Thomas Vakili Teaching Assistant

Contact

Name and title: Thomas VakiliTeaching Assistant

Phone: +468161659

ORCID0000-0001-8988-8226 Länk till annan webbplats.

Workplace: Department of Computer and Systems Sciences Länk till annan webbplats.

Visiting address Nodhuset, Borgarfjordsgatan 12

Postal address Institutionen för data- och systemvetenskap164 25 Kista

Research group

Natural Language Processing Research Group

The Natural Language Processing Research Group develops, applies and evaluates NLP methods, in particular involving large language models, across various domains. We focus on topics such as privacy, explainability, and domain adaptation.

About me

I have a PhD from the Department of Computer and Systems Sciences where I am currently working as a university teacher. My research revolves around the intersection of natural language processing (NLP) and privacy.

The NLP and AI fields have seen great advances through the introduction of large language models (LLMs), like BERT, Llama, and GPT-5. At DSV, we have successfully applied these language models for medical applications by training on large amounts of electronic health record data.

One of the main reasons for the success of these language models is that they are very large, and that they are trained on enormous corpora. Because of this, the success of these models comes with an important drawback: they have a tendency to leak information about their training data. My research tackles this issue, and my goal is to find ways of creating models that preserve the privacy of people in the training data.

I defended my licentiate thesis in May of 2023 and defended my doctoral dissertation in January of 2026. My supervisors were Professor Hercules Dalianis and Professor Aron Henriksson. You can read more about my research at my academic webpage.

I teach several courses and I also supervise bachelor's and master's theses. I am teaching or have taught in the following courses:



Privacy-Preserving Techniques for Large Language Models

Recent breakthroughs in AI have been driven mainly by large language models. While they can be very useful, they also threaten privacy – they leak private information. This project aims to identify these risks and develop privacy-preserving techniques.

Federated Health: A Nordic Federated Health Data Network

Electronic health records are full of important information about diagnoses and treatments of patients. Sharing this information in a federated health data network, across hospitals in the Nordic countries, will improve the quality of health care.

DataLEASH: LEarning And SHaring under Privacy Constraints

With massive amounts of personal data being generated, privacy has become a great challenge. This project studies how machine learning can be used for sharing language models without risking to share information that may identify individuals.

Contact

Name and title: Thomas VakiliTeaching Assistant

Phone: +468161659

ORCID0000-0001-8988-8226 Länk till annan webbplats.

Workplace: Department of Computer and Systems Sciences Länk till annan webbplats.

Visiting address Nodhuset, Borgarfjordsgatan 12

Postal address Institutionen för data- och systemvetenskap164 25 Kista

Research group

Natural Language Processing Research Group

The Natural Language Processing Research Group develops, applies and evaluates NLP methods, in particular involving large language models, across various domains. We focus on topics such as privacy, explainability, and domain adaptation.