Monday, September 23, 2024 @ 14:00 – 15:00

Tua Gyldenholm

MD, PhD Student at Aarhus University

My research is focused on the blood coagulation system and mechanisms of bleeding and blood clots in critically ill patients, especially patients with cancer and patients undergoing surgery. My on-going PhD investigates whether prolonged prophylactic treatment with a blood thinning drug reduces the risk of blood clots following surgery for cancer in the oesophagus. I believe interdisciplinary collaboration is invaluable in order to develop the new methods and analyses that may help future patients, and I am committed to try and bridge the gap from basic science to clinical application.

Tuesday, October 29, 2024 @ 16:00 – 17:00

Arianna March & Mads Alexander Weile

Novo Nordisk

Arianna is a biotech professional with a background in wet lab research and bioinformatics, primarily focus on proteomics. She is currently a Data Scientist at Novo Nordisk, participating in their Graduate Program. Within this program, Arianna is exploring innovative data sources within the pharmaceutical industry, including registries and databases in the Real-World Science team, as well as connected/wearable devices in the Digital Data Science team.
Passionate about practical applications, moving things forward and public health, Arianna was part of INTU diagnostics’ founding team, a start-up dedicated to developing in-vitro diagnostics solutions for infectious diseases. Arianna is currently based in Boston, where she is soaking up the street dance culture.

Mads Weile is a graduate data scientist at Novo Nordisk having recently received his master’s degree in physics and Nanotechnology from DTU. His journey began with a focus on applying deep learning in electron microscopy, including research at MIT. Originally set on pursuing a PhD in physics, Mads found his passion for data science and now contributes to the AI and ML Sciences team at Novo Nordisk in Boston. Personally, Mads is passionate about human-centric and data-centric solutions, often pondering the question: when does AI make sense? Beyond data science, Mads finds enjoyment in collaborating with people, particularly through the sport of rowing.

Thursday, November 21, 2024 @ 16:00 – 17:00

Niels Orbæk Chemnitz

Analyse & Tal

Niels has a background in computer science from the IT University of Copenhagen, where he also started a PhD in distributed ML and data governance. He dropped out of the PhD to join the research cooperative Analyse & Tal (Analysis & Numbers), where he works as partner and data scientist.

Analyse & Tal is a radical company with experimental approaches to organization and leadership: No bosses, equal pay and equal responsibility. It is a highly multi-disciplinary environment where personal & professional freedom to evolve, and the ability to convey complex topics to both colleagues and the public are extremely important.

Within Analyse & Tal Niels has developed state-of-the-art models for hate-speech and verbal attack detection in the Scandinavian languages. Alongside this he has worked at ITU as external lecturer in IoT (Internet of Things) and large-scale data analytics.

Thursday, December 5, 2024 @ 15:00 – 16:00

Anthea Bott

GN Hearing

Thea currently manages the R&D Insights and ML Team at GN. Holding a PhD in Audiology from the University of Queensland, her journey with data science and AI first started when she joined GN 5 years ago. Thea is responsible for people management, project management, and setting GN’s strategic direction around leveraging AI to empower operational efficiencies within R&D. Her team typically provide end-to-end cloud based solutions – from data engineering through to monitoring models in production – and work with more traditional ML algorithms and neural networks. 

Tuesday, January 21, 2025 @ 16:00 – 17:00

Davide Martizzi

PlayStation

Davide Martizzi, Ph.D., is a Senior Data Scientist at PlayStation, Sony Interactive Entertainment, where he designs and deploys quantitative models for user behavior and content engagement. He previously led a team at Albeado, focusing on model design and implementation for their ML platform, and has held academic roles, including Assistant Professor at the University of Copenhagen. His technical expertise spans machine learning, statistical modeling, numerical simulations, and high-performance computing, with over 30 peer-reviewed publications to his name.