Mirek Riedewald

Professor of Computer Science

As a professor in the Khoury College of Computer Sciences at Northeastern University in Boston, MA, I perform cutting-edge research, teach advanced computer-science courses, and serve in various leadership roles in both my institution and my research community. I enjoy collaborative research with scientists and I am always looking for new opportunities to apply my expertise in scalable data management and analysis to new domains in both academia and industry. This includes consulting and hands-on implementation of AI techniques for startups and established companies, as well as expert testimony in legal cases.

Research interests and expertise:

Databases
Design and implementation of algorithms and systems that scale in the size, arrival speed, and complexity of data
Distributed big-data management and analysis in the cloud
Data-stream processing
Data-driven science
Solving real-world data problems using large language models (LLM), AI, and machine learning (ML)

Mirek Riedewald in front of a whiteboard full of scribbles from a research discussion or office hours.

Opinion Corner

Here I share my thoughts on various topics related to technology and education. Please note the following, especially if you disagree:

The opinions expressed here are my own and do not represent those of my employer or any other organization I am affiliated with.
Given the venue and space constraints, these posts are brief and may not fully capture the complexity of the topics discussed.
These posts are meant to stimulate thought and discussion; they may not reflect my most current views as I continue to learn.

I am discussing insights from a unique experience working with an innovative startup, PredictAP, that leverages AI to solve hard problems in the real-estate management domain. This collaboration began in the summer of 2021 with AI Jumpstart, a program initiated by visionaries in the Massachusetts government and at Northeastern University with the goal of bringing together AI-focused small Massachusetts businesses and faculty experts to create synergies and foster innovation. Since then, I have been deeply involved in this project, which included spending an entire year during my sabbatical as a member of the engineering team.

Depth Over Breadth: Why the Next Wave of AI Belongs to Domain Experts (June 23, 2026)

TL;DR

Modern AI's breadth—a single model that can write, summarize, code, and answer almost anything—started losing its role as a differentiator, since the best general models are converging and access is becoming universal. The new frontier is depth: solving domain-specific problems that may look easy from the outside but require expert knowledge to see "the part that is not written down." Invoice coding represents a good running example: seemingly dull data entry that is really a hard prediction-and-reasoning problem, closer to self-driving cars than OCR, because the knowledge that matters most almost never appears on the document or in public training data. The winning approach is not to discard the general model but to build domain-specific AI on top of it, specializing a foundation model's fluency through prompting, retrieval, fine-tuning, or agentic workflows so the domain expert can supply the depth that establishes the real competitive advantage. Rather than replacing experts, the right design keeps them in the loop, and the lasting value belongs to those who understand a domain well enough to see what the general model cannot.

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