DMBI Data Hackathon
Ben Gurion University of the Negev
The DMBI data-hackathon will take place between May 16th-17th, 2019
BGU Main Campus, Carole and Marcus Weinstein Information Systems Engineering and Cyber Security Building (96)
Number of participants is limited! Hurry up and register
** Don't have a group yet/missing a group member? Check out our forum
** Check out the presentations from the preparation meet up
** Join the Facebook group for all the latest updates
About the DMBI Data Hackathon
The SISE DMBI annual conference will be preceded by a Data Hackathon for leveraging the utilization of advanced machine learning and deep learning algorithms and techniques for solving real world challenges.
The hackathon is planned to be a 30-hours event where data enthusiastic programmers, designers and researchers combine their skills to build innovative solutions for real world problems aiming at empowering our society.
The hackathon will include practical tutorials from senior data-scientists from various fields.
The winners will be announced at the conference.
**Please note that the DMBI conference registration is done separately in the DMBI conference website
The DMBI Data Hackathon 2018 will take place between Tuesday, May 8th and Wednesday, May 9th
The winner will be announced @ DMBI conference on May 10th.
BGU Main Campus Beer-Sheva, Israel, Carole and Marcus Weinstein Information Systems Engineering and Cyber Security
The DMBI Data Hackathon 2019 will take place between Thursday, May 16th and Friday, May 17th
The winner will be announced @ DMBI conference on July 4th.
The DMBI Data Hackathon 2019 will host many mentors from several companies from the industry.
You will have an opportunity to work, learn and mingle with the best in the field
The DMBI Data Hackathon 2019 will contain 3 parallel group challenges.
Each group may contain up to five members
The Data Hackathon will include challenges related to the following domains
Special Creative Challenge:
ACC - Predicting Cryptocurrency Behavior (Details)
The winners of each of the main challenges will receive a prize of:
The winners of the creative challenge will receive a new set of headphones
Teams of up to 5 participants are allowed. All team members must have registered to the hackathon.
The hackathon's organizers do not claim ownership of your solutions built during the hackathon; but by participating you are granting the organizers a free right to use, reproduce, publish and sublicense your solution.
You should start building your solution only after the competition officially begins
Teams may be subject to a code-review towards the end of the competition.
The Schedule & Judges
Day 1, 16/5:
8:30 - Arrival and light breakfast
9:00 - Hackathon starts! Challenges overview
11:30 - Tutorial - "Keras++" by Yam Peleg
14:00 - Lunch
19:00 - Tutorial - "Best practices for applying deep learning models on GPU" by Lior Sidi
20:30 - Dinner
Day 2, 17/5:
00:00 - Night snacks
7:00 - Breakfast
9:00 - First judging round
11:30 - Winners announcement, Hackathon ending!
Ziv Gome, Algo Be'er Sheva Lead, Taboola
Itsik Mantin, Lead Scientist, Imperva
Tal Sinko, Director of Energy Department, ICL
Prof. Lior Rokach, Head of the Department of Software and Information Systems Engineering, BGU
Prof. Bracha Shapira, Vice Dean for research - Faculty of Engineering Science, BGU
Nathaniel Shimoni, Research Data Scientist, Grid4C
Liat Antwarg, data scientist - PhD student
Guy Shtar, data scientist - PhD student
Adir Solomon, data scientist - PhD student
Amit Livne, data scientist - MSc student
If you want to be a sponsor, please contact :
11:30 - "Keras++" by Yam Peleg
Yam Peleg is the founder of Deep Trading ltd. He is also a major contributor to the Python community and to Keras specifically who spoke at dozens python conferences around the world, including PyData, PyCon, SciPy and many more.
Lior is the Co-founder and CEO at braincast.ai, a new startup that model humans decisions using sequential patterns. He holds 10 years of experience in data-driven projects, mostly worked with ML and Deep-Learning to solve fraud and cybersecurity problems.
Last year, Lior and his team won the first place at the medical challenge and second place in the entire competition using deep-neural collaborative filtering network to identify drug-to-drug interaction.