Patryk Laurent

Building edge AI systems to convert video and IoT data into actions that reduce cost, improve comfort, and increase safety and security. * Directing AI/ML and Data Management initiatives * Enabling customers to derive actionable insights from onsite multimodal analytics via ML models trained on their own data — including large language models (LLMs) * Optimizing Edge-versus-Cloud AI infrastructure for cost and accuracy while preserving data security and privacy.

Advising on orchestrating machine learning and data infrastructure — MLOps, analytics, and human-in-the loop feedback/UX.

Providing technical guidance, leadership, and rapid application development to organizations needing assistance in machine learning, data science, and ML Ops (the tracking and improvement of machine learning models).

Advising and collaborating on edge AI, ML Ops, and advanced algorithm development. Serving as fractional Advisor, Chief Technology Officer, Chief Data Officer, or VP of AI/Machine Learning/Data Science.

Created a Center of Excellence for Data Science and significantly increased value/exit multipliers of DMGT portfolio companies by improving their data science and technology organizations. Contributed rapid prototyping, depth of ML knowledge, and my ability to remove impediments. Implemented, consulted on, and supervised data science across DMGT portfolio companies, sharing knowledge and best practice. Built ML models to test hypotheses on the value of business proprietary data. Established automated data/ML pipelines. Accelerated sales and product organization by implementing authorization systems to rapidly bundle new SKUs with minimal involvement of the technology organizations.

Improved loan default model performance using machine learning and larger datasets. Identified new product opportunities leveraging Trepp’s data lake. Implemented models using numerical and textual data in CRE and CMBS. Began growing data science team, including hiring VP of data science.

Implemented models and supervised data scientists and engineers getting models into production. Models processed real-time data from a multitude of sensor types and data sources. Grew the team as analysts transitioned into full time data science roles.

Led a team of data scientists and engineers in applying AI/machine learning techniques to real-time physiological data. Developed pipeline for rapid iteration of ML features and stakeholder feedback.

Implemented and deployed AI algorithms on an ecosystem of Android smartphones and televisions for novel gestural+voice UIs. Leveraged on-device computer vision models (TensorFlow, OpenCV).

Implemented and directed the implementation of machine learning and software for robots, IoT systems. Integrated off-the-shelf algorithms with newly developed custom algorithms. Enlisted scientists in creating new algorithms. Actively researched how to improve perception and learning for mobile robots and stationary camera-based IoT systems. Tested and evaluated AI in real-world embodiments.

Applied reinforcement learning models and neural networks to understand mechanisms of reward attentional capture. Analyzed large datasets of human behavior and fMRI data. Supervised graduate students.

Computational neuroscience research into the reinforcement learning of cognitive operations. Also used neuroimaging techniques. Supervised and advised researchers.

Developed low-level communications software and high-level analysis tools for the power industry.