23rd November, Fall meeting 2023: Anomaly Detection
On Thursday 23rd of November 2023 we hope to see you all again in Amsterdam for an afternoon around Anomaly Detection.
Anomaly detection (also referred to as outlier detection) is the identification of rare observations in data that deviate significantly from normal. It finds applications in many areas, such as cyber security, medicine, machine vision, statistics, and neuroscience. Machine learning techniques play an increasing role in anomaly detection (supervised, semi-supervised and unsupervised). For the coming NVPHBV Fall Meeting, we invite you to submit abstracts on your research in the field of anomaly detection (in a broad sense).
In another inspiring afternoon, we are going to explore these important questions with three invited keynote speakers:
- Estefanía Talavera (University of Twente)
- Samet Akcay (Intel Corporation)
- Eric Nalisnick (University of Amsterdam)
And we will have a series of short research presentations again.
You are invited to submit your abstract (max. 1 A4 plus 1 or 2 illustrations) for a short oral or posterpresentation. Please submit your abstract as PDF to firstname.lastname@example.org before Nov. 10th. You will receive a notice of acceptance before Nov. 16th. Topics include, but are not limited to: The field of anomaly detection (in a broad sense).
By doing so you might win the best-abstract award of €200. Furthermore this is the seond time the abstracts for a NVPHBV meeting will be published in online proceedings.
As always there will be ample time to connect with all attendees from industry and academia.
- 12:00 - 13:00
- Lunch and poster session
- 13:00 - 13:10
- Member Meeting (NVPHBV members only)
Speakers:Prof. Dr. Clarissa Sánchez
- 13:10 - 14:00
- Keynote: Detecting Distribution Shift with Deep Generative Models
Speakers:Dr. Eric Nalisnick
- 14:00 - 15:00
- Abstract Presentations
Speakers:Prof. dr. Marcel Breeuwer
- 15:00 - 15:30
- Break - Coffee & Tea
- 15:30 - 16:20
- Understanding anomalies related to human behavior
Speakers:Dr. Estefania Talavera
- 16:20 - 17:10
- Bridging the Gap: Anomalib - Open-Source Innovation in Anomaly Detection
Speakers:Dr. Samet Akcay
- 17:10 - 17:15
- Abstract Award Ceremony
Speakers:Prof. Dr. Clarissa Sánchez
- 17:15 - 18:15
- Drinks and Networking
Dr. Eric NalisnickAssistant professor, University of Amsterdam
Eric Nalisnick is an assistant professor at the University of Amsterdam. He is also an ELLIS scholar and NWO Veni fellow. His research interests span statistical machine learning and probabilistic modeling, with an emphasis on human-in-the-loop learning, specifying prior knowledge, detecting distribution shift, and quantifying uncertainty in deep learning. He previously was a postdoctoral researcher at the University of Cambridge and a PhD student at the University of California, Irvine. Eric has also held research positions at DeepMind, Microsoft, Twitter, and Amazon.
Dr. Estefania Talavera
Estefania Talavera is an Assistant Professor in the Data Management and Biometrics group at the University of Twente. Her research interests include computer vision, machine learning, and their intersection for human behaviour understanding. Prior to joining the University of Twente, she was a lecturer and researcher in the Information Systems group at the University of Groningen. She received her PhD in Computer Science in 2020 from the University of Barcelona and the University of Groningen, under the supervision of Prof. Petia Radeva and Prof. Nicolai Petkov.
Dr. Samet AkcayAI research engineer and a tech lead, Intel AI, London
Samet Akcay, an AI research engineer and a tech lead, specializes in semi/self-supervised, zero/few-shot anomaly detection, and multi-modality. He is recently known for his open-source contributions to the ML/DL community. He is the lead author of anomalib, a major open-source anomaly detection library. He also maintains the OpenVINO Training Extensions, a low-code transfer learning framework for building computer vision models.
Samet received his Ph.D. from the Department of Computer Science, Durham University, UK, and his MSc from the Department of Electrical Engineering, Penn State University, US. His research has been published at top-tier computer vision and machine learning conferences and journals.