Duke Undergraduate Machine Learning Day

Saturday, March 31, 2018 | Gross Hall, Duke University

Come to Duke's first-ever Machine Learning Day, for undergraduates, on Saturday, March 31 in Gross Hall! At ML Day, you'll have the opportunity to learn about Duke's exciting machine learning research. And, if you're a student researcher in ML, you'll have the opportunity to present your work! The day's activities include:
  • Panel discussions with upperclassmen exploring careers in machine learning post-graduation, as well as professors and graduate students pursuing research in machine learning to learn what graduate-life is like;
  • Workshops in areas such as deep learning, algorithm selection for privacy, entity resolution, and more; and
  • Research and poster presentations and a keynote on ML-related research.
Register for the event here. Companies can sponsor the event by sponsoring Duke Undergraduate Machine Learning.

Schedule

  • 9am-10am: Registration and breakfast (Energy Hub Atrium)
  • 10am-10.15am: Introduction (Ahmadieh Family Auditorium, 107)
  • 10.15am-10.45am: Entity Resolution with Societal Impacts in Statistical Machine Learning (Keynote) by Dr. Rebecca Steorts (Ahmadieh Family Auditorium, 107)
  • 11am-12pm: An Introduction to Machine Learning (Workshop) by Dr. Kyle Bradbury (Ahmadieh Family Auditorium, 107)
  • 11am-12pm: An Introduction to Entity Resolution (Workshop) by Dr. Rebecca Steorts (Ahmadieh Family Grand Hall, 330)
  • 12pm-1pm: Networking lunch and poster presentations (Energy Hub Atrium)
  • 1pm-1.45pm: Deep Learning and Neural Networks (Workshop) by Dr. Lawrence Carin (Ahmadieh Family Grand Hall, 330)
  • 1.55pm-2.40pm: Differential Privacy and Algorithm Selection (Workshop) by Dr. Ashwin Machanavajjhala (Ahmadieh Family Grand Hall, 330)
  • 2.30pm-3pm: Refreshments (Energy Hub Atrium)
  • 3pm-3.45pm: Research spotlight presentations (Ahmadieh Family Auditorium, 107)
    • What Does Facebook Know? Predicting Rationality by Trenton Bricken, Elle Deich, Kenneth Green, and James Wang
    • Electronic Health Record Representations for Interpretable Machine Learning by Cathy Chi, Divya Koyyalagunta, and Anna Sun
    • Convolutional Neural Network, Support Vector Machine-based Automated Software for Gestational Age Estimation by Arjun Desai
    • Single-Image Footstep Prediction for Versatile Legged Locomotion by Wuming Zhang
  • 4pm-4.45pm: Undergraduate student panel of Serge Assaad, Rohith Kuditipudi, Yixin Lin, Angie Shen, and Mary Ziemba, moderated by Dr. Rebecca Steorts (Ahmadieh Family Auditorium, 107)
  • 4.45pm-5.30pm: Graduate and post-graduate panel of Dr. Brenda Betancourt, Fan Bu, Dr. Andee Kaplan, and Nisarg Raval, moderated by Dr. Rebecca Steorts (Ahmadieh Family Auditorium, 107)

Speaker & Panelist Bios

  • Serge Assaad
  • Serge Assaad is a senior at Duke University studying Biomedical and Electrical & Computer Engineering with a minor in Mathematics. He is interested in the application of machine learning to medical problems. He has worked on classifying vascular anomalies from Doppler ultrasound audio data.
  • Dr. Brenda Betancourt
  • Dr. Brenda Betancourt is a Postdoctoral Associate in the Department of Statistical Science at Duke University working with Dr. Rebecca Steorts. She is currently working on developing new Bayesian models and algorithms for entity resolution used to identify duplicate records in large noisy databases with applications on human rights violations and social statistics, among others. Brenda is originally from Bogotá, Colombia. She obtained her undergraduate degree at Universidad Nacional de Colombia where she was trained in classical Statistics. In 2008, she completed an M.S. in Statistics from the University of Puerto Rico, Río Piedras where she started to work on Bayesian Statistics. She moved to California in 2010 to pursue a Ph.D. in Statistics at the University of California, Santa Cruz where she worked on modeling and link prediction of dynamic network data.
  • 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.
  • Trenton Bricken
  • Trenton Bricken is a sophomore Robertson Scholar majoring in "Minds and Machines: Biological and Artificial Intelligence," which spans Computer Science, Neuroscience, Genetics, and Statistical Science. His research spotlight presentation perfectly fits into his interest in psychometrics and using data to better understand and predict human behaviour.
  • Fan Bu
  • Fan Bu is a Ph.D. student in the Department of Statistical Science at Duke University. She is interested in statistical machine learning methods with applications in network modeling and social sciences. She is currently working with Dr. Katherine Heller and Dr. Alexander Volfovsky to study player value assessment and network dynamic modeling based on Duke Basketball tracking data.
  • Dr. Lawrence Carin
  • Dr. Lawrence Carin earned his B.S., M.S., and Ph.D. degrees in Electrical Engineering at the University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989 he joined the Electrical Engineering Department at Polytechnic University (Brooklyn) as an Assistant Professor, and became an Associate Professor there in 1994. In 1995 he joined the Electrical Engineering Department at Duke University, where he is now a Professor, and Vice Provost for Research. From 2003-2014 he held the William H. Younger Distinguished Professorship, and he was ECE Department Chair from 2011-2014. Dr. Carin's early research was in the area of electromagnetics and sensing, and over the last 15 years his research has moved to applied statistics and machine learning. He is an IEEE Fellow.
  • Cathy Chi
  • Cathy Chi is a junior at Duke University majoring in Computer Science and minoring in Mathematics and Global Health. She became interested in machine learning when she was abroad at the University of Sydney and worked on a melanoma image classification research project. In her free time, she enjoys yoga, running, and hiking/traveling.
  • Elle Deich
  • Elle Deich is a sophomore A.B. Duke Scholar studying Biological and Artificial Intelligence, an independent major that includes courses in Biology, Statistical Science, Neuroscience, and Computer Science. As an effective altruist, her research for her spotlight presentation has allowed her to combine her many interests by focusing on the prediction of both human rationality and IQ.
  • Arjun Desai
  • Arjun Desai is a senior pursuing a B.S.E. in Biomedical Engineering and a B.S. in Computer Science at Duke University. He is a Pratt Fellow researcher under Dr. Sina Farsiu focused on applying deep learning and machine learning techniques to medical image analysis.
  • Kenneth Green
  • Kenneth Green is a sophomore studying Computer Science with a minor in Economics. He is on the executive board of the Jewish Student Union, an analyst for the Investment Club, and a Duke University tour guide. In his free time, he likes hiking and running. This coming summer, he will be interning as a Software Engineer on a driver team at Uber.
  • Dr. Andee Kaplan
  • Dr. Andee Kaplan is a postdoctoral associate at Duke University working with Dr. Rebecca Steorts in the Department of Statistical Science. She recently completed her Ph.D. in Statistics at Iowa State University working with Dan Nordman and Steve Vardeman in the Summer of 2017. Additionally, she also has an M.S. in Statistics from Iowa State University with Heike Hofmann and Dan Nordman and an M.A. in Mathematics at The University of Texas at Austin under the direction of John Luecke and Martha Smith. She received her B.S. in Mathematics with a certificate in Computing from The University of Texas at Austin. Her interests lie in the intersection of Statistics and Computing, with a penchant for statistical graphics and reproducibility. Andee enjoys struggling with JavaScript and learning new languages, R being her first love.
  • Divya Koyyalagunta
  • Divya Koyyalagunta is a senior majoring in Computer Science with a minor in Neuroscience. She’s interested in the applications of machine learning in the health field, and will be working at Apple Health next year. In her free time, she likes to dance, hike, and eat tacos.
  • Rohith Kuditipudi
  • Rohith Kuditipudi is a junior at Duke University majoring in Mathematics and Computer Science, and is interested in deep learning and robotics. He has worked with various machine learning groups on campus since freshman year, and is interested in going to graduate school.
  • Yixin Lin
  • Yixin Lin is a senior Computer Science major at Duke University. Previously, he interned as a KPCB Engineering Fellow at Gusto, a computer vision researcher at AMALTHEA REU, facial recognition at Facebook AML, and deep reinforcement learning at Google Brain. On campus, he has been involved as a director of HackDuke, a venture partner at Contrary Capital, exec on Effective Altruism, as well as research on deep learning with the Carin lab and DNA automata with Dr. John Reif. He's interested in the long road to AGI, and will be joining Facebook after graduation to pursue machine learning research.
  • Dr. Ashwin Machanavajjhala
  • Dr. Ashwin Machanavajjhala is an Assistant Professor in the Department of Computer Science, Duke University and an Associate Director at the Information Initiative@Duke (iiD). Previously, he was a Senior Research Scientist in the Knowledge Management group at Yahoo! Research. His primary research interests lie in algorithms for ensuring privacy in statistical databases and augmented reality applications. He is a recipient of the National Science Foundation Faculty Early CAREER award in 2013, and the 2008 ACM SIGMOD Jim Gray Dissertation Award Honorable Mention. Ashwin graduated with a Ph.D. from the Department of Computer Science, Cornell University and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Madras.
  • Nisarg Raval
  • Nisarg Raval is a Ph.D. student in the Computer Science department at Duke University. He is interested in developing privacy solutions for mobile systems to protect user privacy without compromising utility of the applications running on those platforms. His research lies at the intersection of privacy, machine learning and mobile systems.
  • Angie Shen
  • Angie Shen recently graduated from Duke University with a B.S. in Statistics. As an undergraduate, she participated in statistical machine learning research, which involves predicting patient deterioration using electronic health records data. She also interned with a financial consulting company specializing in credit risk prediction. She will be pursuing a Ph.D. in Biostatistics in the Fall.
  • 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.
  • Anna Sun
  • Anna Sun is a senior majoring in Computer Science with a Markets & Management Studies certificate. She’s interested in applying machine learning and artificial intelligence to produce interpretable, actionable insights in a variety of sectors. In her free time, she enjoys jump roping, rock climbing, backpacking, and Guasaca.
  • James Wang
  • James Wang (Statistics '19) is the president of Duke East Asia Nexus club, executive member of American Grand Strategy, and leader of the quant team in his Bass Connection research. James has helped multiple local startups in business decision making and plays soccer and tennis in his free time. He will be a corporate strategy intern at Chewy.com this summer.
  • Wuming Zhang
  • Wuming Zhang is a senior at Duke University, majoring in Computer Science and Statistical Science. Since freshman year, he has participated in several research projects, including a deep learning and robotics project with Dr. Kris Hauser, a reinforcement learning project with Dr. Ronald Parr, and a computer vision project with Dr. Kyle Bradbury. He will be pursuing an M.S. in Computer Vision at Carnegie Mellon University this Fall.
  • Mary Ziemba
  • Mary Ziemba is a senior at Duke University from New Jersey studying Computer Science and Innovation & Entrepreneurship. Her computer science interests include ethics of algorithms, data privacy, and machine learning. She did machine learning this past summer at Apple, where she will return full-time in the Fall.