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title: Research Plan 2020

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# Research Plan 2020

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- Table of Content:
[ToC]

## Research

* R1: Authomatic design in machine learning
-- Automatic design and tuning of neural networks
-- Automatic design and tuning of training/validation/testing machine learning pipelines
* R2: Simulation models in medical applications
-- Develop domain oriented plugins for BioDynaMo (under CERN and University of Newcastle)
-- Targetted domain: modelling o virus spreading and alternative controlling approachings
* R3: Optimisation and Operations Research in Additive Manufacturing
-- Multi-objective optimisaton of productive capacity and quality of parts
-- Use of combinatorial optimisation algorithms
* R4: Students' research monitoring
-- Combinatorial optimisation and AI in games developed in-house (e.g. Snakes Game, which was approved in IEEE COG Japan 2020)
-- Individual performance monitoring in DataOps using NLP and Anomaly Detection
-- ML based algorithm selection in single-objective optimisation bechmark functions and insights for real-world constrained problems

## Publications

Targeted journals for this year:

| Research | Journal      |
| :------: |:------------ |
| R1 | IEEE Transactions on Neural Networks and Learning Systems (Q1)      |
| R1 | Journal of Machine Learning ResearchOpen Access (Q1) |
| R2 | European Journal of Operational Research (Q1) |
| R2 | Applied Mathematical Modelling (Q1) |
| R3 | Computers and Operations Research (Q1) |
| R3 | International Journal of Production and Research (Q1) |
| R3 | Journal of Intelligent Manufacturing (Q1) |

This is NOT a comprehensive list of all the targetted and expected publications for the year. Parallel and smaller research projects have several conferences and journal papers outcome.

## Grants

**Objective:** Apply to one or two grant projects to attract investiments to the lab. Hopefully, the investiments will be translated into more dedicated machines and temporary personal. The grant projects to be addressed are in the field of authomatic 3D modelling of industrial parts and the use of machine learning for optimising the aerodynamics.

## Teaching

* I intend to improve and possibily lecture the course *'Advanced Databases'* and adapt it to make the course more Data Science driven. Approach tools including ETL processes, tools for data analysis in databases, etc.
* I envision developing an intro-level course on ‘Operations Research’ that can be offered in the undergraduate curriculum.

## Service

* Support for the Admission Department
* Preparation of course on AI and ML, as well as tasks for the AI Cup for the Pre-university Department (319)
-- The objective is to attract students specifically interested in the Data Science track
-- Gain visibility for the university