Computer Science Department and Centre for Cognitive Science, TU Darmstadt Altes Hauptgebäude, Room 074, Hochschulstrasse 1, 64289 Darmstadt, Germany
+49-6151-16-24411 kersting (at) cs (dot) tu-darmstadt (dot) de
Meetings by appointment, general consultation: thursdays, 13:30-14:30 o'clock
In case of important appointments and requests, please make sure to contact my administrative assistant Ira Tesar.
Mission. My team and I in the Artificial Intelligence and Machine Learning (AIML) group would like to
make computers learn so much about the world,
so rapidly and flexibly, as humans. This poses many deep and fascinating scientific problems:
How can computers learn with less help from us and data? How can computers reason about and learn with complex
data such as graphs and uncertain databases? How can pre-existing knowledge be exploited? How can computers
decide autonomously which representation is best for the data at hand? Can learned results be physically
plausible or be made understandable by us? How can computers learn together with us in the loop? To this end,
my team and I develop novel machine learning (ML) and artificial intelligence (AI) methods, i.e., novel computational
methods that contain and combine for example search, logical and probabilistic techniques as well as (deep)
(un)supervised and reinforcement learning methods.
Bio. Kristian Kersting is a Full Professor (W3) at the
Computer Science Department
of the TU Darmstadt University, Germany. He
heads the Artificial Intelligence and Machine Learning (AIML) lab and is also
a Deputy Director of the Centre for Cognitive Science.
After receiving his Ph.D. from the University of Freiburg in 2006, he was with the MIT, Fraunhofer IAIS, the
University of Bonn, and the TU Dortmund University.
His main research interests are statistical relational artificial intelligence (AI), probabilistic programming, and
deep probabilistic learning. Kristian has published over 170 peer-reviewed technical papers
and co-authored a book on statistical relational AI.
Kristian is a Fellow of the European Association for Artificial Intelligence (EurAI), the key European association for AI researchers,
a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), the key European society for machine learning, and a key supporter of the Confederation of Laboratories for Artificial Intelligence in Europe (CLAIRE), the key European research network for Artifial Intelligence.
He was named a Top 100 Influential Scholar 2018 for Artificial Intelligence by AMiner and
received the Inaugural German AI Award (Deutscher KI-Preis) 2019, accompanied by a prize of EURO100.000, as well as several best paper (TPM 2019, AIIDE 2015, ECML 2006), outstanding reviewer awards (ECCV 2016, AAAI 2013),
a Fraunhofer Attract research grant with a budget of 2.5 Million Euro over 5 years (2008-2013), and the
EurAI (formerly ECCAI) AI Dissertation Award 2006
for the best Ph.D. thesis in the field of Artificial Intelligence in Europe.
several tutorials at top AI and Ml conferences, co-chaired several international workshops,
and cofounded the international workshop series on Statistical Relational AI (StarAI).
He regularly serves on the PC (often at senior level) for several top conference (AAAI, ICML, IJCAI,
NeurIPS, UAI, WWW, ICLR, KDD, CVPR and others), is elected PC co-chair of ECML PKDD 2020,
was the General Co-Chair of UAI 2018, and
co-chaired the scientific program committees (PC) of
UAI 2017 as well as
ECML PKDD 2013.
He is the founding Editor-in-Chief of Frontiers in Machine Learning and AI and is (past) action editor of
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Journal of Artificial Intelligence Research (JAIR), Artificial Intelligence Journal (AIJ),
Data Mining and Knowledge Discovery (DAMI), and Machine Learning Journal (MLJ) as well as on the editorial
board of KI, the German AI Journal.
Inaugural German AI Award (Deutscher KI-Preis) 2019, accompanied by a prize of EURO100.000, for key contributions to the advancement of Artificial Intelligence.
Fellow of the European Association for Artificial Intelligence (EurAI)
for exceptional contributions to the field of Artificial Intelligence.
Fellow of the European Laborator for Learning and Intelligent Systems (ELLIS), the key European society for machine learning.
Best Paper Award at the ICML 2019 Workshop on Tractable Probabilistic Models (TPM).
Named Top 100 Influential Scholar 2018
for Artificial Intelligence by AMiner. The 2018 winners are the most cited scholars
in the AAAI and IJCAI conferences between 2007 and 2017, which are identified as
the top venues of Artificial Intelligence.
P5 Award of the Alumni Computer Science Dortmund for the high practical relevance of the Project Group 608 "Manipulation - Development and critical examination of a framework for extracting and transferring semantic information between videos".
Distinguished Lecturer Award of the Faculty of Mathematics and Computer Science at the Friedrich-Schiller-University Jena
Outstanding Reviewer Award of the European Conference on Computer Vision (ECCV)
Best Paper Award of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE)
Best Presentation Award of the New Challenges in Neural Computations (NC2) workshop
Outstanding PC Member Award of the AAAI Conference on Artificial Intelligence (AAAI)
Best Poster Award of the ACM SIGSPATIAL Advances in Geographic Information Systems (GIS)
Fraunhofer Attract research grant (2.5 Million Euros over 5 years), the excellence stipend programme of Fraunhofer
European Association for Artificial Intelligence (EurAI, formerly ECCAI) Dissertation Award for the best AI dissertation in Europe
Best Student Paper Award of the European Conference on Machine Learning (ECML)
Wolfgang-Gentner Young Talent Award for an Outstanding Diploma Thesis at the CS Department of the University of Freiburg
Kristian Kersting, Christoph Lampert, Constantin Rothkopf (Hrsg.)(2019):Wie Maschinen lernen - Künstliche Intelligenz verständlich erklärt.Springer, ISBN: 978-3-658-26762-9.Do you know what artificial intelligence and machine learning are? This popular science book in German explains the basic methods and procedures of machine learning in an easy to understand way and without complicated formulas. Mathematical prior knowledge is not necessary. Lisa, the book's protagonist, uses
everyday situations to illustrate these in an entertaining and informative way.
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole (2016):Statistical Relational Artificial Intelligence: Logic, Probability, and Computation.Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 9781627058414.This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations.
Christian Bauckhage, Kristian Kersting (2016):Collective Attention on the Web. Foundations and Trends in Web Science 5(1-2):1-136.Understanding the dynamics of collective human attention has been called a key scientific challenge for the information age. Tackling this
challenge, this monograph explores the dynamics of collective attention
related to Internet phenomena such as Internet memes, viral videos,
or social media platforms and Web-based businesses. We discuss mathematical models that provide plausible explanations
as to what drives the apparently dominant dynamics of rapid
initial growth and prolonged decline.
Jörg Lässig, Kristian Kersting, Katharina Morik (2016):Computational Sustainability.Studies in Computational Intelligence, Vol. 645, Springer, ISBN:978-3-319-31856-1.This editorial book gives an overview of the state of the art research in Computational Sustainability as well as case studies of different application scenarios. This covers topics such as renewable energy supply, energy storage and e-mobility, efficiency in data centers and networks, sustainable food and water supply, sustainable health, industrial production and quality, etc. The book describes computational methods and possible application scenarios.
Jude Shavlik, Kristian Kersting, Sriraam Natarajan, Tushar Khot (2015):Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine. SpringerBrief in Computer Science, 2015, ISBN: 978-3-319-13643-1.This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. It reviews the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems.
Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen Muggleton (2008):Probabilistic Inductive Logic Programming: Theory and Applications.Lecture Notes in Computer Science, Vol. 4911, Springer, ISBN: 978-3-540-78651-1.This editorial book provides an introduction to statistical relational learning with an emphasis on those methods based on logic programming principles. The question of how to combine
probability and logic with learning is getting an increased attention as there is an explosive growth
in the amount of heterogeneous data that is being collected in the business
and scientific world. The structures encountered can be as simple
as sequences and trees or as complex as citation graphs, the
World Wide Web, and relational databases
Kristian Kersting (2006):An Inductive Logic Programming Approach to Statistical Relational Learning. IOS Press, ISBN: 978-1-58603-674-4.This book addresses Probabilistic Inductive Logic Programming. The new area is closely tied to, though strictly subsumes, a new field known as ‘Statistical Relational Learning’ which has in the last few years gained major prominence in the AI community. The book makes several contributions, including the introduction of a series of definitions which circumscribe the new area formed by extending Inductive Logic Programming to the case in which clauses are annotated with probability values. Also, it introduces Bayesian logic programs and investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher kernels.
Co-Chair of the ECML PKDD 2020 Program Committee, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Co-Chair of the ICML 2018 Workshop Track, the 35th International Conference on Machine Learning
General Co-Chair of UAI 2018, the 34th International Conference on Uncertainty in AI
Co-Chair of KogWis 2018 Program Committee, the 14th Biannual Conference of the German Society for Cognitive Science
Co-Chair of the UAI 2017 Program Committee, the 33rd International Conference on Uncertainty in AI
Co-Chair of the KDD 2015 Best Paper Award Committee, the 21st ACM SIGKDD Concerence on Knoweldge Discovery and Data Mining
Co-Chair of the ECML PKDD 2013 Program Committee, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Workshop and Symposium Organization
Co-organizer of a Dagstuhl Seminar on "SE4ML - Software Engineering for AI-ML-based Systems", 2020
Co-organizer of a Dagstuhl Seminar on "Logic and Learning", 2019
Co-organizer of the ECML PKDD 2019 Workshop on Deep Continuous-Discrete Machine Learning, 2019
Co-organizer of the IJCAI 2019 Workshop on Declarative Learning Based Programming (DeLBP), 2019
Co-organizer of the Symposium about the beginnings, the present and the future of AI-research on the occasion of Prof. Dr. Wolfgang Bibel's 80th birthday.
Co-chair of the IROS 2018 Workshop on "Robots that learn and reason"
Co-chair of the ACAI 2018 Summer School on Statistical Relational AI
Co-Chair of the ICML 2018 workshop on Enabling Reproducibility in Machine Learning MLTrain@RML
Co-chair of the German Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML) 2018
Co-Chair of the AAAI 2018 workshop on Declarative Learning Based Programming (DeLBP)
Co-chair of the NIPS 2018 Highlights Workshop
Co-Chair of the KDD 2018 workshop on Data Science, Journalism, and Media (DSJM)
Co-Chair of the IJCAI 2017 workshop on Declarative Learning Based Programming (DeLBP)
Co-Chair of the KDD 2017 workshop on Data Science + Journalism (DS+J)
Co-Chair of the AAAI 2017 workshop on Symbolic Inference and Optimization (SymInfOpt)
Co-Chair of the IJCAI 2016 workshop "BeyondLabeler - Human is More Than a Labeler"
(Founding) Co-Chair of the international workshops on Statistical Relational AI (StarAI) 2014, 2012 , 2010
Co-Chair of the SIGMOD/PODS 2014 workshop on Big Uncertain Data (BUDA)
Co-Chair of the international workshops on Statistical Relational Learning (SRL) 2012, 2009
Co-chair of the NIPS 2012 workshop on Collective Learning and Inference on Structured Data (CoLISD)
Co-Chair of the AAAI Student Abstract and Poster Program 2012-14
Co-Chair of the 4th European Starting AI Researcher Symposium (STAIRS)
Co-Chair of the international workshops on Mining and Learning with Graphs (MLG) 2011, 2007
Co-chair of the NIPS 2011 workshop on Choice Models and Preference Learning (CMPL)
Co-organizer of the Dagstuhl Seminar 07161 "Probabilistic, Logical and Relational Learning - A Further Synthesis", 2007
Selected Program Committees/Reviewing
ICML 2020 (AC), AIES 2020, SDM 2020 (SPC), IJCAI-PRICAI 2020 (AC), ICLR 2020, UAI 2020 (SPC), AAAI 2020 (SPC), ECAI 2020 (AC), StarAI 2020, PADL 2020, KR2ML 2019, NeurIPS 2019 (AC), ICDM 2019, IJCAI 2019 (SPC), GCPR 2019 (Track on pattern recognition in the life and natural sciences), DSAA 2019, KDD 2019 (SPC, Member of Best Paper Award Committee for the Applied Data Science Track), AKBC 2019, AAAI 2019 (SPC, Senior Member Track, Demo Track), ICML 2019, ICLR 2019, KI 2019, NeSys 2019 NIPS 2018, ICDM 2018, BNAIC 2018, ICLR 2018, NAMPI 2018, AIKE 2018, AIMSA 2018, ILP 2018, TPM 2018, DSAA 2018, CP 2018, MLG 2018, KI 2018, NAACL-HLT 2018, WWW 2018, CVPR 2018, IJCAI-ECAI 2018 (AC), KDD 2018 (SPC), ICRL 2018, SIGMOD 2018, AAAI 2018 (SPC, Senior Member Track), ILP 2018, ECMLPKDD 2017 (Nectar, PhD), ICDM 2017, CEx 2017, ENIC 2017, GenPlan 2017, KDML 2017, NLP/Journalism 2017, ISWC 2017, SIGMOD 2017, MLG 2017, SUM 2017, IJCAI 2017 (SPC), AAAI 2017 (SPC), MLSA 2017, KI 2017, ACML 2016 (SPC), ICDM 2016, UAI 2016, ECCV 2016, ECML PKDD 2016 (AC), ECAI 2016, IJCAI 2016, ICML 2016, KDD 2016 (AC), AAAI 2016 (SPC), DS 2016, KI 2016, MOD 2016, ICDM 2015, NIPS 2015, ECML PKDD 2015 (GEB, AC), IJCAI 2015 (SPC), MPD 2015, SUM 2015, CVPR 2015, ICML 2015, CoDS 2015, AAAI 2015 (Main, AIW), AAAI 2014 (SPC, SM, SA) , ECML PKDD 2014 (GEB, AC), ICDM 2014 (AC), ECAI 2014 (AC), PODS 2014, KDD 2014 (PC and Best Paper Award Committee), UAI 2014, NIPS 2014, SDM 2014, ACML 2014 (AC and Best Paper Award Committee), CIKM 2014 (KM Track), ESWC 2014, ILP 2014, KR 2014, PGM 2014, DS 2014, CoDS 2014, DATA 2014, LTPM 2014, Know@LOD 2014, MUSE 2014, SenseML 2014, ICML 2010 (AC and Best Paper Award Committee)
German Science Foundation (DFG), European Commission, European Research Council (ERC),
US National Science Foundation (NSF), German-Israeli Foundation for Scientic Research and
Development (GIF), Freie und Hansestadt Hamburg - BWFG, Swiss National Science Foundation, The Netherlands Organisation for Scientific
Research, Research Foundation - Flanders (FWO), The Ministry of Science and Technology
of Israel, Alexander von Humboldt-Stiftung, Carl-Zeiss-Stiftung, The Royal Society of New Zealand, German Academic Exchange Service (DAAD)
Frontiers in Machine Learning and Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Action Editor (2017-)
Machine Learning Journal (MLJ)
Action Editor (2011-)
Data Mining and Knowledge Discovery (DAMI)
Action Editor (2011-)
Journal of Artificial Intelligence Research (JAIR)
Action Editor (2011-2017)
Artificial Intelligence Journal (AIJ)
Action Editor (2013-)
KI - Künstliche Intelligence
New Computing Generation (EB, 2011-), Information (EB, 2016-), Big Data and Cognitive Computing (EB, 2016-)
Advisory Boards and Expert Groups
IAG on Machine Learning, CLAIRE, 2018-
IAG on the Implications of Digitalization on the Quality of Science Communication, BBAW, 2018-
Expert Committee of the Association of German Engineers (VDI) for fundamentals of intelligent learning systems 2019-
BMBF Expert Platform "Lernende Systeme", 2018-
External Advisory Board, NSF Expeditions in Computing network "CompSustNet", 2018-
Technology Review Board, Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, India, 2018-
Board of Academic Advisors, Competence Center "Consumer Research NRW", 2018-
Board of Academic Advisors, BMBF Project ABIDA - Assessing Big Data, 2015-
Scientific Advisor of goedle.io, 2015-2017
Scientific Advisor, heydeal.de, 2016
Scientific Advisor, pflegix.de, 2016-2017
Scientific Advisor, GameAnalytics, 2012-2014
Invited Talks and Panels
Scientific Conferences and Meetings
"Deep machines that know when they do not know", University of Waterloo, Canada, winter 2019.
"Overcoming the Reproducibility Crisis in Sciences using AI?", 50 Years Anniversary Conference " 'The Theoretical University'in the Data Age", University of Bielefeld, Germany, winter 2019.
"The Third Wave of AI", DFG Committee on Scientific Instrumentation and Information Technology, Berlin, Germany, winter, 2019.
"Deep Machines That Know When They Do Not Know", Computer Science Colloquium, University of Hamburg, spring 2019.
"Deep Machines That Know When They Do Not Know", ZIH Colloquium, TU Dresden, summer 2019.
"Towards Reproducibility in Machine Learning and AI", DFG conference on "Traceability and securing of results as essential
challenges of research in the digital age", Berlin, spring 2019.
"Deep machines that know when they do not know and how to exploit symmetries for modelling and solving quadratic programs", 3rd ETAPS Workshop on Learning in Verification (LiVe), Prague, spring 2019.
"What is Artificial Intelligence?", Leibniz Convent on Artificial Intelligence, Berlin, spring 2019.
"Deep Machines That Know When They Do Not Know", DINFO, University of Florence, spring 2019.
"What is Artificial Intelligence?", EFL Joint Spring Conference 2019 on AI in the Finanical Services Industry, Frankfurt, spring 2019.
"The Automatic Data Scientist", Probabilistic Machine Learning Group, Aalto University, Helsinki, Finland, winter 2018.
"Systems AI: The computational and mathematical modeling of complex AI systems", Symposium about the beginnings, the present and the future of AI-research on the occasion of Wolfgang Bibel's 80th birthday, Darmstadt, winter 2018.
"Probabilistic Programming is great", 1st Conference on Probabilistic Programming (ProbProg), MIT, Boston, USA, fall 2018
"The Automatic Data Scientist: Making Data Science Easier using High-level Languages, Fractional Automorphisms, and Arithmetic Circuits ", Highlights of Logic, Games and Automata, Session on Logic and Learning, Berlin, fall 2018.
"Feeding the World with Big Data: Machines Uncover Spectral Characteristics and Dynamics of Stressed Plants": Plants and Animals: Bridging the Gap in Breeding Research 2018.
"Tractable Data Journalism using deep learning": "Plotting Poetry II: Bringing Deep Learning to Computational Poetry Analysis", 2018.
"Optimization for Advancing AI": Birds of a Feather "Artificial Intelligence and Performance Analysis/Optimization" at ISC High Performance 2018.
"Systems AI: Computational modeling of complex AI systems that learn and think": IJCAI-ECAI Workshop on "Learning and Reasoning" (L&R 2018)
"Relational Quadratic Programming": 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2017)
"Declarative Data Science Programming": 25th Annual Machine Learning Conference of Belgium and The Netherlands (BeneLearn 2016)
"Collective Attention on the Web": 19th International Conference on Discovery Science (DS 2016)
"Declarative Programming for Statistical ML": The 2016 Machine Learning Confrence (MLconf 2016 Seattle)
"Democratization of Optimization": 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)
"Collective Attention on the Web":
Winter Conference on Network Science (NetSci-X 2016)
"Lifted Probabilistic Inference": Frontiers in AI Track of the 20th European Conference on Artificial Intelligence (ECAI 2012)
"Increasing Representational Power and Scaling Inference in Reinforcement Learning": 9th European Workshop on Reinforcement Learning (EWRL 2011)
"Probabilistic Logic Learning and Reasoning": 14th Annual Machine Learning Conference of Belgium and the Netherlands (BeneLearn 2005)
"Thinking Machine Learning": NIPS 2016 Workshop on Neurorobotics: A Chance for New Ideas, Algorithms and Approaches
"Declarative Data Science Programming": Software Engineering and Machine Learning Workshop at the 10th Heinz Nixdorf Symposium 2016
"Lifted Machine Learning": International School on Human-Centred Computing (HCC 2016)
"Collective Attention on the Web": International School and Conference on Network Science (NetSci-X 2016)
"Democratization of Optimization" AAAI 2016 Workshop on Declarative Learning Based Programming (DeLBP 2016)
"Democratization of Optimization": 5th International Workshop on Statistical Relational AI (StarAI 2015)
"Democratization of Optimization": IJCAI 2015 Invited Sister Conference Presentations ML Track
"Poisson Dependency Networks": 2nd International Workshop on Mining Urban Data (MUD 2015)
"High Throughout Phenotyping: A Big Data Mining Challenge": 3rd Brazilian-German Frontiers of Science and Technology Symposium (BRAGFOST 2012)
"High Throughout Phenotyping: A Big Data Mining Challenge": Lernen, Wissen, Adaptivität (LWA 2012)
"From Lifted Probabilistic Inference to Lifted Linear Programming": 7th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2011)
"From Lifted Probabilistic Inference to Lifted Linear Programming": 7th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2012)
"Perception and Prediction Beyond the Here and Now": 2nd International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011)
"Lifted Message Passing": 6th International Workshop onNeural-Symbolic Learning and Reasoning (NeSys 2010)
"Lifted Message Passing": International Workshop on Graphical Models in Robotics (GraphBot 2010)
"Relations and Probabilities: Friends, not Foes": Lernen, Wissen, Adaptivität (LWA 2009)
"Probabilistic Logic Learning and Reasoning": 14th Annual Machine Learning Conference of Belgium and the Netherlands (BeneLearn 2005)
Other Meetings such as Meetups, Industry and Public Meetings
"Deep machines that know when they do not know" Borealis.AI, Waterloo, Canada, winter 2019
"Künstliche Intelligenz in der Polizeiarbeit" BKA-Herbsttagung 2019, Wiesbaden, winter 2019
"Moral Choice Machine: Kann man Maschinen Moral beibringen?" Hessischer Verbrauchertag 2019, Darmstadt, winter 2019
"Künstliche Intelligenz: Wann kontrollieren die Maschinen unser Leben?" "Stunde der Wahrheit" of the Hessen Ministry for Higher Education, Research and the Arts. Bensheim, fall 2019
"Künstliche Intelligenz", Podcast "Hessen schafft Wissen", fall 2019
"Machines can learn our moral compass", Bad Homburg Conferene — "Künstliche Intelligenz: Wie können wir Algorithmen vertrauen?", Exzellenzcluster Normative Orders, fall 2019
"Maschinelles und Tiefes Lernen sind der Motor für 'KI made in Germany'": Jahreskonferenz der BMBF Plattform "Lernende Systeme", Berlin, summer 2019
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": Public series of lectures on "Was stackt dahinter?", Darmstadt, summer 2019
"What is AI? And why is calibration central to AI?": Debating the ‘update-mode’, Munich Center for Technology in Society, summer 2019
"Towards Reproducibility in Machine Learning and AI": Second Annual Merck Data Science & Analytics Days, Frankfurt, spring 2019
"What is Artificial Intelligence and can it improve cardiac care?": 25th Friederger Symposium, Bad Nauheim, spring 2019
"Deep Machines That Know When They Do Not Know": Prof. Dr. Nhan, Secretary of the Ho Chi Minh City Party Committee, visiting the TU Darmstadt, spring 2019
"Deep Probabilistic Programming (for Healthcare)": Machine Learning in Healthcare - an IQVIA Meet-up, Frankfurt, spring 2019
"Can we teach morality to machines?": Union Investment Wissenschaftsdialog, Frankfurt, spring 2019
"The Third Wave of AI": ABB and Phoenix Contact, Ladenburg, spring 2019
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., Rhein-Main, Jahreshauptversammlung, spring 2019
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VCI Verband der chemischen Industrie e.V., AK Digitalisierung, fall 2018
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., Rhein-Main, Vortragsreihe Informations- und Kommunikationstechnologie, fall 2018
"The Automated Data Scientist": Cologne AI and Machine Learning Meetup, fall 2018
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": Stadtsparkasse Darmstadt, fall 2018
"Made in Germany – Lernende Systeme als Standortvorteil von morgen": BMBF, Expert Stage, CEBIT 2018"
"The Automated Data Scientist": 2nd Jaenaer Data Science Day 2018"
"Tractable Data Journalism": SciCAR - Where Science Meets Computer Assisted Reporting 2017
"Journalist plus Wissenschaftler: Dreamteam für post fact checking": nr.Jahreskonferenz 2017
"Datensammeln: Messies oder der Sieg der Induktion?": Stadtgespräche im Musuem 2017
"Thinking Data Science Machines": Distinguished Lecturer Series, Jena, 2017
"Modelling Traffic Counts": GCRI, New York, 2016
"Populisten, Autokraten, Despoten-Wie wehrhaft ist unsere Demokratie?"
"Data Mining": THINK BIG: nrvision.de
"Algorithmen-Wer kontrolliert die neuen Machthaber?": nr.Jahreskonferenz 2015
"Tractable Data Journalism": Berlin Machine Learning Meetup Group, October 2017
"Daten! Sind sie Leben?" Kneipengespräch der "Lust an Wissenschaft?" 2016 Serie der Mercator Global Young Faculty
Tutorials, Seminars and Training
"No more data gibberish: Design your Automatic Data Scientist and simplify your decision-making processes" Deutscher IT-Leiterkongress 2019, Duesseldorf, fall 2019
"A Short Tutorial on AI, Deep Learning, and Probabilistic Circuits", High Performance Computing in Hessen (HiPerCH), Darmstadt, Germany, fall 2019.
"Sum-Product Networks: The Third Wave of Differentiable Programming", DeCoDe Workshop 2019, Frankfurt, Germany
"Statistical Relational AI", KI 2018, Berlin, Germany
"Deep Neural and Probabilistic Learning", KogWis 2018, Darmstadt, Germany
"Lifted Statistical ML: Computational modelling of complex AI systems that learn and think", ACAI 2018, Ferrara, Italy
"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", NIPS 2017
"Feeding the World with Big Data", Computational Sustainability Virtual Seminar Series, Cornell University, USA, fall 2017
Probabilistische Graphische Modelle, ACATECH Massiv Open Online Course on "Machine Learning", spring 2017
"Artificial Intelligence - Facts, Chances, Risks", Research Training Group of the German National Academic Scholarship Foundation 2017-18
"Was ist eigentlich Künstliche Intelligenz?", Children's university lecture, comprehensive school Gänsewinkel Schwerte, Germany, fall 2017
"Blade Runner und Künstliche Intelligenz": Schulkinowochen Hessen, spring 2019
"Tractable Probabilistic Graphical Models", 4th International Summer School on Resource-aware Machine Learning, Dortmund, 2017
"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", AAAI 2017
"Data-Diven Plant Phenotyping", PHENOMICS Workshop Berlin 2016