The Friday Seminars, presented on Friday, April 24, 2020, offer researchers and practitioners an opportunity to develop new skills, explore new topics, and to keep up with cutting-edge advances in research and practice. The invited experts will provide a thorough discussion of the topics in an interactive learning environment (e.g., lecture accompanied by break-out discussions, case studies, experiential exercises, and networking) during these three-hours long sessions.

Space is limited and Friday Seminars do sell out, so we encourage you to register early to secure your spot. 

Session Details:

The cost for each Friday Seminar is $175.00. Registration for Friday Seminars is done through the regular conference registration process, and the cancellation policy can be found HERE.

Friday Seminars

Python Programming for I-O Psychology: How to Start and How to Grow. - 8:00 a.m


This seminar will guide participants through the basics of programming in Python and survey specific tools that are important for I-O Psychology professionals using relevant real-world examples. Most importantly, the seminar is designed to help the less experienced practitioner or academic become comfortable using a powerful general programming language to solve new problems as they are discovered.

Learning Objectives: 

  • Python programming basics for beginners from control structures to functions.
  • How to understand a Python library, how to use an API.
  • Using libraries in Python to analyze and visualize data.


Computational tools are widely used in I-O Psychology but formal training in computing has only recently been integrated into relevant degree programs. Although many practitioners are already experts at applying computational techniques, some may feel left behind. This seminar starts from the beginning with the goal of teaching enough about general programming in Python to allow participants to feel confident using existing Python libraries and comfortable in the future learning to use the libraries that have yet to be created.

Intended Audience:

This session is intended for a general audience at a post-graduate level. Some knowledge of statistics may be useful but no prior computer programming experience is assumed.

Presenter biographies:

Adam Cannon is a Senior Lecturer in the Department of Computer Science at Columbia University. Dr. Cannon joined Columbia in the Fall Semester of 2000. Until 2005 he also worked as a visiting scientist at Los Alamos National Laboratory in New Mexico. Dr. Cannon came to Columbia after earning a Ph.D. in applied mathematics from Johns Hopkins University. He holds B.S. and M.S. degrees in aerospace engineering from the University of California, Los Angeles. Dr. Cannon’s current research interests are in computer science education, machine learning and statistical pattern recognition.

Machine Learning powered Talent Assessments: Vision, Practicalities, and Techniques - 8:00 a.m.

This seminar introduces attendees to how machine learning may be used to enhance the efficiency, accuracy, and experience of talent assessments. It is organized around three questions: What could be done (e.g., automating role plays, processing resumes)? What are key considerations (e.g., privacy, compliance)? What techniques are most relevant (e.g., natural language processing, deep learning)?

Learning Objectives: 

  • Explain at least three possible use cases for machine learning in the talent assessment domain.
  • Describe at least two practical considerations for the use of ML in talent assessment.
  • Describe at least two ML techniques relevant for talent assessment.

Description of session:

This seminar will introduce attendees to ways in which machine learning can be applied to enhance the efficiency, accuracy, and experience of talent assessments.  The session will be organized around three questions: What could be done with ML in assessment (e.g., automating role plays, processing resumes, using trace data to enhance scoring)? What are the practical considerations and watchouts (e.g., ensuring accuracy, privacy, compliance)? What are some of the ML techniques that are most relevant for assessment (e.g., supervised learning, natural language processing, deep learning). No Amazon data, initiatives, or product information will be shared in this seminar.

Intended Audience: 

This session is intended for a general audience at a post-graduate level; no specific content knowledge is required.

Presenter biographies:

Anthony S. Boyce, PhD, is a principal research scientist at Amazon.  In this role, he helps set and execute global pre-hire talent assessment strategy to support the continued growth of Amazon’s 650K+ employee workforce.  Previously, Tony was a partner in Aon’s Assessment Solutions practice where he directed a team of PhD's, data scientists, and other colleagues to develop assessment and leadership strategies, tools, and points-of-view that help organizations identify, develop, and retain top talent. He has been the recipient of several industry awards for his collaborative research innovations in the assessment space and has published several book chapters and journal articles on related topics.  Tony received his Ph.D. in Industrial-Organizational Psychology from Michigan State University.  


Tracey L. Tafero, PhD, is a manager on the Talent Assessment team at Amazon. In this role, she manages a team of I-O psychologists focused on development and implementation of assessments. Prior to joining Amazon, Tracey worked with Select International for 14 years where she served as the Director of Consulting Services. Throughout her years of experience, Tracey has engaged with internal and external clients on employee selection and development projects spanning all industries and all levels within organizations.  Tracey is currently an Adjunct Professor at Clemson University and regularly conducts research within the field, presenting at various professional conferences, as well as co-authoring book chapters and journal articles. Tracey received her Ph.D. in Industrial/Organizational Psychology from Clemson University. 


MQ Liu, PhD, is a Research Scientist on the Talent Assessment team at Amazon. MQ’s work focuses on leveraging machine learning and natural language processing on assessment and selection strategies that continuously improve the prediction of a candidate’s success at Amazon. Prior to joining Amazon, MQ

was a consultant at Development Dimensions International (DDI) where she led and managed organizational research and product development using a variety of big data techniques. MQ received her Ph.D. in Industrial/Organizational Psychology from Wayne State University. Her work has been published in journals such as Journal of Applied Psychology, Journal of Business and Psychology, and Journal of Personality and Social Psychology.

Conducting Pay Equity Analyses: The Essentials - 8:00 a.m.


In this interactive Friday seminar, two consultants and an employment attorney will help the audience understand the essentials of conducting pay equity analyses. The seminar will begin with a discussion of how to interpret wage gap statistics, move to a discussion of relevant laws and regulations regarding pay, and finish with a tutorial and hands-on exercises in conducting a pay equity analysis.

Learning Objectives:

  • Understand how to interpret wage gap statistics
  • Learn about the laws and regulations related to pay equity
  • Learn how to determine if jobs are similarly situated
  • Understand the basic steps in conducting a pay equity analysis


In this interactive Friday seminar, two consultants and an employment attorney will help the audience understand the essentials of conducting pay equity analyses. The seminar will begin with a discussion of how to interpret wage gap statistics, move to a discussion of relevant laws and regulations regarding pay, and finish with a discussion of the basic steps in conducting a pay equity analysis. Audience members will participate in several hands-on exercises such as creating similarly situated pay groups and interpreting the statistical results of a pay equity analysis.

Presenter Biographies:

Michael G. Aamodt, Ph.D. is a Principal Consultant for DCI Consulting Group, Inc. He spends most of his days conducting pay equity analyses, computing adverse impact statistics, and helping develop employee selection systems for federal contractors and other organizations. Prior to working for DCI, Mike spent 26 years as a professor of industrial/organizational psychology at Radford University.  He received his B.A. in psychology from Pepperdine University in Malibu, California and both his M.A. and Ph.D. from the University of Arkansas. He is the author of Applied Industrial/Organizational Psychology, now in its 8th edition, Research in Law Enforcement Selection and the coauthor of Understanding Statistics: A Guide for I/O Psychologists and Human Resource Professionals and Human Relations in Business.       


David Cohen is the founder and President of DCI Consulting Group, Inc. He provides consulting services to employers and management law firms on a wide range of human resource risk management strategies, particularly in the areas of EEO/affirmative action program development, systemic compensation statistical analyses, comprehensive human resources self-audits, and employee selection and test validation.

In addition, Mr. Cohen is the co-founder of The Institute for Workplace Equality, a national nonprofit employer association that trains and educates federal contractors in understanding and complying with their affirmative action and equal employment obligations.

Recognized as a national EEO and affirmative action compliance expert, Mr. Cohen speaks frequently before corporate leaders from Fortune 500 companies, and at regional and national ILG conferences and OFCCP events. In 2006, he co-authored a book entitled, Understanding Statistics: A Guide for I/O Psychologists and Human Resource Professionals.  

Mr. Cohen has a master’s degree in Industrial and Organizational Psychology from Radford University and a bachelor’s degree in Psychology from West Virginia University.


Michelle Duncan, Esq. is a Principal in the Denver office of Jackson Lewis P.C.  Michelle is a member of the Affirmative Action Compliance and OFCCP Defense Practice Group and the Pay Equity Resource Group. Michelle represents employers in affirmative action and employment discrimination matters before OFCCP.  She counsels employers on the design and implementation of company-wide AAP structures, applicant tracking systems, pre-employment tests and other compliance issues. Michelle advises employers on pay equity issues and directs pay equity analyses for employers in many industries including higher education.

Michelle joined the firm after working for nearly fourteen years as a trial attorney with the U.S. Department of Labor, Office of the Solicitor. During her tenure with the Solicitor’s Office, Michelle was widely regarded as a leading expert on OFCCP litigation. She litigated numerous OFCCP cases and provided counsel to high-level OFCCP officials. This experience allows Michelle to offer unique insights into the inner-workings of OFCCP and the Solicitor’s Office as well as what she is seeing in audits today. Michelle is Co-Chair of the Colorado ILG, on the faculty of The Institute for Workplace Equality and is a frequent speaker on topics related to affirmative action and employment discrimination.

Social Network Analysis of Teams and Organizations - 3:00 p.m.


PREREGISTRATION AND ADDITIONAL FEE REQUIRED. This seminar introduces social network analytics to understand and enable teams and organizations. Topics include collecting network data from surveys and digital sources, visualizing networks, descriptive analytics (who are influencers/brokers, how siloed is the organization?) and predictive analytics (who is likely to leave? who will work well on a team?). Analytics will be demonstrated using R.


Learning Objectives: 

At the end of this workshop, the learner will be able to:

  • Explain how network-based approaches to understanding social processes in teams and organizations add insights above those available from other (individual attribute based) approaches.
  • Understand the pros and cons of various network data collection techniques and how network data can be securely stored and manipulated in R.
  • Perform descriptive and predictive statistical analysis (e.g. QAP, ERGM) of networks using the SNA/Statnet package in R to address specific organizational pain points.


Decades of research have demonstrated that social networks play an increasingly important role in our understanding of attitudes, behavior and performance in the workplace. And today those networks are not static. Contemporary organizations are constantly evolving dynamic communities as new network links are created and dysfunctional ones dissolved.

This seminar provides an introduction to theoretical, conceptual, and analytic issues associated with network perspectives on organizing. The course will review key network insights that will help us understand and enable teams and organizations.

In addition, this session will provide attendees with a practical introduction to descriptive and predictive methods for analyzing social networks and how to perform these analyses in R. This will include how to collect, visualize, and analyze networks to help address organizational “pain points.”

Attendees should bring a laptop computer to the session, with R and RStudio installed.

Intended Audience: 

This session is intended for a general audience at a post-graduate level; beginning programming experience in a language such as R is recommended.

Presenter Biographies: 

Noshir Contractor is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management at Northwestern University, USA. He is the Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University. He is investigating factors that lead to the formation, maintenance, and dissolution of dynamically linked social and knowledge networks in a wide variety of contexts including from business enterprises, scientific communities, global health and space missions. His research program has been funded continuously for over 25 years by major grants from the U.S. National Science Foundation with additional funding from the U.S. National Institutes of Health (NIH), NASA, DARPA, Air Force Research Lab, Army Research Institute, Army Research Laboratory, Army Research Office, Bill & Melinda Gates Foundation, Rockefeller Foundation and the MacArthur Foundation.


Professor Contractor has published or presented over 250 research papers dealing with communicating and organizing. He has been at the intellectual and institutional forefront of three emerging interdisciplines: network science, computational social science and web science. His

book titled Theories of Communication Networks (co-authored with Professor Peter Monge and published by Oxford University Press, and translated into simplified Chinese in 2009) received the 2003 Book of the Year award from the Organizational Communication Division of the National Communication Association. He received the National Communication Association Distinguished Scholar Award in 2014, was elected a Fellow of the International Communication Association in 2015 and received the 2019 Lifetime Service Award from the Organizational Communication & Information Systems Division of the Academy of Management. In 2018 he received the Distinguished Alumnus Award from the Indian Institute of Technology, Madras where he received a Bachelor’s degree in Electrical Engineering. He received a Ph.D. from the Annenberg School of Communication at the University of Southern California.


Brennan Antone is a PhD candidate in the Industrial Engineering and Management Sciences program at Northwestern University. His research focuses on analyzing social selection and influence in networks, as well as applying social network models for predictive and prescriptive purposes.

Interactive Data Visualization Apps with Shiny - 3:00 p.m.


This seminar focuses on building interactive data visualization and data analysis apps using the Shiny package in R. Participants will gain hands-on experience in developing, styling, optimizing, and deploying Shiny apps of their own creation.

Learning Objectives: 

  • Understand reactivity, input-output control, design, and debugging in Shiny
  • Develop a simple, working Shiny app to build on
  • Discuss and explore new opportunities to add value using Shiny

Description of session:

Data science skills are increasingly expected from non-data scientists. Shiny provides a comprehensive and accessible way for people who are not expert programmers or professional data scientists to create engaging, modern data applications comparable to industry leaders such as This is a hands-on seminar, and participants will receive instruction and support to implement the lecture material by building their own Shiny app throughout the course of the seminar. Group discussion will also focus on opportunities for I-O professionals to use Shiny to add new value for their employers or clients.

Intended Audience: 

This seminar is intended for professionals or students who are interested in creating interactive, modern data visualizations and are already at least somewhat familiar with R.

Materials will be made available in advance of the seminar via GitHub, including illustrative datasets. R and RStudio can be downloaded at, and Shiny can be installed by running the command install.packages(“shiny”)

Presenter biographies:

Ryan Derickson is a lead data scientist at the U.S. Department of Veterans Affairs National Center for Organization Development. Ryan earned his M.A. in I-O Psychology from Xavier University, and is working towards his quantitative PhD at the University of Cincinnati. He has extensive experience building analytic and data visualization apps in Shiny for technical and executive audiences.

Managing and Engaging External Workers for Maximum Potential: Research and Practice - 3:00 p.m.


The external workforce (e.g., contractors, gig workers) has grown in size and importance, but it is still understudied, misunderstood, and mismanaged. Based on a research program by SHRM and SAP SuccessFactors, this interactive session uses the external worker lifecycle as the basis for describing research results and sharing practical tools for how to improve the external workforce experience. 

Learning Objectives: 

At the end of this seminar, the learner will be able to: 

  • Describe what constitutes external work and the motives for organizations to employ external workers and external workers to pursue that type of work  
  • Understand how HR, managers, internal employees, and external workers perceive the practices used by businesses to manage external workers
  • Define the dimensions and maturity indicators of an external workforce management strategy
  • Identify recommendations for how to better manage and engage external workers across their lifecycle in an organization

Description of session:  

Based on an extensive applied research program with over 3,000 total participants undertaken by SHRM and SAP SuccessFactors, this session will provide a comprehensive point of view on how to more effectively utilize and recognize external workers as an important part of the total workforce. The research considers the external workforce from employer, worker, and societal perspectives to ultimately provide evidence-based guidance, resources, and tools for companies 

to better understand the external worker experience and more effectively manage and engage the external workforce through their lifecycle within an organization. 

We will start by sharing the applied research methodology that was used to gather data from HR practitioners, external workers, internal employees who work alongside external workers, and managers of external workers.

Then, the structure of the seminar will follow that of an external workforce management toolkit, which was jointly developed by SHRM and SAP SuccessFactors based on the findings from the applied research program. As each topic is addressed, relevant applied research findings will be shared, practical recommendations will be offered, and participants will have an opportunity to contemplate how the specific topic, research results, and recommendations apply to their particular situation and interest (e.g., an internal practitioner will consider how their organization currently onboards their external workforce and how they might be able to do it more effectively). Given the many topics addressed in the toolkit, as outlined below, the seminar’s focus can be tailored to those topics of greatest interest to the participants, which can be gauged at the beginning of the seminar either via discussion or online polling based on the size of the audience.

Foundational topics that will be shared and discussed include an external workforce management maturity model, key considerations for building a business case so companies will invest in their external workforce, creating an external workforce philosophy, strategy, and governance model, important legal and compliance considerations when employing an external workforce, and how technology can be used to facilitate the management and engagement of external workers.  

Then, the bulk of the time will be focused on considering each stage of the external worker lifecycle within an organization and considering how external workers, internal employees, managers, and HR experience the stage and how organizations can make that experience more effective. The stages include planning (e.g., External Worker or Internal Employee? A Hiring Decision Matrix), sourcing (e.g., Curating an external worker employment brand), onboarding (e.g., Best Practices for Onboarding Your External Workforce), working and engaging (e.g., Developing Leaders to Manage a Blended Workforce; Best Practices for Including and Engaging External Workers), paying (e.g., Considerations for External Worker Compensation), and exiting (e.g., Key Considerations and Recommendations for Exiting External Workers). 

Attendees will be provided with the whitepaper summarizing the research findings and the full external workforce management toolkit. Additionally, based on the exercises throughout the seminar, they will leave with a plan of practical solutions tailored to their unique context to more effectively manage and engage the external workforce in ways that will be beneficial to both organizations and workers alike.  

Intended Audience:

This session is intended for a general audience at a post-graduate level. While no specific content knowledge or expertise is required, prior exposure to the topic of the external workforce generally may be useful. While this seminar will be mainly tailored towards HR internal practitioners and external consultants, it will also be beneficial to academic scholars and applied 

researchers who are interested in pursuing a research program on the external workforce. 

Presenter biographies:

Autumn Krauss is a Principal Scientist in SAP SuccessFactors’ Human Capital Management Research Team. Her role is focused on conducting and applying research on the psychology of work to inform the solutions that SAP SuccessFactors develops and delivers to its clients, as well as providing consultative guidance to companies so they can best leverage human capital management practices to foster a strong positive company culture and improve employee experience and well-being. Dr. Krauss has over 15 years of experience consulting to senior leaders around the globe, as well as speaking, writing, and conducting applied research on topics across the spectrum of human capital management, with her applied research programs funded by the SHRM Foundation, the CDC, and NIOSH. With her primary areas of expertise being assessment development, culture change, talent management, and employee well-being, she has overseen the creation and implementation of large-scale culture assessments, leadership development programs, and employee selection systems across organizations operating in industries such as power generation and distribution, mining, oil and gas, healthcare, and retail. She recently led SAP’s efforts in partnership with SHRM to conduct a comprehensive applied research program focused on the external workforce and how best to source, manage, and engage external workers as part of an effective total workforce management strategy. Autumn holds a Ph.D. in Industrial and Organizational Psychology from Colorado State University. 

Cassondra Batz-Barbarich is an Assistant Professor in Business at Lake Forest College. She earned her PhD in Industrial-Organizational Psychology at Purdue University, and her BS in psychology at Loyola University Chicago. She is actively engaged as an educator, researcher, and practitioner in her field. Her research on well-being and gender challenges in the workplace have been featured in top scientific journals including Psychological Science and she has worked for Fortune 500 companies in the areas of human resources and human capital management. 

Xiaoyuan (Susan) Zhu is a post-doctoral research fellow in the Department of Research and Insights at the Society for Human Resource Management (SHRM). She received her Ph.D. in I/O Psychology from University of Connecticut and B.A. in psychology and biology from Wake Forest University. Susan’s research focuses on recruitment, staffing and leader decision-making; her work has been published in peer-reviewed journals such as Journal of Business and Psychology, International Journal of Selection and Assessments, and Journal of Behavioral Decision Making. Susan has previously worked at IBM-Kenexa and American Institutes for Research, where she consulted on recruitment, selection, employee engagement, and organizational change projects for a variety of Fortune 100 clients. She has also received scholarships and awards from SIOP, NIOSH, and University of Connecticut for her research, practice, and teaching efforts. 

Liz Supinski is Director of Data Science and Research Products in the Department of Research & Insights at the Society for Human Resource Management (SHRM). Prior to joining SHRM in 2015. Prior to joining SHRM, she had a nearly 20-year consulting career in I/O psychology and 

technology including personnel assessment, training, data analytics and visualization, user experience design, training and testing for software and project management. Her current focus spans research methods, data management and analytics. She received her M.S. in Data Science from Indiana University, Bloomington, her M.A. in I/O Psychology from George Mason University and her B.S. in Microbiology from the Pennsylvania State University.