The Minds Behind xeronilavethys

Meet the researchers and developers who are reshaping how machine learning transforms investment decision-making across global markets.

Neural Networks Risk Assessment Data Mining

Cassius Fletcher

Lead Research Scientist

Cassius spent eight years at Cambridge developing predictive models for commodity markets before joining our team in early 2023. His doctoral research focused on pattern recognition in volatile trading environments, which became the foundation for our risk assessment algorithms.

What sets Cassius apart is his ability to spot market anomalies that traditional analysis misses. He developed our core prediction engine that processes over 2 million data points daily, helping identify investment opportunities with remarkable precision.

Published 15 papers on machine learning applications in finance
Created risk assessment protocols used by three major UK investment firms
Guest lecturer at Imperial College London since 2024

Deep Learning Portfolio Optimization Quantitative Analysis

Indira Voss

Head of Algorithm Development

Indira's background in computational mathematics and her five years at Goldman Sachs give her a unique perspective on how algorithms can enhance investment strategies. She joined xeronilavethys in mid-2024, bringing expertise in high-frequency trading systems.

Her latest project involves developing adaptive learning models that adjust investment strategies based on market sentiment analysis. This work has reduced portfolio volatility by 23% in our testing environments.

Designed trading algorithms managing £2.8 billion in assets
PhD in Computational Finance from Oxford University
Holds three patents in automated portfolio management

How xeronilavethys Started

The idea for xeronilavethys came during a late-night discussion at a Liverpool tech meetup in February 2023. Three researchers were frustrated by how slowly traditional investment firms adopted proven machine learning techniques.

We saw brilliant algorithms sitting in academic papers while investment decisions relied on outdated methods. That disconnect bothered us enough to start building something different – a platform that brings cutting-edge research directly into practical investment applications.

Our first prototype processed market data for local investment groups. By December 2024, we were working with firms across the UK, helping them integrate machine learning into their decision-making processes.

Our Mission

Bridge the gap between academic machine learning research and practical investment applications. We focus on making sophisticated algorithms accessible to investment professionals who want data-driven insights.

Our Values

Transparency in our methods, rigorous testing of our models, and honest communication about both capabilities and limitations. We believe good investing requires understanding risks, not ignoring them.

Research-First Culture

Our Liverpool office operates more like a research lab than a typical tech company. Team members spend 20% of their time exploring new approaches, testing hypotheses, and publishing findings in peer-reviewed journals.

This research focus means our algorithms evolve constantly. We don't just update software – we fundamentally improve how our systems understand market behavior and identify opportunities.

Research workspace with multiple monitors displaying financial data and algorithms

Innovation Highlights

Adaptive Risk Modeling

Our systems adjust risk assessments based on changing market conditions, improving accuracy during volatile periods by 34% compared to static models.
Multi-Source Data Integration

We combine traditional financial metrics with alternative data sources including sentiment analysis and economic indicators for comprehensive market analysis.
Explainable AI Framework

Every algorithm decision includes clear explanations, helping investment professionals understand and validate automated recommendations.

Team collaboration session with whiteboards showing algorithm flowcharts and market analysis