Han (Hannah) Ye: Curriculum Vitae

Han (Hannah) Ye
Department of Statistics and Operations Research
University of North Carolina
Chapel Hill, NC 27599-3260
Education
Phone:
Email:
Web:
(919) 428-1583
[email protected]
http://www.unc.edu/∼hanye/
University of North Carolina at Chapel Hill
Ph.D., Interdisciplinary Statistics and Operations Research
• Adviser: Haipeng Shen
• Dissertation: Data-driven Service Operations Management
• GPA: 3.94/4.0
University of Science and Technology of China (USTC)
B.S., Applied Mathematics
• Cumulative GPA: 3.8/4.0
Major GPA: 3.9/4.0
Research Interests
Expected 2014
2008
• Applications:
data analytics, healthcare, service operations (demand forecasting, efficiency&quality
estimation, staffing, scheduling, workforce behaviors), anomaly detection
• Methodologies:
forecasting, data mining, time series, multivariate analysis, stochastic programming,
optimization
Publications and
Working Papers
• Noah Gans, Nan Liu, Avishai Mandelbaum, Haipeng Shen, Han Ye, “Service Times
in Call Centers: Agent Heterogeneity and Learning, with some Operational Consequences”. A Festschrift for Lawrence D. Brown, IMS Collections, 6 (2010), 99-123.
• Han Ye, James Luedtke, Haipeng Shen, “Forecasting and Staffing Call Centers with
Multiple Interdependent Uncertain Arrival Streams”. Submitted to Manufacturing
and Service Operations Management.
• Noah Gans, Haipeng Shen, Han Ye, Yong-Pin Zhou, “Asymptotic Stability of AR(p)Driven Workforce Scheduling Models”. In preparation for submission to Operations
Research.
• Han Ye, Haipeng Shen, Shi Zhao, “A Novel Operational-data Based Method for
Estimating Service Resolution”. In preparation.
Work in Progress
• “A Review of Call Center Forecasting”, with Rouba Ibrahim, Pierre L’Ecuyer, Haipeng
Shen. Invited for submission to International Journal of Forecasting.
• “Effectiveness of Triage Nurse Ordering on Mitigating ED Overcrowding and the
Associated Routing Policies”, with Lan Ding, Shuangchi He, Melvyn Sim.
• “Data Analytics for Surgery Sequencing Based on Skewness-aware Deviation”, with
Jin Qi, Melvyn Sim.
Patents
• Han Ye, Shi Zhao. (2011) “Methods and Systems for Detecting Unusual Patterns in
Functional Data”, U.S. patent filed.
• Han Ye, Shi Zhao. (2011) “Service Center Issue Resolution Estimation Based on
Probabilistic Model using Graphical Network”, U.S. patent filed.
Han (Hannah) Ye,
December 2013
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Awards &
Fellowships
• Excellence in Teaching Award, Department of Statistics and Operations Research,
UNC-CH, December 2012.
• GPSF Travel Award, UNC-CH, 2012.
• Graduate Student Fellowship, Statistical and Applied Mathematical Sciences Institute (SAMSI), 2012.
• Teaching Fellow, UNC-CH, spring 2010, spring 2012, spring 2013, summer 2013.
• China National First-Class Scholarship, 2007.
• Outstanding Student Scholarship, USTC, 2004 - 2006.
Professional
Experience
Research Associate
National University of Singapore Business School, Singapore Aug 2013 - Present
• Conducting empirical data analysis on hospital emergency department operations.
Modeling patients’ resource utilizations. Studying the effects of triage nurse ordering protocols and the associated routing models.
Business Analytics Intern
Xerox Research Center, Webster, NY
May 2012 - August 2012
• Manipulated and analyzed call center data of 20 million call logs.
• Developed a new operational-data based method for estimating service resolution.
Performed descriptive and predictive statistical modeling for agents’ service resolution rates and service times.
• Methods: mixed models, clustering, probabilistic graphical network, sensitivity analysis, variable selection, mixture models.
Business Analytics Intern
Xerox Research Center, Webster, NY
May 2011 - August 2011
• Developed anomaly detection methods to identify unusual agent behaviors in customer service center. Performed predictive modeling to explore driving factors for
service efficiency and customer satisfaction.
• Methods: anomaly detection, robust PCA, smoothing, nonparametric estimation,
variable selection.
Student Consultant
UNC Statistical Consulting Center, Chapel Hill, NC
August 2009 - May 2010
• Provided consulting service to clients from UNC Medical School and Physics Department.
• Methods: logistic regression, variable selection, smoothing, robust regression.
Teaching
Experience
Sole Instructor
University of North Carolina - Chapel Hill
STOR155 - Introduction to Applied Statistics
• Undergraduate course in basic statistics and data analysis.
• Recent student evaluation: 4.6/5.0 from 43 students.
• Received Excellence in Teaching Award in 2012.
SP10,SP12,SP13,SS13
Lecturer
Statistical and Applied Mathematical Sciences Institute (SAMSI), Research Triangle
Park, NC
Undergraduate Workshop in Statistics and Applied Math
February 2013
• Tutorial lecturer in Computer Simulation and ARENA Software.
Han (Hannah) Ye,
December 2013
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Presentations
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Skills
• Computer skills
Programming language: SQL, Python, C
Software:
R, SAS, Matlab, Mathematica, Arena, JMP
Text formatting:
LATEX, MS Office
Operating system:
Windows, Linux
• Coursework
Statistical Inference, Applied Statistics i&ii, Measure and Integration, Probability,
Bayesian Statistics, Statistical Consulting, Nonparametric Smoothing and Functional
Data Analysis, Object Oriented Data Analysis, Time Series and Multivariate Analysis, Stochastic Models in Operations Research i&ii, Linear Programming, Dynamic
Programming and Optimal Control, Discrete Event Simulation, Design and Control
of Queueing Systems, Operations Research Methods in Healthcare
References
INFORMS Annual Meeting, Minneapolis, MN, October 2013. (Invited)
INFORMS Annual Meeting, Phoenix, AZ, October 2012. (Invited).
Joint Statistical Meeting, San Diego, CA, August 2012.
Data Driven Decisions in Healthcare, Opening Workshop, Statistical and Applied
Mathematical Science Institute (SAMSI), August 2012. (Poster).
• A conference at the Wharton school in honor of L. D. Brown, Philadelphia, December
2010. (Poster).
Haipeng Shen
Associate Professor (dissertation advisor)
Department of Statistics and Operations Research
University of North Carolina, Chapel Hill, NC 27599
Email: [email protected]
Phone: (919) 962-1358
Noah Gans
Joel S. Ehrenkranz Family Professor
Operations and Information Management Department
The Wharton School
University of Pennsylvania, Philadelphia, PA 19104
Email: [email protected]
Phone: (215) 573-7673
Serhan Ziya
Associate Professor
Department of Statistics and Operations Research
University of North Carolina, Chapel Hill, NC 27599
Email: [email protected]
Phone: (919) 843-6022
Melvyn Sim
Professor
Department of Decision Sciences
NUS Business School
Singapore 119245
Email: [email protected]
Phone: +65 6516-6274
Han (Hannah) Ye,
December 2013
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Paper Abstracts • Han Ye, James Luedtke, Haipeng Shen, “Forecasting and Staffing Call Centers with
Multiple Interdependent Uncertain Arrival Streams”. Under review at Manufacturing & Service Operations Management.
Abstract: We consider forecasting and staffing call centers with multiple interdependent uncertain arrival streams. We first develop general statistical models that
can simultaneously forecast multiple-stream arrival rates that exhibit inter-stream
dependence. The models take into account several types of inter-stream dependence. With distributional forecasts, we then implement a chance-constraint staffing
algorithm to generate staffing vectors and further assess the operational effects of
incorporating such inter-stream dependence, considering several system designs.
Experiments using real call center data demonstrate practical applicability of our
proposed approach under different staffing designs. An extensive set of simulations
is performed to further investigate how the forecasting and operational benefits of
the multiple-stream approach vary by the type, direction, and strength of interstream dependence, as well as system design. Managerial insights are discussed
regarding how and when to take advantage of the inter-stream dependence operationally.
• Han Ye, Haipeng Shen, Shi Zhao, “A Novel Operational-data Based Method for Estimating Service Resolution”.
Abstract: We consider issue resolution probability as the proxy for service quality.
A traditional method for evaluating issue resolution is to conduct customer surveys
or to monitor the service process. Survey driven methods suffer from severe reliability issues due to voluntary responses while the collection process can be expensive;
service monitoring requires hiring an additional group of qualified people evaluating, which is costly and subjective. We propose a novel operational-data driven
method to estimate issue resolution probability. In particular, we model a graphical
probability network to mimic the process of a customer’s psychological behavior
during a service pursuance and build the relationship between agents’ issue resolution probability and customers’ call backs. Our estimation method only requires
operational data that are either readily accessible or very cheap to obtain. Using a
large real data set which contains millions of service logs, we compare our estimates
with survey estimates and show the superiority of our method. With our estimation
method, we further detect factors driving issue resolution such as agents’ intentionally terminating service. We also develop forecasting models for issue resolution
probability that accommodate agent heterogeneity.
• Noah Gans, Haipeng Shen, Han Ye, Yong-Pin Zhou, “Asymptotic Stability of AR(p)Driven Workforce Scheduling Models”.
Abstract: Over the last decade a lot of work has been done to account for arrival
rate uncertainty in workforce staffing and routing. Only a few papers consider
arrival rate uncertainty in scheduling. We consider a recently proposed approach
that simultaneously considers forecasting and scheduling, and study its asymptotic
stability in terms of parameter estimation and stochastic programming. In particular,
we consider a stochastic integer program (IP) for scheduling, that minimizes longrun average objective subject to long-run average constraint. This IP is a function of
continuous forecast for a hidden AR(p) process. We solve an approximation of this
problem by discretizing the forecast distribution, and myopically solving rounded
version of the LP relaxation of the IP. We prove that, as the forecast horizon grows
long, as the level of discretization grows finer, and as the scale of the problem grows
Han (Hannah) Ye,
December 2013
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large, the scheme is O(1) optimal and the optimality gap is driven by the fact that
it’s an integer program being solved. These basic results are readily applied to call
center scheduling problem that minimizes long-run average staffing costs subject to
long-run average constraint on abandonments.
• Noah Gans, Nan Liu, Avishai Mandelbaum, Haipeng Shen, Han Ye, “Service Times
in Call Centers: Agent Heterogeneity and Learning, with some Operational Consequences”. A Festschrift for Lawrence D. Brown, IMS Collections, 6 (2010), 99-123.
Abstract: We study operational heterogeneity of call center agents. Our proxy for
heterogeneity is agent’s service time (i.e. call duration), a performance measure that
prevalently "enjoys" tight management control and great economic impact. Indeed,
managers of large call centers argue that a 1-second increase/decrease in average
service time can translate into additional/reduced operating costs in the order of
millions of dollars per year. We are motivated by an empirical analysis of call-center
data, which identifies both short-term and long-term factors associated with agent
heterogeneity. Operational consequences of such heterogeneity are then illustrated
via discrete event simulation. This highlights the potential benefits of analyzing individual agents’ operational histories. We are thus naturally led to a detailed analysis
of agents’ learning-curves, which reveals various learning patterns and opens up
new research opportunities.
Han (Hannah) Ye,
December 2013
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