Carl H. Lindner College of BusinessCarl H. Lindner College of BusinessUniversity of Cincinnati

Carl H. Lindner College of Business
Machine Learning Day Graphic

Machine Learning Day

February 14, 2018  7:30 AM - 4:15 PM

Sharonville Convention Center   (directions)

Three keynote speakers will present in the morning session, and the afternoon will feature machine learning technical presentations and case studies by data scientists and analytics companies.  The event fee ($225) includes all sessions and meals.  Parking is free at the Convention Center.

The event will begin with a networking breakfast at 7:30 AM with the keynote sessions beginning at 8:00AM.

Speaker presentations (ones we are allowed to share) will be available after the event.


Thank You Event Sponsors

Machine Learning Day Sponsors
Machine Laerning Day February 14 2018


  1. "The Current State of Practical Machine Learning": Mohammad Taghi Saffar, Machine Learning Engineer, Google
    Overall theme is a discussion on the latest trends in machine learning/deep learning in the high-tech industry with specific examples from Google products (with a few short in-depth discussions for our more technical audience) and a discussion on future trends, opportunities and challenges. It is not a technical presentation but hopefully it will spark some interest for followup discussions in more details

  2. “Kroger – Now Hiring Data Scientists”:  Doug Meiser General Manager of R&D Operations Research, Kroger
    Hear what Kroger has learned from ML deployments, the DOs and DONTs of applied ML, and how repeat success across the company is growing the team.

  3. "Advanced Analytics at FICO – Machine Learning with a Human Touch": Jeff Dandridge, VP Product Marketing, FICO
    Intro to FICO and FICO’s Analytic R&D heritage
    Insight into FICO’s use of different ML techniques
    The challenges and problems FICO is exploring today in xAI/ML
    FICO Predicts 2018:  Analytics and AI 



There will be two sessions with 3 speakers in each session.  You can attend one presentation in each session.

Session 1  1:30 - 2:45 PM
  • "Demystify Machine Learning & Take Action with Your Data": Jorge Zuloaga, Senior Director, Data Sciences,  Nick Magnuson, CFA, VP Product & Engineering, Big Squid
    With all of the buzz around machine learning, it can be difficult to find the value in your data and help your business to take action.What does it mean to “operationalize” your data? While Business Intelligence platforms alone are extremely useful for data visualization and in showing you what’s happening “right now” with your data; and while monitoring data activity in real-time allows businesses to react to issues as they occur -  one question still remains: How do you use that information to be proactive and formulate business initiatives that positively affect your future growth? The answer can be found in the utilization of Predictive Analytics with Machine Learning. Learn how to take measurable action based on your data and drive positive change within your organization  We will showcase some of the most common business problems that can be solved with Machine Learning while helping you scale your analytics and data science teams.
  • "Using Deep Learning in Tensorflow to Evaluate Consumers' Credit Worthiness in The Financial Industry": Kaveh Bastani, Hamed Namavari, Data Scientists, Recovery Decision Science
    Conventionally, in the financial services industry, analysts have been cross-referencing consumers’ personally identifiable data against other person-level and aggregate datasets such as Credit Bureau Reports and Census Data to analyze consumer behavior. However, the recent advances in ubiquitous digital imagery and machine vision techniques have given an extreme capability to analysts to dig deeper and extract new consumer insights that were impossible by the conventional methods. In this work, we present a method using deep learning-based computer vision to gain novel consumer insights by analyzing images of consumer's residential address.

  • “Machine Learning at Cloud Speed”: Clark MacDonald, Principal; Melissa Lacefield, Azure Cloud Solution Architect; Andrej Kyselica, Azure Cloud Solution Architect, Microsoft Azure Platform Solutions
    Learn how to leverage the latest Machine Learning technologies and improve the speed of innovation. Build and deploy ML applications in the cloud, on-premises, and at the edge. Get started by wrangling your data into shape easily and efficiently, then experiment quickly with Azure ML Studio.  Envision enterprise possibilities taking advantage of popular tools like Cognitive Toolkit, Jupyter, and Tensorflow in the Azure ML Workbench to build advanced ML models and train them locally or at large scale in the cloud.  Discuss how  real world examples of Machine Learning use cases can help your organization leverage machine learning at cloud speed.
Session 2  3:00 - 4:15 PM
  • “Price and Promotion Optimization in a Python-based Analytics Environment”: Mark Wells, VP Business Development, Pete Cacioppi, Chief Scientist, Opalytics
    This demonstration illustrates a use case (currently in production), that involves ML to predict sales of beverages (given price and other factors), predicting market share of competitors, and optimizing price and promotion recommendations.  A predictive analytics app and a custom optimization app are seamlessly connected with workflow within a Python advanced analytics environment in the cloud (Opalytics).  The predictions leverage the scikit-learn library.  The custom optimization app, instantly deployed as an interactive business app, uses the open source ticdat data library and Gurobi optimization.   The demo will also feature embedded Jupyter-style notebooks for data wrangling, custom visualization, and other solution tailoring.

  • "Sentiment Analysis of Donald Trump’s Tweets" Andre de Waal, Analytics Consultant, SAS Global Acadenmic Program
    Social media generates huge amounts of data every day and most of the data is unstructured. This is an untapped resource that may provide significant benefits to companies able to exploit this data. SAS Visual Analytics is a big data tool that facilitates the visualization of huge data sets. In this talk we demonstrate how insight can be derived from the analysis of Donald Trump’s tweets. First, a word cloud is built and then a sentiment analysis is done on all of his tweets. Tweets are grouped into topics and the sentiment surrounding each topic is analyzed. This leads to the discovery of novel and interesting insights.

  • "Random Forests and Gradient Boosting Machines in R": Brandon Greenwell, Senior Data Scientist, Ascend Innovations
    Good modeling tools should be universally applicable in classification and regression, have state-of-the-art accuracy, scale well to large data sets, and handle missing values effectively. Additionally, it would be nice for these tools to be able to automatically discover which variables are important, how they interact, and whether there are any novel cases or outliers. In this presentation, we discuss two such modeling tools: random forests and gradient boosting machines. The talk will cover a brief background of both methodologies (including decision trees) as well as various implementations of each in the R software environment for statistical computing. The pros and cons of each implementation will also be covered.


Keynote Speakers

Mohammad Saffar, Machine Learning Engineer, Google
Mohammad Saffar is a machine learning engineer at Google. His main focus is to bring the latest research advancements in AI to Google products. He is also an active ML researcher, investigating novel approaches to video understanding and time-series data modeling and prediction.

Doug Meiser, General Manager of R&D Operations Research, Kroger Co.
Doug Meiser leads the Operations Research team within the Research & Development team of Kroger Technology. Doug earned a bachelor’s degree in Mathematics and Physics and an MBA from Northern Kentucky University. He spent three years in Supply Chain Systems before transitioning to the Research & Development team to develop his current team.

Jeff Dandridge, VP Product Marketing, FICO
Jeff Dandridge provides the voice of “advanced analytics” for FICO’s Product Marketing team.  He works closely with the Product Managers and Product Marketers of FICO’s software products including Analytic Tools, Xpress Optimization, the Decision Management Suite and Lifecycle Applications.  He has extensive experience working with clients to develop advanced analytic solutions.  Prior to FICO, Jeff was CEO of InfoCentricity, a software company focused on building advanced analytic applications, which was acquired by FICO in 2014.

Afternoon Session Speakers

Kaveh Bastani, Data Scientist, Recovery Decision Science
Kaveh is a data scientist with Recovery Decision Science. He received his PhD in Industrial and Systems Engineering from Virginia Tech in 2016. His current research interests are predictive modeling, risk analysis, and text mining w, ith applications to financial services. His research has appeared in high-quality journals including IIE-Transactions, Decision Support Systems, IEEE Transactions on Human-Machine Systems, and IEEE Transactions on Automation Science and Engineering.

Pete Cacioppi, Chief Scientist, Opalytics
Pete Cacioppi was Chief Scientist of LogicTools and developed all the optimization engines. While at IBM, he spearheaded the development of the first off-the-shelf commercial bi-objective solve engine and received Patent Application Invention Award for “Interactive Visualization of Multi-Objective Optimization”. He is the co-author of  the books Supply Chain Network Design and Managerial Analytics.

Dr. Brandon Greenwell, Senior Data Scientist, Ascend Innovations
In addition to his role at Ascend Innovations, Brandon is an Adjunct Professor of Statistics at Wright State University. He is the author of several R packages on CRAN and has published several articles, including two in The R Journal, and is currently co-authoring an R book---Advanced Business Analytics with R: Description, Prediction, and Prescription---to be published by CRC Press in early 2019.

Melissa Lacefield, Azure Cloud Solution Architect, Microsoft
Melissa Lacefield has been at Microsoft since Sept 2005, and during her 12 year tenure she has held several positions including an RRE, PFE, Beta Engineer with the SQL Product Group, CSS SQL Escalation Engineer, and a Technical Account manager. She is passionate about advanced analytics, data, and cybersecurity. She has enjoyed working with large enterprise customers in situations from data center floods to sandy military engagements overseas to large Edu and SLG customers.  She has two bachelors, masters and currently working on finishing her Ph.D. in cybersecurity. 

Clark MacDonald, Principal, Azure Platform Solutions
Clark MacDonald celebrates his 4th year anniversary at Microsoft this month.  Clark is a  Principal for Data & AI technologies. He has worked with customers across multiple industries on predictive analytics projects and other advanced analytics efforts leveraging Microsoft's Cloud.  He holds a Bachelor Degree in Computer Science from The Ohio State University and has several professional certifications.

Nick Magnuson, CFA, VP Product & Engineering, Big Squid: Nick is the Vice President of Product & Engineering at Big Squid, overseeing the development and execution of the firm's machine learning software platform. He has been in the analytics field for over 17 years, developing quantitative applications to enhance decision-making capabilities across a variety of fields. He has been with Big Squid since 2015, holds the Chartered Financial Analyst designation, AB from Dartmouth College, and is a former member of the Chicago Quantitative Alliance.

Hamed Namavari, Data Scientist, Recovery Decision Science
Hamed has been with the Analytics Team at Recovery Decision Science (RDS) as Data Scientist since 2012. He has MA in Applied Economics and MS in Business Analytics from University of Cincinnati where he is currently an Econ PhD candidate. His research interests are focused primarily on Bayesian Inference in Spatiotemporal Econometrics.

Andre de Waal, Analytics Consultant, SAS Global Academic Program
André de Waal received his Ph.D. in theoretical computer science from the University of Bristol during 1994. He spent the next year in Germany and Belgium continuing his research in Logic Programming and Automated Theorem Proving. During 1996 he returned to South Africa to take up his position as lecturer at the School of Computer Science and Information Systems at the then Potchefstroom University for Christian  Higher Education (which later became the North-West University), where he was later promoted to Associated Professor. During 1999 he became one of the founder members of the Centre for Business Mathematics and Informatics at the same university. He became responsible for the Data Mining Program in the Centre and shifted his research focus to include Neural Networks and Predictive Modeling. He joined SAS Institute in Cary, NC during December 2010 to take up the position of Analytical Consultant in the Global Academic Program.

Marks Wells, VP Business Development, Opalytics
After serving as a U.S. Marine officer in transportation and logistics, Mark attended Drexel University where he received his MBA, took post-MBA coursework in operations research, and taught Production and Operations Management and Operations Research in the evening college.  While attending graduate school, he drove and loaded package delivery trucks.
    He has held analytical positions in steel distribution, direct mail, and electronic retailing.  He has also had the opportunity to consult with the management of leading global supply chains in the chemicals, medical devices, consumer goods, high tech, and retail industries.  Mark has led projects in multiple areas of process improvement, including facility location, forecasting, inventory planning, order fulfillment and warehousing, cost-to-serve analysis, and more.
    Mark speaks occasionally at industry events and has been published in several periodicals, including the Journal of Enterprise Resource Management, CSCMP Comment, and Analytics.  He is also the publisher and author of the Supply Chain Action blog.
    Prior to joining Opalytics, Mark’s recent positions included Supply Chain Account Executive for retail at SAP, Director of Advanced Analytics Sales at Aera, and member of the worldwide advanced analytics software team at IBM.

Jorge Zuloaga - Senior Director, Data Sciences, Big Squid: My research in computational physics has revolved around performing numerical simulations on large computer clusters. My main interest is in developing mathematical models and translating them into code to run massive and computationally intensive parallel calculations. I have worked extensively in Unix/Linux environments, programming in C++, Java, Fortran, and Matlab, focusing on the development and implementation of cutting-edge algorithms. From a mathematics point of view, my work largely involves the numerical solution of eigenvalue problems, sets of coupled differential equations, and integral equations. From a physics point of view, my work revolves around ab initio descriptions of many-electron systems interacting with light at the nanoscale, mostly within efficient formulations of Time-Dependent Density Functional Theory. My main contributions in physics have been in the areas of nanophotonics and quantum plasmonics.