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Innoplexus AG proudly announces that our founder and CEO, Dr Gunjan Bhardwaj will be on the panel discussion at Machine Intelligence Summit to be held on 14th July 2017, in Berlin, Germany.
The panel discussion is around “From Past Learning to Predictive Future” at 13:45 CET on Friday at Deutsche Telekom Hauptstadtrepräsentanz Französische Straße 33a-c, 10117 Berlin. The other participants on the panel are Prof. Christoph von der Malsburg, Barbara Pogorzelska, Josh Chen.
The discussion is on how machine intelligence is used to accomplish the tasks on reducing efforts and complexity in understanding the data sets to recognise the patterns and to predict future behaviours. The panel of cross industry experts shall discuss use cases where machine intelligence predictions have real impact.
Other team members from Innoplexus who are going to be present at the event are:
- Mr. Gaurav Tripathi, Co-founder and CTO
- Ms Varsha Rohani, Senior Manager, Corporate Development
- Mr Yannik Schelske, Data Scientist
- Mr. Tanay Gahlot, Software Engineer working in our Deep Learning team
If you are in Berlin on 14th July, please do drop by to listen to our views on AI, Deep Learning and Machine Learning, and to meet our amazing team members.
About the Event
Machine Intelligence Summit, organised by SearchInk, gathers some of the most brilliant minds in the field of Artificial Intelligence to discuss the stakes of Deep Learning and Machine Intelligence.
Innoplexus caters to Life sciences industry offering Data as a Service (DaaS) and Continuous Analytics as a Service (CAaaS) products. We leverage Artificial Intelligence and advanced analytics to help reduce the drug development time from synthesis to approval, significantly.
We use proprietary algorithms and technologies to help global Life Sciences and Pharmaceutical organisations across all stages (Pre-clinical, clinical, regulatory and commercial) of drug development.
We automate the collection, curation, aggregation and analysis, of billions of data points from thousands of data sources, using machine learning, network analysis, ontologies, computer vision and entity normalisation.