Meet a Data Scientist Series
- Every week, we begin our team meeting by hearing from a practicing data scientist or data science researcher.<
- Usually 5 to 15 minutes long, these talks provide Colorado Data Science Team members an idea of what data science looks like in practice, beyond classroom projects and competitions.
- Researchers often share what techniques they are developing, or how they use existing techniques to advance scientific knowledge or engineering practice; practicing data scientists in industry often share the techniques and algorithms that they find to be most useful in their work, and their experiences about what works and when it works.
- Aside from keeping team members informed about their options after they graduate from CU, these talks also foster relationships with local data scientists which we hope will continue to grow.
If you are a data scientist (broadly construed) and believe a presentation on your current work would be a good fit for the Colorado Data Science Team, please reach out to the leadership team at firstname.lastname@example.org
Professor in the Department of Computer Science at the University of Colorado at Boulder
- Received his Ph.D. from the University of Illinois, Urbana-Champaign under Dr. Daniel Reed in the Department of Computer Science.
- Research spans several areas in computer systems. This includes network, wireless networking, computer architecture as well as privacy and analysis of data sets.
Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan
- Finished PhD at UC Berkeley with Peter Bartlett in 2011, and was a Simons postdoctoral fellow with Michael Kearns for the following two years.
- Research focus is Machine Learning, and I like discovering connections between Optimization, Statistics, and Economics
Assistant Professor in the Department of Computer Science at the University of Colorado Boulder.
- Was a graduate student at Princeton with David Blei.
- Was a postdoc and then professor in Maryland’s Computational Linguistics and Information Processing lab.
- Research focuses on making machine learning more useful, more interpretable, and able to learn and interact from humans. This helps users sift through decades of documents; discover when individuals lie, reframe, or change the topic in a conversation; or to compete against humans in games that are based in natural language.
Founding Assistant Professor in the Department of Information Science at the University of Colorado Boulder.
- Received a Ph.D. in Computer Science from Johns Hopkins University in 2015, and a B.S. in Computer Science from the University of Illinois at Urbana-Champaign in 2009.
- Worked at Twitter and Microsoft Research in the summers of 2011 and 2013-2014.
- Research is at the intersection of text analysis and health/social science.
- On the methodological side, research machine learning and natural language processing, and in particular develop methods in topic modeling, which is used to discover patterns in large text datasets.
- On the applied side, study social media to learn about human behavior, especially in the context of public health.
Assistant Professor in the Department of Applied Mathematics at the University of Colorado Boulder.
- Was a Herman Goldstine Postdoctoral fellow in Mathematical Sciences at IBM Research in Yorktown Heights, NY
- Was a postdoctoral fellow via the Fondation Sciences Mathématiques de Paris at Paris 6 (JLL lab) with Patrick Combettes, and co-advised by Volkan Cevher (EPFL)
- Interested in information extraction from various types of datasets. Specific topics of research includes
- Optimization: first-order methods, quasi-Newton methods, primal-dual algorithms, convex analysis
- Numerical linear algebra: randomization and its interplay with optimization methods
- Sampling theory: how to make the best use of your resources when confronted with big data
- Mathematical applications: compressed sensing and variants, matrix completion and variants (robust PCA…), non-negative matrix factorization and end-member detection, sparse SVM
- Physical applications: radar ADC using compressed sensing, quantum tomography, MRI, medical imaging, IMRT, renewable energy, big-data