-S198.

Loh, Lindquist and Wager (2008), Residual Analysis for Detecting Mismodeling in fMRI Images, Statistica Sinica, 18, 1421-1448.

Loh (2008), A Valid and Fast Spatial Bootstrap for Correlation Functions, Astrophysical Journal, 681, 726-734.

Loh (2008), Estimating Third-Order Moments for an Absorber Catalog, Astrophysical Journal, 674, 636-643

Loh and Stein (2008), Spatial Bootstrap with Increasing Observations in a Fixed Domain, Statistica Sinica, 18, 667-688.

Guan and Loh (2007), A thinned block Bootstrap Procedure for Modeling Inhomegeneous Spatial Point Patterns, Journal of the American Statistical Association, 102, 1377-1386.

Loh and Zhu (2007), Accounting for Spatial Correlation in the Scan Statistic, Annals of Applied Statistics, 1, 560-584.

Rzhetsky, Iossifov, Loh and White (2006), Microparadigms: Chains of Collective Reasoning in Publications about Molecular Interactions, Proceedings of the National Academy of Science, 103, p4930-4945.

Loh and Stein (2004), Bootstrapping a Spatial Point Process, Statistica Sinica, 14, p69-101.

Loh, Stein and Quashnock (2003), Estimating the Large-Scale Structure of the Universe using Quasi-Stellar Object Carbon IV Absorbers, Journal of the American Statistical Association, 98, p522-532.

Loh, Quashnock and Stein (2001), A Measurement of the Three-Dimensional Clustering of C IV Absorption-Line Systems on Scales of 5-1000 h-1 Mpc, Astrophysical Journal, 560, p606-612.

Stein, Quashnock and Loh (2000), Estimating the K-Function of a Point Process with an Application to Cosmology, Annals of Statistics, 28, p1508-1532.

The definitive version was published in Very Large Databases, 2012. , Volume PVLDB vol.5/VLDB 2012, 2012-08-30

{We introduce the notion of {em statistical distortion} as an essential metric for measuring the effectiveness of data cleaning strategies. We use this metric to propose a widely applicable yet scalable experimental framework for evaluating data cleaning strategies along three dimensions: glitch improvement, statistical distortion and cost-related criteria. Existing metrics focus on glitch improvement and cost, but not on the statistical impact of data cleaning strategies. We illustrate our framework on real world data, with a comprehensive suite of experiments and analyses.}

(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 13th ACM International Conference on Ubiquitous Computing , 2011-09-01.

{Understanding utilization of city roads is important for urban planners. In this paper, we show how to use cellular hand- off patterns from cellular phone networks to identify which routes people take through a city. Specifically, this paper makes the following three contributions. First, we show that cellular handoff patterns on a given route are stable across a range of conditions and propose a way to measure stability within and between routes using a variant of Earth Mover�s Distance. Second, we present two accurate classification al- gorithms for matching cellular handoff patterns to routes: one requires test drives on the routes while the other uses signal strength data collected by high-resolution scanners. Finally, we present an application of our algorithms for mea- suring relative volumes of traffic on routes leading into and out of a specific city, and validate our methods using statis- tics published by a state transportation authority.}

The definitive version was published in PURBA-2011. , 2011-06-12

{Understanding the mix of different types of people in a city is an important input into urban planning. In this paper we identify distinct sectors of a population by their cellular phone usage. In a study of a small suburban city in New Jersey, we use unsupervised clustering to identify the usage patterns of heavy users . We uncover 7 unique usage patterns. We interpret two of the patterns as belonging to commuters and students, and verify these interpretations with deeper analysis of temporal and spatial patterns. }

The definitive version was published in IEEE Pervasive Computing , 2010-04-01, URL: https://ecopyright.ieee.org/ECTT/login.jsp Username: SCHPCSI-2011-01-0005 Password: 1295115660850

{The rapid growth of modern cities leaves urban planners faced with numerous challenges, such as high congestion and pollution levels. Effectively solving these challenges re- quires a deep understanding of existing city dynamics. In this paper, we describe methodology to study and monitor these dynamics by using Call Detail Records (CDRs), rou- tinely collected by wireless service providers as part of run- ning their networks. Our methodology scales to an entire population, has little additional cost, and can be continually updated. This provides an unprecedented opportunity to study and monitor cities in a way that current practices are not able to do.}

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