Daisy Zhe Wang
Director, Data Science Research Lab
Computer and Information Science and Engineering (CISE)
College of Engineering, University of Florida
Gainesville, FL 32611
Office: E456 CSE Building
Phone: (352) 294-6677; Fax: (352) 392-1220
Office Hours: TBA
Daisy Zhe Wang is an Associate Professor in the CISE department at the University of Florida. She is the Director of the Data Science Research Lab at UF. She obtained her Ph.D. degree from the EECS Department at the University of California, Berkeley in 2011 and her Bachelor´s degree from the ECE Department at the University of Toronto in 2005 download daw. At Berkeley, she was a member of the Database Group and the AMP/RAD Lab. She is particularly interested in bridging scalable data management and processing systems with probabilistic models and statistical methods. She currently pursues research topics such as probabilistic databases, probabilistic knowledge bases, large-scale inference engines, query-driven interactive machine learning, and crowd assisted machine learning schlager herunterladen kostenlos. She received Google Faculty Award in 2014. Her research is currently funded by NSF, DARPA, NIST, NIH, Google, Amazon, Pivotal, Greenplum/EMC, Sandia National Labs and Harris Corporation.
- [March 2018] We are part of a larger PRISMA-P (Precision and Intelligent Systems in Medicine) project, funded by NIH, since its inception from 2013 Download netflix movies on ipad. One of key publications is accepted to Annals of Surgery. We continue to expand our research experience in biomedical and transnational research through project such as Rose and PRISMA-P.
- [Jan 2018] We are part of a newly funded NSF IUCRC (Industry and University Cooperative Research Center) program at the University of Florida: Center for Big Learning, whose goal is to push further the research, tech transfer and application of deep learning technologies gimp download kostenlos.
- [Dec 2017] In collaboration with USC ISI, University of Columbia and RPI, we are selected to receive a grant to work on the DARPA Active INterpretation of Desperate Alternatives (AIDA) program. UF team is going to focus on mining hypothesis from probabilistic knowledge graphs constructed from multimedia event driven corpus.
- [Oct 2017] Supported by NIST and co-PIed with Dr rooster excel downloaden. Ethan White from the Weecology Lab, the Data Science Evaluation (DSE) for Plant Identification with Neon Remote Sensing data is well underway. The tasks and evaluation guideline documents are released — Please join!
- [Aug 2017] The Apache Software Foundation Announces Apache® MADlib™ as a Top-Level Project Download ipad games. MADlib 1.12 released recently with Neural Nets implementation of multi-layer perceptron and Jupiter Notebook demonstrating its application over MNIST dataset.
- [May 2017 UF Clinical and Translational Science Institute (CTSI) and UF Institute for Child Health Policies (ICHP) sponsored the research and development of an intelligent virtual health navigator Rose that is supported by research from the DSR Lab neflix.
- [Jan 2017] Our journal paper by Sean Goldberg et. al.: pi-CASTLE: A Probabilistically Integrated System for Crowd-Assisted Text Labeling and Extraction is published in ACM JDIQ (Journal of Data and Information Quality), 2017.
- ProbKB: Large-scale Probabilistic Reasoning over Uncertain Knowledge Bases
- DBlytics/MADLib: Statistical Machine Learning and Text Analytics in MPP DBMS frameworks
- Archer: Query-Driven Machine Learning
- CAMeL: Leverage Crowd Support in Probabilistic Databases
- SigmaKB: Knowledge fusion, cleaning and knowledge base integration
- VITA: Multimodal Knowledge Extraction and Fusion
- SMARTeR: Smarter information retrieval system
- Panda: Knowledge Extraction and Exchange Using Medical Notes
- Past Projects
Current Ph.D wie kann man videos herunterladen. Students
- Christan Grant (2015) University of Oklahoma
- Kun Li (2015) Google Inc
- Morteza Shahriari Nia (2016) Twitter Inc
- Yang Chen (2016) Google Inc
- Yang Peng (2017)
- CAP4773/CAP6779, Project In Data Science, Spring 2018
- CAP4770/CAP5771, Introduction to Data Science, Fall 2017
- CAP4773/CAP6779, Project In Data Science, Spring 2017
- CAP4770/CAP5771, Introduction to Data Science, Fall 2016
- CAP4773/CAP6779, Project In Data Science, Spring 2016
- CAP4770/CAP5771, Introduction to Data Science, Fall 2015
- CIS4301, Information and Data Management Systems, Spring 2015
- CA4773/CIS6930, Projects in Data Science, Fall 2014
- CIS6930, Introduction to Data Science/Data Intensive Computing, Spring 2014
- COP5725, Data Management Systems, Fall 2013
- CIS6930, Data Science: Large-scale Advanced Data Analysis, Spring 2013
- COP5725, Data Management Systems, Fall 2012
- CIS4301, Information and Data Management Systems, Spring 2012
- CIS6930, Data Science: Large-scale Advanced Data Analysis, Fall 2011
- “Weathering the (Technology) Hypes”
- New Researcher Symposium, SIGMOD, May 2017
- “Archimedes: A Probabilistic Master Knowledge Base System”
- Florida HLT Cofab, Feb 2017
- “Deep Learning over Large-scale Databases and Knowledge Graphs”
- NSF IUCRC for Big Learning Planning meeting, Jan 2017
- “Archimedes: A Probabilistic Knowledge Base to Combine Information Extraction from Diverse Sources”
- “UDA-GIST: An In-database Framework to Unify Data-Parallel and State-Parallel Analytics”
- VLDB 2015, Waikoloa Hawii, September 2015
- “Archimedes: A Master Probabilistic Knowledge Base System”
- University of Miami, Nov 2015
- Harris Coorporation, August 2015
- University of Toronto, July 2015
- Berkeley AMP Lab Seminar, April 2015
- Google Research, April 2015
- Sandia Livermore Lab, Jan 2015
- “Probabilistic Knowledge Base Construction from Big Text, Images and Crowds”
- TRUST WISE workshop at Cornell University, June 2014
- UF Big Data Workshop, June 2013
- “Probabilistic Knowledge Base Systems”
- Invited Talk, WACCK workshop at SIGMOD, June 2014
- Shanghai Jiaotong University, China, April 2014
- ECE Department, University of Florida, October 2013
- Fudan University, China, August 2013
- Google Research, EMC, April 2013
- Rochester Big Data Forum, October 2012
- “Hybrid In-Database Inference for Declarative Information Extraction” sigmod11slides
- SIGMOD Conference, June 15, 2011
- “Selectivity Estimation for Extraction Operators over Text Data” icde11slides
- ICDE Conference, April 14, 2011
- “Querying Probabilistic Information Extraction”
- EMC/Greenplum Seminar, July 11, 2011
- CSAIL Seminar, MIT, November 17, 2010 gratis word programmen.
- Database Seminar, University of Toronto, January 5, 2010.
- “Querying Probabilistic Information Extraction” pvldb10slides
- VLDB Conference, September, 2010
- “Probabilistic Declarative Information Extraction” icde10slides
- ICDE Conference, March, 2010
- “Declarative Information Extraction in a Probabilistic Database System”
- Info Lab Seminar, Stanford, May, 2009.
A Parable of Modern Research
Bob has lost his keys in a room which is dark except for one brightly lit corner.
“Why are you looking under the light, you lost them in the dark!”
“I can only see here.”