I am Amandeep Singh

PhD Researcher in Artificial Intelligence, Academic Author, Educator in AI and Data Analytics, Low Code/No Code Technology Advocate, Digital Thread Technology Specialist, Edge Analytics Innovator, IoT Application Developer, Theoretical Astrophysicist and Cosmologist

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Name: Amandeep Singh

Profile: PhD Researcher in AI/ML, University of Limerick, Ireland

Email: amandeep.singh@ul.ie

LinkedIn: amandeepsingh07

Skill

Python 85%
C++ 75%
SQL 80%
LaTeX 90%
Power BI/Tableau 70%
IBM SPSS 80%
Microsoft Dynamics 365 70%
Microsoft Excel 90%
R 60%
HTML 50%
CSS 50%
About me

Originally from India, I am currently engaged in a PhD in Artificial Intelligence and Machine Learning (AI/ML) at the University of Limerick, Ireland. I am deeply involved in research areas such as Machine Learning, Natural Language Processing, and Reinforcement Learning. This pursuit is fully funded by the Science Foundation Ireland Centre for Research and Training in Artificial Intelligence (SFI CRT-AI), reflecting my commitment to advancing in this dynamic field. Prior to this, I had successfully completed my M.Sc. in Data Analytics from the National College of Ireland, where I excelled with First Class Honours.

My academic journey also includes a M.Sc. in Physics with a focus on Theoretical Astrophysics & Cosmology from the University of Zurich, Switzerland, and a B.Sc. (Honours) in Physics from the University of Delhi, India. My educational experiences have been diverse and enriching, providing me with a strong foundation in both theoretical and applied sciences. Alongside my studies, I have developed a proficiency in programming languages like Python, C++, and R, and tools such as SPSS, SQL, and Tableau. I can effectively work in IBM SPSS, SQL, Tableau, PowerBI, Git, Microsoft Dynamics 365, JupyterLab/Notebooks, Google Colab, Latex. I am trying to improve HTML/CSS skills by designing webpages.

In my professional journey, I have contributed significantly as a Module Leader and Teaching Assistant at the University of Limerick, and as an Associate Faculty at the National College of Ireland. These roles have allowed me to engage deeply in subjects like Artificial Intelligence and Data Mining. Additionally, I have authored several publications in renowned conferences and journals, which is a testament to my research capabilities and commitment to advancing the field of AI and Machine Learning.

Expertise

Here are some of my expertise

Mathematics

Exceptionally skilled in complex mathematical calculations and statistical analysis, reflecting a strong foundation from my M.Sc. in Data Analytics and B.Sc. in Physics, underpinned by relevant coursework in analysis, linear algebra, and numerical methods.

Artificial Intelligence & Machine Learning

Deeply involved in AI and ML research, currently pursuing a PhD with a focus on advanced areas like Natural Language Processing and Reinforcement Learning.

Environments

Versatile in various development environments, adept at using Jupyter Lab/Notebooks for AI/ML research and teaching, with proficiency in other platforms like Spyder, PyCharm, RStudio, and Atom for diverse computing needs.

Data Analytics

My experience in Data Analytics, marked by an M.Sc. in the field and extensive practical application, enables me to tackle big-data projects, striving to create impactful real-world solutions.

Software Proficiency & Development

Expert in utilizing industry-standard software like IBM SPSS, PowerBI, and Tableau, with hands-on experience in designing and working on innovative Low Code/No Code (LC/NC) software solutions as part of a team.

Research, Teaching and Mentorship

Authored several publications in renowned conferences and journals. Skilled in imparting knowledge in AI and Data Analytics, with experience as a Module Leader, Teaching Assistant, and Associate Faculty.

32

PROJECTS COMPLETED

124794

LINES OF CODE

5

COUNTRIES

502

CUPS OF TEA

Publications

A comprehensive showcase of my scholarly contributions: Explore my range of publications in esteemed journals and conferences, reflecting my extensive research and innovation in fields like AI, Machine Learning, Low Code/No Code, and IoT.

IoT
AI/ML
Low Code/No Code

Low-code internet of things application development for edge analytics

Internet of Things (IoT) applications combined with edge analytics are increasingly developed and deployed across a wide range of industries by engineers who are non-expert software developers. In order to enable them to build such IoT applications, we apply low-code technologies in this case study based on Model Driven Development. We use two different frameworks: DIME for the application design and implementation of IoT and edge aspects as well as analytics in R, and Pyrus for data analytics in Python, demonstrating how such engineers can build innovative IoT applications without having the full coding expertise. With this approach, we develop an application that connects a range of heterogeneous technologies: sensors through the EdgeX middleware platform with data analytics and web based configuration applications. The connection to data analytics pipelines can provide various kinds of information to the application users. Our innovative development approach has the potential to simplify the development and deployment of such applications in industry.

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IFIP International Internet of Things Conference, Springer International Publishing (Conference)
19 Oct 2022
IoT
AI/ML
Low Code/No Code

Model-driven engineering in digital thread platforms: a practical use case and future challenges

The increasing complexity delivered by the heterogeneity of the cyber-physical systems is being addressed and decoded by edge technologies, IoT development, robotics, digital twin engineering, and AI. Nevertheless, tackling the orchestration of these complex ecosystems has become a challenging problem. Specially the inherent entanglement of the different emerging technologies makes it hard to maintain and scale such ecosystems. In this context, the usage of model-driven engineering as a more abstract form of glue-code, replacing the boilerplate fashion, has improved the software development lifecycle, democratising the access to and use of the aforementioned technologies. In this paper, we present a practical use case in the context of Smart Manufacturing, where we use several platforms as providers of a high-level abstraction layer, as well as security measures, allowing a more efficient system construction and interoperability.

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International Symposium on Leveraging Applications of Formal Methods, Springer Nature Switzerland (Conference)
17 Oct 2022
IoT
AI/ML
Low Code/No Code

Efficient Model-Driven Prototyping for Edge Analytics

Software development cycles in the context of Internet of Things (IoT) applications require the orchestration of different technological layers, and involve complex technical challenges. The engineering team needs to become experts in these technologies and time delays are inherent due to the cross-integration process because they face steep learning curves in several technologies, which leads to cost issues, and often to a resulting product that is prone to bugs. We propose a more straightforward approach to the construction of high-quality IoT applications by adopting model-driven technologies (DIME and Pyrus), that may be used jointly or in isolation. The presented use case connects various technologies: the application interacts through the EdgeX middleware platform with several sensors and data analytics pipelines. This web-based control application collects, processes and displays key information about the state of the edge data capture and computing that enables quick strategic decision-making. In the presented case study of a Stable Storage Facility (SSF), we use DIME to design the application for IoT connectivity and the edge aspects, MongoDB for storage and Pyrus to implement no-code data analytics in Python. We have integrated nine independent technologies in two distinct Low-code development environments with the production of seven processes and pipelines, and the definition of 25 SIBs in nine distinct DSLs. The presented case study is benchmarked with the platform to showcase the role of code generation and the reusability of components across applications. We demonstrate that the approach embraces a high level of reusability and facilitates domain engineers to create IoT applications in a low-code fashion.

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MDPI Electronics 2023 (Journal)
14 Sep 2023
Health Informatics
AI/ML
Low Code/No Code

CNN-based Human Activity Recognition on Edge Computing Devices

Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to issues such as latency, bandwidth requirements, and energy consumption. Transitioning towards Edge HAR can be a more effective and versatile solution, overcoming the challenges of traditional HAR techniques. We present a novel HAR model for computation on edge devices: we design a Convolutional Neural Network (CNN) Deep Learning approach and compare its performance with cloud-computing HAR models. The paper is accompanied by a self-collected dataset. The experiments on this dataset demonstrate that the proposed edge computing model achieves promising results ( ≥ 92 %) in terms of Precision, Recall, and Fl-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory.

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IEEE COINS 2023 (Conference)
23 Jul 2023
Health Informatics
AI/ML
Low Code/No Code

IDPP: Imbalanced Datasets Pipelines in Pyrus

We showcase and demonstrate IDPP, a Pyrus-based tool that offers a collection of pipelines for the analysis of imbalanced datasets. Like Pyrus, IDPP is a web-based, low-code/no-code graphical modelling environment for ML and data analytics applications. On a case study from the medical domain, we solve the challenge of re-using AI/ML models that do not address data with imbalanced class by implementing ML algorithms in Python that do the re-balancing. We then use these algorithms and the original ML models in the IDPP pipelines. With IDPP, our low-code development approach to balance datasets for AI/ML applications can be used by non-coders. It simplifies the data-preprocessing stage of any AI/ML project pipeline, which can potentially improve the performance of the models. The tool demo will showcase the low-code implementation and no-code reuse and repurposing of AI-based systems through end-to end Pyrus pipelines.

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ECBS 2023 (Conference)
16 Oct 2023
Health Informatics
AI/ML
Low Code/No Code

Binary Decision Diagrams and Composite Classifiers for Analysis of Imbalanced Medical Datasets

Imbalanced datasets pose significant challenges in the development of accurate and robust classification models. In this research, we propose an approach that uses Binary Decision Diagrams (BDDs) to conduct pre-checks and suggest appropriate resampling techniques for imbalanced medical datasets as the application domain where we apply this technology is medical data collections. BDDs provide an efficient representation of the decision boundaries, enabling interpretability and providing valuable insights. In our experiments, we evaluate the proposed approach on various real-world imbalanced medical datasets, including Cerebralstroke dataset, Diabetes dataset and Sepsis dataset. Overall, our research contributes to the field of imbalanced medical dataset analysis by presenting a novel approach that uses BDDs and composite classifiers in a low-code/no-code environment. The results highlight the potential for our method to assist healthcare professionals in making informed decisions and improving patient outcomes in imbalanced medical datasets.

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ECEASST 2023 (Journal)
6 Oct 2023
IoT
AI/ML
Low Code/No Code

Edge IoT prototyping using Model-Driven representations: A use-case for Smart Agriculture

Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems incorporate new components that are capable of generating better decision making (humidity/temperature/soil sensors, drones for plague detection, smart irrigation, etc.) and also include novel processes for crop control (reproducible environmental conditions, proven strategies for water stress, etc.). At the same time, advances in model-driven development (MDD) simplify software development by introducing domain-specific abstractions of the code that makes application development feasible for domain experts who cannot code. XMDD (eXtreme MDD) makes this way to assemble software even more user-friendly and enables application domain experts who are not programmers to create complex solutions in a more straightforward way. Key to this approach is the introduction of high-level representations of domain-specific functionalities (called SIBs, service-independent building blocks) that encapsulate the programming code and their organisation in reusable libraries, and they are made available in the application development environment. This way, new domain-specific abstractions of the code become easily comprehensible and composable by domain experts. In this paper, we apply these concepts to a smart agriculture solution, producing a proof of concept for the new methodology in this application domain to be used as a portable demonstrator for MDD in IoT and agriculture in the Confirm Research Centre for Smart Manufacturing. Together with model-driven development tools, we leverage here the capabilities of the Nordic Thingy:53 as a multi-protocol IoT prototyping platform. It is an advanced sensing device that handles the data collection and distribution for decision making in the context of the agricultural system and supports edge computing. We demonstrate the importance of high-level abstraction when adopting a complex software development cycle within a multilayered heterogeneous IT ecosystem.

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MDPI Sensors 2023 (Journal)
12 Jan 2024
Health Informatics
AI/ML
Low Code/No Code

CINCO de Bio: A Future Internet Platform for Domain-Specific Workflows that Leverage AI for Biomedical Research

Background: In biomedical imaging research, experimental biologists generate vast amounts of data that require advanced computational analysis. Breakthroughs in experimental techniques, such as multiplex immunofluorescence tissue imaging, enable detailed proteomic analysis, but most biomedical researchers lack the programming and Artificial Intelligence (AI) expertise to leverage these innovations effectively. Methods: Cinco de Bio (CdB) is a web-based, collaborative low-code/no-code modelling and execution platform designed to address this challenge. It is designed along Model-Driven Development (MDD) and Service-Orientated Architecture (SOA) to enable modularity and scalability, and it is underpinned by formal methods to ensure correctness. The pre-processing of immunofluorescence images illustrates the ease of use and ease of modelling with CdB in comparison with the current, mostly manual, approaches. Results: CdB simplifies the deployment of data processing services that may use heterogeneous technologies. User-designed models support both a collaborative and user-centred design for biologists. Domain-Specific Languages for the Application domain (A-DSLs) are supported through data and process ontologies/taxonomies. They allow biologists to effectively model workflows in the terminology of their field. Conclusions: Comparative analysis of similar platforms in the literature illustrates the superiority of CdB along a number of comparison dimensions. We are expanding the platform’s capabilities and applying it to other domains of biomedical research.

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MDPI BioMedInformatics 2024 (Journal)
01 Sep 2024
Health Informatics
AI/ML
Low Code/No Code

Enhancing Decision-Making for imbalanced medical datasets using BDDs and Low-Code/No-Code

Imbalanced datasets pose a challenge wherever accurate predictions are essential. This paper explores using low-code/no-code platforms, such as Pyrus and ADD-Lib, to apply data resampling techniques and binary decision diagrams for more accessible and reliable ML workflows. Tested on three medical datasets, these techniques improve model performance by addressing class imbalances. The integration of resampling and formal methods enhances prediction accuracy while making ML tools more accessible to professionals, enabling better decision-making in critical applications. The techniques are applicable to any domain, not just in healthcare.

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IT Professional (Journal/Magazine)
30 Sep 2024
Health Informatics
AI/ML
Low Code/No Code

Model driven development for AI-Based healthcare Systems: a review

We review our experience with integrating Artificial Intelligence (AI) into healthcare systems following the Model-Driven Development (MDD) approach. At a time when AI has the potential to instigate a paradigm shift in the health sector, better integrating healthcare experts in the development of these technologies is of paramount importance. We see MDD as a useful way to better embed non-technical stakeholders in the development process. The main goal of this review is to reflect on our experiences to date with MDD and AI in the context of developing healthcare systems. Four case studies that fall within that scope but have different profiles are introduced and summarised: the MyMM application for Multiple Myeloma diagnosis; CNN-HAR, that studies the ability to do AI on the edge for IoT-supported human activity recognition; the HIPPP web based portal for patient information in public health; and Cinco de Bio, a new model driven platform used for the first time to support a better cell-level understanding of diseases. Based on the aforementioned case studies we discuss the characteristics, the challenges faced and the postive outcomes achieved.

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LNCS, Springer Nature Switzerland (Conference)
31 Oct 2024

Project Portfolio

A growing list of projects that I have completed

Comparing Transfer Learning Techniques for Detection of Traffic Signs using Image Recognition

Deep Learning / 10 Aug 2020

Predicting Customer Review Scores using Ancillary Metadata from Customer Details

Predictive Analytics / 5 Aug 2020

Visualising the COVID-19 Pandemic using Reddit posts, Twitter Hashtags and Stock Market fluctuations

Database & Analysis / 05 May 2020

Statistical Analysis using Binary-Logistic Regression, one-way ANOVA, t-Test and chi-SquareTest

Statistics / 01 May 2020

Life Expectancy Prediction using Multiple Linear Regression and Consumer Price Index Forecasting Model

Statistics / 05 Apr 2020

Managing and Visualising the Workings of an Organisation using Microsoft Dynamics 365 and Power BI Dashboards

Business Intelligence / 20 Apr 2020

Rainfall Prediction in Australia, Exoplanet Classification, & Indian Air Quality Index Prediction using Data Mining & Machine Learning Models

Machine Learning / 25 Apr 2020

A Sub-Grid Model for Molecular Gas in a Cosmological Galaxy Formation Simulation

Astrophysics / 19 Jul 2019

Monitoring the properties of the Atmosphere relevant for the observation of Cosmic Gamma Rays with Atmospheric Cherenkov Telescopes

Astro Particle Physics / 19 Mar 2018

Personal Blog

ASTRUM IMBER - The Star Shower : A repository for the knowledge I acquired as a Physics and Data Analytics student

Statistics

Statistics for Data Analytics

"The emphasis is on understanding the appropriate statistical techniques to use and on the interpretation of the results from the application of those techniques. Whether the results are obtained using SPSS, R, SAS, Stata, Minitab, Excel or some other package should not be critical to the understanding required" - Prof. (Dr.) Tony Delaney, NCI, Dublin.

Amandeep Singh
15 min
Philosophy

Artificial Intelligence — Why it would be more good than bad

This article is more philosophical than physics or astrobiology. But sometimes it is necessary to have discussions that explore the boundaries and ethics of the upcoming fields.

Amandeep Singh
6 min
Astrobiology

Can Venus’ Atmosphere Support Life?

Venus has an atmosphere that crushes everything at its surface. Due to the habitability of its clouds, its atmosphere is still the prime candidate to sustain life.

Amandeep Singh
6 min

Medium

Coding

Pandas Cheatsheet

Usually I use Pandas for EDA of datasets for a Data Analytics project. To move things quicker, I made a cheatsheet of the most frequently used commands.

Amandeep Singh
11 min
Data Analytics

Life Expectancy Prediction using MLR

This article presents a model, based on the statistical technique of multiple-linear regression, that analyses different factors that may or may not affect the accurate prediction of the life expectancy of a country in a given year.

Amandeep Singh
16 min
Philosophy

Artificial Intelligence — Why it would be more good than bad

This article is more philosophical than physics or astrobiology. But sometimes it is necessary to have discussions that explore the boundaries and ethics of the upcoming fields.

Amandeep Singh
6 min
Get in Touch

My contact details and socials are listed below:

  • Dublin, Ireland