Jan Philip Göpfert

AI Consultant & Entrepreneur

Transforming complex AI concepts into real-world solutions. Let's innovate together.

About Me

As an AI consultant and entrepreneur with a PhD in Machine Learning, I bridge the gap between cutting-edge research and practical industry applications.

With a decade of experience in delivering practical solutions that maximize ROI, I help businesses leverage the full potential of artificial intelligence. My expertise as a full stack data engineer and developer allows me to assist my clients in all stages of the AI development lifecycle - from project scoping and data collection to model training and deployment.

Years of experience with AI initiatives at multi-billion dollar companies as well as startups allow me to offer unique insights and strategies in AI consulting. I help businesses set their AI initiatives up for success while addressing crucial aspects such as robustness and explainability.

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Areas of Expertise

AI Strategy & Consulting

Developing AI strategies aligned with business goals, offering guidance on implementation, ethics, and best practices in AI adoption.

Data Engineering

Implementing future-ready data pipelines and automations to collect, process and store data for AI applications.

Business Intelligence and Visualization

Transforming complex data into actionable insights using advanced tools and techniques in data science and visualization.

AI System Architecture

Designing scalable and robust AI architectures, integrating machine learning models into production environments.

Teaching & Workshop Facilitation

Teaching advanced topics in AI, machine learning, and computer science during workshops or invited talks.

Machine Learning Research

Investigating robustness, adversarial perturbations, and interpretability of AI systems, with a focus on deep learning and neural networks.

Project Highlights

E-commerce Search

Multi-modal semantic search for e-commerce

Developed AI-driven, multimodal semantic search for e-commerce using Contrastive Embeddings, Transformers, FastAPI, SQLAlchemy, and Python. The architecture performed so well it led to the development of an extended version after months of robust operation.

Model Training Speedup

Revamped training pipeline for 200x speedup

Optimized a training pipeline for fine-tuning contrastive loss models, achieving a 200x speedup through improved data flow and parallel computing, enabling faster iteration and higher model performance.

Community Engagement

Community engagement assistants for content creators

Created assistants for content creators to enhance community engagement, using Graph APIs, Firebase, Serverless, NoSQL, Vue.js, Python, and JavaScript. Early adopters reported significant relief in managing community interactions.

LLMs for Pharmacology

AI applications in pharmacology research

Presented opportunities for applying Large Language Models and Generative AI in pharmacology at a PHUSE event, including using LLMs with RAG to analyze vast databases of Clinical Trial Applications (CTAs).

E-commerce Recommendations

Visual-based recommendations for fashion e-commerce

Developed a smart visual-based recommendation system for fashion e-commerce using distributed computing, Vision Transformers, Large Language Models, Semantic Segmentation, and Python.

Lead Conversion Prediction

Predict lead conversion with customer journey analysis

Analyzed digital customer journeys to predict lead conversion and optimize marketing strategies, providing insights for data-driven decision making in sales and marketing.

Digitization Pipeline

Automated digitization pipeline for scanned documents

Automated the cleaning, analysis, and information extraction from scanned documents using morphological image processing, neural networks for character recognition, and LLMs. Achieved orders of magnitude improvement in scanning, archiving, and accessing information.

Selected Publications

Robustness in Machine Learning: Adversarial Perturbations, Explanations & Intuition

Jan Philip Göpfert

2022 | Bielefeld University

PhD thesis on adversarial robustness, explanations, and intuition in machine learning.

PDF

Deep Learning for Understanding Satellite Imagery: An Experimental Survey

Sharada Prasanna Mohanty, Jakub Czakon, Kamil A. Kaczmarek, ..., Jan Philip Göpfert, …

2020 | Frontiers in Artificial Intelligence

We explore automated satellite image analysis using deep learning and present five approaches based on U-Net and Mask R-CNN models, achieving impressive results in building detection.

PDF DOI

Intuitiveness in Active Teaching

Jan Philip Göpfert, Ulrike Kuhl, Lukas Hindemith

2020 | IEEE Transactions on Human-Machine Systems

We propose intuitiveness as a property of machine learning algorithms, largely impacting how easy it is for users to interact with a given algorithm without any explicit instruction or training.

arXiv

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