Author Archives: Charles Nicholson

NIST-funded COE for Community Resilience: Webinar

Community Recovery Webinar Announcement

Thursday, May 3, 10:00 AM – 12:00 PM (CDT)

In this Community Recovery Webinar learn more about the NIST-funded Center for Risk-Based Community Resilience Planning and how the Center’s research is progressing to models of community recovery and to field studies.

Webinar Link:

A resilient community is prepared for and can adapt to changing conditions and can with stand and recover rapidly from disruptions to its physical, economic, and social infrastructure. Modeling community resilience for purposes of risk-­‐informed decision requires a collaborative effort by experts in engineering, economics, social sciences, and information sciences to explain how community systems interact and affect recovery efforts. Over the last three years, Center researchers have been working on fundamental research on hazard characterization, models of physical, social and economic systems, damage and losses following hazard events, recovery of community systems, and optimization of alternative options to improve resilience – all at the community-­‐scale.

Join this community recovery webinar to learn more about the Center’s recent activities. A brief introduction of the Center and a recap of the past webinars and the Center’s research accomplishments will be followed by two presentations. The first summarizes the interdependent physical, social, and economics models of communities being developed. These include physics-­‐based models of networks and buildings, combined with computable general equilibrium models and population dislocation models, which are presented within the context of several testbed communities. The second presentation explains the approaches being used within the Center to model housing recovery in a testbed community, followed by integrated data sampling techniques developed for the Center’s longitudinal resilience field study of Lumberton, NC. The latter highlights the integration of social science and engineering data collection techniques to better inform community resilience models.

Additionally, the webinar will have an open Q&A “chat” session at the end.

Center information and past webinars are available at:

More center information is available at our website:


Noble Research Institute Summer internship

Undergrad Summer Internship at Noble Research Institute

Job: Computing Services Data Intern 


The Noble Research Institute LLC is accepting applications for an intern in the Computing Services Solutions team for the summer of 2018.  The position is located in Ardmore, OK and is geared toward undergraduates in Analytics, Computer Science, or other related STEM work.

This internship will also provide experience in the daily operations of the solutions team, including assisting with organizing and addressing data formatting and quality issues with spreadsheets; assisting with profiling data sets using SQL queries and other data profiling tools; assisting with data points to be cleaned in source systems; assisting with researching, comparing, and experimenting with data technology products; assisting with written summaries for review; assisting with data modeling and data extraction, transformation, and loading pipelines and help in developing reports and visualizations.

Duration: Approximately 12 weeks during the summer of 2018.

Hourly Rate: This Intern position will earn $12-14 per hour (subject to federal and state income tax withholding). The Intern will work 40 hours per week for up to 12 weeks.


The successful candidate must:

  • Be enrolled in an undergraduate degree program in a college or university within the United States, with such program resulting in the award of a baccalaureate.
  • Have completed his/her sophomore year at the time the internship begins (at least 60 credit hours) with a declared major in Computer Science, MIS, Data Analytics, or related STEM field of study with appropriate fundamental coursework completed.
  • Be legally authorized to work in the United States (for any employer) and WILL NOT require employment visa sponsorship for this internship; and
  • Be capable of working 40 hours per week for 12 weeks. The intern will work in Ardmore, Oklahoma during the program.

Application Submission:

For the online application, please visit Position ID: 2018-1490

See the flyer for more details:  NRI Summer Internship Opportunity




Open Positions for Fall 2018: PhD level teaching/research assistantships

University of Oklahoma

Open positions

PhD-level teaching/research assistantships

The School of Industrial and Systems Engineering at the University of Oklahoma has multiple PhD-level teaching and research assistantships available for the Fall 2018 semester.

In the School of ISE, we are focusing on applying methods from analytics and systems engineering to problems in (i) Cyber-Physical-Social Systems and (i) Health and Medical Systems, and opportunities exist in both of these application domains.

From the Interim Director, Dr. Shiva Raman: “Industrial and Systems Engineering at OU is a dynamic program that maintains great balance between research and teaching. Our faculty have been consistently recognized as outstanding teachers and have received many awards for excellence in teaching. Our faculty have received several grants from external agencies including the National Science Foundation, the Federal Aviation Administration, the Department of Defense, the Department of Transportation, NIST, and NASA. Faculty publications appear in leading journals in the areas of Operations Research, Risk and Reliability, Human Factors, and Manufacturing. The School of ISE provides our students with cutting edge laboratories and other resources to lead them to successful professional careers. Our most recent Ph.D. graduates have found employment at prestigious academic programs as Iowa State University, Texas A&M University and Vanderbilt University. We are very proud of the accomplishments of our Industrial and Systems Engineering family.”

Follow this link for more information regarding admission to the school:


Data Scientist Engineering Intern

I got an email from a current student in the analytics courses.  He is doing an internship this summer with Pioneer Natural Resources and told me that they are looking for additional intern this summer.   If you are interested, please see the following listing for a Data Scientist / Machine Learning Engineering Intern.  Email Dr. Nicholson if you would like to apply.

“Pioneer Natural Resources is a large, Texas-based independent exploration and production company that is focused on helping to meet the world’s energy needs. We deliver industry-leading production and reserve growth through onshore, unconventional, oil and gas resource development in the United States, while providing opportunities for growth and enrichment for our business partners, employees and the communities in which we operate.”


Pioneer Natural Resources

Data Scientist / Machine Learning Engineering Intern

We are currently seeking Graduate/Undergraduate students (advanced degrees preferred – M.S., Ph.D.) for Pioneer’s Analytics intern program with a strong fundamental understanding of various modern Artificial Intelligence (AI) and machine-learning (ML) methods, and with good experience in a few of the following areas: deep neural networks / LSTM, tensor factorization, reinforcement learning, Markov Random Fields, Bayesian networks, signal processing, distributed computing, operations research and large scale optimization. The candidate(s) may work on one or more of the following:

  • Research and develop data analytics (including streaming) and / or machine learning systems for Upstream Exploration & Production (E&P) applications
  • Work with domain experts to understand needs & constraints;
  • Work with software engineers / vendors to integrate ML solutions in E&P workflows and provide scalable solutions in Pioneer’s Big Data Environment;
  • Communicate sophisticated ML concepts to management, clients, business community to influence the strategy of the product.

Qualifications & Experience

  • Graduate / Undergraduate students in Computer Science, Electrical / Mechanical / Industrial / Petroleum Engineering, Applied Mathematics/Statistics or a highly quantitative discipline.
  • Excellent programming skills in Python, R and/or Java/C# is required. Production experience in Pyspark and Scala is a plus.
  • Understanding or working experience in Big Data architectures such as Hadoop, Apache Spark while utilizing MySQL for ETL is a plus
  • Good understanding of Drilling operations, Completions / Production is preferred but not a must and practical experience with other sensor/IoT related datasets.
  • Familiarity with ML / Deep Learning frameworks such as PyTorch, Scikit-Learn, TensorFlow, Theano, Keras, Caffe or completed E&P projects demonstrating usage of these frameworks is a plus.
  • Understanding and working experience of NoSQL/Multimodal Databases – Cassandra, Apache KUDU and/or — Document Store such as MongoDB, GraphDB is a plus.
  • Experience with visualization tools such as TIBCO-Spotfire, Power BI or other BI tools is a plus (having UI experience would be ideal).
  • Be excellent team players


Graduate Seminar: eBay Machine Learning Engineer!

eBay Machine Learning Graduate Seminar

Weili Zhang was the first analytics lab @ OU student to join the team, the first MS Data Science and Analytics graduate from OU, and will be Dr. Nicholson’s first student to complete his PhD in Industrial & Systems Engineering. He accepted a machine learning job at eBay last year in San Jose, CA, but is back this week to defend his PhD research on Friday, December 8, and then at 4:30p, to give a seminar presentation, open to the public, on machine learning at eBay. I expect this to be a pretty casual meeting and expect that Weili will be open to lots of Q&A and discussion.

It is my great pleasure to invite you to attend the seminar if you can: Friday, December 8, 2017 @ 4:30p in the Carson Engineering Center, Room 117 (map below). Also if you would like to join remotely, you can connect via Zoom:

New Masters 2017!

This week I am very happy to congratulate all of the students completing their Master’s of Science and PhD degrees.

Several of these students are my advisees and I am quite proud of their accomplishments.  As of today, all of my MSc students have defended their work.  And on Friday, my first PhD student will defend his research.  I’ll post the results of that as soon as I have it!

For now, lets focus on the Analytics Lab 2017 new masters!

New Masters and the MSc Research Path

The Master’s thesis student has three major components of their academic path: (1) successful completion of rigorous graduate course work; and (2) an in-depth research effort, spanning one to two years, on an area of specialization that results in the Master’s thesis (usually a 50 to 100 page manuscript detailing the background of the problem, the complexities of work, and their results), and (3) the Master’s defense.

The defense is a presentation to a committee of faculty members, and any others present, the summary of their entire research efforts.  During the defense, the committee members  ask questions relating to any detail of the work.   Questions are aimed at determining whether or not the student truly understands the concepts, methods, and results.  These are often open-ended and require critical, yet on-the-spot, reflection about his or her work.

Most defenses last 30 minutes to 1 hour, but some may exceed 1.5 hours, depending on the questions and student responses.  While the process is not ‘grueling’ per se, it is significant.

Successful defenders…

This semester, I am privileged to participate on 8 MS thesis committees and 2 PhD committees of students completing in December.  Most of the defenses are occurring this week.  So it is a busy week!

However, I am particularly happy about the successful results of 4 of the MS students, since I am their advisor.  Congratulations to Yunjie “Nicole” Wen, Gowtham Talluru, Samineh Nayeri, and Pauline Ribeyre!

Yunjie “Nicole” Wen, Masters of Science in Data Science and Analytics

Thesis: Game theory application of resilience community road-bridge transportation system

Abstract: “This paper considers the problem of game theory application in resilience-based road-bridge transportation network. Bridges in a community may be owned and maintained by separated entities. These owners may have different and even competing objectives for the recovering the transportation system after disaster. In this work, we assume that each player attempts to maximize the efficiency of repair to the system from the perspective of their own damaged damaged bridges after a hazard. The problem is modeled as an N-player nonzero-sum game. Strategic form and sequential form game are designed to demonstrate methodology.  A genetic algorithm is applied to the computation of the problem. The transportation network from Shelby County, TN is used to demonstrate the proposed methodology.”

Nicole will be continuing her academic career by pursing a PhD in Industrial and Systems Engineering at the University of Oklahoma.

Gotham Talluru, Masters of Science in Data Science and Analytics

Thesis: Dynamic Uplift Modelling

Abstract: “A new approach to Uplift modelling which considers time dependent behavior of the customers is analyzed. Uplift modelling (also known as true lift or incremental modeling) has applications in marketing, insurance, banking, personalized medicine, among other fields. The objective of an Uplift model is to identify individual entities who should be targeted for treatment (e.g., a marketing campaign) to maximize the incremental impact overall.

Research to-date has considered this as a static problem modelled at a single instance of time.  The method introduced in this work considers modelling uplift in a dynamic environment.  In particular, I consider a series of direct marketing contacts and simulate  periodic purchasing behavior of customers.  In contrast to static uplift models, the uplift in the purchase probability of the customers is dependent on time as well as customers previous purchases and offers received.  Appropriate modifications are made to static model approaches to adapt them to a dynamic model approach.

This study demonstrates significant potential for both researches and retail companies for thinking about the problem of uplift longitudinally.”

Gowtham has accepted a prestigious job in data science with PricewaterhouseCoopers (PwC) in the Oil and Gas sector of their business.

Samineh Nayeri, Masters of Science in Industrial & Systems Engineering

Thesis: Decomposition algorithm in fixed charge time-space network flow problems

Abstract: “A wide range of network flow problems primarily used in transportation is categorized as time-space fixed charge network flow problems. In this family of networks, each node is associated with a specific time and is replicated across all time-periods. The cost structure in these problems consists of variable and fixed costs where continuous and binary variables are required to formulate the problem as a mixed integer linear programming. and the problem is known to be NP-hard.  When the time dimension is added to the problem, solution approaches are even more time-consuming and CPU and memory intensive.

In this work, a decomposition heuristic is proposed that subdivides the problem into various time epochs to create smaller and more manageable subproblems.  These subproblems are solved sequentially to find an overall solution for the original problem. To evaluate the capability and efficiency of the decomposition method vs. exact method, a total of 1600 problems are generated and solved using Gurobi MIP solver, which runs parallel branch & bound algorithm. Statistical analysis indicates that depending on the problem specification, the average solution time in the decomposition is improved by more than four orders of magnitude and the solutions found are high quality (<2.5% from optimal, on average).”

Pauline Ribeyre, Masters of Science in Industrial & Systems Engineering

Thesis: Finding key characteristics of promising drug compounds for anticancer drug discovery

Abstract: “Multidrug resistance is the simultaneous resistance to two or more chemically unrelated therapeutics, including some therapeutics the cell has never been exposed to. It is one of the biggest obstacles to effective cancer chemotherapy treatments. Multidrug resistance can be caused by drug efflux, an otherwise useful body mechanism that prevents a too-high drug concentration in cells, by using proteins called transporters. Some chemical compounds have the ability to sensitize the cells to the drugs by disabling these transporters. The focus of this work is to find key characteristics of compounds that may disable a specific transporter, the P-glycoprotein. Three datasets listing compounds, their values for different features, and their ability to disable the transporters are provided by experts. Using the programming language R, various data analytics methods are applied to these datasets with the objective of predicting whether compounds are P-glycoprotein inhibitors or not. The main issue encountered is the fact that the most important dataset did not contain enough samples for the number of predictor variables. Ultimately, the decision tree and random forest models prove to be the most effective in predicting the compounds’ ability to disable the transporter.”

Congratulations to all the new masters!  May the force be with you.


Probabilistic Prediction of Post-disaster Functionality

Probabilistic Prediction of Post-disaster Functionality Loss of Community Building Portfolios Considering Utility Disruptions

Journal of Structural Engineering

I am proud to announce that the latest collaborative work from the CORE lab has been accepted for publication in the ASCE’s Journal of Structural Engineering.  The new paper title is a mouthful, “Probabilistic Prediction of Post-disaster Functionality Loss of Community Building Portfolios Considering Utility Disruptions”, but the researchers (Weili Zhang, Peihui Lin, Naiyu Wang, Charles Nicholson, and Xianwu Xue) have been just calling the effort the “PPPD” project.

The study proposes a framework for the probabilistic prediction of building portfolio functionality loss in a community following an earthquake hazard. Building functionality is jointly affected by both the structural integrity of the building itself and the availability of critical utilities.

Post-disaster functionality loss relates to direct damage and critical utilities

To this end, the framework incorporates three analyses for a given earthquake scenario:

  1. evaluation of the spatial distribution of physical damages to both buildings and utility infrastructure
  2. computation of utility disruptions deriving from the cascading failures occurring in the interdependent utility networks; the cascading failures are simulated by use of new mixed-integer, multicommodity network flow optimization model
  3. by integrating (1) and (2), a probabilistic prediction of the post-event functionality loss of building portfolios at the community scale.
Framework for Post-disaster Functionality Loss Prediction

Overview of the PPPD Framework

The framework couples functionality analyses of physical systems of distinct topologies and hazard response characteristics in a consistent spatial scale, providing a rich array of information for community hazard mitigation and resilience planning.

Case Study

An implementation of the framework is illustrated using the residential building portfolio in Shelby County, TN, subjected to an earthquake hazard.  A single realization of an earthquake scenario in Shelby Country, TN is depicted below.

Single realization of post-disaster functionality

Single disruptive event simulation realization

Since the building damage, the flow model, the data collection/aggregation can all be complted efficiently, it is easy to extend the single simulation realization to many realizations.  This allows for a spatial probabilistic analysis of the vulnerabilities in the affected area. The figure below depicts the expected impact to the region based on 1,000 simulations of the scenario earthquake.

Multiple realizations of post-disaster functionality

Expected impact based on multiple earthquake simulation realizations

The intricacies that relate how the electric power network (EPN) support the potable water network (PWN), along with the particular individual component vulnerabilities of the EPN and PWN, produce probabilistic failure patterns in building functionality (see sub-figure d. above), that are not obvious!


Additionally, the framework allows us to compare a more traditional building portfolio analysis to with that of the practical implications of disruptive events.  That is, even if your place of employment is not damaged, if the building does not have power or water, then it will be closed for business anyway!

The green line in the figure to the right denotes the probability of exceedance for the ratio of buildings which cannot be occupied (RUO) due to physical damage.  The dotted line relates to the ratio of functional loss of buildings (RFL) which is due to any combination of direct damage and utility loss.   Clearly, the RUO is a conservative estimate compared to RFL.  For example, there is only a 40% chance that 40+% of the buildings will be directly damaged to the extent of restricted occupancy. However, that number jumps to 80% when the utilities are considered!


This work represents a wonderful collaborative effort within the CORE lab.  Weili Zhang developed the interdependency model and worked closely with Peihui Lin, who provided the building analyses.  And both worked closely with Xianwu Xue, the GIS expert.  And of course, I am always pleased to work with my colleague Naiyu Wang in Civil Engineering.   We have much, much collaborative work already in-progress and planned for the future!

Southwest Airlines Operations Tour

NOC at Southwest Airlines

Southwest Airlines Network Operations Control


Southwest Airlines Visit

(back row, left-to-right) Kyle Beatty, Warren Qualley, Kelvin Droegemeier, Hank Jenkins-Smith, Ed Cokely (front row, left-to-right) Carol Silva, Amy McGovern, Radhika Santhanam, Le Gruenwald, Sridhar Radhakrishnan, Charles Nicholson

I was happy to represent the Analytics Lab recently as a part of a larger team from OU who were invited down to Dallas, TX near Love Field to meet with Southwest Airlines (SWA) to learn more about the airline business and operations.   The attendees from OU included the Vice President of Research; directors from the School of Computer Science in the Gallogly College of Engineering and Management Information Systems in the Price College of Business; senior researchers and specialists from political science, psychology, computer science, and of course, data science.

We were privileged to take a tour of the famous Southwest Airlines Network Operations Control, a.k.a., the NOC.  This facility and the employees who work here are at the very core of the SWA network operations.   From dispatchers to air traffic control specialists to flight operations to maintenance to crew schedulers to weather analysts — this is where the major operational decisions are made.  

The unique look of the NOC, bathed in blue as it is, was designed scientifically to help with mood and to reduce eye strain.  And, well, it simply looks cool.

While we were at the NOC, it so happens that Southwest Airlines was actively engaged in planning for the expected impacts from the impending Hurricane Harvey.  Obviously, weather, and especially major weather events like hurricanes, play a huge role in flight delays and cancellations for all airlines. Such disruptive events can have impacts across across an entire transportation network. Analyzing and optimizing under this larger “system-wide” view is what ISE’s are famous for. These are hard problems, but they are worth solving!

Planning for Harvey at Southwest Airlines NOC

Southwest NOC in action planning for Hurricane Harvey


12th International Conference on Structural Safety & Reliability

Several members of the combined CORE lab at OU attended the 12th International Conference on Structural Safety & Reliability (ICOSSAR 2017) at the Technische Universität Wien in Vienna, Austria during the summer.

The CORE lab had a combined 5 presentations during the conference!


On Tuesday, August 8, Naiyu Wang and Xianwu Xue both gave presentations relating to resilience and climate change:

  • 2:30-2:50p Dresback, K., Xue X., Xu J., Wang, N., Kolar, R., Geoghehan K.  “STORM-CoRe: A coupled model system for hurricanes, storm surge and coastal flooding to support community resilience planning under climate change”
  • 5:20-5:40p Xue X., Wang, N., Ellingwood, B., Zhang, K.  “The impact of climate change on riverine flooding at the community scale”.

On Wednesday, August 9, Yingjun Wang and Charles Nicholson gave presentations in the general resilience section; and Peihui Lin gave a presentation in the section relating to urban resilience:

  • 10:50-11:10 Wang, Y., Wang, N. “Retrofitting building portfolios to achieve community resilience goals under tornado hazard”
  • 11:10 -11:30 Zhang, W., Wang, N., Nicholson, C., Hadikhan Tehrani, M. “Stage-wise resilience planning for transportation networks”
  • 4:40-5:00 Lin, P., Wang, N. “A simulation-based model for post-disaster functionality recovery f community building portfolios”

OU was well represented in Vienna: in addition to the five faculty/students from the CORE lab listed above who traveled to ICOSSAR, there were separate presentations from Kash Barker and Hiba Baroud (PhD from OU, now faculty at Vanderbilt).

Several colleagues from the NIST-funded Center of Excellence on Community Resilience also participated in the conference including Bruce Ellingwood (CSU), John van de Lindt (CSU), Paolo Gardoni (UIUC), and Jamie Padgett (Rice), among others.

For fun

Outside of the conference itself, Vienna was a beautiful and interesting place — museums, history, and incredible architecture.  I was very happy to enjoy the trip t Vienna with my father.  We enjoyed Stephansplatz, a main square at the center of Vienna, named after its Vienna’s amazing cathedral (two pictures below).   Also had a chance to visit the Schönbrunn Palace and gardens.  Finally, I also made a side-trip to work out with Cross Fit Vienna, “The Dungeon”!



Sr. Data Analyst Position – Open in Plano, TX

JCPenney is hiring Sr. Data Analyst

A friend of mine who works at the JC Penney HQ in Plano, TX just sent me a new job posting — she would love to hire an OU DSA student!  See below for job description and let me know if you are interested!   Please note that JCPenney is not doing Visa sponsoring for this position.

Job posting

JCPenney is one of the nations largest apparel and home furnishing retailers with more than 1,000 stores and We are a diverse community of people, all working together to bring sensational style, sensible prices and the best service possible to our customers. Were looking for talented individuals who want to work in an energetic, respectful, collaborative environment. With a wide array of jobs, internships, training and more, there are countless opportunities for you to grow your career with us.

JCPenney is looking for an experienced data analyst who is eager to learn, to add value, and to do interesting work as a valued member of the Customer Strategy team. This position is data-intensive and will involve use of SQL and SAS software tools to pull data for analysis and reporting purposes. Insights produced by this team inform business decisions in Marketing and beyond, including those by senior executive leaders.

Primary Responsibilities:

  • Facilitate the definition of analysis needs and work product requirements of internal clients
  • Translate client needs and requirements into specific data, logic and reporting requirements and realistic work plans
  • Understand and have a working knowledge of customer/transactional level data
  • Strive to structure analysis to provide conclusive insights that directly align to decision-making
  • Prioritize and balance multiple activities in parallel and communicate status proactively to manage stakeholder expectations
  • Understand data sources to determine the correct source(s) and logic to ensure accurate, efficient and timely deliverables
  • Build, run and automate data queries, analysis and reports
  • Speak out when business strategies do not align with data insights and when insights suggest new marketing tactics
  • Identify and log data issues and work with department, IT and vendor teammates to understand and resolve them
  • Proactively seek help from and offer help to JCP teammates to accelerate skill development, business understanding and overall goal achievement
  • Anticipate future insight needs/opportunities and deliver self-initiated value to JCP

Core Competencies & Accomplishments:

  • College graduate with 3+ years of experience
  • At least 2 years experience using databases and SQL (structured query language) and SAS
  • Ability to combine, cleanse and harmonize data for descriptive and predictive analytics
  • Strong math, computer and problem-solving skills, including MS Excel
  • Structured thinker and high attention to detail
  • Strong teamwork, communication and interpersonal skills
  • Desire to consistently meet and exceed stakeholder expectations
  • Desire to acquire new technical skills (e.g., R, Hive, Tableau, Datameer) and business knowledge