AISA Kolloquium

If not state otherwise, the talks will take place in U32, Room 101 (see belows sketch for access information).

The Promises and Challenges of Chatbots in Software Engineering

Chatbots are envisioned to dramatically change the future of software engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. In this talk, we will present our work that proposes a framework that uses chatbots to support software engineering tasks, and identifies challenges that developers face when developing these chatbots, including a comparison of popular Natural Language Understanding (NLU) platforms. We will then finish with a demo and our experience deploying a real-world chatbot called AskGit.

Access information

June 1, 2022, 17:30-18:30
The presentation will be given online. Please register here to receive the login information.

About the speaker

Emad Shihab is Associate Dean of Research and Graduate Studies and Associate Professor in the Gina Cody School of Engineering and Computer Science at Concordia University. He holds a Concordia University Research Chair in Software Analytics. His research interests are in Software Engineering, Mining Software Repositories, Software Analytics and Software Bots. Dr. Shihab received the 2019 MSR Early Career Achievement Award and the 2019 CS-CAN/INFO-CAN Outstanding Young Computer Science Researcher Prize. His work has been published in some of the most prestigious SE venues, including ICSE, ESEC/FSE, MSR, ICSME, EMSE, and TSE. He is recognized as a leader in the field, serving on numerous steering and organization committees of core software engineering conferences. Dr. Shihab has secured more than $2.7 Million, as PI, to support his research, including a highly competitive NSERC Discovery Accelerator Supplement. His work has been done in collaboration with world-renowned researchers from Australia, Brazil, China, Europe, Japan, the United Kingdom, Singapore and the USA and adopted by some of the biggest software companies, such as Microsoft, Avaya, BlackBerry, and Ericsson. He is a senior member of the IEEE. His homepage is: http://das.encs.concordia.ca/

(This colloquium is jointly organized with the ellis unit Stuttgart, https://ellis-stuttgart.eu/)

Deep Learning 2.0: Extending the Power of Deep Learning to the Meta-Level

Deep Learning (DL) has been incredibly successful, due to its ability to automatically acquire useful representations from raw data by a joint optimization process of all layers. However, despite this joint optimization of the network weights, current DL practice still requires substantial efforts to manually optimize on the meta-level to define the neural architecture and training hyperparameters for the data at hand. The next logical step is to jointly optimize these components as well, based on meta-level learning and optimization. I predict that this will allow the next generation of DL systems to simply accept data and user objectives to optimize for (which can, e.g., include fairness, robustness, uncertainty calibration, interpretability, etc) and to thereby provide a clean interface between domain experts (who best know the data and the relevant objectives for the application at hand, but do not need to be machine learning experts) on the one hand and the next-generation DL system on the other hand. In this talk, I will discuss several advances towards this goal, focussing on (1) the joint optimization of several meta-choices in the DL pipeline and (2) the efficiency of this meta-optimization, and (3) taking into account user objectives other than performance.

Access information

July 13, 2022, 17:30-19:00 - Universitätsstr. 32, R101.

About the speaker

Frank Hutter is a Full Professor for Machine Learning at the University of Freiburg (Germany), as well as Chief Expert AutoML at the Bosch Center for Artificial Intelligence. Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is a Fellow of ELLIS and EurAI and the recipient of 3 ERC grants. Frank is best known for his research on automated machine learning (AutoML), including neural architecture search and efficient hyperparameter optimization. He co-authored the first book on AutoML and the prominent AutoML tools Auto-WEKA, Auto-sklearn and Auto-PyTorch, won the first two AutoML challenges with his team, co-organized the ICML workshop series on AutoML every year 2014-2021, and is the general chair of the inaugural conference on AutoML 2022.

(webpage at Albert-Ludwigs-Universität Freiburg)

Towards Neuro-Symbolic Learning Systems

Access information

October 26, 2022, 17:30-18:30 - Universitätsstr. 32, R101.

About the speaker

(webpage)

Prof. Pinkwart will talk on "KI In der Bildung - Historie, Lessons Learned und aktuelle Entwicklungen".

Access information

November 30, 2022, 17:30-18:30 - Universitätsstr. 32, R101.

About the speaker

(webpage at Humboldt-Unversität zu Berlin)

Prof. Freitag will talk on "Artificial Neural Networks in Structural Mechanics".

Access information

February 1, 2023, 17:30-18:30 - Universitätsstr. 32, R101.

About the speaker

(webpage at KIT)