Natalia Konstantinova

Personal Details

Bio

I am a great enthusiast with over 10 years’ experience in the application of Natural Language Processing, Artificial Intelligence, IT and machine learning technologies to real world problems.

My interests lie in the areas of:

machine translation,

ontologies,

question answering,

dialogue systems and chatbots,

information extraction (coreference resolution and relation extraction),

machine learning,

recommendation systems.

I am also passionate about project management, agile ways of working and inspiring people to work together and solve challenging problems!

My PhD thesis entitled “Knowledge acquisition from user reviews for Interactive Question” explored the ways Information Extraction and NLP can be used to collect knowledge for Interactive Question Answering.

Current position (1)

Architecture Lead in AI

Achievements (1)

Top 30 women in AI

Natalia was mentioned on the list of top 30 women in AI, UK edition - https://www.linkedin.com/pulse/top-30-women-ai-uk-edition-nikita-johnson/

Degrees (2)

Mathematical linguistics

Saint Petersburg State University

2002 to 2007

PhD, Computer Science

University of Wolverhampton

2009 to 2013

Presentations

Presentations (3)

Reducing customer effort by using AI technology

We all have busy lives and no time to waste. Our expectations of retail businesses in the meantime are getting higher and higher.
AI is here to help to automate mundane tasks and increase customer satisfaction. But! it works only if done properly, so what do we need to remember to make the most of AI?:
- Are all tasks ok for AI delegation?
- Can we have a clever AI assistant for humans?
- How do we still keep our customers happy?

Sustaining innovation: competitive edge

Sustaining innovation at an organization is a key to success.
1. Why is it important?
2. How do we make people engaged:
- Bottom up approach
- Top down approach
3. Challenges to overcome
4. Lessons learnt

Want a recipe for disaster? Try managing data science project as an IT one!

1. How data science projects are different from typical IT projects?
2. Can you adjust scrum to fit in data science projects and then how?:
- Use spikes
- Make sure items you work on are small
- Start on items with highest uncertainty
- Instead of increment focus on reducing uncertainty
3. Key things to keep in mind:
- Educate your business and stakeholders
- Communication is key

Past talks (1)

SUPERWOMEN SHAPING AI

Online

May 13, 2020