Cornell University Graduate School
The theses and dissertations of graduate students at Cornell University have been deposited in Cornell's institutional repository (eCommons) since about 2004. This collection also includes a few earlier Cornell theses.
Students retain ownership of the copyright of their work. Students also have the option of imposing a temporary embargo on access to the full text of their theses for limited amount of time (see eCommons access policy). If access to a thesis is restricted, the metadata record for the thesis is still visible, but the text "Access to Document Restricted" is displayed, and a field labeled "No Access Until," which indicates the date when the full text of the thesis will become accessible.
More information about finding Cornell theses and dissertations is available on this library guide, and the eCommons help page for finding content in specific collections, including theses and dissertations.
In general, older theses and dissertations from Cornell University are not currently available as digital files in eCommons. The Library is willing to digitize and make available older Cornell theses on a cost recovery basis. If you are interested in this service, please contact firstname.lastname@example.org.
Collections in this community
(2007-05-02)We consider the average consensus algorithm under the rate constraint communication network. Average consensus algorithms are protocols to compute the average value of all sensor measurements via near neighbors communications. ...
Atomic Scale Chemistry on Silicon Surfaces Studied with a Variable Temperature Scanning Tunneling Microscope (1998-08)Using a variable temperature scanning tunneling microscope, we have studied several adsorbates on silicon surfaces. We have studied the adsorption characteristics of H2S on Si(111)-(7×7). H2S adsorbs dissociatively at ...
(2018-08-30)Current contributions from public and philanthropic sources, while significant, are insufficient to finance global fisheries reform. Private capital markets are a largely untapped resource that many argue can help support ...
Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks (2018-08-30)Fully automatic processing of images is a key challenge for the 21st century. Our processing needs lie beyond just organizing photos by date and location. We need image analysis tools that can reason about photos like a ...
MULTISCALE MODELING AND MACHINE LEARNING STUDIES OF THE DIFFUSION OF SILICON AND INTRINSIC DEFECTS IN III-V SEMICONDUCTORS (2018-08-30)Integrating III-V semiconductors into next-generation silicon-based processing is a promising alternative being considered as a route to faster and more energyefficient electronic devices. These III-V materials will be ...