machine learning

bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.

René Werner, Institute for Applied Medical Informatics, UKE

For further details and hybrid links, please go to the webpage AI in Biology & Medicine

As the number of job applications increase, hiring managers have turned to artificial intelligence (AI) to help them make decisions faster, and perhaps better. Where once each manager did their own first rough cut of files, now third party software algorithms sort applications for many firms. Human mistakes are inevitable, but fortunately heterogenous. Not so with machine decision-making. Relying on the same AI systems means that each firm makes the same mistakes and suffering from the same biases. When the same person re-encounters the same model again and again, or models trained on the same dataset, she might be wrongly rejected again and again. In this talk, I will argue that it is wrong to allow the quirks of an algorithmic system to consistently exclude a small number of people from consequential opportunities, and I will suggest solutions that can help ameliorate the harm to individuals.

Prof. Dr. Kathleen A. Creel (Northeastern University, Boston, MA, USA)

Kathleen Creel is an Assistant Professor at Northeastern University, cross appointed between the Department of Philosophy and Religion and Khoury College of Computer Sciences. Her research explores the moral, political, and epistemic implications of machine learning as it is used in non-state automated decision making and in science. She co-leads Northeastern’s AI and Data Ethics Training Program and is a winner of the International Association of Computing and Philosophy’s 2023 Herbert Simon Award.

Institutions

• UHH

The impact of artificial intelligence on society is so profound that it can be considered to be disruptive. AI does not only have radical consequences for society - as is expressed by the concept of ‘the Fourth Revolution’ and ‘Society 5.0’ that is emerging from that - but also for ethics itself. Technologies have become ethically disruptive, in the sense that they challenge and affect the very concepts with which we can do ethics in the first place. What do’ agency’, ‘responsibility’ and ‘empathy’ mean when artificial agents are entering society? What does ‘democratic representation’ mean when AI systems interfere with the very idea of representation itself? What can the notion of ‘the humane’ still mean when AI systems become an intrinsic part of human actions and decision-making? This talk will explore phenomenon of ethical disruption in detail, by investigating the various ways in which technologies – and not only human beings – can be ethically significant. Breaking the human monopoly on ethics and expanding it towards technology will make it possible to connect ethics more directly to practices of design. The resulting ‘Guidance Ethics Approach’ enables bottom-up ethical reflection that can foster the responsible design, implementation and use of new and emerging technologies. 

Prof. Dr. Peter-Paul Verbeek (Universiteit van Amsterdam, NL)

Peter-Paul Verbeek (1970) is Rector Magnificus and professor of Philosophy and Ethics of Science and Technology at the University of Amsterdam. His research and teaching focus on the relationship between humans and technology, viewed from an ethical perspective and in close relation to design. He is chair of the UNESCO World Commission for the Ethics of Science and Technology (COMEST), editor-in-chief of the Journal of Human-Technology Relations, and editor of the Lexington book series in Postphenomenology and the Philosophy of Technology. More information: www.ppverbeek.nl

Institutions

• UHH

unfortunately, today's talk has to be cancelled. We are very sorry

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Patrick Sontheimer - Long short-term memory for surrogate modeling

Long-Short Term Memory is a gated architecture designed to combat the vanishing/exploding gradient problem in recurrent neural networks (RNN). In the presentation, time series regression will be performed on multiple-input multiple-output data, to create a surrogate Model.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

Dr. Sonja Hänzelmann and Dr. Fabian Hausmann, Institute of Medical Systems Biology, UKE

In diesem intensiven 3-tägigen Kurs werden Sie in die Welt der Data Literacy (Die Fähigkeit, kompetent mit großen Datenmengen umzugehen) eingeführt. Der Kurs bietet eine praxisnahe Herangehensweise an biomedizinische Probleme, bei denen die Teilnehmer:innen lernen, wie sie relevante Erkenntnisse aus komplexen Datensätzen gewinnen können.

Topics:
Grundlagen der Dateninterpretation und Data Literacy: Verständnis von Schlüsselbegriffen und Konzepten im Bereich Data Literacy
Python für die Datenanalyse: Einführung in die Programmiersprache Python für Datenanalyse und -manipulation
Einführung in Data Mining-Techniken: Überblick über verschiedene Data Mining-Methoden und ihre Anwendungen im biomedizinischen Bereich.
Praktische Anwendung von Clustering, Klassifizierung und automatischer Mustererkennung
Anwendung auf biomedizinische Probleme: Bearbeitung eines ausgewählten biomedizinischen Problems durch ein Gruppenprojekt
Visualisierung und Interpretation der Ergebnisse: Effektive Kommunikation von Analyseergebnissen durch Datenvisualisierung
Interpretation und Diskussion der gewonnenen Erkenntnisse im biomedizinischen Kontext

Prof. Dr. Vincent Cornelius Müller, Lehrstuhl für Theory and Ethics of Artificial Intelligence, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen

Taming the Machine. The Ethics in Information Technology Public Lecture Series

This semester’s edition of "Taming the Machines" explores the interrelated ethical, political, and technological aspects of Artificial Intelligence (AI) in an interdisciplinary way. 
 
AI-driven technologies are increasingly shaping the world we live in, sparking growing ethical scrutiny. As a result, it appears more and more urgent that societies collectively address how and in what way the further development of such technologies might be tangibly influenced. And, importantly, by whom this task should be advanced and according to which agendas? Ethicists, legislators, designers, and engineers, each bring distinct expertise and capacities to the multiplicity of social issues raised by these technologies, yet the perspectives and approaches they offer may or may not be complimentary or even simultaneously realisable (let alone mutually satisfactory). Amidst the rising tensions surrounding the AI driven transformation of our shared social space, particularly in the domain of governance and regulation, this lecture series asks how we might best accompany innovation in AI and realise ethically desirable future outcomes. In other words, to delve into the questions of what it means to live well in a society that is increasingly driven by AI tools? What design and regulative choices ought we make? What social infrastructures and normative frameworks might be needed for the future handling of emerging technologies? How could or should openness to innovation be reconciled with defending and developing the ideals of a free and democratic society under the rule of law? 
 
To explore these and other related questions, this public lecture series invites distinguished researchers from computer science, philosophy, and political theory to present and discuss their work. To get the latest updates and details how to attend the lectures, please visit https://www.inf.uni-hamburg.de/en/inst/ab/eit/taming-the-machines/winter23-24.html

Institutions

• UHH, Ethik in der Informationstechnologie, Koordination: Prof. Dr. Judith Simon

Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train machine learning. Algorithms trained on biased data will, without intervention, produce biased outcomes and increase the inequality experienced by historically disadvantaged groups.
To tackle this issue the EU commission recently published the Artificial Intelligence Act – the world’s first comprehensive framework to regulate AI. The new proposal has several provisions that require bias testing and monitoring. But is Europe ready for this task? 
In this session I will examine several EU legal frameworks including data protection as well as non-discrimination law and demonstrate how despite best attempts they fail to protect us against the novel risks posed by AI. I will also explain how current technical fixes such as bias tests -  which are often developed in the US - are not only insufficient to protect marginalised groups but also clash with the legal requirements in Europe. 
I will then introduce some of the solutions I have developed to test for bias, explain black box decisions and to protect privacy that were implemented by tech companies such as Google, Amazon, Vodaphone and IBM and fed into public policy recommendations and legal frameworks around the world. 

Prof. Dr. Sandra Wachter (Oxford Internet Institute, University of Oxford, GB)
Sandra Wachter is Professor of Technology and Regulation at the Oxford Internet Institute at the University of Oxford where she researches the legal and ethical implications of AI, Big Data, and robotics as well as Internet and platform regulation. Her current research focuses on profiling, inferential analytics, explainable AI, algorithmic bias, diversity, and fairness, as well as governmental surveillance, predictive policing, human rights online, and health tech and medical law.
At the OII, Professor Sandra Wachter leads and coordinates the Governance of Emerging Technologies (GET) Research Programme that investigates legal, ethical, and technical aspects of AI, machine learning, and other emerging technologies. [more]

Institutions

• UHH

Get to know other researchers working in Image computation and AI within UKE and at institutions around Hamburg.

Our workshop will consist of a mixture of elevator pitches and discussions with interesting formats to create dynamic participation. Please indicate if you are willing to give a short pitch (max 3 mins, 1 slide) of your research topic/interests.

We will have drinks and snacks throughout the afternoon and will end the workshop with pizza and drinks.

Registration: Please write to a.reinicke-vogt@uke.de by May 23^rd with your name, email, research topic, and if you are willing to give a short (3 mins, 1 slide) elevator pitch of your research.

Once again, the five platforms Helmholtz.AI, Helmholtz Imaging, HIFIS, HIDA and HMC have teamed up to createa second edition of the Incubator Summer Academy on 18-29 September 2023! We have designed a joint program with a variety course packages covering state of the art Data Science methods and skills, as well as networking opportunities in our Summer Academy Gathertown space!

Ranging from fundamental course packages as for instance “Python”, or “Introduction to Scienctific Metadata” to advanced topics as “Machine Learning Based Image analysis”, the program offers participants to select course packages that best suit their experience levels and interests.

The Incubator Summer Academy is open to all doctoral and postdoctoral researchers in the Helmholtz Association. Additionally, a small number of seats in our workshops are reserved for Master students, doctoral and postdoctoral students from other research institutions and universities.

More detailed information and registration: https://events.hifis.net

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is for students, researchers, and clinicians keen to learn the R programming language and data analysis relevant to biomedicine. The course is designed to be practical and comprehensive with no specific background requirements. We will focus on fundamentals of data analysis with examples of real-life data in biomedicine, such as gene expression. By the end of the course, participants will be familiar with the essentials of data analysis, including statistical tests, linear regression, principal component analysis, clustering and data visualization. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics:
Basics of R programming language
Statistical tests
Linear regression
Principal component analysis (PCA)
Clustering
Data visualization

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is open to students, researchers, and clinicians keen to learn the essentials of machine learning and implementing it via Python. The aim of the course is to provide a comprehensive map of machine learning (and deep learning) methods with no specific background requirements. A little background in python can be helpful, though. We will focus on fundamentals of machine learning, validation methods, linear and nonlinear models, and feature reduction. The students will also get familiarized with the Python packages of Sci-kit Learn and Pytorch. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics
Types of machine learning: supervised and unsupervised
Validation  metrics and cross validation
Introduction to linear and nonlinear models include: Linear regression, Random forest, support vector machines, deep neural networks.
Feature reduction.
Regularization.