Connor Lawless

I am a fifth year PhD candidate at Cornell studying Operations Research and Information Engineering, where I am fortunate enough to be advised by Oktay Gunluk .

My work leverages tools from computational integer programming to build scalable algorithms for interpretable and fair machine learning. Prior to joining Cornell, I completed my undergraduate at the University of Toronto studying industrial engineering and spent a year working at the Royal Bank of Canada building deep reinforcement learning based trade execution algorithms. I was also lucky enough to spend the summer of 2021 working remotely with the Applied AI group at IBM research on interpretable clustering, and the summer of 2023 working at Microsoft Research with the Human Understanding and Empathy Group and the Office of Applied Research on LLMs for Constraint Programming.

In addition to my research, I'm passionate about training the next generation of data scientists through programs such as iXperience where I've taught introductory practical data science tools in Python and R to students around the world. Outside of the office, you're most likely to find me hiking (19 national parks and counting).

I will be on the 2023-2024 job market!

Email  /  CV  /  Google Scholar

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Fair Minumum Representation Clustering
Connor Lawless, Oktay Gunluk

This paper considers the problem of performing k-means clustering while ensuring groups (e.g. demographic groups) have a minimum level of representation in a specified number of clusters. We show that the popular k-means algorithm (Lloyd's heuristic) can result in unfair outcomes where certain groups lack sufficient representation past the minimum threshold in a proportional number of clusters. We formulate the problem through a mixed-integer optimization framework and present a variant of Lloyd's algorithm, called MiniReL, that directly incorporates the fairness constraints.

Enabling Interactive Decision Support via Large Language Models and Constraint Programming
Connor Lawless, Jakob Schoeffer , Lindy Le, Kael Rowan , Shilad Sen , Jina Suh, Bahar Sarrafzadeh
Under review

Mathematical Programming has been a transformative technology for improving decision making in a number of industrial applications but its impact has been largely realized by large institutions that can afford the optimization expertise. We present a novel framework for interactive decision support for non-expert users that leverages large language models (LLM) to translate natural language requests into operations on an underlying constraint programming model. We investigate this framework through the lens of meeting scheduling, and showcase its potential via a user study with a prototype system. Public preprint coming soon (pending Microsoft Patent Office sign-off)!

Interpretable and Fair Boolean Rule Sets via Column Generation
Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei
Journal of Machine Learning Research

This paper considers the learning of Boolean rules in disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. We also consider the fairness setting and extend the formulation to include explicit constraints on two different measures of classification parity: equality of opportunity and equalized odds. Check out a short presenation on this paper at the Machine Learning NeEDS Mathematical Optimization Seminar.

Cluster Explanation via Polyhedral Description
Connor Lawless, Oktay Gunluk
ICML 2023

We extend the idea of using polyhedra to define clusters to the cluster description setting where cluster assignments are fixed and the goal is to explain them as simply as possible. We model the problem as an exponential-sized IP which we use column generation to solve, and introduce a novel grouping scheme to help our approach tackle large datasets.

Interpretable Clustering via Multi-Polytope Machines
Connor Lawless, Jayant Kalagnanam, Lam Nguyen, Dzung T. Phan, Chandra Reddy
AAAI 2022

We propose a novel approach for interpretable clustering that both clusters data points and constructs polyhedra around the discovered clusters to explain them. Our framework allows for additional constraints on the polyhedra including ensuring that the hyperplanes constructing the polyhedra are axis-parallel or sparse with integer coefficients.

A Two-Stage Approach to Routing with Driver Preferences via Heatmaps
Connor Lawless, Sotiris Ntanavaras, Anders Wikum
Proceedings of the Amazon-MIT Last Mile Routing Challenge

As part of the Amazon-MIT last mile routing challenge we presented a novel hierarchical approach to constructing vehicle routes based on historical driver preferences.

Fair Decision Rules for Binary Classification
Connor Lawless, Oktay Gunluk
NeurIPS Workshop on Optimization for Machine Learning 2020
AI for Social Good Workshop, IJCAI 2021

In this work we explore learning boolean rule sets with constraints on group fairness and rule set complexity. Our algorithm FairCG is able to achieve comparable performance to other interpretable models with stronger fairness guarantees.

COVID-19 Class Scheduling
Connor Lawless, Oktay Gunluk, David Shmoys, David Williamson, Brenda Dietrich, Anders Wikum, Henry Robbins, Shijin Rajakrishnan, Matthew Zalesak, Varun Suriyanarayana, Sotiris Ntanavaras, Vidhisha Nakhwa, Frank Chi, Brian Liu, Sam Shvets (and many more!)
Cornell University

As part of Cornell University's re-opening efforts during the COVID-19 pandemic the ORIE department led an effort to re-optmize Cornell's class schedule and room assignments for social distancing and a hybrid education model. I led the implementation of the primary optimization models for both class scheduling and room allocation.

Trade Platform with Reinforcement Learning
Hasham Burhani, David Shi, Connor Lawless
US Patent Application, 2019

Work done while at RBC Capital Markets. This patent outlines our team's approach for building a production grade deep reinforcmenet learning algorithm for trade execution (i.e. buying/selling large quantities of shares with low market impact). Check out our feature on Bloomberg !

Introduction to Data Science and Data Analytics
Remote, 2020-2022

I've been fortunate enough to spend the last few years teaching undergraduate students across 5 continents the fundamentals of data science in Python. Topics included pandas and numpy, machine learning with scikit-learn, deep learning in keras, and data visualization. As part of the program I also supervised 30+ remote data science projects in industries ranging from fintech to social media marketing.

ORIE5270 Big Data Technologies
Cornell University
Ithaca NY, Spring 2023

This course offers a broad overview of computational techniques and mathematical skills useful for data scientists. Topics include: unix shell, regular expressions, version control: (git), data structures and algorithms, working with databases, data analysis using Python and related libraries (Pandas, NumPy/Scipy, scikit-learn), parallel computing (Map-Reduce, Spark, Hadoop), and an overview of standard machine learning and optimization algorithms.

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