Daniel Hauagge's Email & Phone Number

💼 Past Experience

Biography of Daniel Hauagge:Daniel Hauagge was born in Brazil in 1984. He has a degree in Computer Science from the University of Sao Paulo. Hauagge has been working in the technology industry for the last ten years, starting off as a research engineer at LNLS (Brazilian Synchrotron Light Laboratory). He then worked at Apple in Cupertino, California for 2 years. Hauagge moved to San Francisco Bay Area in October 2017 as the Head of Computer Vision & Machine Learning at SelfMade. He then took a position as the Founder at PeachyLabs from April 2015 to August 2016. He also worked at Brown University from November 2008 to August 2009. He then worked at Adobe from May 2011 to August 2011 as a Research Scientist. Hauagge is then a visiting scholar at Brown University from November 2008 to August 2009. He also worked on the project “Embeddings to generate visual summaries of photo collection using Markov Random Fields” as a visiting researcher at the Israel Institute of Technology in Haifa, Israel. He also teaches Intermediate and intro iOS programming courses at Cornell University and at the Ithaca and New York City campuses. Hauagge is also a published author and has given talks on deep learning and machine learning.

🎓 Education

Daniel Hauagge's education and work readiness background is highly versatile and allows him to take on many different types of roles and responsibilities. Hauagge has a technical degree from Universidade Tecnológica Federal do Paraná, which gives him the ability to work in the design and verification of computer systems. He then went on to have a MSc at Universidade de Campinas, where he studied Computer Science. Finally, Hauagge obtained a BSc from Universidade Estadual de Campinas, which in turn gave him the abilities to work in the computer engineering field. His research and development background comes from his work in the computerscience field at Cornell University, where he obtained his PhD in 2009. Overall, Hauagge has a diverse background that allows him to be adaptable and reliable in many different situations.

💡 Technical & Interpersonal Skills

Daniel Hauagge's professional experience:Deep Learning Research Engineer at LNLS: Brazilian Synchrotron Light Laboratory from 2004 to 2004 (less than a year) in Campinas Area, Brazil.Machine Learning Scientist at GrokStyle (acquired by Facebook) from October 2017 to December 2018 (1 year 3 months) in San Francisco Bay Area.Head of Computer Vision & Machine Learning at SelfMade from September 2016 to September 2017 (1 year 1 month) in Greater New York City Area.Founding Member at PeachyLabs from April 2015 to August 2016 (1 year 5 months) in Greater New York City Area.AI company using computer vision and deep learning to recognize and categorize images of food.Visiting Scholar at Brown University from November 2008 to August 2009 (10 months) in Providence, Rhode Island Area.Designed and deployed camera arrays to capture an archeological dig.Developed a new method for calibrating orthopic cameras published in ICCV2009 workshop.Summer Intern at Technion - Israel Institute of Technology from May 2005 to September 2005 (5 months) in Haifa Area, Israel.Lecturer at Cornell University from February 2016 to October 2017 (1 year 9 months) in Ithaca and New York.Taught Intermediate and intro iOS programming courses in Swift and Objective-C at Cornell University, at the Ithaca and New York City campuses.Runway Postdoc at Cornell Tech from August 2014 to August 2016 (2 years 1 month) in Greater New York City Area.Developed technologies for food recognition using deep learning.Conceived of the projects goals, developed the underlying technology. Led development of a mobile app and a backend recognition server.Led the drive to commercialize the technology.Summer Intern at Adobe from May 2011 to August 2011 (4 months) in Greater Seattle Area.Used crowd sourcing to learn semantically meaningful embeddings of large photo collection using SVM Rank.Embeddings were then used to generate visual summaries of photo collection using Markov Random Fields (poster presented at ICCP 2013).