In addition to consultations and workshops, GradQuant also hosts drop-in hours and Hacky Hours. Drop-in hours allow students to utilize GradQuant staff without an appointment, on a “first-come, first-served” basis. Because our drop-in hours are more flexible than our one-on-one consultations, these are also a good time to visit GradQuant with a collaborator or classmate. Drop-in hours function similarly to consultations, so feel free check out our consultations page for more details.

NEW: In Winter 2019, GradQuant is teaming up with the UCR Library to offer Hacky Hours on Thursday afternoons! Come talk to Kat Koziar, Data Librarian at UCR Library, and Lauren Cappiello, GradQuant Lead Consultant, about programming, data management, and more. Hacky Hours are open to all members of the UCR research community, including graduate and undergraduate students, postdoctoral researchers, faculty, and staff.

Please note that GradQuant follows UCR’s academic calendar, but may occasionally have additional closures that will be listed on this page. Closures are also reflected in our event calendar.

Winter 2019 Drop-In & Hacky Hours

This quarter, GradQuant is offering drop-in hours four times per week. Drop-in hours are staffed by the same GradQuant consultants each week. See below for details.

Mondays, 1:00 – 3:00 PM

Consultant: Vashishtha “VJ” Bhatt
Specialties: Python, Machine Learning, MATLAB, Java, Classification & Text Clustering, and SolidWorks

Tuesdays, 9:00 – 11:00 AM

Consultant: Heran Bhakta
Specialties:
Python, LaTeX, JMP, COMSOL, SolidWorks

Consultant: Lauren Cappiello
Specialties:
Basic Statistics, Advanced Statistics, Nonparametric Methods, Mathematical Statistics and Probability Theory, Experimental Design, Data Mining, Machine Learning, LaTeX, Stan, SAS, R

Wednesdays, 9:00 – 11:00 AM

Consultant: Seth Margolis
Specialties: Basic Statistics, Advanced Statistics, Experimental Design, SPSS, R, Excel

Thursdays, 1:00 – 3:00 PM: HACKY HOURS

Consultant: Lauren Cappiello
Specialties: Basic Statistics, Advanced Statistics, Nonparametric Methods, Mathematical Statistics and Probability Theory, Experimental Design, Data Mining, Machine Learning, LaTeX, Stan, SAS, R

Data Librarian: Kat Koziar
Specialties: Python, R, Git, Bash, OpenRefine, Data Management, Data Cleaning, Data Visualization, Data Analysis, Quantitative Methods