Malamas Nikolas

PhD Candidate

Aristotle University of Thessaloniki
Department of Electrical and Computer Engineering
54124 Thessaloniki – GREECE

Email: nikolas [dot] malamas (at) issel [dot] ee [dot] auth [dot] gr

Education

10/2020 – today

PhD Candidate
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki, Greece
PhD Thesis: “Techniques and Algorithms for Optimal Natural Language Understanding for Digital Assistants”

09/2014 – 11/2019

Diploma of Electrical and Computer Engineering
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki, Greece
Diploma Thesis: “Full Coverage of Known Area with Unmanned Ground Vehicle using Path Patterns and Semantic Map Annotation”.

Professional Experience

09/2020 – today Research Associate, Gnomon Informatics SA, Thessaloniki, Greece
Funded Project: Sities (https://issel.ee.auth.gr/gr-sities/)

Research interests

  • Natural Language Processing
  • Chatbots
  • Software Engineering
  • Machine Learning

Languages

  • English: Proficient (Michigan Proficiency)
  • German: Fluent (Goethe-Zertifikat B2)

Memberships

  • Member of the Technical Chamber of Greece

Puplications

2022

Journal Articles

Nikolaos Malamas, Konstantinos Papangelou and Andreas L. Symeonidis
"Upon Improving the Performance of Localized Healthcare Virtual Assistants"
Healthcare, 10, (1), 2022 Jan

Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed “healthcare vocabulary” to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor’s appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant’s behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.

@article{malamas-healthcare,
author={Nikolaos Malamas and Konstantinos Papangelou and Andreas L. Symeonidis},
title={Upon Improving the Performance of Localized Healthcare Virtual Assistants},
journal={Healthcare},
volume={10},
number={1},
year={2022},
month={01},
date={2022-01-04},
url={https://www.mdpi.com/2227-9032/10/1/99},
doi={https://doi.org/10.3390/healthcare10010099},
issn={2227-9032},
keywords={chatbot; virtual assistant; Rasa; ehealthcare},
abstract={Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed “healthcare vocabulary” to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor’s appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant’s behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.}
}

2021

Journal Articles

Nikolaos Malamas and Andreas Symeonidis
"Embedding Rasa in edge Devices: Capabilities and Limitations"
Procedia Computer Science, 192, pp. 109-118, 2021 Jan

Over the past few years, there has been a boost in the use of commercial virtual assistants. Obviously, these proprietary tools are well-performing, however the functionality they offer is limited, users are ”vendor-locked”, while possible user privacy issues rise. In this paper we argue that low-cost, open hardware solutions may also perform well, given the proper setup. Specifically, we perform an initial assessment of a low-cost virtual agent employing the Rasa framework integrated into a Raspberry Pi 4. We set up three different architectures, discuss their capabilities and limitations and evaluate the dialogue system against three axes: assistant comprehension, task success and assistant usability. Our experiments show that our low-cost virtual assistant performs in a satisfactory manner, even when a small-sized training dataset is used.

@article{malamas2021-rasa,
author={Nikolaos Malamas and Andreas Symeonidis},
title={Embedding Rasa in edge Devices: Capabilities and Limitations},
journal={Procedia Computer Science},
volume={192},
pages={109-118},
year={2021},
month={01},
date={2021-01-01},
url={https://www.sciencedirect.com/science/article/pii/S187705092101499X},
doi={https://doi.org/10.1016/j.procs.2021.08.012},
issn={1877-0509},
keywords={Spoken Dialogue Systems;NLU;Rasa;Chatbots},
abstract={Over the past few years, there has been a boost in the use of commercial virtual assistants. Obviously, these proprietary tools are well-performing, however the functionality they offer is limited, users are ”vendor-locked”, while possible user privacy issues rise. In this paper we argue that low-cost, open hardware solutions may also perform well, given the proper setup. Specifically, we perform an initial assessment of a low-cost virtual agent employing the Rasa framework integrated into a Raspberry Pi 4. We set up three different architectures, discuss their capabilities and limitations and evaluate the dialogue system against three axes: assistant comprehension, task success and assistant usability. Our experiments show that our low-cost virtual assistant performs in a satisfactory manner, even when a small-sized training dataset is used.}
}