AI Colloquium
The AI Colloquium is a series of lectures dedicated to cutting-edge research in the field of machine learning and artificial intelligence, coorganized by the Lamarr Institute for Machine Learning and Artificial Intelligence (Lamarr Institute), the Research Center Trustworthy Data Science and Security (RC Trust), and the Center for Data Science & Simulation at TU Dortmund University (DoDas).
Programme
Distinguished researchers deliver captivating lectures followed by vibrant discussions. However, unlike traditional colloquia, the AI Colloquium prioritizes interactive dialogue, fostering international collaboration. Conducted primarily in English, these 90-minute sessions feature hour-long lectures and 30-minute Q&A sessions. Join every Thursday at 10 AM c.t. for a stimulating exploration of cutting-edge topics. Whether in-person at our Lecture Room on Fraunhofer Strasse 25 or via Zoom, our hybrid format ensures accessibility for all.
| Day (usually) | Thursday |
| Start and end time | 10 AM c.t. - 12 AM |
| Duration of Presentation | 60 Minutes |
| Location (usually) | Lecture Room 303 3. Floor Fraunhofer Strasse 25 Dortmund |
Upcomming Events
Accelerating biodiversity restoration and conservation with AI-driven evidence synthesis
- Resource-aware ML
Abstract: Ecosystem degradation driven by human activities and climate change has caused major losses in biodiversity, ecosystem functions, and services worldwide. Global initiatives such as the UN Decade on Ecosystem Restoration (2021–2030) and the Convention on Biological Diversity’s Target 2 call for the effective restoration of at least 30% of degraded ecosystems by 2030. Ecological restoration is an important tool to halt and reverse the collapse of biodiversity decline, but its practice largely relies on ad hoc decisions by busy practitioners and policymakers. Semantic knowledge graphs have the capacity to represent knowledge from both the peer-reviewed scientific literature and experiential knowledge. We are developing a knowledge graph using grassland restoration studies that will be enriched with practitioner interviews and paired with a LLM to make a public-facing tool for grassland restoration. We will test this tool and evaluate its accuracy, trustworthiness and efficacy with a community of practice. I will share more details about the architecture, key decisions and opportunities for research collaboration.
Dr. Tim Alamenciak
Speaker Bio: Tim is a scholar working on engagement, program participation and open knowledge systems in biodiversity restoration and conservation. He is a postdoctoral fellow with the Bennett Lab at Carleton University (Canada), an Adjunct Associate Professor in the School of Environment, Resources and Sustainability at the University of Waterloo (Canada) and a researcher with Waterloo.AI.
Past Events
Accelerating biodiversity restoration and conservation with AI-driven evidence synthesis
- Resource-aware ML
Abstract: Ecosystem degradation driven by human activities and climate change has caused major losses in biodiversity, ecosystem functions, and services worldwide. Global initiatives such as the UN Decade on Ecosystem Restoration (2021–2030) and the Convention on Biological Diversity’s Target 2 call for the effective restoration of at least 30% of degraded ecosystems by 2030. Ecological restoration is an important tool to halt and reverse the collapse of biodiversity decline, but its practice largely relies on ad hoc decisions by busy practitioners and policymakers. Semantic knowledge graphs have the capacity to represent knowledge from both the peer-reviewed scientific literature and experiential knowledge. We are developing a knowledge graph using grassland restoration studies that will be enriched with practitioner interviews and paired with a LLM to make a public-facing tool for grassland restoration. We will test this tool and evaluate its accuracy, trustworthiness and efficacy with a community of practice. I will share more details about the architecture, key decisions and opportunities for research collaboration.
Dr. Tim Alamenciak
Speaker Bio: Tim is a scholar working on engagement, program participation and open knowledge systems in biodiversity restoration and conservation. He is a postdoctoral fellow with the Bennett Lab at Carleton University (Canada), an Adjunct Associate Professor in the School of Environment, Resources and Sustainability at the University of Waterloo (Canada) and a researcher with Waterloo.AI.
Accelerating biodiversity restoration and conservation with AI-driven evidence synthesis
- Resource-aware ML
Abstract: Ecosystem degradation driven by human activities and climate change has caused major losses in biodiversity, ecosystem functions, and services worldwide. Global initiatives such as the UN Decade on Ecosystem Restoration (2021–2030) and the Convention on Biological Diversity’s Target 2 call for the effective restoration of at least 30% of degraded ecosystems by 2030. Ecological restoration is an important tool to halt and reverse the collapse of biodiversity decline, but its practice largely relies on ad hoc decisions by busy practitioners and policymakers. Semantic knowledge graphs have the capacity to represent knowledge from both the peer-reviewed scientific literature and experiential knowledge. We are developing a knowledge graph using grassland restoration studies that will be enriched with practitioner interviews and paired with a LLM to make a public-facing tool for grassland restoration. We will test this tool and evaluate its accuracy, trustworthiness and efficacy with a community of practice. I will share more details about the architecture, key decisions and opportunities for research collaboration.
Dr. Tim Alamenciak
Speaker Bio: Tim is a scholar working on engagement, program participation and open knowledge systems in biodiversity restoration and conservation. He is a postdoctoral fellow with the Bennett Lab at Carleton University (Canada), an Adjunct Associate Professor in the School of Environment, Resources and Sustainability at the University of Waterloo (Canada) and a researcher with Waterloo.AI.