2025 Spring Speaker

Tuesday, April 15, 6:00 pm - 7:00 pm

OSU Hamilton Hall, 1645 Neil Avenue, Room 400, Columbus 43210 (in-person) and Zoom (link will be distributed to registered participants)

Dinner Following @ Bravo Italian Kitchen

10 free spots available for chapter members on a first-come, first-served basis

Event is free but registration is required! 

Register now at

https://osu.az1.qualtrics.com/jfe/form/SV_eDPbBmzgpXkVqrI

Peter White, Ph.D.

Chief Data Sciences Officer

Abigail Wexner Research Institute 

Nationwide Children’s Hospital

"A New Era in Biomedical Research: Harnessing AI

to Advance Diagnosis & Discovery"

Abstract: Biomedical research is entering a transformative new era, driven by advancements in artificial intelligence (AI) and machine learning (ML). At the Abigail Wexner Research Institute, the Office of Data Sciences (ODS) is pioneering initiatives that integrate advanced AI into pediatric healthcare, redefining diagnosis, treatment, and research. Through a centralized data lake, enhanced data intelligence, and robust translational pipelines, ODS enables researchers and clinicians to leverage vast datasets—from genomics to electronic health records—for precision medicine. Highlighting innovative projects such as DREAM, an AI-driven platform transforming mental health research, and GENiUS, which accelerates genomic diagnosis in neonates and automates the genetic variant assessment process, this presentation will explore how AI technologies streamline complex data analysis, enhance clinical decision-making, and facilitate rapid discovery. Ultimately, this seminar underscores how harnessing AI can empower scientific discovery, significantly advance health outcomes, and pave the way for equitable, data-driven pediatric care.

2024 Traveling Course

From Big Data to Better Insights:

A Primer on Using Machine Learning Methods in a Data-Centric World

Trent D. Buskirk, Ph.D.

Old Dominion University

This is a two-day, virtual event from 1:00 pm - 5:00 pm on August 14 and 15

Registration Link 

Description:

The amount of data generated as a by-product in society is growing fast including data from satellites, sensors, transactions, social media, smartphones, and even thermostats, just to name a few.  Such data are often referred to as "big data" and can be leveraged to create value in different areas such as health, crime prevention, commerce and fraud detection, among others.  This course offers a broad overview of big data to allow participants to understand the need for alternate methods to analyze and visualize such data and introduces machine learning framework.  The course will discuss the difference between inference and prediction within the statistical machine learning paradigm as well as the difference between supervised and unsupervised machine learning methods and close with an intuitive, accessible yet rigorous, discussion of four of the most common machine learning methods that every analyst should understand in the era of big data including k-means clustering, k nearest neighbors, tree-based methods and random forest models using examples in R.  Time permitting, we will highlight the Rattle package in R that provides an intuitive and accessible graphic user interface for reproducible specification of a broad assortment of machine learning models within the R environment.

About the instructor:

Trent D. Buskirk, Ph.D., is a Professor and Data Science Fellow in the New School of Data Science at Old Dominion University.  Prior to this appointment, Trent was the Novak Family Distinguished Professor of Data Science and Chair of the Applied Statistics and Operations Research Department at Bowling Green State University.  Dr. Buskirk is a Fellow of the American Statistical Association and his research interests include big data quality, recruitment methods through social media, the use of big data and machine learning methods for health, social and survey science design and analysis, mobile and smartphone survey designs and in methods for calibrating and weighting nonprobability samples and fairness in AI models and interpretable ML methods.

ASA Columbus Chapter 5-Year Anniversary Banquet and Fall Speakers

Please join us to celebrate the Columbus Chapter's 5-year anniversary!

This event will take place at 6:00 pm on Wednesday, November 8, 2023 at The Fawcett Center (2400 Olentangy River Rd., Columbus, OH 43210, Alumni Lounge). 

The cost is: $10 for members, $20 for non-members and $5 for students.  Parking is free.

The evening will include a cash bar, appetizers, a Seminar (see below for speaker info and abstract), a Five Year Anniversary Summary Presentation from Steve MacEachern (OSU Professor of Statistics), and a dinner buffet. 

Seminar Title: "Statistical Insights into the Genomic World of Bioinformatics"

Seminar Speaker:

Meng Wang, PhD, Senior Bioinformatics Scientist

Center for Childhood Cancer Research, The Abigail Wexner Research Institute, Nationwide Children’s Hospital

Bioinformatics is an interdisciplinary field of science involving the collection, analysis, and interpretation of genomic sequencing data to decipher the genetic code. Sequencing data are high-dimension, possibly sparse, and have complex underlying structures. In application, GLM, various machine learning and deep learning models are popular due to their abilities to handle such complex data structures. In recent development, single cell genomics has enabled scientists to profile genomic and transcriptive landscape at a unprecedent resolution, but it also elevated the challenges we face in analysis by increasing the curse of dimensionality, heterogeneity, and sparsity in the data.  In this talk, I will present a statistician’s life in bioinformatics: the role which statistics has played in second generation sequencing, interdisciplinary communication, as well challenges we currently face in application and methods development.

2023 Spring Seminar

Tuesday, June 6th from 3:30 - 5:00 pm.

In Person Option: Fisher Hall Room 168, Franklin University, 300 E. Rich St, Columbus, OH 43215

Virtual Option: Register Here

See the following link for a map of the campus and where visitors can park for free.

Speaker

Susan H. Gawel, MS, PhD. Director Biostatistics, Senior Associate Research Fellow, Abbott Diagnostics Division, Abbott Laboratories

Title

Algorithm Development for Progression of Liver Disease to Liver Cancer

Abstract

This presentation will focus on the development of algorithms that can assist in predicting the likelihood of liver disease and liver cancer. The talk will explore the current challenges in diagnosing liver disease and the transition to liver cancer and how these challenges have prompted the development of novel solutions. Clinical algorithms developed using different statistical models in the diagnosis and prediction of liver disease and liver cancer will be compared, with attention on data training and validation. The presentation will also discuss the potential benefits of using these algorithms in clinical practice and the challenges in clinical adoption.

May 19, 2021 - 2:30-3:30 pm: Virtual Meetup 

Managing Difficult Requests from Difficult People 

Consider this scenario: 

Boss:  ‘”Hey Jesse, would you please bring me a rock from outside?"

Employee:  “Sure Boss.  Here is a nice, round one.”

Boss:  “No, I wanted a flat rock.”

Employee:  “Okay, here’s a different one.  It’s a nice, flat rock.”

Boss:  “No I wanted a large rock.”

Employee:  “How’s this one?  It’s a large, gray, flat rock."

Boss:  “Isn’t there a brown one?”

And on it goes . . . 

Have you ever been in a similar situation?  What did you do, or what should you have done?  Please join us for a lively discussion about dealing with this kind of difficult boss, co-worker, client, etc.  We will brainstorm strategies for handling these and other situations.  Among the types of people we’ll discuss are:

• Mr. "I'm not really sure what I want" -- constant change of heart; says one thing in person and something completely different in an e-mail an hour later.

• Mr. "I need this done yesterday" -- general lack of awareness of the space-time continuum; belief that having an idea means work is accomplished.

• Ms. "Won't this take 5 minutes?" -- belief that she can do what you do in a much shorter time frame than is really possible.

• Ms. "What you did was great, but..." -- goes along with everything you propose, only to basically toss it all out once it's finished and go in a completely new direction.

Check out some other “challenging” examples in this flyer:  https://cdn2.hubspot.net/hub/53/file-1524889572-jpg/blog-files/wonderful-world-of-clients.jpg .