Change Your Password Day is a crucial initiative that underscores the significance of cybersecurity in our interconnected world. Cyber-attacks are becoming more frequent and sophisticated. A vast number of breaches are due to compromised, weak passwords. The most common passwords still include easily guessable sequences like "123456" and "password," making it effortless for cybercriminals to gain unauthorized access. High-profile incidents, such as the Equifax breach or the attack on Yahoo, exemplify the devastating consequences of inadequate cybersecurity measures, affecting millions of users worldwide and leading to significant financial and reputational damage. Moreover, the emergence of generative AI and quantum computing introduces complex challenges in password security. Generative AI can be used to create sophisticated phishing attempts, while quantum computing threatens to break current encryption standards, potentially rendering traditional password protection obsolete. It's essential to acknowledge these challenges and advocate for stronger security measures. Adopting multi-factor authentication, using complex and unique passwords, and staying informed about cybersecurity trends are pivotal steps in safeguarding our digital identities. Thanks to CC for the idea and CB for your work in CyberSecurity! More: https://en.wikipedia.org/wiki/List_of_the_most_common_passwords (!!) https://en.wikipedia.org/wiki/Password_cracking Art&Text supported by ChatGPT.

I studied piano performance and toxicology, and am now Master Data Manager at Bayer Pharma in the R&D Master Data Management team of Data Science & AI. I am passionate about team collaboration and rowing. Currently, I'm co-leading the IDMP Ontology, an international cross-industry project with 12 pharma companies for the unique identification of medicinal products to ensure patient safety. Talk to me about common standards and high quality data for better ML and AI outcomes, our next collaboration project, and/or our next jam session at the Bayer Berlin campus.

I work in ALYCE as Tech and Data Lead. I am working towards ALCYE-Spectrum integration from Core side and also involved in Alyce-MIRA use case. I come from Spectrum [Oracle warehouse] background and also work as Spectrum Utility Programmer. I love playing Cricket, Badminton and TT [Ping Pong]. In my free time I listen to Geopolitical news and do Yoga.

Over the past two decades, the interdisciplinary predicTeam has established a prediction platform at Bayer Pharma R&D with the goal to generate state-of-the-art machine learning models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. The predicTeam provides an all-inclusive package covering the data pipeline from experiment to application in projects. In close interaction with experimentalists, we select endpoints for model building that are relevant for drug discovery. We implement and maintain the infrastructure to retrieve and prepare the data and make it accessible as a data lake. For each endpoint, after fully exploring the matrix of data, molecule representations and algorithms, we implement the best-performing and most stable-models models in our internal research platform Pix. A highly automated infrastructure allows us to perform weekly retraining of the models to ensure that the novel chemical space of drug discovery projects is well embedded. We ensure close interaction (e.g. presentations, tutorials, teams channel) with the user base for optimal model use and direct feedback allowing for constant improvements. Finally, our Model Performance Report helps users to assess the applicability of each model to their specific project molecules.

Within DS&AI the Product Owner Hub team, POH, is here to drive the impact of digital products in R&D via a product-centric approach. We conduct this in a hub-and-spoke setting in close collaboration with our R&D colleagues and IT teams and we aim to increase the cross-functional and data-centric mindset throughout the organizations. Our team will establish a dynamic, holistic view on the R&D digital landscape through transparent and value-based digital product roadmaps. We will employ “total cost of ownership” principles that will allow us to better grasp the full cost of digital activities and systems for Bayer – and map this against the expected value. This should allow us to make jointly well-informed decisions about future digital investments investments closely working with our IT colleagues. Artwork created using Stable Diffusion.

ChemogenomicsDB (CGDB) and Älixir are prime examples of creating data assets and insight solutions to help improve digital and data capabilities, enhance scientific productivity, and invigorate early pipeline. Using our products, Bayer R&D colleagues can - access to a broad range of data assets essential for solving key drug discovery questions - explore integrated biomedical and chemical data for scientific curiosity, inspiration, and knowledge discovery - generate insights with the assistance of interactive visualizations for evidence-informed decision making