• Research Areas
  • Special Projects
  • Sponsors and Funding

My research focuses on the development of novel optimization methods and the application of those methods to solve complex decision-making problems primarily in healthcare and sports.

Inverse Optimization

The goal of inverse optimization is to “reverse engineer” parameters of an optimization model that make a given, observed decision optimal. If it is not possible to make the decision exactly optimal, e.g., the data is noisy or the model is an approximation, then a measure of suboptimality is typically minimized. Viewed through the lens of model fitting, my main interest is to develop new approaches for inverse optimization that optimize and measure data-model fit. Given the increasing amounts of data that are generated as the result of a decision process, I am also interested in finding innovative applications for inverse optimization.

RELEVANT PUBLICATIONS

19.

An ensemble learning framework for model fitting and evaluation in inverse linear optimization Journal Article Forthcoming

A. Babier, T. C. Y. Chan, T. Lee, R. Mahmood, D. Terekhov

INFORMS Journal on Optimization, Forthcoming.

18.

Inverse mixed integer optimization: Certificate sets and trust region methods Miscellaneous

M. Bodur, T. C. Y. Chan, I. Zhu

under review at Operations Research, 2020.

17.

An inverse optimization approach to measuring clinical pathway concordance Miscellaneous

T. C. Y. Chan, M. Eberg, K. Forster, C. Holloway, L. Ieraci, Y. Shalaby, N. Yousefi

under second review at Management Science, 2020.

16.

Spatial price integration in competitive markets with capacitated transportation networks Miscellaneous

J. R. Birge, T. C. Y. Chan, M. Pavlin, I. Zhu

under revision for Operations Research, 2020.

15.

The importance of evaluating the complete automated knowledge-based planning pipeline Journal Article

A. Babier, R. Mahmood, A. L. McNiven, A. Diamant, T. C. Y. Chan

Physica Medica, Vol. 72, pp. 73-79, 2020.

14.

Knowledge-based automated planning with three-dimensional generative adversarial networks Journal Article

A. Babier, R. Mahmood, A. L. McNiven, A. Diamant, T. C. Y. Chan

Medical Physics, Vol. 47, pp. 297-306, 2020.

13.

Inverse optimization for the recovery of constraint parameters Journal Article

T. C. Y. Chan, N. Kaw

European Journal of Operational Research, Vol. 282, pp. 415-427, 2020.

12.

Inverse optimization: Closed-form solutions, geometry, and goodness of fit Journal Article

T. C. Y. Chan, T. Lee, D. Terekhov

Management Science, Vol. 65, pp. 1115-1135, 2019.

11.

The importance of evaluating the complete knowledge-based automated planning pipeline Inproceedings

A. Babier, R. Mahmood, A. Diamant, A. McNiven, T. C. Y. Chan

Proceedings of the International Conference on the use of Computers in Radiation Therapy, 2019.

10.

A small number of objective function weight vectors is sufficient for automated treatment planning in prostate cancer Journal Article

A. Goli, J. J. Boutilier, M. B. Sharpe, T. Craig, T. C. Y. Chan

Physics in Medicine and Biology, Vol. 63(Article No. 195004), 2018.

9.

Inverse optimization of objective function weights for treatment planning using clinical dose-volume histograms Journal Article

A. Babier, J. J. Boutilier, M. B. Sharpe, A. L. McNiven, T. C. Y. Chan

Physics in Medicine and Biology, Vol. 63(Article No. 105004), 2018.

8.

Knowledge-based automated planning for oropharyngeal cancer Journal Article

A. Babier, J. J. Boutilier, A. L. McNiven, T. C. Y. Chan

Medical Physics, Vol. 45, pp. 2875-2883, 2018.

7.

Trade-off preservation in inverse multi-objective convex optimization Journal Article

T. C. Y. Chan, T. Lee

European Journal of Operational Research, Vol. 270, pp. 25-39, 2018.

6.

Automated treatment planning in radiation therapy using generative adversarial networks Inproceedings

R. Mahmood, A. Babier, A. McNiven, A. Diamant, T. C. Y. Chan

Proceedings of the 3rd Machine Learning for Healthcare Conference, pp. 484-499, PMLR, 2018.

5.

Models for predicting objective function weights in prostate cancer IMRT Journal Article

J. J. Boutilier, T. Lee, T. Craig, M. B. Sharpe, T. C. Y. Chan

Medical Physics, Vol. 42, pp. 1586-1595, 2015.

4.

Generalized inverse multi-objective optimization with application to cancer therapy Journal Article

T. C. Y. Chan, T. Craig, T. Lee, M. B. Sharpe

Operations Research, Vol. 62, pp. 680-695, 2014.

3.

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning Journal Article

T. Lee, M. Hummad, T. C. Y. Chan, T. Craig, M. B. Sharpe

Medical Physics, Vol. 40(Article No. 121706), 2013.

2.

Examining the LEED rating system using inverse optimization Journal Article

S. D. O. Turner, T. C. Y. Chan

Journal of Solar Energy Engineering, Vol. 135(Article No. 040901), 2013.

1.

Examining the LEED rating system using approximate inverse optimization Inproceedings

S. D. O. Turner, T. C. Y. Chan

Proceedings of the ASME 2012 International Mechanical Engineering Congress and Exposition, 2012.

Robust Optimization

Robust optimization is used to optimize in the face of uncertainty. I am interested in theory, modeling, solution methods, and applications of robust optimization. The majority of my work to date has been in robust optimization, especially applied to radiation therapy treatment planning.

RELEVANT PUBLICATIONS

22.

Ambulance Emergency Response Optimization in developing countries Journal Article

J. J. Boutilier, T. C. Y. Chan

Operations Research, Vol. 68, pp. 1315-1334, 2020.

21.

Process flexibility in baseball: The value of positional flexibility Journal Article

T. C. Y. Chan, D. Fearing

Management Science, Vol. 65, pp. 1642-1666, 2019.

20.

Robust radiotherapy planning Journal Article

J. Unkelbach, M. Alber, M. Bangert, R. Bokrantz, T. C. Y. Chan, J. Deasy, A. Fredriksson, B. L. Gorissen, M. van Herk, W. Liu, H. Mahmoudzadeh, O. Nohadani, J. V. Siebers, M. Witte, H. Xu

Physics in Medicine and Biology, Vol. 63(Article No. 22TR02), 2018.

19.

Robust defibrillator deployment under cardiac arrest location uncertainty via row-and-column generation Journal Article

T. C. Y. Chan, Z.-J. Shen, A. Siddiq

Operations Research, Vol. 66, pp. 358-379, 2018.

18.

Stability and continuity in robust optimization Journal Article

T. C. Y. Chan, P. A. Mar

SIAM Journal on Optimization, Vol. 27, pp. 817-841, 2017.

17.

Robust optimization methods Book Chapter

T. C. Y. Chan, P. A. C. Mar

Terlaky, T; Anjos, M; Ahmed, S (Ed.): Advances and Trends in Optimization with Engineering Applications, Chapter 25, pp. 333-344, SIAM, Philadelphia, 2017.

16.

Robust wind farm layout optimization Book Chapter

P. Y. Zhang, J. Y. J. Kuo, D. Romero, T. C. Y. Chan, C. H. Amon

Terlaky, T; Anjos, M; Ahmed, S (Ed.): Advances and Trends in Optimization with Engineering Applications, Chapter 28, pp. 367-374, SIAM, Philadelphia, 2017.

15.

Constraint generation methods for robust optimization in radiation therapy Journal Article

H. Mahmoudzadeh, T. G. Purdie, T. C. Y. Chan

Operations Research for Health Care, Vol. 8, pp. 85-90, 2016.

14.

Robust PET-guided intensity-modulated radiation therapy Journal Article

H. Li, J. P. Bissonnette, T. Purdie, T. C. Y. Chan

Medical Physics, Vol. 42, pp. 4863-4871, 2015.

13.

The perils of adapting to dose errors in radiation therapy Journal Article

V. V. Mišić, T. C. Y. Chan

PLOS ONE, Vol. 10(Article No. e0125335), 2015.

12.

Adaptive and robust radiation therapy in the presence of drift Journal Article

P. A. Mar, T. C. Y. Chan

Physics in Medicine and Biology, Vol. 60, pp. 3599-3615, 2015.

11.

Robust optimization methods for cardiac sparing in tangential breast IMRT Journal Article

H. Mahmoudzadeh, J. Lee, T. C. Y. Chan, T. G. Purdie

Medical Physics, Vol. 42, pp. 2212-2222, 2015.

10.

A robust-CVaR optimization approach with application to breast cancer therapy Journal Article

T. C. Y. Chan, H. Mahmoudzadeh, T. G. Purdie

European Journal of Operational Research, Vol. 238, pp. 876-885, 2014.

9.

Adaptive and robust radiation therapy optimization for lung cancer Journal Article

T. C. Y. Chan, V. V. Mišić

European Journal of Operational Research, Vol. 231, pp. 745-756, 2013.

8.

The value of flexibility in baseball roster construction Inproceedings

T. C. Y. Chan, D. S. Fearing

Proceedings of the 7th Annual MIT Sloan Sports Analytics Conference, 2013.

7.

Motion-compensating intensity maps in intensity-modulated radiation therapy Journal Article

T. C. Y. Chan

IIE Transactions on Healthcare Systems Engineering, Vol. 3, pp. 1-22, 2013.

6.

Optimal margin and edge-enhanced intensity maps in the presence of motion and uncertainty Journal Article

T. C. Y. Chan, J. N. Tsitsiklis, T. Bortfeld

Physics in Medicine and Biology, Vol. 55, pp. 515-533, 2010.

5.

Experimental evaluation of a robust optimization method for IMRT of moving targets Journal Article

C. Vrančić, A. Trofimov, T. C. Y. Chan, G. C. Sharp, T. Bortfeld

Physics in Medicine and Biology, Vol. 54, pp. 2901-2914, 2009.

4.

Robust management of motion uncertainty in intensity modulated radiation therapy Journal Article

T. Bortfeld, T. C. Y. Chan, A. Trofimov, J. N. Tsitsiklis

Operations Research, Vol. 56, pp. 1461-1473, 2008.

3.

Optimization under uncertainty in radiation therapy PhD Thesis

T. C. Y. Chan

Sloan School of Management, MIT, 2007.

2.

Accounting for range uncertainties in the optimization of intensity modulated proton therapy Journal Article

J. Unkelbach, T. C. Y. Chan, T. Bortfeld

Physics in Medicine and Biology, Vol. 52, pp. 2755-2773, 2007.

1.

A robust approach to IMRT optimization Journal Article

T. C. Y. Chan, T. Bortfeld, J. N. Tsitsiklis

Physics in Medicine and Biology, Vol. 51, pp. 2567-2583, 2006.

Radiation Therapy

Radiation therapy is one of the primary techniques used to treat cancer. Beams of radiation are delivered from different angles around a patient, targeting a tumor at their intersection while aiming to spare nearby critical organs. I deploy a combination of robust optimization, inverse optimization, and machine learning to improve the quality of treatments and the efficiency of the treatment planning process.

RELEVANT PUBLICATIONS

35.

An ensemble learning framework for model fitting and evaluation in inverse linear optimization Journal Article Forthcoming

A. Babier, T. C. Y. Chan, T. Lee, R. Mahmood, D. Terekhov

INFORMS Journal on Optimization, Forthcoming.

34.

Sampling from the complement of a polyhedron: An MCMC algorithm for data augmentation Journal Article

T. C. Y. Chan, A. Diamant, R. Mahmood

Operations Research Letters, Vol. 48, pp. 744–751, 2020.

33.

The importance of evaluating the complete automated knowledge-based planning pipeline Journal Article

A. Babier, R. Mahmood, A. L. McNiven, A. Diamant, T. C. Y. Chan

Physica Medica, Vol. 72, pp. 73-79, 2020.

32.

Knowledge-based automated planning with three-dimensional generative adversarial networks Journal Article

A. Babier, R. Mahmood, A. L. McNiven, A. Diamant, T. C. Y. Chan

Medical Physics, Vol. 47, pp. 297-306, 2020.

31.

Sparse flexible design: a machine learning approach Miscellaneous

T. C. Y. Chan, D. Letourneau, B. Potter

under revision for Flexible Services and Manufacturing Journal, 2019.

30.

Inverse optimization: Closed-form solutions, geometry, and goodness of fit Journal Article

T. C. Y. Chan, T. Lee, D. Terekhov

Management Science, Vol. 65, pp. 1115-1135, 2019.

29.

The importance of evaluating the complete knowledge-based automated planning pipeline Inproceedings

A. Babier, R. Mahmood, A. Diamant, A. McNiven, T. C. Y. Chan

Proceedings of the International Conference on the use of Computers in Radiation Therapy, 2019.

28.

Robust radiotherapy planning Journal Article

J. Unkelbach, M. Alber, M. Bangert, R. Bokrantz, T. C. Y. Chan, J. Deasy, A. Fredriksson, B. L. Gorissen, M. van Herk, W. Liu, H. Mahmoudzadeh, O. Nohadani, J. V. Siebers, M. Witte, H. Xu

Physics in Medicine and Biology, Vol. 63(Article No. 22TR02), 2018.

27.

A small number of objective function weight vectors is sufficient for automated treatment planning in prostate cancer Journal Article

A. Goli, J. J. Boutilier, M. B. Sharpe, T. Craig, T. C. Y. Chan

Physics in Medicine and Biology, Vol. 63(Article No. 195004), 2018.

26.

Inverse optimization of objective function weights for treatment planning using clinical dose-volume histograms Journal Article

A. Babier, J. J. Boutilier, M. B. Sharpe, A. L. McNiven, T. C. Y. Chan

Physics in Medicine and Biology, Vol. 63(Article No. 105004), 2018.

25.

Knowledge-based automated planning for oropharyngeal cancer Journal Article

A. Babier, J. J. Boutilier, A. L. McNiven, T. C. Y. Chan

Medical Physics, Vol. 45, pp. 2875-2883, 2018.

24.

A mixed-integer optimization approach for homogeneous magnet design Journal Article

I. Dayarian, T. C. Y. Chan, D. Jaffray, T. Stanescu

Technology, Vol. 6, pp. 49-58, 2018.

23.

Trade-off preservation in inverse multi-objective convex optimization Journal Article

T. C. Y. Chan, T. Lee

European Journal of Operational Research, Vol. 270, pp. 25-39, 2018.

22.

Automated treatment planning in radiation therapy using generative adversarial networks Inproceedings

R. Mahmood, A. Babier, A. McNiven, A. Diamant, T. C. Y. Chan

Proceedings of the 3rd Machine Learning for Healthcare Conference, pp. 484-499, PMLR, 2018.

21.

Sample size requirements for knowledge-based treatment planning Journal Article

J. J. Boutilier, T. Craig, M. B. Sharpe, T. C. Y. Chan

Medical Physics, Vol. 43, pp. 1212-1221, 2016.

20.

Constraint generation methods for robust optimization in radiation therapy Journal Article

H. Mahmoudzadeh, T. G. Purdie, T. C. Y. Chan

Operations Research for Health Care, Vol. 8, pp. 85-90, 2016.

19.

The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT Journal Article

H. Li, N. Becker, S. Raman, T. C. Y. Chan, J.-P. Bissonnette

Medical Physics, Vol. 42, pp. 4727-4733, 2015.

18.

Robust PET-guided intensity-modulated radiation therapy Journal Article

H. Li, J. P. Bissonnette, T. Purdie, T. C. Y. Chan

Medical Physics, Vol. 42, pp. 4863-4871, 2015.

17.

The perils of adapting to dose errors in radiation therapy Journal Article

V. V. Mišić, T. C. Y. Chan

PLOS ONE, Vol. 10(Article No. e0125335), 2015.

16.

Adaptive and robust radiation therapy in the presence of drift Journal Article

P. A. Mar, T. C. Y. Chan

Physics in Medicine and Biology, Vol. 60, pp. 3599-3615, 2015.

15.

Robust optimization methods for cardiac sparing in tangential breast IMRT Journal Article

H. Mahmoudzadeh, J. Lee, T. C. Y. Chan, T. G. Purdie

Medical Physics, Vol. 42, pp. 2212-2222, 2015.

14.

Models for predicting objective function weights in prostate cancer IMRT Journal Article

J. J. Boutilier, T. Lee, T. Craig, M. B. Sharpe, T. C. Y. Chan

Medical Physics, Vol. 42, pp. 1586-1595, 2015.

13.

A robust-CVaR optimization approach with application to breast cancer therapy Journal Article

T. C. Y. Chan, H. Mahmoudzadeh, T. G. Purdie

European Journal of Operational Research, Vol. 238, pp. 876-885, 2014.

12.

Generalized inverse multi-objective optimization with application to cancer therapy Journal Article

T. C. Y. Chan, T. Craig, T. Lee, M. B. Sharpe

Operations Research, Vol. 62, pp. 680-695, 2014.

11.

A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer Journal Article

Y. Liu, T. C. Y. Chan, C.-G. Lee, Y. B. Cho, M. K. Islam

Medical Physics, Vol. 41(Article No. 021705), 2014.

10.

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning Journal Article

T. Lee, M. Hummad, T. C. Y. Chan, T. Craig, M. B. Sharpe

Medical Physics, Vol. 40(Article No. 121706), 2013.

9.

Adaptive and robust radiation therapy optimization for lung cancer Journal Article

T. C. Y. Chan, V. V. Mišić

European Journal of Operational Research, Vol. 231, pp. 745-756, 2013.

8.

Motion-compensating intensity maps in intensity-modulated radiation therapy Journal Article

T. C. Y. Chan

IIE Transactions on Healthcare Systems Engineering, Vol. 3, pp. 1-22, 2013.

7.

Optimal margin and edge-enhanced intensity maps in the presence of motion and uncertainty Journal Article

T. C. Y. Chan, J. N. Tsitsiklis, T. Bortfeld

Physics in Medicine and Biology, Vol. 55, pp. 515-533, 2010.

6.

Experimental evaluation of a robust optimization method for IMRT of moving targets Journal Article

C. Vrančić, A. Trofimov, T. C. Y. Chan, G. C. Sharp, T. Bortfeld

Physics in Medicine and Biology, Vol. 54, pp. 2901-2914, 2009.

5.

Robust management of motion uncertainty in intensity modulated radiation therapy Journal Article

T. Bortfeld, T. C. Y. Chan, A. Trofimov, J. N. Tsitsiklis

Operations Research, Vol. 56, pp. 1461-1473, 2008.

4.

Tumor trailing strategy for intensity-modulated radiation therapy of moving targets Journal Article

A. Trofimov, C. Vrancic, T. C. Y. Chan, G. C. Sharp, T. Bortfeld

Medical Physics, Vol. 35, pp. 1718-1733, 2008.

3.

Optimization under uncertainty in radiation therapy PhD Thesis

T. C. Y. Chan

Sloan School of Management, MIT, 2007.

2.

Accounting for range uncertainties in the optimization of intensity modulated proton therapy Journal Article

J. Unkelbach, T. C. Y. Chan, T. Bortfeld

Physics in Medicine and Biology, Vol. 52, pp. 2755-2773, 2007.

1.

A robust approach to IMRT optimization Journal Article

T. C. Y. Chan, T. Bortfeld, J. N. Tsitsiklis

Physics in Medicine and Biology, Vol. 51, pp. 2567-2583, 2006.

Cardiac Arrest and Public Access Defibrillator Location

The likelihood of survival from cardiac arrest is very time sensitive. One method to reset the heart’s normal rhythm is to deliver an electric shock using an automated external defibrillator (AED), which can be used by lay responders with little to no training. My research focuses on developing optimization methods for AED deployment, ranging from static deployment in public locations to delivery via drone. Accordingly, I am interested in approaches to estimate cardiac arrest risk and quantify accessibility.

RELEVANT PUBLICATIONS

19.

Public defibrillator accessibility and mobility trends during the COVID-19 pandemic in Canada Miscellaneous

K. H. B. Leung, R. Alam, S. C. Brooks, T. C. Y. Chan

under review at Resuscitation, 2020.

18.

Effect of optimized versus guidelines-based automated external defibrillator placement on out-of-hospital cardiac arrest coverage: an in silico trial Journal Article

C. L. F. Sun, L. Karlsson, L. J. Morrison, S. C. Brooks, F. Folke, T. C. Y. Chan

Journal of the American Heart Association, Vol. 9 (Article No. e016701), 2020.

17.

Improving access to automated external defibrillators in rural and remote settings: A drone delivery feasibility study Journal Article

S. Cheskes, S. L. McLeod, M. Nolan, P. Snobelen, C. Vaillancourt, S. C. Brooks, K. N. Dainty, T. C. Y. Chan, I. R. Drennan

Journal of the American Heart Association, Vol. 9 (Article No. e016687), 2020.

16.

Optimal in-hospital defibrillator placement Journal Article

K. H. B. Leung, C. L. F. Sun, M. Yang, K. S. Allan, N. Wong, T. C. Y. Chan

Resuscitation, Vol. 151, pp. 91-98, 2020.

15.

Improving bystander defibrillation in out-of-hospital cardiac arrests at home Journal Article Forthcoming

L. Karlsson, C. M. Hansen, C. Vourakis, C. L. F. Sun, S. Rajan, K. B. Sondergaard, L. Andelius, F. Lippert, G. H. Gislason, T. C. Y. Chan, C. Torp-Pedersen, F. Folke

European Heart Journal: Acute Cardiovascular Care, Forthcoming.

14.

Response time optimization for drone-delivered automated external defibrillators Miscellaneous

J. J. Boutilier, T. C. Y. Chan

under revision for Manufacturing and Service Operations Management, 2019.

13.

In silico trial of optimized versus real public defibrillator locations Journal Article

C. L. F. Sun, L. Karlsson, C. Torp-Pedersen, L. J. Morrison, S. C. Brooks, F. Folke, T. C. Y. Chan

Journal of the American College of Cardiology, Vol. 74, pp. 1557-1567, 2019.

12.

Health care utilization prior to out-of-hospital cardiac arrest: a population-based study Journal Article

M. Shuvy, M. Koh, F. Qiu, S. C. Brooks, T. C. Y. Chan, S. Cheskes, P. Dorian, G. Geri, S. Lin, D. C. Scales, D. T. Ko

Resuscitation, Vol. 141, pp. 158-165, 2019.

11.

Spatiotemporal AED optimization is generalizable Journal Article

C. L. F. Sun, L. Karlsson, C. Torp-Pedersen, L. J. Morrison, F. Folke, T. C. Y. Chan

Resuscitation, Vol. 131, pp. 101-107, 2018.

10.

Robust defibrillator deployment under cardiac arrest location uncertainty via row-and-column generation Journal Article

T. C. Y. Chan, Z.-J. Shen, A. Siddiq

Operations Research, Vol. 66, pp. 358-379, 2018.

9.

Increased cardiac arrest survival and bystander intervention in enclosed pedestrian walkway systems Journal Article

M. Lee, D. Demirtas, J. E. Buick, M. J. Feldman, S. Cheskes, L. J. Morrison, T. C. Y. Chan

Resuscitation, Vol. 118, pp. 1-7, 2017.

8.

Optimizing a drone network to deliver automated external defibrillators Journal Article

J. J. Boutilier, S. C. Brooks, A. Janmohamed, A. Byers, J. E. Buick, C. Zhan, A. P. Schoellig, S. Cheskes, L. J. Morrison, T. C. Y. Chan

Circulation, Vol. 135, pp. 2454-2465, 2017.

7.

Ranking businesses and municipal locations by spatiotemporal cardiac arrest risk to guide public defibrillator placement Journal Article

C. L. F. Sun, S. C. Brooks, L. J. Morrison, T. C. Y. Chan

Circulation, Vol. 135, pp. 1104-1119, 2017.

6.

Rise and shock: Optimal defibrillator placement in a high-rise building Journal Article

T. C. Y. Chan

Prehospital Emergency Care, Vol. 21, pp. 309-314, 2017.

5.

Overcoming spatial and temporal barriers to public access defibrillators via optimization Journal Article

C. L. F. Sun, D. Demirtas, S. C. Brooks, L. J. Morrison, T. C. Y. Chan

Journal of the American College of Cardiology, Vol. 68, pp. 836-845, 2016.

4.

Optimizing the deployment of public access defibrillators Journal Article

T. C. Y. Chan, D. Demirtas, R. H. Kwon

Management Science, Vol. 62, pp. 3617-3635, 2016.

3.

Modeling the impact of public access defibrillator range on public location cardiac arrest coverage Journal Article

A. A. Siddiq, S. C. Brooks, T. C. Y. Chan

Resuscitation, Vol. 84, pp. 904-909, 2013.

2.

Identifying locations for public access defibrillators using mathematical optimization Journal Article

T. C. Y. Chan, H. Li, G. Lebovic, S. K. Tang, J. Y. T. Chan, H. C. K. Cheng, L. J. Morrison, S. C. Brooks

Circulation, Vol. 127, pp. 1801-1809, 2013.

1.

Determining risk for out-of-hospital cardiac arrest by location type in a Canadian urban setting to guide future public access defibrillator placement Journal Article

S. C. Brooks, J. H. Hsu, S. K. Tang, R. Jeyakumar, T. C. Y. Chan

Annals of Emergency Medicine, Vol. 61, pp. 530-538, 2013.

Global Health

I am interested in bringing operations research methods to bear on important problems in global health. My main research project in this domain focuses on developing optimization models to guide ambulance deployment and routing in developing country settings, which are subject to major uncertainties in demand and travel time. Click here for a two-minute introduction to the project.

RELEVANT PUBLICATIONS

5.

Machine learning-based risk stratification for early detection of diabetes and hypertension in resource-limited settings Miscellaneous

J. J. Boutilier, T. C. Y. Chan, M. Ranjan, S. Deo

under revision for JMIR, 2020.

4.

A drone delivery network for antiepileptic drugs: a framework and modeling case study in a lowest-income country Journal Article

F. J. Mateen, K. H. B Leung, A. C. Vogel, A. Fode Cissé, T. C. Y. Chan

Transactions of the Royal Society of Tropical Medicine and Hygiene, Vol. 114, pp. 308-314, 2020.

3.

Ambulance Emergency Response Optimization in developing countries Journal Article

J. J. Boutilier, T. C. Y. Chan

Operations Research, Vol. 68, pp. 1315-1334, 2020.

2.

Operations Research in Global Health: A scoping review with a focus on the themes of health equity and impact Journal Article

B. D. Bradley, T. Jung, A. Tandon-Verma, B. Khoury, T. C. Y. Chan, Y.-L. Cheng

Health Research Policy and Systems, Vol. 15(Article No. 32), 2017.

1.

Estimating oxygen needs for childhood pneumonia in developing country health systems: a new model for expecting the unexpected Journal Article

B. D. Bradley, S. R. C. Howie, T. C. Y. Chan, Y.-L. Cheng

PLOS ONE, Vol. 9(Article No. e89872), 2014.

Sports Analytics

I am a passionate sports fan and enjoy analyzing interesting (decision) problems in sports. I have worked on topics in hockey, baseball, tennis, golf, football, and curling. Click here for a video of a talk I gave on sports analytics. Here is my TEDxUofT talk on baseball flexibility. A team of students and I developed an interactive NHL Expansion Draft optimization tool, which allows users to optimize protection and selection decisions in real time.

RELEVANT PUBLICATIONS

10.

Points gained in football: Using Markov process-based value functions to assess team performance Journal Article Forthcoming

T. C. Y. Chan, C. Fernandes, M. L. Puterman

Operations Research, Forthcoming.

9.

Predicting plays in the National Football League Journal Article

C. Fernandes, R. Yakubov, Y. Li, A. Prasad, T. C. Y. Chan

Journal of Sports Analytics, Vol. 6, pp. 35-43, 2020.

8.

A mathematical optimization framework for expansion draft decision making and analysis Journal Article

K. E. C. Booth, T. C. Y. Chan, Y. Shalaby

Journal of Quantitative Analysis in Sports, Vol. 15, pp. 27-40, 2019.

7.

Process flexibility in baseball: The value of positional flexibility Journal Article

T. C. Y. Chan, D. Fearing

Management Science, Vol. 65, pp. 1642-1666, 2019.

6.

A Bayesian regression approach to handicapping tennis players based on a rating system Journal Article

T. C. Y. Chan, R. Singal

Journal of Quantitative Analysis in Sports, Vol. 14, pp. 131-141, 2018.

5.

Improving fairness in match play golf through enhanced handicap allocation Journal Article

T. C. Y. Chan, D. Madras, M. L. Puterman

Journal of Sports Analytics, Vol. 4, pp. 251-262, 2018.

4.

A Markov Decision Process-based handicap system for tennis Journal Article

T. C. Y. Chan, R. Singal

Journal of Quantitative Analysis in Sports, Vol. 12, pp. 179-189, 2016.

3.

The value of flexibility in baseball roster construction Inproceedings

T. C. Y. Chan, D. S. Fearing

Proceedings of the 7th Annual MIT Sloan Sports Analytics Conference, 2013.

2.

Quantifying the contribution of NHL player types to team performance Journal Article

T. C. Y. Chan, J. A. Cho, D. C. Novati

Interfaces, Vol. 42, pp. 131-145, 2012.

1.

Split personalities of NHL players: using clustering, projection and regression to measure individual point shares Inproceedings

T. C. Y. Chan, D. C. Novati

Proceedings of the 2012 MIT Sloan Sports Analytics Conference, 2012.

Healthcare Operations

I am interested in a variety of healthcare operations problems including scheduling and process flexibility.

RELEVANT PUBLICATIONS

7.

Modelling resource requirements and physician staffing to provide virtual urgent medical care for residents of long-term care homes: a cross-sectional study Journal Article

F. Razak, S. Shin, F. Pogacar, H. Y. Jung, L. Pus, A. Moser, L. Lapointe-Shaw, T. Tang, J. L. Kwan, A. Weinerman, S. Rawal, V. Kushnir, D. Mak, D. Martin, K. G. Shojania, S. Bhatia, P. Agarwal, G. Mukerji, M. Fralick, M. K. Kapral, M. Morgan, B. Wong, T. C. Y. Chan, A. A. Verma

CMAJ Open, Vol. 8, pp. E514-E521, 2020.

6.

Cherry-picking and its negative effect on system service level: evidence from a radiology workflow platform Miscellaneous

T. C. Y. Chan, N. Howard, S. Lagzi, G. Romero

2020.

5.

An inverse optimization approach to measuring clinical pathway concordance Miscellaneous

T. C. Y. Chan, M. Eberg, K. Forster, C. Holloway, L. Ieraci, Y. Shalaby, N. Yousefi

under second review at Management Science, 2020.

4.

Ambulance Emergency Response Optimization in developing countries Journal Article

J. J. Boutilier, T. C. Y. Chan

Operations Research, Vol. 68, pp. 1315-1334, 2020.

3.

Response time optimization for drone-delivered automated external defibrillators Miscellaneous

J. J. Boutilier, T. C. Y. Chan

under revision for Manufacturing and Service Operations Management, 2019.

2.

Sparse flexible design: a machine learning approach Miscellaneous

T. C. Y. Chan, D. Letourneau, B. Potter

under revision for Flexible Services and Manufacturing Journal, 2019.

1.

Women's College Hospital uses Operations Research to create an ambulatory clinic schedule Journal Article

B. K. Eagen, T. C. Y. Chan, M. W. Carter

Service Science, Vol. 10, pp. 230-240, 2018.

Sustainability

Although I am not currently active in this area, I have previous experience applying optimization methods to problems in green building design and wind farm layout.

RELEVANT PUBLICATIONS

4.

Robust wind farm layout optimization Book Chapter

P. Y. Zhang, J. Y. J. Kuo, D. Romero, T. C. Y. Chan, C. H. Amon

Terlaky, T; Anjos, M; Ahmed, S (Ed.): Advances and Trends in Optimization with Engineering Applications, Chapter 28, pp. 367-374, SIAM, Philadelphia, 2017.

3.

A new mathematical programming approach to optimize wind farm layouts Journal Article

S. D. O. Turner, D. A. Romero, P. Y. Zhang, C. H. Amon, T. C. Y. Chan

Renewable Energy, Vol. 63, pp. 674-680, 2014.

2.

Examining the LEED rating system using inverse optimization Journal Article

S. D. O. Turner, T. C. Y. Chan

Journal of Solar Energy Engineering, Vol. 135(Article No. 040901), 2013.

1.

Examining the LEED rating system using approximate inverse optimization Inproceedings

S. D. O. Turner, T. C. Y. Chan

Proceedings of the ASME 2012 International Mechanical Engineering Congress and Exposition, 2012.

Education

I enjoy developing innovative teaching methods using games and other interactive activities.

RELEVANT PUBLICATIONS

2.

Introducing and integrating machine learning in an operations research curriculum: an application-driven course Miscellaneous

J. J. Boutilier, T. C. Y. Chan

under revision for INFORMS Transactions on Education, 2020.

1.

Deal or No Deal: A spreadsheet game to introduce decision making under uncertainty Journal Article

T. C. Y. Chan

INFORMS Transactions on Education, Vol. 14, pp. 53-60, 2013.

A software tool to optimize matching of available hospital staff to job requests during COVID-19

A suite of optimization models tailored for the 2017 and 2021 NHL Expansion drafts that allow users to modify objectives and constraints, and evaluate what-if scenarios.

High-performance analytics for sports

An initiative to grow research, student training, industry partnerships, and equity, diversity and inclusion (EDI) in sports analytics.

An international competition sponsored by the American Association of Physicists in Medicine to advance dose prediction methods for knowledge-based planning.

Support from the following sponsors is gratefully acknowledged