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.

Learning from Accountants: Human-in-the-Loop Systems Done Right (May 13, 2026)

TL;DR

The best approach to automated invoice coding is not full replacement of AP clerks, but human-in-the-loop systems where AI handles routine cases and human experts focus on complex ones. Effective systems should provide confidence scores and explanations for their predictions so reviewers can quickly decide whether to accept or correct them. The interaction between human and machine creates a powerful feedback loop—both implicit (e.g., corrections and review behavior) and explicit (e.g., structured questions)—that enables continuous improvement of the AI over time. Striking the right balance in learning speed is key: too fast risks overfitting to outliers, too slow frustrates users. Over time, the share of invoices needing significant human intervention should shrink, dramatically reducing manual labor while preserving accuracy and control over financial records.

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