Duke Machine Learning Day

Saturday, March 23, 2019 | Schiciano Auditorium & The Edge, Duke University

Come to Duke's second Machine Learning Day on Saturday, March 23 in Schiciano Auditorium and The Edge! At ML Day, you'll have the opportunity to learn about exciting machine learning research through workshops, talks, and research presentations on machine learning and related topics. This year's event will highlight important women in the field, including a women in data science dinner. The day's activities include:
  • Panel discussions on careers and opportunities post-graduation;
  • Workshops and talks on various areas of machine learning; and
  • Research and poster presentations on ML-related research.
Register for the event here. Students interested in presenting their posters can apply here, and women interested in leading research spotlight presentations can apply here. Applications are due by Friday, March 8, 2019, and decisions will be made by Sunday, March 10, 2019. Companies can sponsor the event by sponsoring Duke Undergraduate Machine Learning. Information about last year's event can be found here.

Schedule

  • 9am-10am: Registration and breakfast (Fitzpatrick Lobby)
  • 10am-10.15am: Opening remarks by Dean Valerie Ashby, Dr. Robert Calderbank, and Dr. Rebecca Steorts (Schiciano Auditorium)
  • 10.15am-11am: Share, Contribute, Collaborate, Broadcast (Keynote) by Dr. Mine Cetinkaya-Rundel (Schiciano Auditorium)
  • 11.15am-12.15pm: Women in Data Science research spotlight presentations (Schiciano Auditorium)
    • Applying Entity Resolution to Conflict Data of El Salvador by Bihan Zhuang
    • Social Network Metrics of Game Success by Fan Bu
    • Storytelling with Data by Julia Donheiser
    • Gaussian Processes for Characterizing Strategic Decision Making by Kelsey McDonald
    • Simultaneous Bipartite Record Linkage and Regression by Jiurui Tang
    • Classical Music Composition Using State Space Models by Anna Yanchenko
  • 12.15pm-1.30pm: Networking lunch and poster presentations (Fitzpatrick Lobby)
  • 1.30pm-2.15pm: No-code Approaches to Machine Learning (Workshop) by Blue Prism (Schiciano A)
  • 1.30pm-2.15pm: Women in Data Science panel of Sarah Sibley, Dr. Maria Tackett, Dr. Ya Xue, and Bihuan Zhuang, moderated by Shelley Rusincovitch and Dr. Rebecca Steorts (Schiciano B)
  • 2.30pm-3.15pm: Preparing for Industry (Workshop) by Josh Jelin from Google (Schiciano A)
  • 2.30pm-3.15pm: An Introduction to Machine Learning (Workshop) by Dr. Kyle Bradbury (Schiciano B)
  • 6pm-7.30pm: Women in Data Science networking dinner (The Edge, Lounge)

Speaker & Panelist Bios

  • Dean Valerie Ashby
  • Dean Valerie Ashby is the Dean of Trinity College of Arts & Sciences at Duke University. She received her B.A. and Ph.D. degrees in Chemistry from UNC Chapel Hill and completed her postdoctoral research at the Universitat Mainz, Germany in 1994 as a National Science Foundation Postdoctoral Fellow and NATO Postdoctoral Fellow. She is the recipient of the National Science Foundation Career Development Award, the DuPont Young Faculty, and 3M Young Faculty Awards. As an educator, she was recognized with the UNC Chapel Hill General Alumni Association Faculty Service Award, the Bowman and Gordon Gray Distinguished Term Professorship for excellence in undergraduate teaching and research, the J. Carlyle Sitterson Freshman Teaching Award, the UNC Student Undergraduate Teaching Award, and the Johnston Teaching Award for Undergraduate Teaching.
  • Dr. Kyle Bradbury
  • Dr. Kyle Bradbury is the Managing Director of the Energy Data Analytics Lab at the Duke University Energy Initiative. He brings experience in machine learning and statistical modeling to energy problems. He completed his Ph.D. at Duke University, with research focused on modeling the reliability and cost trade-offs of energy storage systems for integrating wind and solar power into the grid. Dr. Bradbury holds an M.S. in Electrical Engineering from Duke University, where he specialized in statistical signal processing and machine learning, and a B.S. in Electrical Engineering from Tufts University. He has worked for ISO New England, MIT Lincoln Laboratories, and Dominion.
  • Sarah Brandsen
  • Sarah Brandsen is a third-year graduate student with Duke University studying Physics. Her research focus is in quantum information theory, more specifically in using machine learning and dynamic programming techniques to find efficient locally adaptive protocols for quantum state discrimination. Prior to enrolling at Duke, she completed her undergraduate degree from the California Institute of Technology and studied device-independent protocols in quantum information at the Centre for Quantum Technologies in Singapore.
  • Fan Bu
  • Fan Bu is a Ph.D. student in Statistics at Duke University. Her research interests include statistical machine learning, stochastic modeling, and social network analysis. Before coming to Duke, she received a B.S. in Data Science and Big Data Technology at Peking University, China.
  • Dr. Robert Calderbank
  • Dr. Robert Calderbank is the Director of the Information Initiative at Duke University, where he is Professor of Electrical Engineering, Computer Science, and Mathematics. He joined Duke in 2010, completed a three-year term as Dean of Natural Sciences in August 2013, and also served as Interim Director of the Duke Initiative in Innovation and Entrepreneurship in 2012. Before joining Duke, he was Professor of Electrical Engineering and Mathematics at Princeton University, where he also directed the Program in Applied and Computational Mathematics.
  • Dr. Mine Cetinkaya-Rundel
  • Dr. Mine Cetinkaya-Rundel is an Associate Professor of the Practice in the Department of Statistical Science at Duke University. She is also a Data Scientist and Professional Educator at RStudio. Mine’s work focuses on innovation in statistics pedagogy, with an emphasis on data science, computation, reproducible research, student-centered learning, and open-source education. She organizes ASA DataFest, an annual international two-day data science competition. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. She also teaches the popular Statistics with R MOOC on Coursera as well as numerous other online courses.
  • Julia Donheiser
  • Julia Donheiser is a senior studying Data Journalism through Program II. Their work focuses on merging statistics and storytelling to make research more accessible to the people most affected. They have worked with various non-profit and investigative newsrooms, including the Center for Public Integrity and education-focused Chalkbeat.
  • Kat Hefter
  • Kat Hefter is an undergraduate student studying the intersection of engineering and neuroscience. She hopes to use machine learning to help understand the neural circuitry involved in psychiatric disorders.
  • Josh Jelin
  • Josh Jelin is a senior product analyst and data scientist at Google. He currently works on developer productivity. His primary work has been in Google Cloud, with a focus on building scalable unified analytics from disparate data sources, and using ensemble methods to identify extreme outliers. In the past, Josh has worked at Wizards of the Coast, as well as Capital One's fraud protection division. Josh completed his undergraduate and graduate work at Carnegie Mellon University, completing an M.S. in Statistics in 2014.
  • Chris Kapu
  • Chris Kapu is a graduate student in engineering working on applying Bayesian machine learning to trends relating agriculture and water across North America. He is broadly interested in conducting work focused on monitoring large numbers of dynamic environmental systems with a synthesis of physical and data-driven modeling approaches.
  • Kelsey McDonald
  • Kelsey McDonald graduated from Princeton University with a B.A. degree in Psychology and a minor in Neuroscience. Her undergraduate research focused on reinforcement learning with Dr. Yael Niv. After completing her undergraduate studies, Kelsey worked with Dr. Catherine Hartley at Weill Cornell Medical College as a research assistant studying the development of reinforcement learning processes in children, adolescents, and adults. Kelsey is now a Ph.D. candidate in the Department of Psychology and Neuroscience at Duke University, studying strategic decision-making using computational modeling with Dr. John Pearson and Dr. Scott Huettel.
  • Peter Mikhael
  • Peter Mikhael is a senior pursuing a double major in Chemistry and Mathematics. As a member of the Locasale Lab, his research focuses on applying machine learning and systems biology to understanding the one-carbon metabolic network in cancer metabolism. After graduation, Peter will be applying to M.D.-Ph.D. programs and pursuing a career in oncology and computational cancer research.
  • Shelley Rusincovitch
  • Shelley Rusincovitch is an informaticist and technical leader with extensive background in healthcare data, clinical trials, and outcomes registries. In her role with the Forge, she co-leads the Demonstration Program with a portfolio of project illustrating the “art of the possible” in the vision of actionable health data science, and, in collaboration with the Forge Principal Data Scientist, provides direction and operational leadership for the transdisciplinary teams. She also manages the Data Science Core and oversees the Health Data Science Internship Program in partnership with the Duke Clinical Research Institute.
  • Sarah Sibley
  • Sarah Sibley is a senior studying Statistical Science and Political Science at Duke University. She will be headed to Facebook after graduation.
  • Dr. Rebecca Steorts
  • Dr. Rebecca Steorts received her B.S. in Mathematics in 2005 from Davidson College, her M.S. in Mathematical Sciences in 2007 from Clemson University, and her Ph.D. in 2012 from the Department of Statistics at the University of Florida under the supervision of Malay Ghosh. She was a Visiting Assistant Professor in 2012-2015, where she worked closely with Stephen E. Fienberg. She is currently an Assistant Professor in the Department of Statistical Science at Duke University. Rebecca was named to MIT Technology Review's 35 Innovators Under 35 for 2015 as a humanitarian in the field of software. Her work was profiled in the September/October issue of MIT Technology Review and she was recognized at a special ceremony along with an invited talk at EmTech in November 2015. In addition, Rebecca is a recipient of an NSF CAREER award, a collaborative NSF award, a collaborative grant with the Laboratory for Analytic Sciences (LAS) at NC State University, a Metaknowledge Network Templeton Foundation Grant, the University of Florida (UF) Graduate Alumni Fellowship Award, the U.S. Census Bureau Dissertation Fellowship Award, and the UF Innovation through Institutional Integration Program (I-Cubed) and NSF for development of an introductory Bayesian course for undergraduates. Rebecca was the recipient of an Honorable Mention (second place) for the 2012 Leonard J. Savage Thesis Award in Applied Methodology. Her research interests include large scale clustering, record linkage, privacy, network analysis, and machine learning for computational social science applications.
  • Dr. Maria Tackett
  • Dr. Maria Tackett is an Assistant Professor of the Practice in the Department of Statistical Science. She received a Ph.D. in Statistics from the University of Virginia; prior to pursuing a Ph.D., she received an M.S. in Statistics from the University of Tennessee and spent several years working as a statistician in industry. She is currently a member of the Center for Statistics and Applications in Forensic Evidence (CSAFE), a collaboration of researchers and forensic practitioners working to deepen the statistical foundation for the analysis of forensic evidence. Her work focuses on using Bayesian methods to inform the interpretation of pattern evidence, with an emphasis on interpreting latent fingerprint evidence. In addition to her work in forensics, Maria is interested in how technology and active learning spaces can be used to enhance student learning.
  • Jiurui Tang
  • Jiurui Tang is a Ph.D. student at Duke University studying Statistical Science.
  • Dr. Ya Xue
  • Dr. Ya Xue is the Director of Data Science Operations at Infinia ML, a machine learning company located in RTP. She manages a team of about 15 data scientists and oversees a portfolio of service projects with Fortune 500 companies as clients. Prior to Infinia ML, Ya was a senior machine learning scientist for Align Technologies. Ya has worked for Siemens Medical Solutions, GE Global Research, and two startups. She has worked on numerous projects spanning a wide range of application domains, such as industrial-system monitoring, security surveillance, renewable energy, online recommendations, and treatment planning. Ya received B.S., M.S., and Ph.D. degrees in Electrical Engineering from Tsinghua University, Arizona State University, and Duke University, respectively. The focus of her Ph.D. work was machine learning and statistical data modeling, and her work was published in top machine learning journals and conferences such as JMLR, NIPS and ICML.
  • Anna Yanchenko
  • Anna Yanchenko is a Ph.D. student in the Department of Statistical Science at Duke. She is interested in machine learning for time series applications, especially with applications to modeling music. She currently works with Professors Sayan Mukherjee and Peter Hoff on algorithmic composition and hierarchical audio modeling. Anna holds an M.S. in Statistical Science from Duke University and a B.S. in Physics from the University of Virginia. Prior to starting the Ph.D., Anna worked at the MIT Lincoln Laboratory.
  • Bihan Zhuang
  • Bihan Zhuang is a senior majoring in Computer Science and Statistical Science. She became interested in machine learning after attending the “Young Bayesians and Big Data for Social Good” workshop and conference in Marseille, France. She is currently doing a thesis with Dr. Rebecca Steorts on the application of record linkage to a real-world conflict dataset. After graduation, she will be working at Apple.