Look out for important updates in the fall semester for 2022's D4 Conference!
The annual D4 Conference, enacted and hosted by Iowa State's D4 Institute, is a two-day workshop intended for early and later career researchers to network, present and critique research, and discuss data science at Iowa State, and what that means for graduate students in particular.
- Panel discussions will inform students of opportunities for data science graduate studies in the four departments leading Iowa State's NSF-funded TRIPODS D4 Institute (Computer Science, Mathematics, Statistics, and Electrical Engineering). Panelists will cover topics including programs and departments, admissions and funding, research areas, and activities, with scheduled time for a Q&A.
- The Conference solicits both technical and position papers. One goal of the conference is for these papers to act as motivation for new researchers to include the topics of this workshop into their research. To accomplish this, we will make all received papers available on ISU’s open-access digital repository; and we intend to informally publish the workshop proceedings – consisting of selected high-quality papers and extended abstracts of the remaining accepted papers. The workshop is also open to researchers without accepted papers. It is our intention to receive submissions from researchers new to the field as well as experienced researchers and practitioners, and will benefit all involved parties. For the former, we want to offer a platform for discussing their ideas and receiving feedback on them. This will be supported by question and answer sessions as well as by a session of group discussions.
- Keynote speakers discuss the intersections of data science, problems facing the field, new innovations, and more. Dr. Ming Li, a Research Science Manager at Amazon, was 2021's keynote speaker.
- Keynote Title: Bridge the Gaps Between Traditional Quantitative Training and Data Science
- Abstract: With recent developments in data science, there are a few gaps between traditional quantitative training and data science. In this talk, we walk through these gaps in details from the lens of industry applications. First, we discuss the difference between statistician and data scientist and how cloud platforms help. Then, we focus on the project cycles and common pitfalls of typical data science projects. Lastly, we describe the roles of deep learning in data science.
- Speaker Biography: Dr. Ming Li is currently a research science manager at Amazon’s last mile team and was a research scientist at the customer service team and a senior research scientist at the seller partner services team. He organized and presented the 2018 Joint Statistical Meetings Introductory Overview Lecture: Leading Data Science: Talent, Strategy, and Impact. He was the Chair of Quality & Productivity Section of the American Statistical Association. Before joining Amazon, he was a data scientist at Walmart and a statistical leader at General Electric Global Research Center. He holds a Ph.D. in Statistics and Physics from Iowa State University. He was also an instructor of Amazon’s internal Machine Learning University and the recipient of Amazon's Best Science Mentor Award. He is also an adjunct instructor at the University of Washington. He has trained and mentored numerous junior data scientists with different backgrounds such as statistician, software developer, and business analyst.