Data Science & Artificial Intelligence

IBM and Binghamton University logos     

Data Science & Artificial Intelligence Virtual Workshop

- Learn how data science and artificial intelligence (AI) can add value to your business or organization.  


In this free 3-day workshop, you will be able to interact with IBM and SUNY technical leaders in the Data Science and Artificial Intelligence profession. You will hear from SUNY and IBM experts on topics including: Industry 4.0, Business Impact (ROI), Ethics, Big Data and Data Analytics. The workshop will be split between talks and hands-on labs and each day will end with a Q&A panel session. The workshop will culminate with a panel discussion on "How to get started with AI"

  • DATE: Jan. 26-28                                            
  • TIME: 9 a.m. - 12:30 p.m.            
  • LOCATION: Live Virtual on Zoom. A meeting link will be sent to participants.
  • WHO CAN ATTEND: This workshop is intended for industry professionals, academic scholars and students with interest in emerging data science and machine learning/artificial intelligence (AI).
    • Participants who attend all three days will receive a workshop completion certificate for 1.0 Continuing Education Units (1.0 CEU = 10 instructional contact hours). 
    • All participants will receive free e-books, whitepapers and IBM Cloud Services for a 90-day free trial.
    • All participants will be entered into a contest for a free online Data Science or Computer Science course of their choice delivered through Watson Continuing Professional Education.
(more details on the labs and panel sessions in the detailed printable agenda below)

Day 1 (Tuesday Jan. 26, 2021)
09:00 AM - 09:15 AM     Welcome by M. Testani and M. O’Malley-Trumble (Endicott,IBM)
09:15 AM - 10:00 AM     Keynote: Dr. Ben Amaba, CTO, IBM
"Why is AI an industry imperative and what are some ways it is benefiting the bottom line?"
10:00 AM – 11:45 PM     Lab 1 and 2: Catalogs and projects, Data Engineering, Data Refinery
11:45 PM - 12:30 PM     Panel Session 1: Ethical considerations for DS & AI

Day 2 (Wednesday, Jan. 27, 2021)
09:00 AM – 09:45 AM     Keynote: Dr. Sangwon Yoon, Professor, SSIE, Binghamton University
“Industry 4.0: Smart Electronics Manufacturing Using DS/AI”
10:00 AM – 11:45 PM     Lab 3: Data Science and Machine Learning
11:45 PM - 12:30 PM     Panel Session 2: Industry 4.0

Day 3 (Thursday, Jan. 28, 2021)
09:00 AM - 9:45 AM       Dr. Sreekanth Ramakrishnan, STSM and Strategy Leader, IBM
"Improving business performance and the customer experience through Data Science & AI"
10:00 AM - 11:30 PM      Lab 4: Build a Supply Chain Optimization application
11:30 PM - 12:30 PM      Panel Session 3: “How to get started with AI”

*More details in Agenda  




Many CEOs, CTOs and other decision-makers are seeing an advantage from the rise of big data and faster computing power. The question arises: How can all this great data drive innovation? Being able to harness the power of data through data science can help to accelerate financial, sales, manufacturing and supply-chain operations; enable a better, more intimate customer experience; or reduce downtime.

For many manufacturing plants with capital intensive equipment and rising labor costs, downtime is not an option. Predictive and preventative models can be applied to mitigate downtime, offset losses and enable safer working environments.

AI and machine learning also can help drive better faster products to market. For example, when launching a new product or service, it can be imperative to use data analytics for insight on market, demand and the target demographics.

AI and machine learning are being adopted at a rapid pace. McKinsey estimates that AI could potentially deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year.


  • Using predictive maintenance to improve equipment uptime
  • How can AI enhance the human-computer interaction (HCI)
  • Using Artificial Intelligence to reduce scrap and better manage materials
  • Supply Chain optimization and prescriptive risk management
  • Using AI to improve the customer experience
  • Converging Artificial Intelligence (AI) and Industry 4.0 (i4.0)
  • Ethic and Artificial Intelligence
  • How to start your AI journey


Ben Amaba photo  

Ben Amaba:  CTO Industrial Sector, DS & AI, IBM

"Why is AI an industry imperative and what are some ways it is benefiting the bottom line?"

Sreekanth Ramakrishnan Photo 

Sreekanth Ramakrishnan: STSM Strategy Lead, IBM Z

"Improving business performance and the customer experience through data science & AI"

sangwon yoon

Sangwon Yoon: Professor, SSIE, Binghamton University. 

“Industry 4.0: Smart Electronics Manufacturing Using DS/AI”


There will be a panel session each day with selected panelists. The topics, panelists and times are:

Panel 1: Ethical considerations for DS & AI (1/26, 11:45 at 12:30 pm)

Daehan Won: Professor, SSIE, Binghamton University (1)
Jake Brodsky: SCADA and ICS security engineer (1)
Philip Laplante: Professor, Software and Systems Engineering, Penn State University (1)
Dale Mumper: Data Science & Visualization Technical Specialist, Industrial Sector, IBM (1)
Ben Amaba: PhD, PE, CPIM®, LEED®AP, CTO Industrial Sector, Data Science & AI, IBM (1)

Panel 2: Industry 4.0 (1/27, 11:45 am at 12:30 pm)

Sangwon Yoon: Professor, SSIE, Binghamton University (2)
Yong Wong: Professor, SSIE, Binghamton University (2)
Jeff Daniels: Director Business Transformation & Systems Modernization Intelligent Factory, Asset Intelligence,Manufacturing Execution Systems, and Product Lifecycle Management, Lockheed Martin (2)
Mike McMahon: AI & Data Science Technical Lead, Industrial Sector, IBM (2)
Jesse Slater: Cloud and Cognitive Software Cloud Pak Accelerations Team Data Scientist, IBM Garage (2)

Panel 3: How to get started with AI (1/28, 11:30 am at 12:30 pm)

Kenneth Chiu: Professor, Computer Science, Binghamton University (3)
Bing Si:
Professor, SSIE, Binghamton University (3)
Andrei S. Popa:
Supervisor Reservoir Management at Chevron and Adjunct Associate Professor at University ofSouthern California: Viterbi School of Engineering. (3)
Jeffrey Baetz: Global Industry CTO, Industrial Market for Data, AI, ML and Data Science Elite, IBM (3)
Sreekanth Ramakrishnan: STSM and Strategy Leader, IBM (3)

HANDS-ON LABS (Details on lab topics on page 3 in Agenda

Data is everywhere these days and organizations are looking for more ways to differentiate themselves through analyzing that data. Projects can quickly become complex and overmanaged as more roles are added, such as data engineer, data steward, analysts, data scientists, and administrative personnel. In this series of labs we will execute hands-on labs exercises showing new ways to both analyze data and manage analytics projects. The example use case that forms the background of the exercises is based on analyzing streaming data to gain insight on machinery performance. You will apply data governance to help analysts find and use data efficiently and consistently. You will then use that data to create machine learning predictive models that can be used in applications to help inform business decisions. Insights for how to successfully execute your own analytics projects will be shared throughout the sessions.

Mike McMahon Photo 

Mike McMahon: AI & Data Technical Lead, IBM

Dale Mumper Photo 

Dale Mumper: Data Science & Visualization Technical Specialist, IBM


For technical questions, contact:  Mike McMahon at 
All other questions can be directed to Astrid Stromhaug at