About the CIMPA Summer School
The main objective of this summer school is to enrich collaboration between Nepalese and international research scholars, especially those early in their careers, while providing opportunities for data analysts, statisticians, biologists and mathematicians in Nepal and neighboring countries to develop modeling, computational and analytical skills that can be applied to address problems in public health and natural sciences. The summer school aims at introducing graduate students, early career faculty members, and young researchers to diverse topics from data modeling and visualization as well as algebraic, topological and analytic tools to study mathematical models via differential equations. The courses will be focused on foundational topics in statistical inference; linear and nonlinear regression analysis; time-series regression; reliability and survival analysis; data-driven mathematical modeling via ordinary and partial differential equations; data fitting and parameter estimation with computer coding; analysis of ODE and PDE models and their numerical solutions; and algebraic, variational and nonvariational methods for analyzing mathematical models.
Committees
Administrative and Scientific Coordinators
Local Coordinator: Shree Ram Khadka, Tribhuvan University, Nepal, shree.khadka@cdmath.tu.edu.np
External Coordinator: Abdenacer Makhlouf, Université de Haute Alsace, France, abdenacer.makhlouf@uha.fr
Scientific Committee
Dhruba Adhikari, Kennesaw State University, USA
Maya Chhetri, University of North Carolina at Greensboro, USA
Mohamed Elhamdadi, University of South Florida, USA
Daniel Kaplan, Macalester College, Minnesota, USA
Abdenacer Makhlouf, Université de Haute Alsace, France
Adnan Sljoka, RIKEN Center for Advanced Intelligence Project (AIP), Japan
Gauri Shrestha, Tribhuvan University, Nepal
Nicoleta Tarfulea, Purdue University Northwest, USA
Naveen Vaidya, San Diego State University, USA
Organizing Committee
Pitambar Acharya, University of Alabama at Birmingham , USA
Gokarna Aryal, Purdue University Northwest, USA
Maya Chhetri, University of North Carolina at Greensboro, USA
Shree Ram Khadka, Tribhuvan University, Nepal
Netra Khanal, University of Tampa, USA
Abdenacer Makhlouf, Université de Haute Alsace, France
Anjana Pokhrel, Tribhuvan University, Nepal
Keshav Pokhrel, University of Michigan-Dearborn, USA
Neelam Subedi, Tribhuvan University, Nepal
Instructors/Mentors
Gokarna Aryal, Purdue University Northwest, Indiana, USA
Hum Nath Bhandari,
Roger Williams University, USA
Gauri Shrestha, Tribhuvan University, Nepal
Naveen Vaidya, San Diego State University, USA
Adnan Sljoka, RIKEN Center for Advanced Intelligence Project (AIP), Japan
Abdenacer Makhlouf, Université de Haute Alsace, France
Maya Chhetri, University of North Carolina at Greensboro, USA
Dhruba Adhikari, Kennesaw State University, USA
Registration
Registration and Financial Support
For registration and application to a CIMPA financial support, follow the instructions given at the webpage
www.cimpa.info/en/node/40.
Deadline for registration and application: February 01, 2023
For questions related to the summer school registration, contact Dr. Keshav Pokhrel, University of Michigan-Dearborn, USA, at kpokhrel@umich.edu.
Scientific Program & Structure
Course 1: Data Visualization and Statistical Inference
Instructors: Dr. Daniel Kaplan and Dr. Hum Nath BhandariAbstract: Visualization and modeling of data generally requires hefty work to clean, reshape, condense, and bring data together from multiple sources. This course aims to teach (i) how to work with and manipulate unstructured data (ii) identify appropriate data visualization techniques for complex and unstructured data (iii) ability to effectively communicate inherent stories behind data through visualization (iv) analyze, critique, and revise data visualizations. We plan to teach pertinent statistical measures of the data and illustrate different tasks with case studies. We will also explore best possible probability distributions that fit the given data set, design research hypotheses with supporting statistical inference.
Course 2: Data Modeling and Model Diagnostics
Instructors: Dr. Keshav Pokhrel and Dr. Gauri ShresthaAbstract: The aim of this course is to explore data modeling methodologies with the goal of understanding how to select, apply, and interpret appropriate statistical methodologies through real data problems from a variety of resources. Topics covered include understanding the relationships between the variables in the data, theory and application of linear and non-linear regression models, time series regression models, model building steps, model diagnostics, and remedial measures.
Course 3: Basics and New Trends of Matrix Theory and Applications
Instructor:
Dr. Abdenacer Makhlouf
Abstract: The aim of the lectures is to provide the basics on matrix theory such as spectral theory and perturbative matrix theory. Moreover, we will discuss sparse matrices which play an important role in data science. Furthermore, we show applications in control theory and data science.
Course 4: Mathematical Models
Instructors: Dr. Elissa Schwartz and Dr. Naveen VaidyaAbstract: The objective of this course is to teach techniques for developing a variety of mathematical models. These techniques will focus on data-driven and problem-oriented approach to formulate mathematical equations. In particular, we will emphasize on developing various ordinary differential equations (compartmental, meta-populations, initial value, boundary-value) and partial differential equations (age-structure, parabolic, elliptic). The topics will also include basic qualitative analyses of the models, numerical and computational methods for model solutions, data-fitting for parameter estimation, and sensitivity analysis.
Course 5: Large-scale Modeling, Rigidity Theory and Application
Instructor: Dr. Elissa Schwartz and Dr. Adnan SljokaAbstract: The objective of this course is to introduce the basic concepts in rigidity theory with focus on algorithms and applications in large-scale modeling. We will present the rigidity theory as a blend of combinatorics, geometry, and algebra. We will focus on applications in many areas of science, engineering and design, where geometric constraint systems serve as suitable mathematical models for various kinds of structures, be they man-made (e.g., robots, sensor networks, materials, and Computer-Aided Design software) or found in nature (e.g., proteins and crystals). Students will also learn how rigidity theory can be used to obtain fast and practical combinatorial algorithms for studying complex problems such as protein flexibility and dynamics.
Course 6: Periodic Dynamical System Models
Instructor: Dr. Mohamed Elhamdadi
Abstract: The goal of these lectures is to explain how dynamical systems can be used to study some evolutionary systems with periodic phenomena. We will show how systems of differential equations can predict flows with periodic orbits. Some applications to biology will also be given.
Course 7: Analysis of Nonlinear Partial Differential Equations
Instructors: Dr. Maya Chhetri and
Dr. Dhruba Adhikari
Abstract: The aim of this course is to introduce modern methods for studying nonlinear partial differential equations that arise from modeling of various phenomena in science and engineering. The content of the course will be built around some of the following themes: strong and weak maximum principles, variational and nonvariational techniques, weak convergence techniques, regularity (interior and global) of solutions. There will be problem sets for participants to work on and deepen the understanding of the theories and methods.
Sponsors
