Upcoming Mathematical Meetings and Conferences

 Inquiry Based Learning Workshop

April 6, 2019, Florida Atlantic University 

The 5th International Computational and Mathematical Population Dynamics

May 19-24, 2019, Bahia Mar Fort Lauderdale Beach  

Young CryptograpHers: A Cybersecurity Summer Camp for High School Girls

July 29-August 2, 2019, Harold and Marleen Forkas Alumni Center, Florida Atlantic University


Recent Math Conferences/Events

Florida Women in Mathematics Day (FWIMD)

Saturday, February 9, 2019, Florida Atlantic University 

Florida Geogebra, 2019

February 15 & 16, 2019, Florida Atlantic University, Science and Engineering Complex

50th Southeastern International Conference on Combinatorics, Graph Theory and Computing, 2019

March 4-8, 2019, Florida Atlantic University, Student Union

View our CGTC Photos! 


Math Boot Camp - FAU Students

Need to improve your mathematical skills? Check out the Math Boot Camp! New sessions open throughout the semester!

Math Day Events - K12 Community Events

American Mathematics Competitions - AMC-10/12A and AMC-10/12B 

The AMC 10/12A will be held on TBA. 

The AMC10/12B will be held on TBA. 

Math Seminars and Colloquium 

Department of Mathematical Sciences Colloquium

Thursday, March 28, 2019; SE 215, 11:00 a.m.

Speaker: Professor Louis Menzer, Chemistry and Physics, Npva Southeastern University

Title:  Seizure Prediction with Machine Learning using Real and Simulated Electrocorticography Data


Epilepsy is the most common chronic neurological disorder, affecting approximately one percent of people worldwide. Patients with symptoms not well controlled with medication often suffer significantly reduced quality of life due to the unpredictable nature of seizures, which are periods of pathological synchronization of neural activity in the brain. Using a surgically-implanted intracranial electrode grid, electrocorticography (ECoG) provides better spatial and temporal resolution of brain electrical activity, compared with conventional scalp electroencephalography (EEG). We combine this patient data with simulated output from a full Hodgkin-Huxley calculation using in silico neurons connected with a small-world network topology. Supervised Machine Learning, a set of powerful and flexible artificial intelligence techniques that allow computers to classify complex data without the need for explicit programming, along with topological data analysis methods, are employed with a goal of developing an algorithm that can be used for the real-time clinical prediction of seizure risk. 

All are cordially invited. Coffee and donuts will be served.


Wednesday, April 3, 2019; SE 215, 2:30 p.m.

Speaker: Ziyu Hu

Title: The HARQ model for Realized Volatility


Among models for the realized volatility (RV) and RV-based forecasting, the issue of measurement errors has been largely ignored, while in the others the errors are treated as homoskedastic. In this presentation, we introduce a new family of models by Bollerslev et al. which allows the parameters to vary explicitly with the (estimated) degree of measurement error. The models exhibit stronger persistence, and in turn generate more responsive forecasts when the measurement is relatively low. The HARQ model is one from the family that adds time-varying components to the popular Heterogeneous Autoregression (HAR) model of Corsi. Significant improvements in the accuracy of the resulting forecasts are documented implementing the new class of models for the S&P 500 equity index and the individual constituents of the Dow Jones Industrial Average, as compared to the models that ignore the temporal variation in the magnitude of the realized volatility measurement errors.

All are cordially invited.