| WUSS |
2026 |
A Structured Agent-Based Framework for Early-Stage Data Science Pipelines |
Dheerusha Tiwari,
Siddharth Birajdar |
source |
14 |
| PharmaSUG |
2026 |
From Molecular Subtypes to Bedside Decisions: An AI Approach for Personalized Critical Care Recommendations |
Xi Jiang,
Scott McClain |
source |
8 |
| PHUSE APAC |
2026 |
AI for Adverse Event Signal Detection: Moving Beyond |
Monika Monika |
source
presentation source |
10 |
| PHUSE APAC |
2026 |
Bridging Statistical Expertise Through Intuitive Design: A Modern Web Interface for Automated SAS Procedure Selection in Cardiovascular Clinical Trials |
Likith Gopikumar |
source
presentation source |
10 |
| PharmaSUG |
2025 |
Advanced Programming with R: Leveraging Tidyverse and Admiral for ADaM Dataset Creation with a Comparison to SAS |
Joshua J. Cook,
Richann J. Watson |
source |
20 |
| PHUSE US |
2025 |
Accelerating PKPD Analysis with Automated DDI Variable Creation: An SAS-Based Approach |
Sunil Mummidisetty,
Harish Ganesan |
source
presentation source |
7 |
| PHUSE US |
2025 |
Streamlining Machine Learning Modelling in Neuroscience Clinical Trials Through Cross-Functional Collaboration |
Jing Dai |
source
presentation source |
5 |
| PHUSE EU |
2023 |
Streamlining Clinical Data Analysis and Reporting with R: Best Practices, Tips and Techniques for Efficient Workflow Management and Code Optimisation |
Vaibhavi Govind Bhide,
Perry Meeks |
source
presentation source |
12 |
| WUSS |
2023 |
Leveraging Teradata ClearScape Analytics to Improve ROI [presentation] |
Gregory Goralski,
Paul Segal |
source |
40 |
| SESUG |
2023 |
Predicting Employees' Preference for Remote or On-site Jobs |
Livingstone Gadzanku |
source |
10 |
| SESUG |
2022 |
Don't be so One-Dimensional: How to Engineer Multi-Dimensional, High Cardinality Categorical Inputs for Machine Learning |
Aleksandar Nikolic |
source |
25 |
| SAS Explore |
2022 |
SAS Model Studio: Building ML, Text Analytics and Forecasting Pipelines [video] |
Wendy Czika |
source
youtube |
|
| WUSS |
2022 |
Quick Jumpstart into Pharmaceutical SAS Programming |
Jim Box,
Matthew J. Becker |
source |
7 |
| PharmaSUG |
2021 |
How I became a Machine Learning Engineer from a Statistical Programmer |
Kevin Lee |
source |
4 |
| SGF |
2020 |
Apply the Key Ideas from Andrew Ng's Machine Learning Certification (Coursera) in SAS Viya |
Petri Roine |
source |
9 |
| SGF |
2020 |
Automation in SAS Visual Data Mining and Machine Learning |
Wendy Czika |
source |
26 |
| SGF |
2020 |
How to Explain Your Black-Box Models in SAS Viya |
Funda Güneş |
source |
26 |
| PHUSE US |
2020 |
A Comparison of Machine Learning Algorithms for Binary Classification of Free-text Fields Using SAS, R, and Python |
Michael S Rimler |
source
presentation source |
12 |
| SGF |
2019 |
Playing Favorites: Our Top 10 Model Studio Features in SAS Visual Data Mining and Machine Learning |
Wendy Czika,
Peter Gerakios |
source |
16 |
| PHUSE US |
2019 |
Quantifying Dynamic Risk with Predictive Analytics |
Susan Olson,
Connor English |
source
presentation source |
7 |
| SCSUG |
2018 |
Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model |
Mei Najim |
source |
17 |
| SGF |
2018 |
Analytics Applications in Targeted Marketing and Forecasting Demand: A Two-Stage Model |
Anurag Hardikar |
source |
7 |
| SGF |
2018 |
Automated Assistants for Fraud Investigation Productivity |
Michael Ames |
source |
6 |
| SGF |
2018 |
Claim Analytics: A Litigation Prediction Case Study |
Mei Najim |
source |
18 |
| SGF |
2018 |
Navigating the Analytics Life Cycle with SAS Visual Data Mining and Machine Learning |
Susan Haller,
Brett Wujek |
source |
29 |
| MWSUG |
2017 |
Claim Analytics |
Mei Najim |
|
18 |
| SGF |
2017 |
Interactive Modeling in SAS Visual Analytics |
Donald R. Chapman,
Jonathan Wexler |
source |
16 |
| SGF |
2017 |
Property and Casualty Insurance Predictive Analytics in SAS |
Mei Najim |
source |
17 |
| MWSUG |
2016 |
Property and Casualty Insurance Predictive Analytics in SAS |
Mei Najim |
|
17 |
| SGF |
2016 |
A Property and Casualty Insurance Predictive Modeling Process in SAS |
Mei Najim |
source |
14 |
| SGF |
2016 |
SAS Visual Statistics 8.1: The New Self-Service, Easy Analytics Experience |
Xiangxiang Meng,
Donald R. Chapman,
Cheryl LeSaint |
source |
11 |
| SGF |
2016 |
Virtual Accessing of a SAS Data Set Using OPEN, FETCH, and CLOSE Functions with %SYSFUNC and %DO Loops |
Amarnath Vijayarangan |
source
presentation |
6 |
| MWSUG |
2015 |
A Property & Casualty Insurance Predictive Modeling Process in SAS |
Mei Najim |
|
14 |
| MWSUG |
2012 |
Reproducible Research Two Ways: SASweave vs. StatRep |
Shannon M. Morrison,
Matthew T. Karafa |
|
16 |
| SUGI |
2005 |
Automating Predictive Analysis to Predict Medicare Fraud |
Christine L. Warner |
source |
9 |
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