Artificial intelligence · Data mining · Optimization

Research contributions from methods to applications

My publications span artificial intelligence, machine learning, data mining, optimization, and applied data science, with an emphasis on reproducible methods and open-source software.

Optimization

[1]	Michael Hahsler and Anthony R. Cassandra. Pomdp: A computational infrastructure for partially observable Markov decision processes. R Journal, 16:116--133, 2025. [ DOI ] Many important problems involve decision-making under uncertainty. For example, a medical professional needs to make decisions about the best treatment option based on limited information about the current state of the patient and uncertainty about outcomes. Different approaches have been developed by the applied mathematics, operations research, and artificial intelligence communities to address this difficult class of decision-making problems. This paper presents the pomdp package, which provides a computational infrastructure for an approach called the partially observable Markov decision process (POMDP), which models the problem as a discrete-time stochastic control process. The package lets the user specify POMDPs using familiar R syntax, apply state-of-the-art POMDP solvers, and then take full advantage of R's range of capabilities, including statistical analysis, simulation, and visualization, to work with the resulting models.
[2]	Farzad Kamalzadeh, Vishal Ahuja, Michael Hahsler, and Michael E. Bowen. An analytics-driven approach for optimal individualized diabetes screening. Production and Operations Management, 30(9):3161--3191, September 2021. [ DOI | preprint (PDF) | at the publisher ] Type 2 diabetes is a chronic disease that affects millions of Americans and puts a significant burden on the healthcare system. The medical community sees screening patients to identify and treat prediabetes and diabetes early as an important goal; however, universal population screening is operationally not feasible, and screening policies need to take characteristics of the patient population into account. For instance, the screening policy for a population in an affluent neighborhood may differ from that of a safety-net hospital. The problem of optimal diabetes screening—whom to screen and when to screen—is clearly important, and small improvements could have an enormous impact. However, the problem is typically only discussed from a practical viewpoint in the medical literature; a thorough theoretical framework from an operational viewpoint is largely missing. In this study, we propose an approach that builds on multiple methods—partially observable Markov decision process (POMDP), hidden Markov model (HMM), and predictive risk modeling (PRM). It uses available clinical information, in the form of electronic health records (EHRs), on specific patient populations to derive an optimal policy, which is used to generate screening decisions, individualized for each patient. The POMDP model, used for determining optimal decisions, lies at the core of our approach. We use HMM to estimate the cohort-specific progression of diabetes (i.e., transition probability matrix) and the emission matrix. We use PRM to generate observations—in the form of individualized risk scores—for the POMDP. Both HMM and PRM are learned from EHR data. Our approach is unique because (i) it introduces a novel way of incorporating predictive modeling into a formal decision framework to derive an optimal screening policy; and (ii) it is based on real clinical data. We fit our model using data on a cohort of more than 60,000 patients over 5 years from a large safety-net health system and then demonstrate the model’s utility by conducting a simulation study. The results indicate that our proposed screening policy outperforms existing guidelines widely used in clinical practice. Our estimates suggest that implementing our policy for the studied cohort would add one quality-adjusted life year for every patient, and at a cost that is 35% lower, compared with existing guidelines. Our proposed framework is generalizable to other chronic diseases, such as cancer and HIV.
[3]	Michael Hahsler, Matthew Piekenbrock, and Derek Doran. dbscan: Fast density-based clustering with R. Journal of Statistical Software, 91(1):1--30, 2019. [ DOI ] This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Package dbscan uses advanced open-source spatial indexing data structures built in C++ to speed up computation. An important advantage of this implementation is that it is up-to-date with several primary advancements that have been added since their original publications, including artifact corrections and dendrogram extraction methods for OPTICS. We provide a consistent presentation of the DBSCAN and OPTICS algorithms, and compare dbscan’s implemen- tation of DBSCAN and OPTICS with implementations in other popular libraries such as the R package fpc, ELKI, WEKA, PyClustering, SciKit-Learn, and SPMF in terms of available features and using an experimental comparison.
[4]	Michael Hahsler. An experimental comparison of seriation methods for one-mode two-way data. European Journal of Operational Research, 257:133--143, February 2017. [ DOI | preprint (PDF) ] Seriation aims at finding a linear order for a set of objects to reveal structural information which can be used for deriving data-driven decisions. It presents a difficult combinatorial optimization problem with its roots and applications in many fields including operations research. This paper focuses on a popular seriation problem which tries to find an order for a single set of objects that optimizes a given seriation criterion defined on one-mode two-way data, i.e., an object-by-object dissimilarity matrix. Over the years, members of different research communities have introduced many criteria and seriation methods for this problem. It is often not clear how different seriation criteria and methods relate to each other and which criterion or seriation method to use for a given application. These methods are represent tools for analytics and therefore are of theoretical and practical interest to the operations research community. The purpose of this paper is to provide a consistent overview of the most popular criteria and seriation methods and to present a comprehensive experimental study to compare their performance using artificial and a representative set of real-world datasets.
[5]	Michael Hahsler and Kurt Hornik. Dissimilarity plots: A visual exploration tool for partitional clustering. Journal of Computational and Graphical Statistics, 10(2):335--354, June 2011. [ DOI | preprint (PDF) ] For hierarchical clustering, dendrograms are a convenient and powerful visualization technique. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this paper we extend (dissimilarity) matrix shading with several reordering steps based on seriation techniques. Both ideas, matrix shading and reordering, have been well-known for a long time. However, only recent algorithmic improvements allow us to solve or approximately solve the seriation problem efficiently for larger problems. Furthermore, seriation techniques are used in a novel stepwise process (within each cluster and between clusters) which leads to a visualization technique that is able to present the structure between clusters and the micro-structure within clusters in one concise plot. This not only allows us to judge cluster quality but also makes mis-specification of the number of clusters apparent. We give a detailed discussion of the construction of dissimilarity plots and demonstrate their usefulness with several examples. Experiments show that dissimilarity plots scale very well with increasing data dimensionality.
[6]	Michael Hahsler, Kurt Hornik, and Christian Buchta. Getting things in order: An introduction to the R package seriation. Journal of Statistical Software, 25(3):1--34, March 2008. [ DOI | at the publisher ] Seriation, i.e., finding a linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides the infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.
[7]	Michael Hahsler and Kurt Hornik. TSP -- Infrastructure for the traveling salesperson problem. Journal of Statistical Software, 23(2):1--21, December 2007. [ DOI | at the publisher ] The traveling salesperson (or, salesman) problem (TSP) is a well known and important combinatorial optimization problem. The goal is to find the shortest tour that visits each city in a given list exactly once and then returns to the starting city. Despite this simple problem statement, solving the TSP is difficult since it belongs to the class of NP-complete problems. The importance of the TSP arises besides from its theoretical appeal from the variety of its applications. Typical applications in operations research include vehicle routing, computer wiring, cutting wallpaper and job sequencing. The main application in statistics is combinatorial data analysis, e.g., reordering rows and columns of data matrices or identifying clusters. In this paper we introduce the R package TSP which provides a basic infrastructure for handling and solving the traveling salesperson problem. The package features S3 classes for specifying a TSP and its (possibly optimal) solution as well as several heuristics to find good solutions. In addition, it provides an interface to Concorde, one of the best exact TSP solvers currently available.
[8]	Christoph Breidert and Michael Hahsler. Adaptive conjoint analysis for pricing music downloads. In R. Decker and H.-J. Lenz, editors, Advances in Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pages 409--416. Springer-Verlag, 2007. [ DOI | preprint (PDF) ] Finding the right pricing for music downloads is of ample importance to the recording industry and music download service providers. For the recently introduced music downloads, reference prices are still developing and to find a revenue maximizing pricing scheme is a challenging task. The most commonly used approach is to employ linear pricing (e.g., iTunes, musicload). Lately, subscription models have emerged, offering their customers unlimited access to streaming music for a monthly fee (e.g., Napster, RealNetworks). However, other pricing strategies could also be used, such as quantity rebates starting at certain download volumes. Research has been done in this field and Buxmann et al. (2005) have shown that price cuts can improve revenue. In this paper we apply different approaches to estimate consumer's willingness to pay (WTP) for music downloads and compare our findings with the pricing strategies currently used in the market. To make informed decisions about pricing, knowledge about the consumer's WTP is essential. Three approaches based on adaptive conjoint analysis to estimate the WTP for bundles of music downloads are compared. Two of the approaches are based on a status-quo product (at market price and alternatively at an individually self-stated price), the third approach uses a linear model assuming a fixed utility per title. All three methods seem to be robust and deliver reasonable estimations of the respondent's WTPs. However, all but the linear model need an externally set price for the status-quo product which can introduce a bias.
[9]	Christoph Breidert, Michael Hahsler, and Lars Schmidt-Thieme. Reservation price estimation by adaptive conjoint analysis. In Claus Weihs and Wolfgang Gaul, editors, Classification - the Ubiquitous Challenge, Studies in Classification, Data Analysis, and Knowledge Organization, pages 577--584. Springer-Verlag, 2005. [ preprint (PDF) | at the publisher ] Though reservation prices are needed for many business decision processes, e.g., pricing new products, it often turns out to be difficult to measure them. Many researchers reuse conjoint analysis data with price as an attribute for this task (e.g., Kohli and Mahajan (1991)). In this setting the information if a consumer buys a product at all is not elicited which makes reservation price estimation impossible. We propose an additional interview scene at the end of the adaptive conjoint analysis (Johnson (1987)) to estimate reservation prices for all product configurations. This will be achieved by the usage of product stimuli as well as price scales that are adapted for each proband to reflect individual choice behavior. We present preliminary results from an ongoing large-sample conjoint interview of customers of a major mobile phone retailer in Germany.