idalab seminar

idalab seminar #16: How to unlock valuable personal data for analysis – Shedding light on the byzantine world of privacy-enhancing technology

At the heart of privacy preserving data analysis lies a fundamental paradox: privacy preservation aims to hide, while data analysis aims to reveal. The two concepts may seem completely irreconcilable at first, but – using the right approach – they need not be. Our Data Strategist Lisa Martin spent two months researching this topic extensively, conducting interviews with industry experts and Startups alike. In this talk, Lisa will share her insights and we invite you to join us in discussing one of the most pressing issues of the 21st century: data privacy.

idalab seminar #15: What makes an algorithm ethical? – Defining and implementing a Quality Criteria Catalogue for Algorithms

Algorithms are increasingly relied upon in decision making processes that can have far-reaching implications for all of us. They help doctors diagnose diseases and develop treatment plans. They tell police officers where to patrol. They decide who is going to be invited to the job interview. If these decisions are made by people and the way they decide seems harmful or unjust, our laws enable us to hold them accountable for their actions and correct them if necessary.

idalab seminar #14: Academia to Industry: Looking back on a decade of machine learning

Machine Learning is one of that areas that has seen a rapid transformation from a purely academic topic to becoming a driving technology in the industry these days. Mikio has seen both sides of the coin and will share his experience. What is the difference between academic research and bringing a ML driven product live? What does it take to productionize ML? And finally, how close are we to true AI?

idalab seminar #13: Exploring Chemical Space with Deep Learning

What if we could build batteries for electric cars that would take us further than a full tank of gasoline? If we could grow affordable, tasty and nutritious meat in the laboratory instead of occupying one third of the land on our planet with animal farming? What if we could easily identify promising targets in the human body for new cancer drugs?

idalab seminar #12: The data-privacy dilemma: How full homomorphic encryption could bring healthcare into the digital era

Imagine this: The key to better cancer treatments is within reach, based on patterns from data that is scattered across various locations all over the world. This data could be digitalised, labelled, collected, stored and interpreted. However, this data belongs to a countless number of individuals – and their right to data privacy weighs just as much as the dream of curing a lethal disease.

idalab seminar #11: SELECT * FROM … natural language: databases, we need to talk!

Do you know the feeling? All you want is a break-down of last year’s sales numbers and suddenly you find yourself typing tedious heaps of SQL-statements, clicking through complicated dashboards or looking for the right number in an Excel sheet. The vast majority of decision makers has better things to do with their time. That’s why business intelligence software was originally born. But only 20% of users are actually coping with the solutions BI software provides. Instead, the BI team is spammed with ad hoc data requests. What if everyone had a personal virtual data analyst at hand?

idalab seminar #10: Precision Medicine – How to Identify Biomarkers that Help Patients Choose Their Best Treatment Option

In idalab seminar #10, we turn to the field of Precision Medicine. Dr. Nicole Krämer from Staburo GmbH will give a talk on how Data Science can help physicians to predict a patient’s reaction to a certain treatment in advance.

idalab seminar #9: When you’re Sick, Please Stay at Home – Making Sense of Spreading Phenomena using Human Contact Data

The next idalab seminar will take place on Friday April 27th at 5pm, as always in our office at Potsdamer Straße 68. We will host a talk by Benjamin F. Maier, PhD student at the Robert Koch Institute and freelance data scientist.