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Meet Our Team

Meet the bright minds behind NIMI: a team of passionate researchers united in our commitment to developing foundational and theoretical approaches in artificial intelligence. Driven by curiosity and guided by leading scientific methods, we are inspired to make impactful contributions in areas such as healthcare, environmental studies, and AI for good, aiming to pave the way for advancements that benefit society and the planet.

Group Leader

Dr. Sahar Vahdati

Group leader

Nature-Inspired Machine Learning (NIMI)

Knowledge-driven machine learning, representation learning and reasoning methods over knowledge graphs are the core part of my research that I had the opportunity to work on, in several different research groups.

Here is the list of my academic degrees and the research organizations in which I conducted my research over the past years:

  • Research Group Lead at the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig.

  • Research Group Lead of Nature-Inspired Machine Intelligence at the Institute for Applied In-formatics (InfAI) Institute in Dresden.

  • Postdoctoral Fellow in the Intelligent Information Systems group at the Department of Computer Science, University of Oxford, UK – led by Prof. Georg Gottlob.

  • Postdoctoral Fellow in the department of Intelligent Systems (Institute of Computer Science III) at the University of Bonn.

  • PhD of Natural Science (Dr. rer. nat) in the department of Intelligent Systems (Institute of Computer Science III) at the University of Bonn, Germany – doctoral dissertation under supervision of Prof. S¨oren Auer at the Enterprise Information System (EIS) group.

  • Master of Computer Science in the department of Intelligent Systems (Institute of Computer Science III) at the University of Bonn, Germany – master thesis under the supervision of Prof. Rainer Manthey, and Prof. Andreas Behrend at the the Intelligent Databases (IDB) group.

  • Bachelor of Software Engineering in Tabriz, Iran – bachelor thesis under the supervision of Prof. Farhad Pourreza.

The details related to the development of my research in each of the above-mentioned career steps can be summarized as the following disciplines:

Knowledge-driven AI

Since 1st September 2023, I joined TU Dresden and the Scdas.AI center as research group. I mainly target to build up a group with a focus on the connection of knowledge graphs and large language models, and the role of Neuro-symbolic methods for future learning models. My position is funded with additional two doctoral students with whom I am exploring the above-mentioned topics.

Representation Learning

Since I joined InfAI in 2020 a research group leader, I worked on my vision of Nature-Inspired Machine Intel-ligence. We acquired group budget through EKFZ funding, and EU projects. I have been directing the group research activities, and research mainly on my vision for research in machine intelligence inspired by natural science that can result in innovations to address weaknesses of current ML approaches. The main activities of the group and planned research directions are focused on representation learning.

Learning and Reasoning in Large Scale Knowledge Graphs.

Shortly before finishing my dissertation, I became very interested in using link prediction techniques over the scholarly knowledge graph we had constructed by then. I used link prediction techniques to provide recommendations in the context of scholarly communication. Initially, I used graph partition approaches relying on semantic similarity measures to determine the relatedness between scholarly entities. I further continued with this line of research by using knowledge graph embedding (KGE) models in several other use cases, as well proposing new models. This is still a focal research direction of myself and my group.

Knowledge Graph-empowered Intelligent Information Systems

The core of my PhD topic was to explore the challenges and approaches for automated knowledge acquisition and curation, integration and management of heterogeneous metadata on the Web towards. This research has been conducted using the example of scholarly metadata towards a collaborative construction and management of a science knowledge graph. The aim was to facilitate the integrated use of different knowledge-aware AI-based methods, analytical techniques, and tools for improving scholarly communication. My research contributions provide useful approaches by following the FAIR data principles and providing metadata in a findable, accessible, interoperable, and reusable format. Efficient and scalable methods for integrating large amounts of data, as well as knowledge representation and discovery, were key challenges that I tackled. As a major part of my research activities, I constructed a specific knowledge graph for which I also provided quality-based assessments and meta research analytics by applying data mining and link discovery approaches.

Scientific publishing I published at venues that target core of artificial intelligence that deals with knowledge representation, learning and management such as ECAI, AAAI, EMNLP, IJCNN, IEEE Access, PAKDD, EDBT, ILP, ESWC, EKAW, TPDL, ICSC, MTSR, ECIR, and SEMANTiCS. I value collaborative research work and have conducted research not only with members of the Computer Science department at the University of Bonn and University of Oxford but also with the Knowledge Media Institute at the Open University London – UK, the CNR research council of Italy, Institute Mihajlo Pupin, Belgrade in Serbia, the University Hospital Dresden, the University Hospital Leipzig, the L3S Research Center in Hanover, and University of Vienna . Community involvement and Event Organization Active community involvement is very rewarding and beneficial for increasing the impact of science in general. I am the co-general chair of SEMANTICS conference with approximately 500 visitors per year.

84 entries « 1 of 28 »

2023

Karami, Saeed; Saberi-Movahed, Farid; Tiwari, Prayag; Marttinen, Pekka; Vahdati, Sahar

Unsupervised feature selection based on variance-covariance subspace distance Journal Article

In: Neural Networks, vol. 166, pp. 188–203, 2023.

BibTeX | Links:

Mohiuddin, Karishma; Alam, Mirza Ariful; Alam, Mirza Mohtashim; Welke, Pascal; Martin, Michael; Lehmann, Jens; Vahdati, Sahar

Retention is All You Need Proceedings Article

In: Frommholz, Ingo; Hopfgartner, Frank; Lee, Mark; Oakes, Michael; Lalmas, Mounia; Zhang, Min; Santos, Rodrygo L. T. (Ed.): Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023, pp. 4752–4758, ACM, 2023.

BibTeX | Links:

Language Models as Controlled Natural Language Semantic Parsers for 
 Knowledge Graph Question Answering

Lehmann, Jens; Ferré, Sébastien; Vahdati, Sahar

Language Models as Controlled Natural Language Semantic Parsers for Knowledge Graph Question Answering Proceedings Article

In: Gal, Kobi; Nowé, Ann; Nalepa, Grzegorz J.; Fairstein, Roy; Radulescu, Roxana (Ed.): ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland - Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023), pp. 1348–1356, IOS Press, 2023.

BibTeX | Links:

84 entries « 1 of 28 »

Senior Researchers

Dr. Kossi Amouzouvi

Senior Researcher

Nature-Inspired Machine Learning (NIMI)

Kossi Amouzouvi is a postdoctoral researcher and is involved in joint collaboration with Scads.AI and InfAI. He is by background a mathematician and holds a PhD in Density Functional Theory, from the University of the Witwatersrand, he is a member of Science in Mathematics, African Institute for Mathematical Sciences (AIMS – Ghana) where he did his master on Hochschild Cohomology and Deformation Theory, and his second master is on Mathematics, from his hometown, University of Lomé (Togo), with focus on Differential Geometry. He is focusing on representation learning and quantum machine learning.

Dr. Roman Liessner

Senior Researcher

Nature-Inspired Machine Learning (NIMI)

Roman Liessner is a mechatronics engineer with over 10 years of experience in developing cutting-edge applications with machine learning. He has a PhD in reinforcement learning with application in energy management. In his spare time, he enjoys spending time with his son, cycling and pursuing his curiosity in artificial intelligence. Connect with him on LinkedIn


Farhad Safaei

Senior Researcher

Nature-Inspired Machine Learning (NIMI)

Farhad Safaei is a skilled AI and Reinforcement Learning expert, committed to harnessing the power of AI to tackle real-world challenges across diverse domains. With experience in various AI-driven projects in the industry, Farhad has gained valuable insights into addressing complex problems and developing effective solutions. In 2019, he joined Deutsche Bahn (DB) as a Senior Reinforcement Learning Expert, where he played a significant role as an early contributor to an innovative project aimed at creating an AI-based capacity and traffic management system using Multi-Agent Reinforcement Learning. Alongside his position at DB, Farhad is pursuing academic research focused on enhancing the robustness and reliability of reinforcement learning methods for critical and practical applications. Driven by a strong passion for making a tangible impact on human lives, Farhad is highly motivated about his research collaboration with the InfAI Institute, through which he can leverage his AI expertise to develop innovative solutions for pressing healthcare challenges.


Robert Wardenga

Senior Researcher

Nature-Inspired Machine Learning (NIMI)

Robert Wardenga  is a researcher in the NIMI group within InfAI. His main research interest is the integration of knowledge into the Machine Learning Pipeline to improve Factuality and Truthfulness. This entails research in Representation Learning, Natural Language Generation and Reinforcement Learning. Before joining the InfAI I worked as a mathematician at the TU Dresden.


Prof. Dr. Jens Lehmann

PhD Supervisor

Nature-Inspired Machine Learning (NIMI)

rincipal Scientist, Amazon AGI (Artificial General Intelligence)

Honorary Professor at TU Dresden
Member of InfAI

Prof. Dr. Jens Lehmann is a Principal Scientist at Amazon where he works at the AGI (Artificial General Intelligence) organisation on advancing large language models, conversational AI and knowledge graphs. He also holds an honorary professorship at TU Dresden, was selected as a fellow of ELLIS and is a member of  InfAI. His academic activities at TU Dresden and InfAI support the Smart Data Analytics research group. Previously, he was jointly appointed full professor at the University of Bonn and Fraunhofer IAIS leading approximately 40 researchers. In this role, he was a lead scientist at Fraunhofer IAIS and coordinated the Dresden branch of the institute. His research interests involve knowledge graphs, machine learning as well as question answering & dialogue systems. His main research goal is to investigate and build generally intelligent systems by combining knowledge- and data-driven approaches. Prof. Lehmann authored more than 200 articles in international journals and conferences winning 15 best paper awards and obtaining more than 25000 citations and h-index 60+. He is a supporter and contributor to community research projects, including DBpedia, DL-Learner and LinkedGeoData. Previously, he led the AKSW research group and completed his PhD with „summa cum laude“ at the University of Leipzig with visits to the University of Oxford.

30 entries « 1 of 6 »

2023

Mohiuddin, Karishma; Alam, Mirza Ariful; Alam, Mirza Mohtashim; Welke, Pascal; Martin, Michael; Lehmann, Jens; Vahdati, Sahar

Retention is All You Need Proceedings Article

In: Frommholz, Ingo; Hopfgartner, Frank; Lee, Mark; Oakes, Michael; Lalmas, Mounia; Zhang, Min; Santos, Rodrygo L. T. (Ed.): Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023, pp. 4752–4758, ACM, 2023.

BibTeX | Links:

Language Models as Controlled Natural Language Semantic Parsers for 
 Knowledge Graph Question Answering

Lehmann, Jens; Ferré, Sébastien; Vahdati, Sahar

Language Models as Controlled Natural Language Semantic Parsers for Knowledge Graph Question Answering Proceedings Article

In: Gal, Kobi; Nowé, Ann; Nalepa, Grzegorz J.; Fairstein, Roy; Radulescu, Roxana (Ed.): ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland - Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023), pp. 1348–1356, IOS Press, 2023.

BibTeX | Links:

Song, Bowen; Xu, Chengjin; Amouzouvi, Kossi; Wang, Maocai; Lehmann, Jens; Vahdati, Sahar

Distinct Geometrical Representations for Temporal and Relational Structures in Knowledge Graphs Proceedings Article

In: Koutra, Danai; Plant, Claudia; Rodriguez, Manuel Gomez; Baralis, Elena; Bonchi, Francesco (Ed.): Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Proceedings, Part III, pp. 601–616, Springer, 2023.

BibTeX | Links:

Mohiuddin, Karishma; Alam, Mirza Ariful; Alam, Mirza Mohtashim; Welke, Pascal; Martin, Michael; Lehmann, Jens; Vahdati, Sahar

Retention Is All You Need Journal Article

In: CoRR, vol. abs/2304.03103, 2023.

BibTeX | Links:

2022

Alam, Mirza Mohtashim; Rony, Md. Rashad Al Hasan; Nayyeri, Mojtaba; Mohiuddin, Karishma; Akter, M. S. T. Mahfuja; Vahdati, Sahar; Lehmann, Jens

Language Model Guided Knowledge Graph Embeddings Journal Article

In: IEEE Access, vol. 10, pp. 76008–76020, 2022.

BibTeX | Links:

30 entries « 1 of 6 »

Research Associate (PhD)

Bowen Song

Research Associate

Nature-Inspired Machine Learning (NIMI)

Bowen is a PhD student, and holds a CSC grant that supports his exploration into the world of Representation Learning. Focused on temporal knowledge graph embedding models, Bowen’s research deals with the dynamic interplay of data over time. Bowen emerges as a pioneering force, navigating the complex path of knowledge and leaving an indelible mark on the future of artificial intelligence


Jason Li

Research Associate

Nature-Inspired Machine Learning (NIMI)

Jason Li is a PhD student who is working on Reinforcement Learning. He received his M.Sc. Computer Science from University College London and is currently actively working in the EU funded research project IntelliLung which aims to develop an AI based decision support system in intensive care units. His research focuses on the practical application and validation of RL, in particular offline RL and RL in the medical domain. 


Preetam Gattogi

Research Associate

Nature-Inspired Machine Learning (NIMI)

Preetam, a researcher at the ScadDS.AI center, focuses on investigating the factual accuracy of large language models. His research is uniquely centered on human and mind-inspired intelligence to refine the precision of these language models. Within the dynamic landscape of artificial intelligence, Preetam’s work contributes to the advancement of understanding and improving the accuracy of language models.


Dhananajay Bhandiwad

Research Associate

Nature-Inspired Machine Learning (NIMI)

Dhananajay, a PhD student at the ScadDS.AI center, is dedicated to researching the factual accuracy of large language models and knowledge graphs. His work in the language and knowledge intersect, exploring ways to enhance the precision of these expansive information systems. As a scholar within the ScadDS.AI center, Dhananajay is positioned at the forefront of advancing our understanding of how accuracy can be refined in the expansive landscape of language models and knowledge representation.


Muhammad Hamza Yousuf

Research Associate

Nature-Inspired Machine Learning (NIMI)

Hamsa Yousuf is a researcher in the InteliLung project where a decision support system is under development based on  Reinforcement Learning for mechanical ventilation in intensive care units.


Student Assistants

 Prathmesh Dudhe

Student Assistant

Nature-Inspired Machine Learning (NIMI)

Prathmesh Dudhe is a master student of TU Dresden and working on the IntelliLung project. He is working on data preparation and exploration of reinforcement learning-based algorithms for supporting improvement of clinician decisions in intensive care units.  


Qasid Saleem

Student Assistant

Nature-Inspired Machine Learning (NIMI)

Qasid Saleem has been working on several projects, and has a broad range of skills, from reinforcement learning, to knowledge graphs and language models. Qasid finished his master thesis also in the NIMI group on link prediction. And recently has been heavily involved in E-vita project, building a conversation AI system for elderly people based on RAZA system and language models.


Abrar Hyder Mohammed

Student assistant

Nature-Inspired Machine Learning (NIMI)

Abrar Hyder is a student assistant in the E-vita project and works on building dialogue systems for elderly people. He is heavily involved in data preparation, and system maintenance.


Nur A Zarin Nishat

Student Assistant

Nature-Inspired Machine Learning (NIMI)

A Master’s student in the computational modeling and simulation in life sciences program at TU Dresden. Previously worked at Siemens. Current research interests lie in the application of knowledge graph embedding and machine learning in the field of molecular biology, specifically in genomics.


Kartikey Nagpal

Student Assistant

Nature-Inspired Machine Learning (NIMI)

Student Research Assistant, working on GeoSpacial data and advanced querying 


Kevin

Student Assistants

Nature-Inspired Machine Learning (NIMI)

Kevin  has a bachelor’s degree in media informatics and is particularly interested in machine learning, especially natural language processing. As part of the iDoks project, he is actively working on the development of a summary generation demonstrator and a library for evaluating the generated summaries. This library also enables the creation of datasets for improving summary models through human feedback.


Pavan Singavarapu

Student Assistants

Nature-Inspired Machine Learning (NIMI)

Pavan Singavarapu is working on two main projects, both iDoks and E-vita. Pavan is helping in text summarization tasks, and preparation of stories for conversation AI-based systems for elderly people in the E-vita project. 


Ashish Knagen

Student Assistants

Nature-Inspired Machine Learning (NIMI)

Ashish Knagen is working on large language models.


Visiting Researchers

Wiem Gargouri

DAAD Research

Nature-Inspired Machine Learning (NIMI)

Wiem Gargouri holds a DAAD scholarship to do a research visit at NIMI. She is joining us from the Institut Supérieur des Sciences Appliquées et de Technologie de Sousse (Higher Institute of Applied Science and Technology of Sousse)


Rutuja Mohekar

Visitor Researcher

Nature-Inspired Machine Learning (NIMI)

Student Researcher on Research Project in collaboration with TU Dresden


Abigail Amankwah

 Visitor Researcher

Nature-Inspired Machine Learning (NIMI)

Abigail is currently doing research on Deep Nonnegative Matrix Tri-Factorization for Image Clustering in collaboration of NIMI and the University of Cape Coast (UCC), Ghana. In 2022 ranking, UCC was adjudged the best university in Ghana, in West Africa, the 4th best university in Africa and the best university globally for research influence.


Developer

Sanaz Alipour Mazloumi

Software Developer

Nature-Inspired Machine Learning (NIMI)

I am a software development engineer with over 10 years of experience in developing web , desktop and mobile applications. I have worked on various projects, ranging from e-commerce platforms, social media networks, to data analysis tools. I am also passionate about learning new technologies and keeping up with the latest trends in the industry. I enjoy collaborating with other developers, designers, and clients to deliver innovative and user-friendly solutions. I am looking for a challenging and rewarding position where I can apply my skills and experience to create impactful products.


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