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This lecture is relevant for any student in the KIT Department of Informatics, who intends to write Orientierungsprüfungen ("Orientation Exams"),   The event is dedicated to optimizing the exam period. It will not only provide subject-specific tips but also present proven methods and techniques that can contribute to a successful exam phase. The focus will be on effective learning strategies, time management during the exam period, and stress management techniques. This part of the event can be attended both online and on site.   There will also be a learning partnership exchange on site.   Study Group: At the end there will be an opportunity to find a study partner for the exam phase and/or coming summer semester.   The workshop will be held in German. You can ask questions in English or German.   https://kit-lecture.zoom-x.de/j/61457862779?pwd=bvb07vxsW93TIxYsYdwGsBv2iqVfzG.1

„Real-time Voice AI: Extracting structured data from live phone calls using LLMs"   In the real world, things rarely go according to plan for call centre agents. The customers calling may be stressed (e.g., broken car, flooded house), and they may give unprompted, out-of-order answers. The call centre agents’ job is not easy - they may need to navigate a huge conditional question tree from memory during a high-stakes situation. They will often be required to enter large amounts of structured data during or after the call, based on handwritten or typed notes.   The results are missing, incorrect data for an economically relevant process. What if the agents could follow a dynamic script that changes in real time based on what the caller just said? And what if the structured data could be extracted directly from the audio stream in real time, so that by the time the call is finished, all forms are already filled out? This is what we set out to build.   Our approach has been an LLM-based application that guides call centre agents through the call, adapting to new information as soon as it is available. It both selects the questions and picks out the answers. In order to implement this, we’ve had to solve many challenges - in context engineering, networking, and system design. The aim of the talk is to guide you through the trade-offs and decision that we made.   Referenten: Izer Onadim und Theodor Nguyen, Members of Technical Staff at QuantCo

The KIT Department of Informatics cordially invites you to the Semester Colloquium on February 16 2026, 5:30pm, at InformatiKOM 1.   As part of the event, the Dean of the Faculty will give a brief semester report, providing an overview of current developments and perspectives. The focus of the colloquium will then be on the inaugural lectures of Professor Nadja Klein and Professor Henning Meyerhenke, both of whom are based at the Scientific Computing Center in addition to the Department.   In her inaugural lecture, entitled “Bayesian Statistics and Machine Learning: Leveraging the Best of Both Worlds”, Nadja Klein introduces Bayesian learning as a principled framework for combining prior knowledge with data, quantifying uncertainty, and enhancing the transparency of modern machine-learning systems. By incorporating expert information, structural assumptions, or sparsity‑inducing mechanisms, Bayesian methods can make models more accurate, robust, and data‑efficient, thereby addressing key limitations of black‑box approaches. Nadja Klein's research brings together theoretical analysis, methodological innovation, and real‑world applications. This includes work on spatial statistics, sparse and scalable Bayesian models, Bayesian neural networks, and techniques for interpretability and explainability of complex systems. On the applied side, her group collaborates across disciplines, from analyzing complex biomedical and neuroimaging data, to predicting weather and environmental patterns, to supporting safer autonomous‑driving technologies. This talk will highlight selected recent methodological advances from her group and illustrate their impact through concrete applications, showcasing how Bayesian ideas can strengthen modern machine‑learning pipelines.   In the inaugural lecture “Graph Algorithms for Large Complex Systems” Henning Meyerhenke addresses research challenges arising from massive networks in different application areas. The talk focuses on recent algorithmic results that solve problems in algorithmic network analysis, carbon-aware workflow scheduling, and graph robustness optimization.   Following the colloquium, you are warmly invited to a small reception, offering the opportunity for discussion and personal exchange.

If you are an international Students in the KIT Department of Informatics who will be starting in the coming semester this online Pre-Arrival Orientation will provide vital information regarding our department, KIT and living in Karlsruhe. It is also an opportunity to ask questions you may have. We want you to be prepared and get off to a good start, therefore we will cover topics including: ∎ Checklists for getting started ∎ Academic calendar and key dates and time periods ∎ Planning your Studies and Selecting Modules ∎ Accommodations  ∎ Living in Karlsruhe and Student Life at KIT ∎ Points of Contact for Students ∎ and much much more    The workshop will be held in English, but you can ask questions in either German or English.   We want the international students to be well informed and have information that will help navigate their start to their studies and life in Karlsruhe. Therefore make sure to take part in this online Pre-Arrival Orientation. https://kit-lecture.zoom-x.de/j/62903484183?pwd=a1EcbtkbhtJFhgCSXIm2RbhO8aWi9H.1