Introducing the team

Please meet the team of ML2R! The speakers, the representatives of the partner organizations as well as the employees of ML2R introduce themselves with a brief profile. The scientists reveal the topics they are currently working on, what they are particularly interested in and which of the ML2R research focuses their work is aligned with. The team members come from the four partner organizations: the TU Dortmund University, the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, the Fraunhofer Institute for Material Flow and Logistics IML and the University of Bonn. The teams from Dortmund, Bonn and Sankt Augustin are interdisciplinary and work cooperatively across locations.

The spokespersons

Prof. Dr. Katharina Morik

Katharina Morik received her doctorate from the University of Hamburg in 1981 and her habilitation from the TU Berlin in 1988. In 1991, she established the chair of Artificial Intelligence, which focuses on Machine Learning, at the TU Dortmund University. The current focus lies on learning algorithms for distributed, real-time applications, for example in astrophysics, industry 4.0 or traffic infrastructure.

In 2011, she acquired the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Data Analysis”, of which she is the spokesperson. Katharina Morik has been involved in numerous EU projects: She has coordinated the MiningMart project and worked in the projects VaVel and Insight on the analysis of data streams for traffic planning.

Katharina Morik has been a member of the Academy of Technical Sciences since 2015 and of the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts since 2016. She is the author of more than 200 publications in prestigious journals and conferences. She was a member of the editorial board of the journal “Machine Learning” and is currently one of the editors of the international journal “Data Mining and Knowledge Discovery”. She was a founding member, Program Chair and Vice Chair of the conference series IEEE International Conference on Data Mining (ICDM) and Program Chair of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).

The first efficient implementation of the Support Vector Machine (SVM) and the globally successful data analysis tool RapidMiner were developed at her department. Together with Volker Markl, Katharina Morik heads the working group “Technological Pioneers and Data Science” of the platform “Learning Systems” of the German Federal Ministry of Education and Research (BMBF).

In 2019, Katharina Morik was recognized as pioneer of Machine Learning and awarded with the GI Fellowship by the Gesellschaft für Informatik e.V. (GI).

Prof. Dr. Katharina Morik

Technical University of Dortmund
Faculty of Computer Science
Otto-Hahn-Str. 12
44227 Dortmund
Germany

katharina.morik@tu-dortmund.de
www-ai.cs.uni-dortmund.de/en
www.sfb876.tu-dortmund.de/en

Prof. Dr. Stefan Wrobel

Stefan Wrobel studied Computer Science in Bonn and Atlanta, Georgia, USA (M.S., Georgia Institute of Technology) with a focus on Artificial Intelligence and received his doctorate from the TU Dortmund University. After working in Berlin and Sankt Augustin, he became Professor of Computer Science at the University of Magdeburg, before accepting a call to his current position in 2002. Since 2014, he has also been one of the directors of the Bonn-Aachen International Center for Information Technology (b-it).

Professor Wrobel has been working on aspects of digitization for many years, particularly in regard to intelligent algorithms and systems for the analysis of large amounts of data and the influence of Big Data/Smart Data on the use of information in companies and society. He is the author of a multitude of publications in the fields of Data Mining and Machine Learning, a member of the editorial board of several leading journals and a founding member of the “International Machine Learning Society”. Wrobel was honored by the German Computer Science Society (Gesellschaft für Informatik (GI) e.V.) as one of the most influential people in German AI history.

As speaker of the “Fraunhofer Alliance Big Data and Artificial Intelligence”, director of the “Fraunhofer Research Center Machine Learning”, deputy chairman of the “Fraunhofer Information and Communication Technology Group” and speaker of the group “Knowledge Discovery, Data Mining and Machine Learning” of the Society for Computer Science, he is advancing the topics of digitization, intelligent use of Big Data and Artificial Intelligence on a national and international level.

Prof. Dr. Stefan Wrobel

Fraunhofer Institute for Intelligent Analysis
and Information Systems IAIS
Schloss Birlinghoven
53757 Sankt Augustin
Germany

stefan.wrobel@iais.fraunhofer.de
www.iais.fraunhofer.de/en
www.iai.uni-bonn.de/en

The representatives of the partners

Prof. Dr. Dr. h. c. Michael ten Hompel

Professor Dr. Dr. h. c. Michael ten Hompel studied Electrical Engineering at the RWTH Aachen and received his doctorate from the University of Witten/Herdecke. He holds the chair of Materials Handling and Warehousing (FLW) of the TU Dortmund University and is the Managing Director of the Fraunhofer Institute for Material Flow and Logistics. Before his scientific work he was an entrepreneur. Among other things, he founded GamBit GmbH (today Vanderlande Industries) in 1988 and headed the company, which is primarily involved in the development and implementation of warehouse management systems and industrial control systems, as managing partner until 2000.

Michael ten Hompel is considered to be one of the fathers of the Internet of Things and has published numerous reference books and over four hundred articles on logistics and IT. He is co-editor of the “Lecture Notes in Logistics” (Springer) and other specialist publications. Michael ten Hompel is a member of the Academy of Engineering Sciences and in 2012, he was inducted into the Logistics Hall of Fame. In 2017, he received an honorary doctorate from the University of Miskolc (Hungary). In 2018, he was awarded the honorary distinction “Citizen of the Ruhr Area” by the Pro Ruhr Area Association. In 2019, he received the international HERMES Logistics Award in Vienna.

Prof. Dr. Dr. h. c. Michael ten Hompel

Fraunhofer Institute for Material Flow und Logistics IML
Joseph-von-Fraunhofer-Str. 2-4
44227 Dortmund
Germany

michael.ten.hompel@iml.fraunhofer.de
www.iml.fraunhofer.de/en

Prof. Dr. Christian Bauckhage

Christian Bauckhage studied Computer Science and Physics at the University of Bielefeld, was a Research Intern at INRIA in Grenoble and received his PhD in Computer Science from the University of Bielefeld. He then worked as a Postdoctoral Researcher at the Centre for Vision Research in Toronto, Canada, and as a Senior Scientist at Deutsche Telekom Laboratories in Berlin. In 2008, he was appointed as a Professor of Computer Science at the University of Bonn and became Lead Scientist for Machine Learning at Fraunhofer IAIS.

Prof. Bauckhage has more than 20 years of experience as a Data Scientist in industry and science and is (co-)author of numerous publications on Pattern Recognition, Data Mining and Intelligent Systems. His current research focuses on techniques of Informed Machine Learning, which integrate knowledge and data Driven methods, and on Quantum Computing solutions for Machine Learning. Applications can be found in areas such as Physics, Agriculture, Social Media or Business Analytics. He is a sought-after speaker and passionate advocate of open innovation and open science. Christian Bauckhage advises companies and public institutions on the development and deployment of solutions for Machine Learning and Artificial Intelligence.

Prof. Dr. Christian Bauckhage

Fraunhofer Institute for Intelligent Analysis
and Information Systems IAIS
Schloss Birlinghoven
53757 Sankt Augustin
Germany

christian.bauckhage@iais.fraunhofer.de
www.iais.fraunhofer.de/en

The administrative office

Heike Horstmann

Managing Director ML2R

Heike Horstmann is Managing Director of the Competence Center Machine Learning Rhine-Ruhr at the Bonn site.

Heike Horstmann is an experienced project manager at the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS. In the past, she successfully coordinated various funding and business projects. Most recently, she headed a Project Management Office at Fraunhofer IAIS.

After graduating as an engineer in Communications Technology at the University of Applied Sciences Cologne (TU Köln) Heike Horstmann conducted research on interactive digital TV formats at the Fraunhofer Institute for Media Communication IMK and participated in the standardization and market launch of the European Multimedia Home Platform (MHP). With the re-establishment of Fraunhofer IAIS in 2006, Heike Horstmann took over the technical management of the EU project “LIVE – Staging of media events” and continued her research, which culminated in an interactive live broadcast of the 2008 Olympic Games by the project partner ORF. From 2012 on she led a national funding project on targeted advertising formats and coordinated the EU project “LinkedTV”, in which research was successfully conducted on the linking of TV and web content through automatic annotation processes and semantic enrichment. Since 2016 Heike Horstmann has been working on approaches to cognitive process automation and has been leading data science projects for business clients.

Heike Horstmann

Fraunhofer Institute for Intelligent Analysis
and Information Systems IAIS
Schloss Birlinghoven
53757 Sankt Augustin
Germany

heike.horstmann@iais.fraunhofer.de
www.iais.fraunhofer.de/en

Dr.-Ing. Stefan Michaelis

Managing Director ML2R

Stefan Michaelis has been Managing Director of the Competence Center Machine Learning Rhine-Ruhr for the Dortmund location since 2018. He manages the operative business together with the representatives of the other locations. He supports the scientists in their tasks and coordinates cooperations internally as well as with project management agencies, the German Federal Ministry of Education and Research (BMBF) and cooperation partners from the private sector.

Prior to this, he was Managing Director of the Collaborative Research Center 876 ““Providing Information by Resource-Constrained Data Analysis” funded by the German Research Foundation (DFG) from 2011. The Collaborative Research Center investigates possibilities of Intelligent Data Analysis under consideration of available resources: from small cyber-physical systems to mainframe computers for the analysis of large amounts of data. The goal is to extract information from ever larger amounts of data – in a timely manner, without large energy requirements and directly on site. Stefan Michaelis was also involved in the successful application for both the second and third funding phases until 2022.

After studying engineering informatics, Stefan Michaelis received his doctorate in Electrical Engineering with applications in Machine Learning for quality improvement in mobile communications. In his doctoral thesis, he investigated how predictions of expected movements of mobile phone users – independent of the profiles of specific users – allow for an outlook on the future utilization of mobile phone cells.

Dr. Ing. Stefan Michaelis

Technical University of Dortmund
Otto-Hahn-Str. 12 / R4.020
44227 Dortmund
Germany

stefan.michaelis@tu-dortmund.de
www-ai.cs.tu-dortmund.de/PERSONAL/michaelis

Katrin Berkler

Head of Communications

Katrin Berkler is Head of Press and Public Relations at the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS. After studying Cultural, Media and Economic Sciences at the Universities of Siegen and Orléans (France) and working as a research assistant at a chair of Media Science in Siegen and at the Siegen Institute for small and medium-sized enterprises, she joined the Fraunhofer Institute SCAI in 2009, working in the field of marketing and communications.

Since 2010, she has been in charge of PR activities at Fraunhofer IAIS. In her role as Head of Communications she is responsible for numerous strategic projects and initiatives in all aspects of communication and public relations. Since 2014, Katrin Berkler has also been in charge of public relations for the Fraunhofer Alliance Big Data and Artificial Intelligence, a network of over 30 Fraunhofer Institutes for cross-sector research and technology development in the field of Big Data and Artificial Intelligence. In addition, she and her team are responsible for the press and public relations of the Competence Center Machine Learning Rhine-Ruhr (ML2R), the Competence Platform KI.NRW, the Fraunhofer Research Center Machine Learning as well as the Roberta Initiative, one of the largest initiatives for digital education in Germany.

Katrin Berkler

Fraunhofer Institute for Intelligent Analysis
and Information Systems IAIS
Schloss Birlinghoven
53757 Sankt Augustin
Germany

katrin.berkler@iais.fraunhofer.de 
www.iais.fraunhofer.de/en

Ann-Kathrin Oster

Associate

In her work at the Competence Center Machine Learning Rhine-Ruhr ML2R, Ann-Kathrin Oster strives to communicate scientific results and events in the context of the Competence Center in a way that is understandable and comprehensible. Her goal is hereby to make research on Machine Learning as a key technology of Artificial Intelligence accessible to a broad public. She also holds the position of the coordinator for the cooperation with the German state of North Rhine-Westphalia and other state actors.

During her studies, which she completed with a Master of Arts degree in Media and Political Communication at the Free University of Berlin, Ann-Kathrin Oster specialized in political communication and public relations. She strengthened this focus by working in political institutions on the federal and state level.

Ann-Kathrin Oster

Technical University of Dortmund
Otto-Hahn-Str. 12 / R4.022
44227 Dortmund
Germany

ann-kathrin.oster@tu-dortmund.de
https://www-ai.cs.tu-dortmund.de/PERSONAL/oster

Vanessa Faber

Coordinator

Vanessa Faber has been a coordinator for the Dortmund location of the Competence Center Machine Learning Rhine-Ruhr since 2018. In addition to her role as Coordinator within ML2R, she is responsible for the cooperation and communication between the German competence centers at a national level. She is in constant communication with the Federal Ministry of Education and Research (BMBF) and act as point of contact for the cooperation of the German and French competence centers.

Vanessa Faber studied Computer Science at the Technical University of Dortmund. After her studies she was involved in the successful application for the first funding phase of the Collaborative Research Center 876 “Providing Information by Resource-Constrained Data Analysis” funded by the German Research Foundation (DFG) before she worked in industry for several years.

Dipl.-Inform. Vanessa Faber

Technical University of Dortmund
Otto-Hahn-Str. 12 / R4.022
44227 Dortmund
Germany

vanessa.faber@tu-dortmund.de
https://www-ai.cs.tu-dortmund.de/PERSONAL/faber

The scientists

Katharina Beckh

Research Focus:

Human-oriented modeling, ML and Complex Knowledge

What problems are you currently working on?

  1. Modeling visual attention of anesthesiologists with focus on effort
  2. Steering active learning models with expert input
  3. Identification of bad medical reporting with text mining tools

What are you particularly interested in?

Interactive Machine Learning, improving the interaction between humans and machine learning algorithms by designing suitable interfaces and models.

David Biesner

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Interpretable NLP: Human readable approaches to word embeddings and topic modeling using matrix factorization
  2. Deep-learning based methods for domain-specific NLP, mainly text classification and text generation

What are you particularly interested in?

My main research focus is natural language processing (NLP). I am interested in the power of modern deep-learning architectures for general language understanding and transfer learning, and the possibilities of augmenting these approaches with human knowledge.

Daniel Boiar

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Building a knowledge base about multivariate time series algorithms
  2. Outlier detection with Support Vector Machines

What are you particularly interested in?

I am interested in derivation of new and reliable time series algorithms to optimize the analysis of heterogeneous sensor time series in an industrial context.

Kostadin Cvejoski
Kostadin Cvejovski

Awards:

Syngenta 2019 Crop Challenge

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Improvement of recommender systems by using the text and time component of user-item interaction
  2. Use of deep neural networks to model queuing systems

What are you particularly interested in?

Development of methods that incorporate temporal and textual information to model how the inspection data of objects (products, companies, …) change over time.

Dr. Tiansi Dong

Awards:

Best Paper Award at ICANN 2019

Research Focus:

Trustworthy ML

What problems are you currently working on?

  1. Surveying recent papers on ‘Vision and Language’
  2. Reproduction of experiments in some of the papers
  3. Region-based knowledge graph reasoning

What are you particularly interested in?

I am interested in the unification of symbolic structures and in deep learning and its applications in Natural Language Understanding, knowledge graph reasoning and Visual-Language interaction.

Raphael Fischer
Raphael Fischer

Research Focus:

Human-oriented modeling, ML and Complex Knowledge

What problems are yoou currently working on?

Application of probabilistic ML to spatio-temporal data, such as image time series, using generative ML to fill gaps in incomplete data sets, such as cloudy satellite image data

What are you particularly interested in?

To investigate how probabilistic ML can contribute to understanding our world while transferring knowledge and perception to other research areas and even to society in general.

Bogdan Georgiev
Dr. Bogdan Georgiev

Awards:

  1. Syngenta 2019 Crop Challenge
  2. Graduate Scholarship, Max Planck Institute for Mathematics, Bonn

Research Focus:

ML and Complex Knowledge, modular ML, human-oriented ML, trustworthy ML, Quantum ML

What problems are you currently working on?

  1. Theory and applications of representation and manifold Learning
  2. Statistical Learning Theory – compression, complexity and generalization bounds
  3. Autoencoding techniques with applications to image and speech data
  4. Quantum Machine Learning – Variational Quantum Circuits

What are you particularly interested in?

The mathematics behind deep learning models and discrete-program inference with applications in training algorithms and generalization performance.

Sven Giesselbach
Sven Giesselbach

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Transferring knowledge from “labeled” domains to “unlabeled” domains with our algorithm named Corresponding Projections
  2. Including structured knowledge in text representations such as Word2Vec or BERT

What are you particularly interested in?

I think the assumption that computers can learn to understand natural language simply by consuming as many texts as possible is fundamentally flawed and I would like to explore mechanisms to incorporate external knowledge in natural language understanding.

Felix Gonsior

Research Focus:

Human-oriented ML, trustworthy ML

What problems are you currently working on?

  1. Interpreting optimization problems originating from training Machine Learning models as probabilistic models. This adds the possibility to evaluate and compare many possible alternative solutions.
  2. Inference via sampling on PGMs with quality guarantees for given sample sizes.

What are you particularly interested in?

Understanding the role of uncertainty in ML training and inference and it’s effect on the generalization ability of machine learning models.

Lukas Heppe

Research Focus:

Distributed Learning, High performance computing, Probabilistic graphical models

What problems are you currently working on?

  1. Resource-Constrained Distributed Model aggregation for improving communication efficiency
  2. Modular parameter aggregation for Markov Random Fields
  3. Distributed and parallel inference for Markov Random Fields

What are you particularly interested in?

Development of communication-efficient distributed learning algorithms that enable the dissemination of machine learning methods to a large number of devices, which in turn can jointly benefit from each other.

Lars Patrick Hillebrand

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Development of matrix factorization methods for joint learning of word embeddings and extraction of topics and their relations among each other.
  2. Metric learning for automatic evaluation of text summarization systems based on common human criteria.

What are you particulary interested in?

Combining structured knowledge and statistical learning in order to improve text representation learning and knowledge extraction (e.g. topics extraction, text summarization) in the context of Natural Language Processing and Understanding.

Philipp-Jan Honysz

Research Focus:

Human oriented modeling, Industrial application of machine learning

What problems are you currently working on?

  1. Detection of outliers in industrial process data with focus on reduction of rejects and energy demand
  2. Production support through explanation of predicted results

What are you particulary interested in?

Definition of adequate ML-pipelines for the processing of high-volume process data.

Real-time forecasting supported by modern hardware.

Matthias Jakobs

Research Focus:

Trustworthy ML

What problems are you currently working on?

  1. Providing guarantees in theory and application for explainability methods using Shapley values
  2. Combining explainability models with Bayesian Neural Networks (BNN)

What are you particularly interested in?

Shining a light into the decision-making process of black-box models giving users and experts confidence in the decisions produced by Neural Networks

Birgit Kirsch
Birgit Kirsch

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Cold-start knowledge base population by utilizing prior knowledge, e.g. ontologies, domain-specific constraints, …
  2. Noise reduction in distantly supervised datasets for relation extraction via statistical relational learning

What are you particularly interested in?

Joint learning of neural networks and logic based systems

Helena Kotthaus
Dr. Helena Kotthaus

Research Focus:

Trustworthy and Resource-Constrained ML

What problems are you currently working on?

  1. Transfer of machine learning methods between research and industrial projects
  2. Extraction of ML expert knowledge to generate checks for typical pitfalls in machine learning pipelines, aiming at trustworthiness

What are you particularly interested in?

An unified system for the certification and explanation of ML processes for software engineers and scientists who are no experts in the field of AI or ML.

Jens Leveling

Research Focus:

Resource-Constrained Learning

What problems are you currently working on?

  1. Resource-Constrained Learning for Logistics Use-Cases
  2. Industry Transfer of ML2R results – Main focus on Logistics Use-Cases

What are you particularly interested in?

Machine Learning on Internet-of-Things devices: Machine Learning for Classification, Detection and Tracking in Logistics Use-Cases

Industry transfer issues: Getting and creating training data shifting Machine Learning results on an operational level

Mojtaba Masoudinejad

Research Focus:

Resource-Constrained Learning, Industrial application of Machine Learning

What problems are you currently working on?

6D pose estimation of logistics objects in production and warehousing

What are you particularly interested in?

Low cost industrial object detection using computer vision and machine learning

Sascha Mücke

Research Focus:

Resource-Contrained ML, Quantum ML

What problems are you currently working on?

  1. Implementing and continually improving a FPGA-based hardware solver for Ising models
  2. Exploring applications of MAP solvers, specifically for analyzing probabilistic models

What are you particularly interested in?

Probabilistic graphical models; quantum-based optimization and its potential applications to machine learning

Andreas Pauly

Research Focus:

Resource-Constrained Learning, Theory of Modular Machine Learning

What problems are you currently working on?

  1. Transferring Machine Learning methods from research to industry
  2. Regularization of deep learning methods to reduce the required training resources

What are you particularly interested in?

I am interested in enhancing low-resource Machine Learning methods to enable an inexpensive industry transfer.

Dr. Nico Piatkowski

Research Focus:

Resource-Constrained Learning, Quantum Machine Learning

What problems are you currently working on?

  1. Structure learning for graphical and neural machine learning models
  2. Non-classical hardware (like quantum gate circuits or field programmable gate arrays) for probabilistic inference in high-dimensional models
  3. Accurate probabilistic modelling of physical, industrial, and economic systems and their simulation

What are you particularly interested in?

  • Theoretical and practical insights into finding the most resource-efficient realization of particular machine learning models together with an accurate analysis of its inherent uncertainties.
  • Learning model structures which are tailored to a specific computational architecture, to identify important and unnecessary parts of a model, free unused resources, and make use of all available compute capabilities.
Dr. Ramses Sanchez

What problems are you currently working on?

  1. Dynamic generative language models, that is, language models that leverage representations from point process models in time to capture how e.g. reviews or news evolve with time
  2. Learning both discrete and continuous representation for generative models of text using mutual information metrics
  3. Neural network models for switching dynamical systems
  4. Generative neural network models for point processes and queueing systems

What are you particularly interested in?

I’m interested in using Bayesian nonparametric processes as prior distributions in deep generative models of text, with the goal of representation learning for text summarization and controllable text generation.

Till Hendrik Schulz

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Graph Kernels based on a distance measure using tree edit distances of local tree structures
  2. Computationally feasible graph pattern matching algorithms using constrained homomorphism

What are you particularly interested in?

Learning on graphs, particularly knowledge extraction from single networks or sets of graphs with provable bounds or guarantees

Florian Seiffarth

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Enriching abstract closure systems by additional information, such as distances, with the goal of efficient classification, e.g. achieving maximal separations in closure systems based on monotone linkage functions
  2. Neural network layers with weight sharing based on data dependent rules (expert knowledge), applications to graph and node classifications and learning on “complex” data structures

What are you particularly interested in?

Mathematical understanding of Machine Learning with applications to abstract closure systems, e.g. closed frequent item sets, concept lattices etc. and learning on graphs, e.g. graph neural networks based on data dependent rules.

Eike Stadtländer

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Learning closure systems, e.g. from transactional databases for application in frequent itemset mining
  2. Convexity conditions in graphs, e.g. for simultaneous clustering

What are you particularly interested in?

Bridging the gap between theory and practical algorithms to improve the interpretability of learning systems.

Dr.-Ing. Oliver Urbann

Awards:

Multiple awards in robot soccer, particularly world championship on RoboCup

Resarch Focus:

Resource-Contrained ML

What problems are you currently working on?

Machine Learning compiler

What are you particularly interested in?

Software engineering and Machine Learning in a hardware focus context, e.g. SIMD vectorization, caching, memory limits etc.

Laura von Rueden

Research Focus:

Machine Learning and Complex Knowledge, Trustworthy Machine Learning

What problems are you currently working on?

  1. Informed Machine Learning: A taxonomy and survey of integrating prior knowledge into learning systems
  2. Street-map based validation of semantic segmentation in autonomous driving

What problems are you particularly interested in?

The combination of data-based Machine Learning with knowledge-based modelling (hybrid AI)

Dr. Pascal Welke

Research Focus:

ML and Complex Knowledge

What problems are you currently working on?

  1. Efficient search of frequent subgraphs
  2. Core methods for graphs
  3. Analysis of ML models