Connor Makowski
Research Associate MIT Center for Transportation & Logistics
- Three Rivers MI
Connor Makowski specializes in algorithms and coding for simulation, optimization, AI, and visualization.
MIT Center for Transportation & Logistics
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Biography
In the CAVE Lab, Connor leads multiple small teams to develop interactive web applications for corporate partners. These applications range in function from helping companies work on new corporate strategies all the way down to determining warehouses that should be open and how to route delivery trucks. Connor also manages day-to-day activities in the CAVE lab and the CAVE server farm.
For the MicroMasters, Connor researches user patterns, develops applications and implements them into the learning environment. These applications include simple tools that engage students to advanced machine learning algorithms that ensure academic honesty and prevent students from dropping out. His team also automates redundant processes for the course staff.
Outside of development, Connor has instructed short courses on Artificial Intelligence, Blockchain, Visualization, Statistics and Supply Chain Leadership at MIT. He has also helped to develop and teach content in optimization, statistics, regression, database systems, SQL and Machine Learning as part of the MITx courses.
Areas of Expertise
Education
Zaragoza Logistics Center
MEng
Supply Chain Management
2016
Western Michigan University
BBA
Actuarial Science and Business Management
Languages
- English
Media Appearances
After only online collaborations, four MicroMasters students meet in person for the first time — and go on to win a worldwide competition.
MIT News online
2017-10-06
"While other teams were arriving hours early, the MITx students were very relaxed, enjoying breakfast,” says Connor Makowski, digital learning lead for CTL’s MicroMasters courses in supply chain management, who attended the competition. “Their focus was not on winning the competition, but on building a good strategy, performing well and having fun.”
Research Papers
Type Enforced: A Python type enforcer for type annotations
Journal of Open Source SoftwareConnor Makowski, Willem Guter, Timothy Russell
2025-09-30
Python’s dynamic typing system offers flexibility but can lead to runtime errors that are difficult to diagnose in web applications, complex scientific software, and research applications. Static type checking tools such as Mypy provide valuable compile-time validation; however, they do not prevent runtime type errors. Existing runtime enforcement libraries often require extensive boilerplate code or lack support for advanced typing features and nested structures.
SCGraph: A Dependency-Free Python Package for Road, Rail, and Maritime Shortest Path Routing Generation and Distance Estimation
MIT Center for Transportation & Logistics Research PaperConnor Makowski, Austin Saragih, Willem Guter, Timothy Russell, Arne Heinold, Spyridon Lekkakos
2025-08-11
SCGraph (Supply Chain Graph) is a dependency-free Python toolkit that provides fully offline, accurate multimodal routing and network-based distance estimation across global road, rail, and maritime networks. SCGraph leverages optimized adjacency list data structures and customized implementations of Dijkstra and A* algorithms, specifically designed for sparse and large-scale transportation graphs. Accompanying SCGraph, SCGraph Data delivers global geospatial graphs that include worldwide highways, railways, and maritime networks. Based on our numerical results, SCGraph calculates the shortest path distances 10 5× faster than other Python toolkits such as OSMnx with NetworkX and 86× faster than Google Maps API, while remaining within 4% of Google Maps distances.
Bmsspy: A python package and empirical comparison of bounded multi-source shortest path algorithm
MIT Center for Transportation & Logistics Research PaperConnor Makowski, Willem Guter, Timothy Russell, Austin Saragih, Lucas Castro
2025-03-04
We introduce BMSSPy, the first open-source Python package faithfully implementing the Bounded Multi-Source Shortest Path (BMSSP) algorithm, recently proposed as a breakthrough beyond Dijkstra's sorting barrier for single-source shortest paths. BMSSPy translates theoretical advances into practice: it preserves the algorithm's asymptotic performance guarantees while returning both distance labels and route predecessors. Our implementation resolves core ambiguities in pivot selection, tight-edge forest construction, and frontier management that prior work left open for practitioners.
Awards
MIT Prize for Open Data
2025
