Update publications

config.toml

@@ -23,7 +23,7 @@ defaultContentLanguageInSubdir = false
   role = "PhD Candidate"
   organization = "University of Illinois Urbana-Champaign"
   organization_url = "http://www.ece.illinois.edu/"
-  avatar = "headshot.jpeg"
+  avatar = "headshot.jpg"
   email = "pearson@illinois.edu"
   address = "227 Coordinated Science Lab MC 228, 1308 W. Main St., Urbana, Illinois 61801"
   office_hours = "Face-to-face by appointment"

content/home/positions.md

@@ -16,11 +16,13 @@ weight = 30
 
 ## Industry
 
-Summer 2017 - Research Intern, IBM TJ Watson Research Center, Yorktown Heights, NY
+Summer 2017 - Research Intern for Optimized CLOUD Systems, IBM TJ Watson Research Center, Yorktown Heights, NY
 
 ## Teaching
 
-I am a University of Illinois [Mavis Future Faculty Fellow](http://publish.illinois.edu/engr-mavis/2017-2018-mavis-fellows/) for the 2017-2018 academic year.
+- 2017 Fall University of Illinois Head TA for ECE408/CS483
+- 2017-2018 University of Illinois [Mavis Future Faculty Fellow](http://publish.illinois.edu/engr-mavis/2017-2018-mavis-fellows/).
+
 
 I have been a teaching assistant for the following courses:
 

content/publication/2017hwu.md

@@ -1,23 +0,0 @@
-+++
-draft = false
-
-date = "2017-06-22"
-title = "Massively-Parallel Heterogeneous Computing for Solving Large Problems"
-authors = ["Wen-Mei Hwu", "Mert Hidayetoglu", "Carl Pearson", "Simon Garcia de Gonzalo", "Sitauo Huang", "Abdul Dakkak"]
-
-abstract = ''
-
-image = ""
-image_preview = ""
-math = false
-publication = "*Computing and Electromagnetics International Workshop.* IEEE, 2017."
-
-url_code = ""
-url_dataset = ""
-url_pdf = ""
-url_project = ""
-url_slides = ""
-url_video = ""
-
-selected = true
-+++

content/publication/2017pearson.md

@@ -1,23 +0,0 @@
-+++
-draft = false
-
-date = "2017-06-22"
-title = "Comparative Performance Evaluation of Multi-GPU MLFMM Implementation for 2-D VIE Problems"
-authors = ["Carl Pearson", "Mert Hidayetoglu", "Wei Ren", "Weng Cho Chew", "Wen-Mei Hwu"]
-
-abstract = 'We compare multi-GPU performance of the multilevel fast multipole method (MLFMM) on two different systems: A shared-memory IBM S822LC workstation with four NVIDIA P100 GPUs, and 16 XK nodes (each is employed with a single NVIDIA K20X GPU) of the Blue Waters supercomputer. MLFMM is implemented for solving scattering problems involving two-dimensional inhomogeneous bodies. Results show that the multi-GPU implementation provides 794 and 969 times speedups on the IBM and Blue Waters systems over their corresponding sequential CPU executions, respectively, where the sequential execution on the IBM system is 1.17 times faster than on the Blue Waters System.'
-
-image = ""
-image_preview = ""
-math = false
-publication = "*Computing and Electromagnetics International Workshop.* IEEE, 2017."
-
-url_code = ""
-url_dataset = ""
-url_pdf = "pdf/mlfmm-cem2017.pdf"
-url_project = ""
-url_slides = ""
-url_video = ""
-
-selected = true
-+++