SHORT COURSE
Mathematical Analysis and its Modern Applications
Function Spaces & Operator Theory to Integral Equations & Artificial Intelligence
Online Registration FormWelcome
The Abdus Salam School of Mathematical Sciences (ASSMS), Government College University, Lahore, is pleased to offer a two-week intensive short course entitled Mathematical Analysis and Its Modern Applications. This course is designed to introduce participants to the fundamental concepts of modern mathematical analysis and demonstrate their significance in contemporary research and applications.
Beginning with measure theory and function spaces, the course gradually develops advanced topics in operator theory and Fredholm integral equations, culminating in an introduction to the mathematical foundations of Artificial Intelligence. Through a combination of theoretical discussions and modern applications, participants will gain a deeper understanding of how abstract mathematical concepts provide the framework for many emerging scientific and technological developments.
We warmly welcome graduate students, researchers, and young faculty members to join this exciting learning opportunity and explore the beauty and power of modern mathematical analysis.
Course Description
This short course provides a comprehensive introduction to modern mathematical analysis through the study of function spaces and operator theory. The course covers the foundations of measure theory, Lebesgue integration, classical and variable Lebesgue spaces, classical and variable Sobolev spaces, and the theory of bounded and compact operators.
The course further explores the application of these concepts to Fredholm integral equations and concludes with an introduction to the mathematical foundations of modern Artificial Intelligence. Particular emphasis is placed on the role of function spaces, operator theory, optimization, and approximation in understanding machine learning algorithms, neural networks, and data-driven models. Throughout the course, participants will develop a rigorous mathematical foundation while gaining insight into how abstract concepts from modern analysis underpin many techniques used in contemporary AI research and other interdisciplinary applications.
Course Highlights
- Rigorous introduction to modern mathematical analysis.
- Foundations of measure theory and Lebesgue integration.
- Classical and variable Lebesgue spaces.
- Classical and variable Sobolev spaces.
- Bounded and compact operators in functional analysis.
- Fredholm integral equations and operator-theoretic techniques.
- Mathematical foundations of Artificial Intelligence and machine learning.
- Modern applications of functional analysis in science and technology.
- Research-oriented lectures that bridge theory and applications.
Learning Outcomes
Target Audience
This course is intended for: MPhil and PhD students in Mathematics. Advanced undergraduate students with a strong mathematical background. Early-career researchers and university faculty members. Anyone interested in functional analysis, operator theory, partial differential equations, integral equations, or the mathematical foundations of Artificial Intelligence.
Prerequisites
Participants are expected to have:
- Participants are expected to have a basic background in:
- Real Analysis.
- Linear Algebra.
- Elementary Topology (recommended but not mandatory).
- Mathematical maturity and an interest in modern analysis and its applications.
Instructor
Dr. Ali Raza
Dr. Ali Raza is an Assistant Professor at the Abdus Salam School of Mathematical Sciences (ASSMS), Government College University, Lahore. He conducted postdoctoral research and earned his PhD and MPhil degrees in Mathematics, specializing in Mathematical Analysis and its Applications, at ASSMS, Government College University, Lahore. He received his Master's degree in Mathematics from the University of the Punjab, Lahore.
His research interests include Functional Analysis, Harmonic Analysis, Operator Theory, Interpolation Theory, Classical and Variable Lebesgue Spaces, Sobolev Spaces and their applications to various areas of mathematics and emerging interdisciplinary fields. He has extensive teaching and research experience and has taught a wide range of courses in mathematical analysis at the MPhil and PhD levels. He has also supervised, and is currently supervising, MPhil and PhD research students working in diverse areas of mathematical analysis and its applications.
Through this short course, Dr. Ali Raza aims to equip participants with a rigorous understanding of modern mathematical analysis and demonstrate how its fundamental concepts are applied to operator theory, integral equations, and the mathematical foundations of Artificial Intelligence.
Registration
Interested applicants are required to fill out the registration form for this short course.
Registration Deadline: 31 July 2026
Venue
Abdus Salam School of Mathematical Sciences
Government College University (GCU), Lahore, 68-B, New Muslim Town, Lahore, Pakistan.
Contact Information
🌐 Website: https://sms.edu.pk
✉️ Email: events@sms.edu.pk
SHORT COURSE
Quantitative Finance Skills Bootcamp
Option Pricing, Market Risk, Financial Simulations, Excel, and Python
Download Registration FormWelcome
The Abdus Salam School of Mathematical Sciences (ASSMS), Government College University Lahore, is pleased to announce the two-week intensive short course, Quantitative Finance Skills Bootcamp.
This course is designed to provide participants with a strong foundation in quantitative finance, option pricing, market risk, financial simulations, the Black–Scholes framework, Brownian motion, and risk-neutral valuation through a combination of theoretical concepts, practical demonstrations, hands-on sessions, and applied financial examples.
Participants will gain practical experience in developing financial models, simulating stock-price movements, analyzing market risk, and implementing option-pricing techniques using Microsoft Excel, Python, and modern computational tools.
Course Description
Quantitative Finance has become one of the most important areas of modern finance, enabling advanced applications in option pricing, market-risk analysis, financial forecasting, portfolio management, and investment decision-making.
This short course introduces participants to the fundamentals of Quantitative Finance, Excel, and Python. Participants will learn about stock-price modelling, Brownian motion, Geometric Brownian Motion, financial simulations, option pricing, the Black–Scholes framework, and risk-neutral valuation through lectures, practical exercises, and hands-on laboratory sessions.
The course emphasizes practical implementation. Participants will also complete a mini project applying quantitative-finance concepts and computational techniques to a real-world financial problem.
Course Highlights
- Quantitative Finance and Its Applications
- Introduction to Financial Markets, Risk, and Derivatives
- Fundamentals of Brownian Motion and Geometric Brownian Motion Stock-Price
- Modelling and Financial Simulations
- Introduction to Option Pricing and the Black–Scholes Framework
- Market-Risk Analysis and Risk-Neutral Valuation
- Scientific Computing with Python
- Hands-on Financial Modelling using Microsoft Excel
- Building Option-Pricing Calculators in Excel and Python
- Practical Quantitative Finance Mini Project
Learning Outcomes
By the end of the bootcamp, participants will be able to:
- Understand the basic mathematics of financial markets;
- Explain how options are priced;
- Derive and interpret the Black–Scholes model;
- Build option-pricing calculators using Excel and Python;
- Compute and visualize Delta and Gamma;
- Understand risk-neutral pricing and measure-change techniques;
- Simulate stock-price movements;
- Complete a practical quantitative finance mini-project suitable for academic, professional, or research use.
Target Audience
The proposed bootcamp is designed to make advanced financial mathematics more accessible, practical, and attractive for Students, researchers, faculty members, finance graduates, data science learners, banking professionals, fintech enthusiasts, and interested participants from the general public. The purpose is to connect rigorous mathematical theory with practical financial modelling skills that are increasingly relevant in banking, investment analysis, insurance, fintech, actuarial science, data analytics, and risk management.
Prerequisites
Participants are expected to have:
- A basic understanding of Calculus and Linear Algebra
- Familiarity with Differential Equations and Partial Differential Equations.
- Basic knowledge of Probability Theory and Random Variables.
- Introductory knowledge of Stochastic Processes and Itô Calculus (recommended).
- Basic understanding of financial derivatives and option pricing is helpful but not mandatory.
Instructor
Dr. Fahim Ud Din
Dr. Fahim Ud Din holds a Ph.D. in Mathematics from Quaid-I-Azam University, Islamabad Pakistan.
Dr. Fahim has extensive experience in teaching pure mathematics courses and conducting research in Stochastic, Differential Equations, Fixed Point Theory and Its Applications, Fuzzy Fixed Point Theory, Contractive Type Mappings, Iterated Function System.
Registration
Interested applicants are required to fill out the registration form for this short course.
Registration Deadline: 23 July 2026
Venue
Abdus Salam School of Mathematical Sciences
Government College University (GCU), Lahore, 68-B, New Muslim Town, Lahore, Pakistan.
Contact Information
🌐 Website: https://sms.edu.pk
✉️ Email: events@sms.edu.pk
SHORT COURSE
A Friendly Introduction to Artificial Intelligence
Foundations of Deep Learning — Building Your First Neural Network
Download Registration FormWelcome
The Abdus Salam School of Mathematical Sciences (ASSMS), Government College University, Lahore is pleased to announce this two-week intensive short course. This course is designed to provide participants with a strong foundation in Artificial Intelligence, Machine Learning, and Deep Learning through a combination of theoretical concepts, practical demonstrations, hands-on programming sessions, and project-based learning.
Participants will gain practical experience in developing and training neural networks using Python and modern computational tools.
Course Description
Deep Learning has emerged as one of the most powerful areas of Artificial Intelligence, enabling advanced applications in computer vision, medical imaging, natural language processing, robotics, and intelligent systems.
This short course introduces participants to the fundamentals of Python programming and Deep Learning. Participants will learn how to design, build, train, and evaluate basic neural network models through lectures, coding exercises, and laboratory sessions.
The course emphasizes practical implementation. Participants will also complete a mini project applying Deep Learning concepts to a real-world Artificial Intelligence problem.
Course Highlights
- Python Programming for Artificial Intelligence Applications
- Introduction to Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning
- Fundamentals of Artificial Neural Networks (ANNs)
- Introduction to Convolutional Neural Networks (CNNs)
- Computer Vision Applications
- Scientific Computing with Python
- Hands-on Coding using Jupyter Notebook and Google Colab
- Building and Training Neural Networks
- Practical AI Mini Project
Learning Outcomes
Upon successful completion of this course, participants will be able to:
- Understand the fundamental concepts of Artificial Intelligence and Deep Learning.
- Develop Python programs for AI applications.
- Work with scientific computing and machine learning libraries.
- Understand the architecture and working principles of neural networks.
- Build and train neural network models.
- Explore practical applications of Computer Vision.
- Design, develop, and present a practical AI project.
Target Audience
This course is designed for:
- Undergraduate students
- Graduate students
- Researchers
- Engineers
- Software developers
- Faculty members
The course is particularly suitable for individuals who want to develop practical programming skills in Python and gain hands-on experience in designing and training Deep Learning models for real-world applications.
Prerequisites
Participants should have:
- Basic understanding of mathematics.
- Basic programming knowledge.
- A personal laptop for coding exercises and laboratory sessions.
Instructor
Dr. Jamshaid Ul Rahman
Dr. Jamshaid Ul Rahman holds a Ph.D. in Deep Learning from the University of Science and Technology of China (USTC), one of the world's leading research universities. His doctoral research focused on advanced Deep Learning techniques and their applications. He is currently associated with the Abdus Salam School of Mathematical Sciences, Government College University, Lahore. His research and teaching interests include Applied and Computational Mathematics, Artificial Intelligence, Machine Learning, Deep Learning, Computational Biology and Chemistry, Mathematical Modeling, Computer Vision, and Data Science.
Dr. Rahman has extensive experience in teaching AI-related courses and conducting research in intelligent systems. His teaching approach focuses on practical learning through programming exercises, real-world examples, and project-based implementation.
Registration
Interested applicants are required to fill out the registration form for this short course.
Registration Email: events@sms.edu.pk
Registration Deadline: 10 July 2026
Venue
Abdus Salam School of Mathematical Sciences
Government College University (GCU), Lahore, 68-B, New Muslim Town, Lahore, Pakistan.
Contact Information
🌐 Website: https://sms.edu.pk
✉️ Email: events@sms.edu.pk
