Nadi Serhan Aydın, FRM

Sustainable Aggregate Production Planning using Multi-Criteria Decision-Making, Mathematical Modeling and Dynamic Programming

The project aims to contribute to the current theoretical as well as practical approaches in the domain of sustainable aggregate production planning (APP). In this context, it will focus on developing a hybrid multi-criteria production optimization model that incorporates sustainability as a factor and harnesses both conventional mathematical optimization and dynamic programming methods (such as reinforcement learning). The project also aims to develop a decision support system to enhance the utilization of the developed algorithm.

Financial Pattern Recognition through Deep-Learning-based Image Processing

the present project departs from the conventional approach to the prediction problem based on financial data. To put it differently, the project falls apart from the broader part of the literature which deals with conventional stochastic and/or autoregressive models for predicting the financial data based solely on temporal dynamics within the numerical data. That said, the project can be deemed as part of literature that mainly deals with extracting signals from financial data based mainly on identifying patterns. It further departs from the latter group by adopting a relatively new approach, namely, the image-based approach, to identifying the visual patterns in the financial data. This novel approach is in fact largely encouraged by the recent success of deep neural networks in image-based pattern recognition (also known as computer vision). This attracted the attention of many researchers from other fields, including quantitative finance. To that end, the project seeks to extend the current body of literature on image-based financial pattern recognition with a view to developing a model with a higher predictive performance.

Deep Learning based Diabetic Retinopathy Stage Diagnosis, Change and Risk Prediction

Examining the retinal fundus images of the diabetes patients is one common way to go for investigating DR. With the increasing amount of those fundus images, it can be possible to derive computer-based systems that can perform those DR investigations. Such systems can offer fast diagnosis with high accuracy, which is helpful both for the patients and their doctors – so the hospitals. The present project aims at devising a Deep Learning (DL) based DR diagnosis and prediction tool.

Model Factory Concept and Digital Transformation Applications in Manufacturing

Model factories can be defined as a tool that enables the dissemination of operational excellence principles in enterprises in a scalable manner by using experiential learning techniques. The most basic feature that distinguishes model factories from other structures established to increase the productivity and competitiveness of employees and therefore businesses is that they combine theory and practice by using experiential learning techniques and thus make their competence gains permanent. As a part of the model factory concept, digital twin or virtual commissioning methods will result in the digitization of physical production activities, digital monitoring of production flow, cycle times, line occupancy rates, efficiency and quality. benefits will be discussed. It is also aimed to provide training and consultancy services that enable the teaching and dissemination of operational excellence principles by using experiential learning techniques in the model factory to be established. In this way, it is aimed to change the existing perspectives of enterprises on issues such as continuous improvement, lean production and digital transformation, to use human, machine / equipment, raw materials, materials, time and energy resources in the most efficient way and to increase their competitive power in a short time.

Systems Simulation Istinye University, 2021/22 Spring

Simulation modelling concepts and discrete-event simulation, Random number and random variate generation, Selection of probability distributions for model inputs, Validation and verification, Output analysis, Comparison of alternative systems.

Introduction to Optimization Istinye University, 2021/22 Spring

This is a university elective course which covers basics of optimization. Topics include linear programming, model formulation, graphical method, simplex metod, sensitivity analysis, duality, Python/Excel examples, transportation/network models, integer optimization.

Financial Engineering Istinye University, 2021/22 Fall

Financial markets, derivative securities, risk management, mathematical models in finance. Foreign exchange, debt equity, commodity markets. Investing, trading, hedging arbitrage. Forwards, futures, options, swaps, exotic derivatives. Models of price dynamics, binomial model, introduction to Black-Scholes theory and Monte Carlo simulation.

Engineering Economics Istinye University, 2020/21 Fall, 2021/22 Fall

Time value of money, economic equivalence, present worth (PW), annual equivalent worth (AEW), internal rate of return (IRR), benefit-cost analysis, uncertainty, replacement analysis.

Computational Thinking Istinye University, 2019/20 Spring

Timeline of computing; representation and processing of numbers, texts, images; understanding patterns; loops and iterative functions, Python.

Probability and Statistics Istinye University, 2019/20 Spring, 2020/21 Spring

Probability theory, axioms, discrete and continuous random variables, moments and moment generating functions, maximum likelihood estimation, statistical inference.

Operations Research (Linear Programming) Istinye University, 2019/20 Fall

Formulating LPs, graphical method, simplex method, sensitivity, duality, transportation models, network models.

Differential and Integral Calculus Istinye University, 2018/19 Fall

Functions, limits, continuity, rules of differentiation and integration, rotational areas and volumes.

Lean Process Design TED University, 2016/17 Spring

The course covers lean techniques that are catered towards analysis, measurement, design, and improvement of business processes and work systems.