The team of Datathon mentors has never been so big and diverse – we have 30 mentors who will help the international teams to go through the challenging data cases! We are proud to announce the last group of mentors and are looking forward to a really interesting and enriching Datathon. We are also sure that there is a lot to learn during the challenge for both mentors and participants. Here are some of our mentors, joining from India, Bulgaria, Austria and Malaysia!
Mr. Mohan Kumar Silaparasetty
Mr. Mohan Kumar Silaparasetty is a Consultant and Faculty at Manipal Academy of Higher Education-South Bangalore Campus (Manipal ProLearn-Data Science).
Mr. Mohan Kumar Silaparasetty is very Senior Data Science and AI Professional and comes with 25+ years of rich industry experience in the field. He is a well-known expert in the field of Big Data, Artificial Intelligence, and Machine Learning and trained numerous professionals in the Data Science and Big Data field.
He is the founder and CEO of Trendwise Analytics which is into consulting and provides AI, Machine Learning and Data Science solution to the corporates. He worked in senior leadership roles in the field of Data Science and Analytics in the reputed Multinational organizations like IBM and SAP Labs.
His areas of expertise include Artificial Intelligence, Big Data, Hadoop (Cloudera/Hortonworks), Spark, Data Science, Machine Learning, R, Python, Deep Learning and Tensorflow. He is also an organizer for Bangalore Hadoop Meetup Group and Bangalore AI Meetup group in India. He is Engineering Graduate from India’s premier technical institute, Indian Institute of Technology – Kharagpur.
Anit Bhandari is a lead data scientist with VMware. He has over 6 years of experience working in various machine learning and deep learning applications. His research interest includes customer relationship management, customer churn, loyalty programs, supply chain, e-commerce analytics, digital marketing in both B2C and B2B domains. He has worked with eBay, Capillary, Manthan and some of the top retail chains in India to develop, build and implement machine learning and data science models for various business problems.
Currently, he works on a variety of problems associated with B2B digital marketing and channel sales to generate incremental sales for next-gen products of VMware.
Anit holds a Masters in Technology with specialization in Data Sciences and B.Tech in Computer Science and is also currently part of Executive MBA program from Indian Institute of Management, Bangalore. He has multiple publications in several international journals to his credit. His areas of expertise include Machine Learning, Deep Learning, Sales and Marketing Analytics, Retail, E-Commerce & Marketplace Analytics, Project Management and his technical skills include R, Python, Teradata, Google Analytics, Adobe Omniture, SQL / Greenplum, Talend and Alpine Data Labs
Anit shared his expectations and reasons to join #GlobalDatathon2018:
” I have been part of a couple of Hackathons and so I was quite interested in another version of that which is multinational and cross-cultural. The knowledge and expertise of the participants and their teams bring to such events is just exhilarating. I am looking forward to guiding the teams to understand their strengths, establish goals and prioritize them to come up with a best possible and presentable solution. Even as a mentor my expectation is to leave the datathon with new learnings and reinvigorated passion for data science as a discipline.
The Datathon is a great initiative which will encourage learning, celebrate new data science enthusiasts, foster inclusion and building innovative solutions for each of the cases. I also congratulate Data Science Society for organizing such an event.”
Mladen Savov is a Probability mentor and an Associate Professor at IMI-BAS, Bulgaria. He is a mathematician doing research in the area of probability. Mladen’s main interests are stochastic processes and their applications. Occasionally, he applies his expertise in areas such as Monte-Carlo numerical methods for problems stemming from quantum physics. He teaches probability in FMI.
“I am joining the Datathon with the hope that I would help with mathematics should such questions arise. I expect excellent solutions with sound mathematical justification: 🙂!”
Lars Martens is currently working at A1 Austria Telekom AG as a data scientist and also manages educational programs, community events and partnerships of the Data Science Society (AT) which he co-founded. While completing his studies for a Bachelor’s degree in business administration he worked on various projects based on predictive modeling as well as on applications of NLP as part of a research cooperation between the Vienna University of Economics and Business and the conversational AI start-up Ondewo. In a similar vein, he collaborated on creating an NLP-based application, which was exhibited at the IBM Watson Summit 2017.
At the moment, Lars’ main areas of interest are process-mining, reinforcement learning and translating real-world problems into analytics tasks. Here is what Lars expects from the upcoming Datathon:
“Data science is incredibly easy to get into, but challenging to stay with. When you google “how to begin data science”, you’ll get hit with an avalanche of courses, wikis and guides from the world’s leading experts and institutions for free. What do you do, though, after you have learned a bit about linear algebra, know how to deal with messy datasets and can confidently create good prediction models? It’s a bit like launching into space with a rocket. In the beginning, you just want to be able to be able to get out of earth’s gravitational pull. Although it may not be easy, it is very simple, with the number of sensible actions being rather limited. Go up. But what do you do once you get to the point of being able to float and look into space? There’s a practically infinite number of clusters of methods, applications and fields you could check out. This is the point where it’s really helpful to have a community of equally curious and passionate people around you, with whom you can explore this space together. And the datathon is a great initiative that is fostering just the kind of community you would want.
I expect people to learn, have fun, connect, and to come up with smart ideas, all of which I’ll do my best to facilitate!”
Jan Sauer is a Data Scientist at The Center of Applied Data Science & Bioinformatician at the German Cancer Research Center.
Jan Sauer was a biostatistician in the field of deep learning and image/pattern recognition. He has a master’s degree in physics and have extensive experience as a software developer.
Throughout his career, he has been involved in different areas of data science, ranging from automated data collection and data analysis, data pipeline and database design, and advanced machine learning where he uses Tensorflow extensively in image processing.
“I enjoy exploring data as well as working together with others to discover what insights a dataset can deliver. From the Datathon, I expect to see many creative approaches and solutions to the problems and hopefully learn something from the participants.”
Anton Nenov is a Data Scientist at NetInfo, Bulgaria. He often says: “If you torture the data long enough, it will confess”
Anton’s favorite tools are: IBM SPSS Modeler (Clementine), Business Objects, Tableau, R, Salesforce, Google Analytics.
Anton is an experienced professional with the following specialties: Data Mining, Churn Prediction Models (Classification), Social Network Analysis (SNA), Behavioral Segmentation (Clustering), Sales Forecast (Time series), Market Basket analysis (Association Rules), Brand Management and FMCG Marketing.
Laleh Asadzadeh is a Senior Data Scientist at The Center of Applied Data Science & Data Scientist at Potentia Analytics Inc.
Laleh received her MSc. in Computer Science in 2016 from Southern Illinois University. Her research focused on the modelling and analysis of social network users’ activities. Laleh then was a data scientist at Potentia Analytics Inc. and was in charge of developing and implementing several research projects that enhance the quality of service in hospitals.
Before that, she received her MSc. In Mathematics in 2001 from Sharif University of Technology. Her masters thesis on defining sets in combinatorial structures and their applications in cryptography. After graduation, Laleh has been a Mathematics instructor, researcher, and research mentor at Isfahan Mathematics House.
She specializes in Mathematical Modelling, Statistical Analysis, Machine Learning, and programming languages, such as Python and R.
Premkumar Chandra Shegaran
Premkumar Chandra Shegaran – Product Lead @ The Center of Applied Data Science
Prem is the currently the Product Lead at The Center of Applied Data Science, heavily involved in business innovation planning and strategizing the product plan for capability building, utilizing latest technologies focusing on Data Science and AI. He holds a MSc in Petroleum Engineering, Heriot-Watt University and a BEng Electrical and Electronics Engineering (Hons) (major in AI and Robotics), UNITEN.
Prem had his early career days in the Oil and Gas Sector, where he was a Research for Manufacturing Engineer at Schlumberger and an Instrumentation Engineer at Ranhill Worley Parsons. It was here, where he gained tremendous experience dealing with massive amount of data and analyzing trends. He then moved on to the academics and enjoyed stints lecturing in UCSI and Heriot-Watt University. He has strong experiences in data extraction, mining and processing where he immerses himself in different algorithms of machine learning.
His strengths in machine learning include Supervised and Unsupervised learning and as well as Reinforcement Learning. With the combination of technical and business skills, Prem always sees the opportunity to create intelligent solutions to cater the mass market. He enjoys the thrill of challenges, puzzles and problems knowing that discovering the solution is a satisfying experience.
Stay tuned for the data articles of our amazing mentors who will support the participants even before the challenge by writing some helpful guidelines on approaches for the data cases’ solutions.
Here are Prem’s expectations for the upcoming Datathon:
“I would like to work with great minds and share ideas to help and solve some interesting real-world problems. As I am a fan of the KISS (keep it simple, smarty-pants!) methodology, I wish to see teams using an ensemble of very simple machine learning algorithms to achieve high accuracy in problems such as fraud-detection, product recommendation etc, of course while keeping the end customer’s goal in mind!”
Don’t forget to book your ticket before September 24 at http://bit.ly/2C4Di6b!