Legal IT Group, Head of IT law practice

Anton Tarasiuk is a lawyer, the Head of the IT -practice at Legal IT Group. He has a vast experience in providing lawyer’s consultations for foreign customers who seek to outsource IT- services. Anton has also led many projects within the Big Data, within the framework of the agreements with Data Scientist and Analytics. With more than 5 years of legal experience, he has provided advice and done documents for many IT companies and IT start-ups, including #medtech, #adtech, #fintech. Mr. Tarasiuk was a speaker at AI & BigData Day, California In Ukraine, Target School, “Free School of Journalism and Marketing”, LIOF, GETIT and other events. He has conducted law workshops, given lectures at Unit Factory and Projector.

Topic of presentation: Contracts for the Big data

CEO of project “The Smart Country”

The CEO of project "The Smart Country" Sergey and his team have found out interesting solutions aimed at supervising state companies, hospitals and retail chains. Sergey’s team have won in Ukraine's largest competitions such as Kyiv Smart City, 1991 Open Data, Egup Challenge. Our team creates e-services for direct democracy and provides top managers of the largest companies with a simple and effective mechanism for full control of customer service.

Topic of presentation: Electronic services that can control large systems. Simulation of the behavior of a large system.

RingUkraine, Head of FaceRecognition department

Oleksandr is Head of Face Recognition research department at RingUkraine. He has wide experience in sound analysis, computer vision, deep learning and machine learning. Also Oleksandr taught ML at LITS Kharkiv, CS@UCU, KWSchool on Data Analysis, etc.

Topic of presentation: Affine transforms and how CNN lives with them

The main points of the presentation: The talk is about convolutional neural networks their advantages and disadvantages. In particular, we’ll talk about not only common methods of increasing robustness to affine transformations over the input but also about more complex special architectures, like Spatial Transformer Networks from DeepMind and Capsule Networks recently proposed by J. Hinton.

CEO and CTO at Syndicode

CTO and co-founder at Syndicode. Deputy CIO at Nova Poshta LLC. Private entrepreneur, consultant with 15+ years of experience in software engineering. Holding Ph.D. Anton wrote more than 20+ scientific papers on Semantic Web, Agent technologies and Security.

Topic of presentation: Machine Learning for Inventory Balancing

Eleks, Enterprise Platform Architect, Big Data Architect

I’ve been designing data-centric systems for last 20 years and have gained huge experience in developing and performance tuning of OLTP, DW and BI applications. Last 3 year I’m focused on Data Analytics in Big Data Clusters and implementing different innovative approaches for Fast Data Processing. At the moment my responsibility is to develop competencies for Enterprise level Data Platform at Eleks company.

Topic of presentation: Scalable Business Intelligence on Cloud Big Data Cluster. Architecture And Approaches.

ELEKS, Senior Data Scientist, Ph.D.

Taras is a Senior Data Scientist at ELEKS, working with statistical analysis, forecasting, optimization, machine learning and other math stuff. He is the Doctor of Philosophy in Differential equation and Assistant Professor in Lviv Polytechnic National University, that teaches operations research, numerical and data analysis.

Topic of presentation: Anomaly detection on images

Svitla, Vice President of Development

The Vice-President on Development Strategies at the company “Svitla”, with over 10 years experience of working in the media industry. Currently, helping to customers to understand the need for Digital Transforma

Topic of presentation: 10 mistakes that startups make

thredUP, Machine Learning Engineer

7+ years of experience in Data Science. Application of Machine Learning for the world largest consignment store. Successful data-driven consulting projects for FinTech.

Topic of presentation: Machine Learning for data-driven marketing

Analysis of clickstream data for omnichannel acquisition and retargeting optimization. Survival analysis for cost per acquisition (CPA) optimization.
Use cases of successful optimization of marketing budget allocation using machine learning and big data approaches.
Challenges
- We have billions of clickstream events
- We must predict on an extremely unbalanced sample Data processing solution
- Complex processing of clickstream data for further analysis
- For proof of concept: AWS Redshift + Python (processed < 10% of users)
- For processing all users: AWS Redshift + Apache Spark (Databricks) + Python (sklearn)
ML for omnichannel attribution:
- Re-create the user journey (the sequence of the attribution channels driving user visits to thredUP.com) between signup and purchase
- Define the importance of each attribution channel for user conversion
ML for retargeting optimization:
- We used Gradient Boosting Machines (GBM) to predict conversion after clicking on ad
ML for CPA optimization:
- We used survival analysis to optimize cost per acquisition for paid marketing campaigns.
Business insights & impact:
- This analysis provided the marketing department full visibility into the user journey
- High prediction quality for conversion after ad click. Predictive model developed solely based on clickstream data, complimentary to the model developed using traditional data sources for CR prediction.
- High prediction quality and newly developed approach for prediction conversion rate using survival analysis
- Target ads toward people with high likelihood of clicking on the ad and converting
- Retargeting optimization project is expected to improve retargeting spend efficiency by 10%
- CPA optimization project improved efficiency of acquisition campaigns by 15%

IT Step University, Vice-Rector for research

Vice-Rector for research at IT Step University. Doctor of Science, Professor, Senior Member of IEEE. Scientific specialty – systems and methods of artificial intelligence Publication. He has more than 200 scientific publications, including 10 inventions and 10 monographs and book chapters. My current scientific areas are machine vision; computational and artificial intelligence; data mining; data stream mining; digital image, video series, video streams processing in on-line mode. Practical results are video motion detector, fire detector, solution for tracking the objects under conditions of noisiness and high level of separation of motion subject.

Topic of presentation:  Convolutionary Detection of Flame in Real Time Video Stream

SoftElegance, System Architect

Yaroslav Nedashkovsky got Master’s degree in Applied Mathematics and System Analysis in 2008 from National Technical University of Ukraine, however made the first steps in software development around 2004. From 2004 to 2008 worked under a contract for Institute of Petroleum UAS, Kyiv, Ukraine. Started as a software engineer in projects related with data mining and visualization of seismic data. Since 2011 works in SoftElegance. He has a profound experience in building various successful SaaS solutions, data lake, mostly specialized in distributed system, IoT, and Big Data. From 2015 works as a System Architect. Big fan of machine learning.

Topic of presentation: Data Engineering in Information Security: how to collect, store and process terabytes of data from viruses

Researcher at Videogorillas LLC

Candidate of Physics and Mathematics, senior researcher of the V.M. Glushkov Institute of Cybernetics of NAS of Ukraine. I have been teaching at the Institute since 2009, actually in IT he is from student years. The last year is working at Videogorillas.

Topic of presentation: iOS object detection app on rendered dataset in practice

CEO, Ricker Lyman Robotic

Jeffrey Ricker is the CEO and founder of The Ricker Lyman Robotic Company which provides hardware, software and professional services for big data and the internet of things. He has a 25 year career working with startup companies, the financial industry and the US Defense Department. Previously, Jeffrey led the big data team that generated all the daily financial metrics at Amazon. He built the original big data team at Point72 Asset Management (formerly SAC Capital). He was the founder of XMLSolutions Corp, a business-to-business e-commerce company. He was also the founder of Distributed Instruments, a sensor data fusion company. Jeffrey began his career working in defense research where his assignments included the DOD High Performance Computing Modernization Program and the US Army Future Battle Directorate. He also served as an armored cavalry officer in the US Army Reserve. He received his bachelor of science in mechanical engineering from Tulane University.

Topic of presentation:  Big Data Governance

QuantUp, Owner & Data Scientist

Artur has over twenty years of experience in deep business analytics, data science, and machine learning projects. He worked for various companies: from start-ups to international corporations, and in various roles: as an employee, a consultant, and a business owner. He is an experienced programmer and project manager. He spent over ten years working as a statistician in a commercial bank, where he dealt mainly with construction of predictive models and development of supporting software. At the same time he received Ph.D. in Mathematics and wrote several scientific papers. He currently runs his company QuantUp (http://quantup.pl), focusing on data science, machine learning, software development and commercial trainings. He has led nearly one hundred of projects and several thousands hours of commercial trainings related to deep business analytics. He is a co-owner, Vice CEO and CSO of a Swedish bioinformatics company MedicWave. Artur has a long-time experience working with open source software and promotes its use in business applications during numerous conferences. He is a fan of the R language and a co-author of a book on forecasting in R.

Topic of presentation: What are common mistakes in Data Science projects and how to avoid them?

Topic of presentation: Predictive modeling with R

Dexis Partners, Managing Partner

Ivan has nine years of experience in legal practice, managing partner of Dexis Partners Law Firm, Director of the Law School at the Ukrainian Catholic University. In 2013-2015 worked as a Legal Counsel at Lviv IT Cluster. As an expert, he cooperates with the Council of Europe and the World Justice Project. Member of the Association of Ukrainian Lawyers, Ukrainian Association of International Law and International Association of Artificial Intelligence and Law. Co-founder and member of the Board of the Lviv Center for International Law and Human Rights.

Topic of presentation: Legal limits of the AI software development: what should be taken into account?

WIX, Data Scientist

Doing Data Science since 2014. Experienced in analyzing and predicting user behavior. Since 2016 joined Wix to develop and deploy DS & AI models. Currently passionate about neural networks, specifically image processing and restoration

Topic of presentation: Improving your image: neural network style

Data Sceintist (Ph.D.) at SoftServe, assoc.prof. at LNU

Bohdan combines academic theories and practical approaches in the data science area. He is an associate professor (Ph.D.) in Electronics and Computer Technologies Faculty at Lviv National University and has more than 70 scientific publications. His scientific interest lies in the area of semiconductors physics, quantum computing, quantitative linguistics, machine learning, predictive analytics, computer vision, social networks mining, business intelligence, time series analytics, numeric modelling, risk assessment, reliability theory, financial modelling. He has strong practical experience in retail and supply chain analytics, customers behavior analytics, fraud detection. In predictive analytics models, he combines machine learning and Bayesian inference that is an effective approach for forecasting and risk assessment in the business processes with non-Gaussian statistics. He works on the state of the art predictive analytics solutions, taking part in Kaggle competitions where he has Master degree and 3 gold medals for top positions in leaderboards. As a teammate, he won one Kaggle competition among nearly two thousand teams where his team proposed the best solution for sales forecasting in the chain of nearly 800 thousand stores.

Topic of presentation:  Modern methods of forecasting in Business Intelligence