Publications

JANOS: An Integrated Predictive and Prescriptive Modeling Framework
David Bergman
Teng Huang
Philip Brooks
Andrea Lodi
Arvind U. Raghunathan
Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling… (see more) framework JANOS that seamlessly integrates the two streams of analytics, allowing researchers and practitioners to embed machine learning models in an end-to-end optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables: regular and predicted. The relationship between the regular and the predicted variables is specified by the user as pretrained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation. Summary of Contribution. This paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.
2022-03-01
INFORMS Journal on Computing (published)
doi.org
arxiv.org
Multistep networks for roll force prediction in hot strip rolling mill
Shuh-Rong Shen
Denzel Guye
Xiaoping Ma
S. Yue
Narges Armanfard
2022-03-01
Machine Learning with Applications (published)
doi.org
Multistep networks for roll force prediction in hot strip rolling mill
Shuh-Rong Shen
Denzel Guye
Xiaoping Ma
S. Yue
Narges Armanfard
2022-03-01
Machine Learning with Applications (published)
doi.org
Single Allocation Hub Location with Heterogeneous Economies of Scale
Borzou Rostami
Masoud Chitsaz
Okan Arslan
Gilbert Laporte
Andrea Lodi
2022-03-01
Operations Research (published)
doi.org
Software-Engineering Design Patterns for Machine Learning Applications
Hironori Washizaki
Foutse Khomh
Yann‐Gaël Guéhéneuc
Hironori Takeuchi
Naotake Natori
Takuo Doi
Satoshi Okuda
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (see more) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
2022-03-01
Computer (published)
doi.org
Software-Engineering Design Patterns for Machine Learning Applications
Hironori Washizaki
Foutse Khomh
Yann‐Gaël Guéhéneuc
Hironori Takeuchi
Naotake Natori
Takuo Doi
Satoshi Okuda
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings… (see more) suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
2022-03-01
Computer (published)
doi.org
The Role of Robotics in Achieving the United Nations Sustainable Development Goals - The Experts' Meeting at the 2021 IEEE/RSJ IROS Workshop [Industry Activities]
Vincent Mai
Bram Vanderborght
Tamás Haidegger
Alaa Khamis
Niraj Bhargava
Dominik B.O. Boesl
Katleen Gabriels
An Jacobs
AJung Moon
Robin Murphy
Yasushi Nakauchi
Edson Prestes
Bhavani Rao R.
Ricardo Vinuesa
Carl-Maria Mörch
The development and deployment of robotic technologies can have an important role in efforts to achieve the United Nations’ (UN) Sustainab… (see more)le Development Goals (SDGs)—with both enabling and inhibiting impacts. During a workshop at the 2021 IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems (IROS 2021), experts from various disciplines analyzed the role of robotics in achieving the SDGs. This article provides a summary of the most important outcomes of the workshop. During the workshop panels, the variety of roles that robots can play in enabling the SDGs was underlined. The panelists discussed the challenges to the adoption of robots and to their deployment at their full potential. The probable undesirable effects of robots were also considered, and the panelists suggested approaches to correctly design SDG-relevant robotic solutions. Governance frameworks were also discussed, with respect to their contents as well as the challenges to build them. The role of military funding was briefly analyzed. Finally, several proposals for actions and policies were made. The contents of the workshop, including contributing papers and videos from the panelists, as well as additional information about future initiatives regarding robotics and the SDGs, are available at www.sustainablerobotics.org.
2022-03-01
IEEE Robotics Autom. Mag. (published)
doi.org
The Role of Robotics in Achieving the United Nations Sustainable Development Goals - The Experts' Meeting at the 2021 IEEE/RSJ IROS Workshop [Industry Activities]
Vincent Mai
Bram Vanderborght
Tamás P. Haidegger
Alaa M. Khamis
Niraj Bhargava
Dominik B. O. Boesl
K. Gabriels
An Jacobs
AJung Moon
R. Murphy
Yasushi Nakauchi
Edson Prestes
Bhavani Rao R.
Ricardo Vinuesa
Carl-Maria Mörch
2022-03-01
IEEE Robotics Autom. Mag. (published)
doi.org
Combining Modular Skills in Multitask Learning
Edoardo Ponti
Alessandro Sordoni
Siva Reddy
2022-02-28
ArXiv (preprint)
arxiv.org
arxiv.org
Multiscale PHATE identifies multimodal signatures of COVID-19
Manik Kuchroo
Je-chun Huang
Patrick Wong
Jean-Christophe Grenier
Dennis Shung
Alexander Tong
Carolina Lucas
Jon Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Bastian Rieck
Benjamin Israelow
Michael Simonov
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
Camila D. Odio
Arnau Casanovas-Massana … (see 10 more)
John Fournier
Shelli Farhadian
Charles S. Dela Cruz
Albert I. Ko
Matthew Hirn
F. Perry Wilson
Julie Hussin
Guy Wolf
Akiko Iwasaki
Smita Krishnaswamy
2022-02-28
Nature Biotechnology (published)
doi.org
Multiscale PHATE identifies multimodal signatures of COVID-19
Manik Kuchroo
Je-chun Huang
Patrick W. Wong
Jean-Christophe Grenier
Dennis L. Shung
Alexander Tong
C. Lucas
J. Klein
Daniel B. Burkhardt
Scott Gigante
Abhinav Godavarthi
Bastian Rieck
Benjamin Israelow
Michael Simonov
Tianyang Mao
Ji Eun Oh
Julio Silva
Takehiro Takahashi
C. Odio
Arnau Casanovas‐massana … (see 10 more)
John Byrne Fournier
Shelli F. Farhadian
C. D. Dela Cruz
A. Ko
Matthew Hirn
F. Wilson
Julie Hussin
Guy Wolf
Akiko Iwasaki
Smita Krishnaswamy
2022-02-28
Nature Biotechnology (published)
doi.org
Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Brennan Nichyporuk
Douglas Arnold
Tal Arbel
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (see more)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties).
2022-02-28
MIDL.io/2022/Conference (published)
doi.org
openreview.net