Join Paper Talks

Join our Paper Talks

Live meeting with experts: chance to discuss, network, share knowledge and experience

Follow the newest achievements in the ML world and stay up to date with state-of-the art developments

Regular meetings that anyone interested can participate in and discuss a
particular scientific paper

JOIN NEXT SESSION

15 December 2022, 3pm CET, Zoom meeting

Next Paper:
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (link)

What are Paper Talks?

Paper Talks is our internal GetInData initiative created within the Advanced Analytics team. It was born from the need to follow the newest achievements in the machine learning world and stay up to date with state-of-the art developments. Our field of interest is around different hot topics in data science, especially machine learning and deep learning.

For whom?
Everyone interested in data science and machine learning is welcome to join the discussion, however, we used to be pretty curious about the details and did many deep dives, so the atmosphere is definitely more technical and academic than business oriented. If you are a data scientist or a machine learning engineer and you work either in industry or academia, surely you will be able not only to get something interesting from it for you, but also to contribute to the discussion by sharing your ideas and experience.
Formula
One-hour zoom meeting that will be recorded (but recordings won't be public). We encourage you to turn on the camera!
Paper
Everyone can submit candidate papers for next paper talks and before each meeting we vote for the one that we want to discuss next. The person who submitted a winning paper starts and moderates the meeting, but it's never a one-man-show; it's a free discussion where everyone is encouraged to share thoughts!

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Who you gonna meet at Paper Talks

Data Science and Machine Learning practitioner with a couple of years of industry experience in applying analytics to solve business problems in various domains. Currently working as a data science consultant in GetInData, being engaged with both commercial projects as well as internal R&D and business development initiatives.

Piotr Chaberski

Machine Learning Engineer with several years of experience. Obtained his BA in Mathematics from the University of Cambridge and later Msc in Mathematics from Adam Mickiewicz University in Poznań. Passionate about both theory and practice and solving business problems with whatever tools are necessary. Currently working on a custom solution in the property appraisal market. He also likes nerd-sniping :)

Aleksander Jaworski

Head of Advanced Analytics at GetInData. He helps companies build a data-driven perspective on different business problems. He has 10+ years of experience working for various clients in different business sectors from startup, through scale-ups, to corporation. He guides companies through 1. defining needs, 2. translating them into project streams and data products 3. aligning diverse experts to deliver high-quality data products (e.g. ML models, dashboards, applications).

Borys Sobiegraj

Data scientist with experience in both research and industry. He received his bachelor's and master's degrees in data science at the Faculty of Mathematics and Information Science, Warsaw University of Technology. Passionate about deep learning, cutting-edge technology and solving real-world problems. Currently working on research in the field of graph neural networks and building machine learning solutions at GetInData.

Michał Stawikowski

Previous Paper Talks

So far we managed to organize nearly 20 such meetings internally and since the formula seems to be working nicely, we want to share this initiative with a broader machine learning community.
We have discussed several topics, mostly in the area of advanced machine learning and deep learning. Usually, the papers were about:

Novel approaches to certain business use cases (e.g. state of the art designs in recommender systems, image synthesis with diffusion models etc.)

Cross-cutting comparative studies of different methods (e.g. modern applications of graph neural networks, evaluation of different methods in time-series anomaly detection etc.)

Empirical studies of important ML concepts (deep double descent, comparison of deep learning vs. tree-based algorithms on tabular data etc.)

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We help data-oriented organizations to succeed using open-source and cloud technologies such as Flink, Kafka, Spark, Hadoop, Google Cloud Platfrom by providing outsourcing, consulting and training services.

We've been already working for tens of companies ranging from fast-growing European startups to global corporations in pharmacy, FMCG, banking and media sectors. We trully focus to help our customers achieve true ROI from their data processing.