DISA

When? Friday May 16th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/DKAh2iCN5EGEth9F6 by May 14th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Secure On-Premises Deployment of Large Language Models for Enhanced Patent Drafting – Homam Mawaldi, AWA
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Enhancing E-commerce Personalization with a Hybrid Recommendation and Advanced Search System – Kailash Chowdary Bodduluri, Enode
15.50 -16.00 Sum up and plan for our seminars in June

Abstracts

Secure On-Premises Deployment of Large Language Models for Enhanced Patent Drafting – Homam Mawaldi, AWA

Patent drafting is a complex and high-stakes process for securing intellectual property rights. During the patent prosecution phase, maintaining confidentiality is crucial, which makes cloud-based third-party services inadequate. This study explores the feasibility of AWACopilot, a secure, on-premise solution comprising a web service that leverages open-source large language models (LLMs) to assist patent attorneys in the intricate patent application drafting process. AWACopilot generates key patent sections such as background, abstract, detailed description, etc., from human-crafted claims, addressing the data security risks posed by cloud-based AI services. Its modular architecture enables customization and adaptability to different patent tasks. Although challenges remain—including reliance on LLM capabilities and the need for rigorous content verification—this study demonstrates the potential for secure, AI-driven solutions to enhance patent drafting workflows.

Enhancing E-commerce Personalization with a Hybrid Recommendation and Advanced Search System – Kailash Chowdary Bodduluri, Enode

In the evolving landscape of e-commerce, personalizing user experience through recommendation systems has become a way to boost user satisfaction and engagement. However, small-scale e-commerce platforms struggle with significant challenges, including data sparsity and user anonymity. These issues make it hard to effectively implement recommendation systems, resulting in difficulty in recommending the right products to users. This study introduces an innovative Hybrid Recommendation System (HRS) to address challenges in e-commerce personalization caused by data sparsity and user anonymity. By blending multiple dimensions of the data into one unified system for producing recommendations, this system represents a notable advancement in achieving personalized user experiences in the context of limited data. In addition to the recommendation system, we have also developed an effective search feature with capability of leveraging fuzzy matching, TF-IDF vectorization, and a Swedish language synonym model for query expansion. Our current research focuses on integrating these two independent systems—recommendations and search—to address their individual limitations and create a unified discovery ecosystem. By combining explicit search behaviors with implicit user preferences and exploring technologies such as large language models and sequential recommendation frameworks, we aim to further improve and optimize product discovery in data-sparse environments.

When? Friday April 25th 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/BTAE5wY4XW9TDB2A9 by April 23 (especially important if you plan on attending onsite so we have fika for everyone)

Agenda
14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Enhancing Efficiency in Industry: Automation, Analytics, and Digital Twins – Arslan Musaddiq
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Machine-Learning Models for Two-Dimensional Antiferromagnetic Materials – Shahid Sattar
15.50 -16.00 Sum up and plan for our seminars in May

Abstracts

Enhancing Efficiency in Industry: Automation, Analytics, and Digital Twins –Arslan Musaddiq
This presentation will be about ongoing industry collaborations focused on data-driven optimization in manufacturing, energy, and logistics. These projects aim to improve operational efficiency, enhance decision-making, and drive digital transformation across various sectors. The presentation will cover the automation of waste input measurement and real-time thickness monitoring in construction board production, energy data analysis for baseload identification and prediction, digital twin development for sawmill process optimization, and scheduling optimization for mobile blood donation units. The seminar will explore the challenges, methodologies, and practical impact of integrating smart systems into traditional industries.

Machine-Learning Models for Two-Dimensional Antiferromagnetic Materials – Shahid Sattar
Two-dimensional antiferromagnets (2D AFMs) recently gained tremendous scientific interest owing to their use in next-generation spintronic devices. In this talk, I will discuss about our recent effort to develop machine-learning (ML) models for 2D AFMs. More specifically, how ML models together with molecular dynamics simulations can be effectively used to capture surface reconstructions in topological 2D AFMs [1]. Additionally, I will show how ML models can be employed to compute thermal properties and heat transport in 2D MnX and Janus XMnY (X,Y=S, Se, Te). Finally, I will talk about potential new avenues which can be explored combining first-principles calculations and ML models.

[1] S. Sattar, D. Hedman and C. M. Canali, Phys. Rev. Research (2025).