Jeffrey Saltz

Jeffrey Saltz

Associate Professor

233 Hinds Hall

jsaltz@syr.edu

Overview

I received my B.S. in computer science from Cornell University, an M.B.A. from The Wharton School at the University of Pennsylvania and a Ph.D. in Information Systems from the New Jersey Institute of Technology. 

My 20+ years of industry experience has often focused on building new teams that leverage emerging technologies and data analytics to deliver innovative business solutions.  In my last corporate role, I worked at JPMorgan Chase. At JPMC, I reported to the firm's Chief Information officer while helping to drive innovation throughout the firm. I also held several other key management positions at the company, including CTO (Consumer and Community Banking Risk Management), Chief Information Architect (Chase Financial Services), global head of eBusiness technology and vice president of computational technology. I previously served as chief technology officer and principal investor at Goldman Sachs/Goldman Sachs Ventures, where I invested and helped incubate technology start-ups. I started my career as a programmer, project leader and consulting engineer with Digital Equipment Corp (now part of HP). 

Earlier in my career, I developed and led an award-winning collaboration between JP Morgan and the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.

 

Research

Big Data Science: Methodologies to improve the process teams use while performing data projects

My key research question is “would having a well defined methodology improve the results of teams doing big data projects”. While data science projects are growing in frequency and importance, the growth in the use of data science has outstripped the knowledge of how to structure projects and project teams to ensure that they perform reliably and effectively. This is similar to the early days of software development: software was being developed but organizations had little ability to predict whether a project would be successful, on time or on budget and projects were overly reliant on the heroic efforts of particular individuals. 

Startup Ecosystems

My focus is on the support structures that are created “for the people, by the people” via a “community of service” type of culture. Specifically on the Philadelphia startup community. Note that even though the group is focused on supporting Philadelphia startups, much of the work happens virtually, via computer mediated communication.

 

Experiential Learning

How to integrate "work" and the classroom, or provide meaningful classroom experiences that simulate the work environment.

 

Teaching


Semester Course Section Title
Fall 2017 IST687 M001 Applied Data Science
Fall 2017 IST387 M001 Intro to Applied Data Science
Spring 2018 IST687 M001 Applied Data Science
Spring 2018 IST687 M400 Applied Data Science
Semester Course Section Title
Fall 2016 IST687 M001 Applied Data Science
Fall 2016 IST687 M400 Applied Data Science
Spring 2017 IST687 M001 Applied Data Science
Spring 2017 IST687 M400 Applied Data Science
Summer 2017 IST687 M001 Applied Data Science
Semester Course Section Title
Fall 2015 IST687 M001 Applied Data Science
Fall 2015 IST687 M002 Applied Data Science
Spring 2016 IST687 M001 Applied Data Science
Spring 2016 IST687 M004 LAB: Applied Data Science
Semester Course Section Title
Fall 2014 GET472 M001 Professional Experience in GET
Fall 2014 GET410 M800 Contemporary Issues in GET:
Fall 2014 IST687 M001 Applied Data Science