Events Calendar

Speaker: Jun Xu, Ball State University

Abstract: Once portrayed as a heretical paradigm and subjective doctrine, Bayesian inference has emerged from this abject oblivion to a tidal wave to sweep through the world of statistics and data science. This talk begins with the origin of Bayesian statistics, the Bayes theorem, and recounts how and (possibly) why this framework was created.  Formerlly called the inverse probability approach, and probably more appropriately—Laplacian statistics—Bayesian statistics has undergone the nadir and zenith of its practice, due in part to its computational inconvenience and subjective assignment of priors. With the computational breakthroughs, especially those in the 1980s and early 1990s, several seemingly unrelated dots were connected to create the Markov chain Monte Carlo (MCMC) methods. This has completely changed the landscape in the field and revolutionized the estimation methods for Bayesian statistics. Unlike the classical frequentist statistics with the null hypothesis significance testing (NHST), Bayesian statistics usually uses Bayes factors, probabilities (not the confusing and problematic p-values), and credible intervals (not confidence intervals) to make inferences. Along with prior information integrated into the current iteration of estimation, the Bayesian approach dovetails well with how information is processed and updated epistemologically. This talk is based on the introductory sections of this recently published book.  

Symposium on ASIA-USA Partnership Opportunities (SAUPO) 2023

Time: 8:00am – 3:30pm, Friday April 7, 2023

Location: KSU Center, 3333 Busbee Drive, Kennesaw GA 30144 (metro Atlanta)

SAUPO 2023 Agenda: https://conference.kennesaw.edu/saupo/agenda.php

Registration at $249, covering breakfast, lunch, snacks, coffee, tea, panels & networking

Registration link: https://conference.kennesaw.edu/saupo/registration.php

[About SAUPO 2023] Organized by the Asian Studies Program at KSU, the Symposium on ASIA-USA Partnership Opportunities (SAUPO) is the largest Asia business conference in the USA since 2011. Next SAUPO will be held on Friday April 7, 2023 (Good Friday) from 8am to 3:30pm at KSU Center in metro Atlanta. We anticipate about 300 participants attending 2023 SAUPO in person, and an additional 200 watching SAUPO via live streaming. These are business leaders, government officials, community organizers, diplomats, scholars and students from the USA and Asia. You will be entertained with innovative business ideas, exciting discussions, valuable networking opportunities, and nonstop service of coffee, tea, food and drinks.

SAUPO Panel#1). Opportunities and Challenges in ASIA-USA Partnerships

SAUPO Panel#2). Post Pandemic Technology and Innovation

SAUPO Panel#3). Trends in Global Manufacturing and Business

Featured Speakers at SAUPO 2023 include:

• Honorable Matt Murray, U.S. Senior Official for APEC, U.S. Department of State  
• Honorable Mio Maeda, Consul General of Japan in Atlanta  
• Honorable Andre Omer Siregar, Consul General of Indonesia in Houston 
• Ivan Pulinkala, Provost and Senior VPAA, Kennesaw State University  
• DeAnn Golden, President & CEO, Berkshire Hathaway HS Georgia Properties  
• Catherine “Katie” Kaukinen, Dean, Radow College of Humanities & SS, KSU  
• Jessica Cork, Vice President, YKK Corporation of America  
• Steven Jahng, Director for External Affairs, SK Battery America  
• Jae Kim, Korean Practice, Aprio 
• Hajime Yokoyama, Vice President, Marketing, Kajima Int’l Inc.  
• Marcy Sperry, Founder, Vivid IP  
• Nagendra Roy, CEO, AanseaCore  
• Bill Strang, President, TOTO USA  
• Georgia Mui, Managing Partner, Global Consultants United 
• Allan (Yun-Chin) Lin, CEO, Winmate Inc.  
• John Rustin, Founder, Yereq Geo Energy 
• Stella Xu, Managing Director, Greater China Region, Georgia Dept. of Eco. Dev. 
• Masae Okura, Partner, Taylor English and Duma  

See SAUPO 2023 Flyer attached. Here is the SAUPO Sponsorship page if you want to contribute at a proper capacity:

Speaker: Jun Xu, Ball State University

Abstract: Once portrayed as a heretical paradigm and subjective doctrine, Bayesian inference has emerged from this abject oblivion to a tidal wave to sweep through the world of statistics and data science. This talk begins with the origin of Bayesian statistics, the Bayes theorem, and recounts how and (possibly) why this framework was created.  Formerlly called the inverse probability approach, and probably more appropriately—Laplacian statistics—Bayesian statistics has undergone the nadir and zenith of its practice, due in part to its computational inconvenience and subjective assignment of priors. With the computational breakthroughs, especially those in the 1980s and early 1990s, several seemingly unrelated dots were connected to create the Markov chain Monte Carlo (MCMC) methods. This has completely changed the landscape in the field and revolutionized the estimation methods for Bayesian statistics. Unlike the classical frequentist statistics with the null hypothesis significance testing (NHST), Bayesian statistics usually uses Bayes factors, probabilities (not the confusing and problematic p-values), and credible intervals (not confidence intervals) to make inferences. Along with prior information integrated into the current iteration of estimation, the Bayesian approach dovetails well with how information is processed and updated epistemologically. This talk is based on the introductory sections of this recently published book.  

Speaker:  Tim Liao, State University of New York at Stony Brook 

Abstract: The application of sequence analysis (SA) in the social sciences, especially in life course research, has mushroomed in the last decade and a half. Using a life course analogy, I examine in this talk the birth of SA in the social sciences and its childhood (the first wave), its adolescence and young adulthood (the second wave), and its future mature adulthood in the paper. The talk provides a summary of (1) the important SA research and the historical contexts in which SA was developed by Andrew Abbott, (2) a brief review of the manymethodological developments in visualization, complexity measures, dissimilarity measures, group analysis of dissimilarities, cluster analysis of dissimilarities, multidomain/multichannel SA, dyadic/polyadic SA, Markov chain SA, sequence life course analysis, sequence network analysis, SA in other social science research, and software for SA, and (3) reflections on some future directions of SA including themethods currently being developed, and some remaining challenges facing SA for which we do not yet have any solutions. The talk builds on this recently published paper in Social Science Research.

Speaker: Ian Lundberg, Cornell University 

Abstract: This talk is based on a paper with Jennie Brand and Nanum Jeon. Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science research. We aim to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.

Speaker: Ana Macanovic, Utrecht University 

 Abstract: The emergence of big data and computational tools has introduced new possibilities for using large-scale textual sources in social science research. We discuss five computational text analysis methods that can help researchers analyze large quantities of textual data and discuss exemplary applications in recently published research. First, we show how dictionary methods can assist the quantification of concepts of interest in texts; then, we summarize the potential of using semantic text analysis to extract information on social actors, social actions, and relationships between them. We move on to explore how unsupervised machine learning clustering methods assist inductive exploration of underlying meanings and concepts present in texts and how supervised machine learning classification methods support replication of manual coding onto new data. Finally, we discuss how powerful language models can help us map complex meanings, explore the evolution of meanings over time, and follow the emergence of new concepts in texts. We conclude by emphasizing the important implications of using large datasets and computational methods to infer complex meaning from texts in social sciences. This talk builds on this recently published paper.