Philipp Singer

Data Scientist

About

Philipp Singer is a Senior Principal Data Scientist at H2O.ai. He obtained a PhD in computer science with honors at the Technical University of Graz where he also finished his Master studies in Software Development and Business Management.

Philipp is passionate about machine learning, statistics, data mining, programming, and many further data science related fields. During his career, he has proven his expertise on several occasions, including multiple winning and top placements on Kaggle as well as several scientific honors such as a best paper award at the renowned World Wide Web Conference. He loves supporting and enabling data-driven decision making, developing data services, as well as teaching and mentoring. Philipp is also always eager to delve into new bleeding-edge fields and technologies.

Curriculum Vitae

Kaggle

I am competitively participating in competitions on Kaggle and my highest rank on global competition leaderboard has been 1st. The following list summarizes my first place achievements on Kaggle.

Publications

Selected publications

  • Philipp Singer*, Florian Lemmerich*, Robert West, Leila Zia, Ellery Wulczyn, Markus Strohmaier and Jure Leskovec (* equal contribution), Why We Read Wikipedia, 26th International World Wide Web Conference, Perth, Australia, 2017 (acceptance rate 164/966, 17% quota) [PDF] [arXiv]
  • Claudia Wagner*, Philipp Singer*, Fariba Karimi, Jürgen Pfeffer and Markus Strohmaier (* equal contribution), Sampling from social networks with attributes, 26th International World Wide Web Conference, Perth, Australia, 2017 (acceptance rate 164/966, 17% quota) [PDF] [arXiv]
  • Dimitar Dimitrov*, Philipp Singer*, Florian Lemmerich and Markus Strohmaier (* equal contribution), What Makes a Link Successful on Wikipedia?, 26th International World Wide Web Conference, Perth, Australia, 2017 (acceptance rate 164/966, 17% quota) [PDF] [arXiv]
  • Florian Lemmerich, Martin Becker, Philipp Singer, Denis Helic, Andreas Hotho and Markus Strohmaier, Mining Subgroups with Exceptional Transition Behavior, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 2016 (full paper acceptance rate 70/784, 9% quota) [PDF]
  • Philipp Singer, Denis Helic, Andreas Hotho and Markus Strohmaier, HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web, 24th International World Wide Web Conference, Florence, Italy, 2015 (Best Paper Award) (acceptance rate 131/929, 14.10% quota) [PDF] [arXiv] [Slides] [Tutorial] [Talk] [Code]
  • Philipp Singer, Denis Helic, Behnam Taraghi and Markus Strohmaier, Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order, PLoS ONE, vol 9(9), 2014 [HTML/PDF] [arXiv] [Code]
  • Philipp Singer*, Fabian Flöck*, Clemens Meinhart, Elias Zeitfogel and Markus Strohmaier (* equal contribution), Evolution of Reddit: From the Front Page of the Internet to a Self-referential Community?, Web-Science Track at the 23rd International World Wide Web Conference, Seoul, South Korea, 2014 [PDF] [arXiv] [Data]

Theses

  • Philipp Singer, Modeling Aspects of Human Trails on the Web, Ph.D. Thesis,  Graz University of Technology, Graz, 2014. Supervisor: Dr. Markus Strohmaier [PDF]
  • Philipp Singer, Time Series Analysis of Online Social Network Data and Content, Master's Thesis, Graz University of Technology, Graz, 2011. Supervisor: Dr. Markus Strohmaier [PDF]

The following link refers to a list of all peer-reviewed journal, conference, workshop, and poster/demo track publications, as well as book chapters and tutorials on Google Scholar.

Full publication list
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Impressum

Philipp Singer
Engerthstraße 171
1020 Wien

Contact: [email protected]

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Teaching

I gladly offer services in the area of teaching. The format, complexity, and corresponding topic of lectures and courses can be individually tailored to your needs and requirements. It is my deepest belief, that advanced knowledge and attention around the topic of data plays a crucial role for companies. Consequently, it is also important to build up internal knowledge within a company in order to foster sustainable effects. The following list offers a few examples of potential situations, teaching formats and scenarios:

  • Over the years, your company has collected a large amount of data, but has not fully utilized the data beyond simple statistics. The topic of data, potential benefits, but also risks, is present not only in todays media, but also increasingly in the enterprise sector. Yet, the topic appears often more complex and unclear, as it actually is. What do topics like artificial intelligence or machine learning even mean? Also on a management level these topics can often lead of lack of clear knowledge. I can teach about specific topics with targeted and structured education for stakeholders and relevant employees. My objective is to not only offer introduction to specific topics around data science, but also unveil potential use cases for certain sectors and respective expectations.
  • Your company already employs statistical data analysts. However, the data science field is extremely fast-paced and weekly new scientific discoveries are made and new methods are developed. I offer targeted education for employees and teams to bring them up to speed with state-of-the-art areas and possibilities.
  • Often dated, and expensive proprietary data analysis tools are deployed within companies. However, the current trend, specifically in the data science field, goes more and more into the direction of open-source software and methodology. State-of-the-art methods are often directly available via open-source tools and are mostly promoted and developed by big players like Google or Microsoft. Specific programming languages like Python or R are the go-to languages for implementation of modern data science methods. I offer an introduction to respective programming languages and tools and showcase emerging potentials and use cases.
  • You are personally interested in the topic of data science, maybe even envision a career in it? Yet, the field can often seem quite obscure leading to questions like "Which methods are important to learn", or "How should I best apply for a data science job?". Consequently, I naturally also offer custom-tailored teaching for private individuals.

Abovementioned examples should only highlight a small excerpt of potential teaching services. Through my scientific and corporate activities, I have already gained manifold experiences in the area of teaching (e.g., lecturing the "Machine Learning and Data Mining" course at the University of Koblenz). It has always been my personal drive to communicate and pass on my knowledge in this area, e.g., through posts and tutorials in my personal Blog. Consequently, I hope to be able to adapt to all your personal needs and requirements. Gladly, I also offer hands-on lecturing in the form of coaching.

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Coaching

Apart from services in the area of Teaching, I also offer direct coaching and mentoring for teams and individuals. Again, corresponding services can be individually adapted and cover a diverse array of topics, as for example:

  • Employees in the area of data analytics have the task of applying state-of-the-art technologies and methods, but require initial coaching and mentoring for respective deployment. To that end, targeted coaching can bring enduring success.
  • You want to enforce data-driven decision making within your company which warrants end-to-end education in different departments. I can offer direct support in this area.
  • Managers might require regular coaching to unfold potentials and possibilities in the area of data science within the company.
  • I also offer profound methodological support for employees and individuals. Exemplary, I can offer theoretical and practical hands-on introduction to neuronal networks and deep learning, or other methods from the area of machine learning.
  • Gladly, I also support private individuals that want to step into the area of data science.

Again, ebovementioned examples should only highlight a few potential scenarios, and respective services I can offer will always be custom-tailored to your requriments. Throughout my previous career, I could obtain wide-ranging experiences in the areas of coaching, mentoring, and tutoring. This includes e.g., successfull tutoring of students in various practical courses, but also the mentoring of Bachelor, Master, and PhD theses. Apart from that, I have also offered and published diverse tutorial at e.g., renowned conferences, but also on my personal Blog. The practical support of teams and individuals is a personal concern of mine, and I hope to be able to offer you appropriate solutions in this area.

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Implementing

I am excited to support you in conducting valuable data science projects. My deepest belief is also that it is crucial to build up knowledge within the company which is why I will actively support teams and employees during the process and communicate all valuable knowledge to stakeholders. My topical and methodological expertise in the field of data science is manifold and covers, among many others, e.g., the following areas:

  • Specific application of state-of-the-art machine learning methods, e.g., from the areas of deep learning or gradient boosted trees.
  • Statistical modeling and examination of problem settings, e.g., hypotheses tests, Bayesian statistics, or regression analysis.
  • Pre-processing of data, e.g., in the areas of acquiring data, extracting valuable information, or the application of techniques like OCR text recognition.
  • Application in specific fields such as Natural Language Processing (NLP), image recognition, network analysis, information retrieval, or data mining.
  • Utiliztation of state-of-the-art technologies for handling big data, e.g., Hadoop, Spark, BigQuery, or CPU and GPU multiprocessing.
  • Implementation and connection of developed models for productive environments, e.g., through Docker deployment or API service development.
  • Diverse activities in the area of project management.

Through my year-long activities in the area of data science, I have conducted a multitude of projects and scientific activities in the area of data science. An overview of some of my activities can be found in my publication list, as also my Github profile, or my personal Blog. Parts of my work has also been awarded, such as a best paper award at the renowned World Wide Web Conference. I would be excited to support you in any applied data science activites.

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