My research journey spans three continents. I began in Brazil, earning a first-class Electrical Engineering degree from the University of Brasília and winning the Brazilian Government Scientific Initiation Scholarship for FPGA research with Professor Alexandre Nery. I then worked in partnership with Cellcrypt under Professor Edson Mintsu Hung, researching machine learning pipelines to improve call quality by optimizing PJSIP codec parameters.

My next step was Japan, where I received the Japanese Government (MEXT) Scholarship to work at The University of Tokyo under Prof. Toyotaro Suzumura, former MIT-IBM Watson AI Lab principal research scientist. There I collaborated with Nikkei Inc. under Dr. Yuichiro Yasui, researching news recommender systems and foundational LLMs. This resulted in a SIAM SDM'25 publication on news recommenders achieving superior multilingual performance, and earned the SIAM Travel Award.

Now in the United Kingdom at Imperial College London, I'm pursuing an MRes in AI/ML supervised by Prof. Pedro Mediano, on generative and contrastive deep learning for predicting rare events in large-scale clinical data in partnership with the NHS.

01

Education

Sep 2025 — Sep 2026London, UK

Imperial College London

MRes, Artificial Intelligence & Machine Learning

Research areas: contrastive deep learning, long-tail learning, generative AI, and large language models.

Supervised by Professor Pedro Mediano on generative and contrastive deep learning for predicting rare events in large-scale clinical data, in partnership with the NHS.

Aug 2016 — Jun 2022Brasília, Brazil

University of Brasília (UnB)

BSc Electrical Engineering — First-Class Honours, GPA 4.35 / 5.0

Research areas: FPGA co-processors, machine learning for audio codecs, and deep learning for finance.

Focused on embedded systems, publishing a paper on FPGAs, and later transitioned to machine learning for codec optimization and financial markets. Final thesis supervised by Professor Edson Mitsu Hung.

Jan 2017 — Dec 2018Brasília, Brazil

University Center of Brasília (CEUB)

Associate Degree, Systems Analysis & Development

Concentrated on Java development, particularly real-time tracking systems, with a final thesis on a pharmacy delivery application under the guidance of Professor Auto Tavares.

02

Experience

Apr 2023 — Apr 2025Tokyo, Japan

The University of Tokyo — Graph Neural Network Laboratory

Research Scholar

  • Two years in Prof. Toyotaro Suzumura's lab, contributing to machine learning research on recommender systems, foundational LLMs, and financial forecasting.
  • Led a research partnership with Nikkei Inc., Japan's largest financial media company and owner of the Financial Times, designing novel recommender system models for user behaviour (under Dr. Yuichiro Yasui).
  • Built LLM systems including multi-agent pipelines, prompt engineering, and long-context handling with Retrieval-Augmented Generation (RAG).
  • Trained large-scale distributed models on the Wisteria supercomputer.
  • Implemented Point-of-Interest Graph Neural Network recommendation models during an industry partnership with Toyota.

Jul 2021 — Apr 2023Brasília, Brazil

VOGA — Technology Division, BTG Pactual

Intern → Developer → Development Team Lead

  • Led system integration for VOGA post-acquisition by BTG Pactual, Latin America's largest bank.
  • Delivered interactive data solutions and analytics for non-technical stakeholders throughout the acquisition, using FastAPI and Plotly to build self-service dashboards integrated with BTG's API.
  • Architected a centralized, real-time data ecosystem managing over USD 300 million in portfolios.
  • Transitioned from full-stack development to DevOps, managing AWS infrastructure (EC2, VPC, RDS, ELB) with Terraform, automating CI/CD with Jenkins, and securing the application with Cloudflare.

Sep 2020 — Jun 2021London, UK (Remote)

Cellcrypt Inc. — Advanced Security Systems

Research Intern

  • Advised by Professor Edson Mintsu Hung and Dr. Rogério Richa, improving VoIP call quality by 7% in voice-encrypted systems through automated ML parameter tuning.
  • Built an ML pipeline for automated PJSIP parameter tuning.
  • Analyzed voice quality metrics (MOS, jitter, latency) under varying network and encryption conditions.
03

Research Papers

  1. A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System
    January - July 2024 | SDM'25 Proceedings | ArXiv
    • Collaboration: Toyotaro Suzumura (The University of Tokyo) and Yuichiro Yasui (Nikkei Inc.)
    • Highlights: Developed AWRS, an Avoidance-Aware Recommender System for news that incorporates article avoidance as a key factor to improve recommendations. Evaluated on datasets in English, Norwegian, and Japanese, AWRS outperformed existing methods by leveraging avoidance as an indicator of user preferences.
  2. NewsReX: An Open-Source Multi-Framework for Neural News Recommendation
    January - August 2025 | ArXiv
    • Collaboration: Toyotaro Suzumura (The University of Tokyo) and Yuichiro Yasui (Nikkei Inc.)
    • Highlights: An open-source framework that unifies multiple neural news recommendation models across two ML backends (PyTorch and JAX/Flax). Models are defined through a structured YAML specification that decouples model definition from framework-specific implementation, with a Hydra-based configuration system for composable experiments and single-command framework switching. Cross-framework parity tests verify numerical consistency across backends, and pre-trained weights are published so results can be reproduced by inference alone.
  3. POPK: Mitigating Popularity Bias via a Temporal-Counterfactual
    April - July 2024 | ArXiv
    • Collaboration: Toyotaro Suzumura (The University of Tokyo) and Yuichiro Yasui (Nikkei Inc.)
    • Highlights: Developed POPK, a model which uses temporal-counterfactual analysis to reduce popularity bias in news recommendations. POPK improves accuracy and diversity by systematically removing the influence of popular articles.
  4. From Votes to Volatility Predicting the Stock Market on Election Day
    August - December 2024 | ArXiv
    • Collaboration: Toyotaro Suzumura (The University of Tokyo)
    • Highlights: Deep learning models for high-frequency stock price forecasting, focused on predicting market behaviour during election periods.
  5. A SHA-3 Co-Processor for IoT Applications
    January - November 2020 | Paper (IEEE - WCNSPS'20)
    • Collaboration: Alexandre S. Nery (University of Brasília) and Alexandre da C. Sena (Rio de Janeiro State University)
    • Highlights: Designed and implemented a SHA-3 hardware co-processor on FPGA for IoT applications, achieving 65% faster performance than ARM Cortex-A9 with improved energy efficiency and reduced circuit area.
04

Awards

  1. 3rd Place – HackTheLaw, King's College Cambridge June 2026 | About
    About: Hosted by the University of Cambridge and Stanford CodeX. Recognized for technical excellence and LLM integration by a panel drawn from academia, industry (Perplexity, Anthropic), and law firms (Clifford Chance and White & Case).
  2. SIAM Travel Award – SDM25 May 2025 | About
    About: Granted by the Society for Industrial and Applied Mathematics (SIAM), this award supported travel to the 2025 SIAM International Conference on Data Mining (SDM25) held in Alexandria, VA. It recognized promising early-career researchers contributing to the field.
  3. Japanese Government (MEXT) Research Scholarship April 2023 - April 2025 | About
    About: The Japanese Government (MEXT) Research Scholarship supports international students conducting research at Japanese higher education institutions. Approximately 9,000 students are accepted out of 100,000 global applicants (~9% acceptance rate), with full funding for tuition, living expenses, and research activities.
  4. Brazilian Government (CNPq) Institutional Scientific Initiation Scholarship (PIBIC) August 2019 - July 2020 | About

    About: The PIBIC program, funded by the Federal Government of Brazil, supports undergraduate students in research, technological development, and innovation. Awarded to the top ~4% of undergraduates nationwide on academic merit; funded research into FPGA-based cryptographic security systems.
05

Open Source

  1. NewsReX - Code | Weights
    An open-source framework for neural news recommendation, built with The University of Tokyo and Nikkei. I am the first author and wrote the entire implementation, and have maintained it for over a year.

    It is research tooling: models are declared in YAML and composed through Hydra, and run on two backends — PyTorch and JAX/Flax — behind a single interface. Cross-framework parity tests verify that both implementations agree numerically before any result is reported, and evaluation is shared and framework-agnostic. Everything is MIT-licensed and every trained weight is public, so results are reproducible by inference alone.

    Shared by François Chollet, the creator of Keras.
  2. AlphaDL - Code
    Lightning AI Project for Cutting-Edge Deep Learning Models in Stock Markets.
  3. NewsrecLib - Code
    Implemented the PP-REC SOTA model into the news recommendation framework.
  4. Qlib - Code
    Added support for the Brazilian stock market, enabling local investors and researchers to use Qlib's machine learning models and data processing pipelines on Brazilian stock data.
06

Certifications

  • IELTS: Overall Band Score: 8.0 (Listening: 8.0, Reading: 8.5, Writing: 7.0, Speaking: 8.5)
07

Essentials

Courses

  • Deep Learning @ Carnegie Mellon University | Certificate
  • Digital Signal Processing @ EPFL | Certificate
  • Information Theory @ The Chinese University of Hong Kong | Certificate
  • Algorithms and Data Structures @ UCSD | Certificate
  • Model United Nations @ Harvard University | Certificate
  • Model United Nations @ Yale University | Certificate

Leadership experiences

  • Commercial Director, Electrical Engineering Junior Enterprise (ENETEC) June 2019 - April 2020

    Led the team to achieve annual goals set by the National Association of Junior Enterprises in Brazil (Brasil júnior), securing projects worth approximately USD 30,000, which funded training and development for company members.

Languages

Portuguese Native
English Near-native
Spanish Conversational
Japanese Basic

Tech stack

Programming Languages

Python JavaScript SQL C Java

Frameworks & Libraries

PyTorch Lightning AI JAX TensorFlow Keras Plotly CrewAI Flask FastAPI

Infrastructure

AWS Cloudflare Docker Nginx

Databases

PostgreSQL

Igor L.R. Azevedo

MRes · AI/ML
Igor L.R. Azevedo
Long-tailed learning Recommenders LLMs Electrical Eng.
🇬🇧 🇯🇵 🇧🇷 igorazevedo.com