Microsoft

Started as a Data Scientist and grew to Principal Data Scientist Manager.
My team has supported multiple industries such as retail, gaming, finance, marketing, e-commerce, energy, media, manufacturing, and others. We implement solutions in a wide range of AI areas including recommendation systems, natural language processing, computer vision, time series forecasting, and other machine learning techniques.
Our repository Recommenders ➡️➡️ https://github.com/recommenders-team/recommenders is a 
world reference in recommendation systems. Starred and used by +19k people.

Principal Data Scientist Manager

IE University

I'm helping a new generation of students to understand and apply AI.

Adjunct Professor

Imperial College London

I help the College to define its strategic development, research themes, academic programs, and partnerships with the industry.

Industry Board Member at Dyson School of Design Engineering

samsamia

We developed a SaaS solution to increase fashion e-commerce revenue through visual search. Our technology allowed users to take a picture of a garment they liked, and find the most similar garment within the customer's catalog. I led the engineering and sales team. Among our customers, we had one of the biggest e-commerce in the UK and one of the biggest media companies in Spain.

CEO & Co-Founder

King's College London

Research topics: Learning from Demonstration, Human-Robot Interaction, Markov Chains, and 
Non-linear control.

External Researcher

Universidad Carlos III de Madrid

Research in Machine Learning for robotics: Learning from Demonstration, Computer Vision, kinematic and dynamic control of humanoid robots, and non-linear control. See my publications: ➡️➡️ https://github.com/miguelgfierro/publications

Professor at several Engineering degrees teaching Control Engineering, Industrial Automation, and Industrial Robotics.

Research Assistant at Robotics Lab

Development of algorithms to predict the energy price fluctuation in Spanish electrical market focusing in wind turbines.

Scholarship Researcher in Electric Department

Asociación de Robótica de la Universidad Carlos III

A non-profit student organization at Universidad Carlos III de Madrid. Development of R&D robotics projects including mobile robots, aerial robots (Unmanned Aerial Vehicles UAVs), 3D printers, and small humanoid robots.

President & Co-Founder

I lead the Personalization team at Microsoft working on Reco Systems, NLP, Computer Vision, and other ML solutions.

In addition, I help people understand and apply AI. Whether you want to switch your career to Data Science, land a job in a big tech company, grow your Data Science career, or apply AI to your business, I can help you.

Subscribe to my list ➡️➡️ https://bit.ly/emaillistmiguel ⬅️⬅️

____________________________
Blog: https://bit.ly/miguelgfierro
GitHub: https://github.com/miguelgfierro
Microsoft Recommenders: https://github.com/microsoft/recommenders
Publications and conferences: https://github.com/miguelgfierro/publications

I am at my maximum connections, please follow me and press the bell to stay connected on LinkedIn.

I’ve finished the book: Building AI Agents with LLMs, RAG, and Knowledge Graphs. Really strong book to get a holistic view on how to build agents.

Here are some ideas I found interesting:

𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥 𝐢𝐧𝐝𝐞𝐱𝐢𝐧𝐠:
Problem: If there are too many documents or they are too long, RAG would struggle with precision (chunks are misaligned) or recall (find all relevant chunks).
Solution: We generate a summary of each document and index it with the chunks. At query time, first search over the summaries, and then retrieve the relevant chunks.

𝐒𝐞𝐥𝐟-𝐑𝐀𝐆:
Problem: The query is too complex.
Solution: Use a multi-step process to critique and reflect on the output.

𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆:
Problem: Traditional RAG fails to capture relationships and struggles with redundant information.
Solution: Use a Knowledge Graph, composed as tripplets of entity-relation-entity, as a controlled context to increase relevance.

𝐆𝐫𝐚𝐩𝐡 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 (𝐆𝐨𝐓):
Problem: The query is too complex.
Solution: Use multiple reasoning steps in graph form to explore alternative answers and verify the final output.

𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐗𝐑𝐋):
Problem: It’s not easy to understand the agent's behavior in an RL setting.
Solution: Use an LLM as a policy interpreter to explain the agent’s behavior.

Link: https://lnkd.in/dNybQPcj

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