Richard Lange
Assistant Professor
Department of Computer Science
Golisano College of Computing and Information Sciences
585-475-7923
Richard Lange
Assistant Professor
Department of Computer Science
Golisano College of Computing and Information Sciences
585-475-7923
Select Scholarship
Invited Keynote/Presentation
Lange, Richard D. "Interpolating between sampling and variational inference with infinite stochastic mixtures." From Theory to Practice: workshop in data science. AIMS Rwanda. Kigali, Rwanda. 16 Apr. 2024. Guest Lecture.
Journal Paper
Lange, Richard D, et al. "Bayesian encoding and decoding as distinct perspectives on neural coding." Nature Neuroscience 26. (2023): 2063–2072. Web.
Currently Teaching
COGS-880
Cognitive Science Dissertation Research
1 - 6 Credits
This course is to fulfill the work plan agreed by the student and the dissertation adviser. The guiding principle of the Dissertation Research course is to complete the doctoral dissertation research proposed by the doctoral candidate and approved by the candidate’s dissertation committee. The course consists of carrying out the thesis research, including collection and analysis of data, and completion and public defense of the dissertation document for partial fulfillment of the requirements of the PhD degree in Cognitive Science. This course can only be taken after successful completion of COGS-800 Cognitive Science Qualifying Examination.
COGS-888
Continuation of Dissertation
0 Credits
Doctoral students in the Ph.D. in Cognitive Science are expected to conduct research under the supervision and guidance of their faculty advisor. After completion of all degree requirements, with the exception of COGS-890 Cog Sci Dissertation and including completion of at least 60 semester credit hours, students may register for COGS-888 Cog Sci Continuation of Dissertation in order to maintain their status in the Ph.D. in Cognitive Science program.
CSCI-631
Foundations of Computer Vision
3 Credits
An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit.