Haifeng Wang
Assistant Professor
McCain Hall, Room 260P
Department of Industrial and Systems Engineering
Bagley College of Engineering, Mississippi State University (MSU)
PO Box 9542, Mississippi State, MS 39762
✉: wang [at] ise.msstate.edu
☎: (662) 325 3923
See also: Google Scholar, ResearchGate, Linkedin, MSU Profile
My research studies machine learning and optimization methods for addressing challenges of high data heterogeneity and model interpretability in complex engineering problems, such as medical image-based diseases analysis, neurological disorder diagnosis, precision agriculture, smart electronics manufacturing, smart warehouse, and other big data applications. I am particularly interested in understanding the algorithmic and statistical properties of machine learning models through both empirical (data-driven methods to develop tools for solving practical problems) and methodological (optimization theories to advance our understanding of machine learning fundamentals) studies.
Recently, my work is focused on 1) developing interpretable machine learning tools to understand extracapsular extension in head and neck cancer treatment, 2) transfer learning and domain adaptation in woodchip quality evaluation, and 3) pattern recognition from multi-channel multi-modal biometric signals.
I joined MSU Industrial and Systems Engineering department as an assistant professor in 2019, I am also an affiliated faculty at the Center for Advanced Vehicular Systems (CAVS) and University of Mississippi Medical Center (UMMC) Department of Radiation Oncology.
I obtained my Ph.D. and M.S. in Industrial and Systems Engineering from the State University of New York at Binghamton in 2019 and 2015, respectively, as advised by Dr. Sang Won Yoon.
I also hold a B.S. degree in Industrial Engineering from Southeast University in Nanjing, China.
From 2014 to 2019, I was a research associate working on the following research projects sponsored by different industry partners under the Integrated Electronics Engineering Center (IEEC) and Watson Institute of Systems Excellence (WISE) at Binghamton University:
- 2017-2019, Koh Young Technology Inc., worked on KSMART: A Smart Factory Solution in Electronics Manufacturing.
- 2016, Koh Young Technology Inc., worked on Machine Learning-Based Pathological Tissue Detection.
- 2014-2016, Innovation Associates (iA), worked on Large-scale Mail Order Pharmacy Warehouse Simulation and Optimization.
[third person bio]
☑ Research Area
- Machine Learning
Methods: machine learning modeling and optimization with high data heterogeneity, image-based machine learning model interpretability, information fusion, ensemble learning
Applications: medical image-based cancer diagnosis and treatment planning, smart agriculture, advanced manufacturing, surface mount technology, virtual reality-integrated intelligence, anomaly event detection - Optimization Algorithms
Methods: first-order methods, large-scale optimization, nonlinear programming, mixed-integer programming, Bayesian optimization, meta-heuristics, multi-objective evolutionary algorithms
Applications: optimization of complex engineering systems (warehouse, supply chain, healthcare), optimization in machine learning - Network Science
Methods: community detection, network dynamics, deep graph neural networks
Applications: human brain functional connectivity pattern analysis for brain disorder diagnosis, attention deficit hyperactivity disorder (ADHD) diagnosis, spatial-temporal graph
📰 News
- Congratulations to PhD student Abdur Rahman for securing first place in the oral presentation category at MSU’s Fall Graduate Research Symposium! His presentation, titled “Integrating Texture Features and Domain Adaptation for Robust Moisture Content Prediction in Wood Chips,” highlights research currently supported by the U.S. Department of Agriculture (USDA).
- Great honor to have been invited as the 2024 Bakowski Visiting Professor to present our work at the Feist-Weiller Cancer Center at LSU Health Shreveport.
- A great pleasure to join Scientific Data's Editorial Board. Scientific Data - Nature is a peer-reviewed open access scientific journal published by Nature Research since 2014. With impact factor 9.55 (2023), it is a prestigious journal focusing on descriptions of datasets, and research that advances the sharing and reuse of scientific data.
- Congrats! PhD student Abdur Rahman, his paper has been selected as a finalist for the IISE DAIS Division Best Student Paper Competition. The paper is titled "Boosting Discriminability of Transferable Features in Unsupervised Domain Adaptation." This is a wonderful honor for a student paper - Congratulations!
- Congrats! Our work related to AI-assisted plant disease diagnosis has been funded by the Mississippi Soybean Promotion Board.
- A great pleasure for serving as a 2024 NSF Reviewer.
2023
- The Data Descriptor for the published brain MRI dataset is now available in Scientific Data journal.
- Excited to be invited to USDA NIFA review panel.
- A great pleasure to join the NSF review panel.
- Congrats! Our review paper about ``Wood chip moisture content assessment and prediction" has been published in Renewable and Sustainable Energy Reviews journal.
- I will serve as a Guest Editor of Information Journal, Special Issue ``From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics." We welcome your submissions. Deadline for manuscript submissions: 31 August 2024
- A great pleasure to serve as a Guest Editor of Mathematics Journal, Special Issue ``Computational Intelligence in Addressing Data Heterogeneity." Welcome to submit your recent work to the issue. Deadline for manuscript submissions: 30 September 2024
- Excited to be invited to join NIH review panel meeting in November.
- Congrats! An NIH grant has been awarded for our work on Interpretable Deep Learning for Head and Neck Cancer Evaluation. This multidisciplinary project, led by both MSU and UMMC, is a fruitful collaboration between engineering and medical professionals. I would like to acknowledge the support of the Industrial and Systems Engineering Department of the Bagley College of Engineering at MSU, as well as the Radiation Oncology and Radiology Departments at UMMC. Their support is essential for the success of this grant.
- Congrats! Our recent research about Robust Unsupervised Domain Adaptation has been published in Journal of Computational and Applied Mathematics.
- Three lab members have successfully defended their doctoral dissertations. Congratulations to Dr. Yibin Wang, Dr. Wesley Chorney, and Dr. Fatine Elakramine. Yibin will be a PostDoc at Michigan State University starting in fall 2023.
- A great pleasure to participate in the 2023 MPS Workshop for Young Investigators in Alexandria, VA. Many thanks to the organizers, The University of Florida and NSF.
- Congrats! Undergraduate Research Assistant Ethan Kang (research topic biometric signal processing)received BCoE Undergraduate Student Research Award.
- Our data about Brain Tumor Recurrence Prediction are now available in The Cancer Imaging Archive (TCIA). This is a collaborative work by both UMMC Department of Radiation Oncology and MSU Department of Industrial and Systems Engineering. Thank you all the team members for the hard work.
- Our work titled "Preoperative Prediction and Identification of Extracapsular Extension in Head and Neck Cancer Patients: Progress and Potential" has been published in the Cureus Journal of Medical Science.
- Congrats! PhD student Abdur Rahman's work “An Interpretable Deep Learning Model for Wood Chip Moisture Content Prediction” is awarded 3rd position in Engineering PhD category in the Graduate Research Symposium, Spring 2023.
2022
- Congrats! Our work about Real-time Collaborative Machine Learning for Plant Disease Diagnosis Intelligence is funded by MSU-MAFES Special Research Initiative.
- Congrats! Our paper about Embedded Machine Learning Model for Early Detection and Intervention of High-Risk ICU Readmission Patients has been accepted by IEEE International Conference in Bioinformatics and Biomedicine (BIBM) (acceptance rate 20%).
- Congrats! Lab member PhD student Abdur's paper about Deep Learning-based Weed Detection for Cotton has been published in Smart Agricultural Technology journal.
- A great honor to be invited to give a seminar talk to the Department of Industrial and Manufacturing Systems Engineering at Iowa State University.
- Excited to be invited to join NIH Emerging Imaging Technologies and Applications (EITA) Panel meeting.
- A great pleasure to organize AI in Radiation Oncology Seminar and welcome the visitors from UMMC Department of Radiation Oncology. Thanks to all the participants for all the great talks.
- Congratulations! Our research related to image-based machine learning in wood industry got funded by USDA-NIFA.
- Congrats! PhD students Abdur and Yibin's work about activation function optimization has been selected as a finalist to the 2022 IISE QCRE/ProcessMiner Data Challenge Competition. Congratulations on this achievement! The work title: Exponential Error Linear Unit (EELU): Optimization-Boosted Activation Function Series for Image Classification.
- Organized a mini symposium (MS) session about Machine Learning Advances in Healthcare Systems in the The Second International Conference in Computational Methods and Applications in Engineering.
- PhD student Abdur Rahman won the 1st place in the MSU Spring 2022 Graduate Research Symposium - Engineering Section. His topic is Activation Function Optimization Scheme for Image Classification. Congrats!
- Organized the 2022 IISE DAIS Data Analytics Competition. This year's topic is about brain tumor progression prediction based on the current radiation therapy.
- Organized the 2022 IISE Best Student Paper Competition in the DAIS division.
- Our research paper about the Dynamic Brain Network has been accepted by IEEE ICHI 2022.
- Congratulations to Zach for successfully defending his Master's thesis, titled: Improving Deep Neural Network Training with Batch Size and Learning Rate Optimization for Head and Neck Tumor Segmentation on CT and PET Medical Images on March 4th, 2022. Zach will join LinkedIn as a Machine Learning Engineer Apprentice. Congratulations, Zach!
See More
2021
- Our research paper about Deep Neural Network Architecture Optimization has been accepted by Applied Soft Computing.
- Our research paper about Multi-scale 3D Dense Neural Network has been accepted by the 12th ACM Conference in Bioinformatics, Computational Biology, and Health Informatics.
- Dr. Wang will join the Board of Directors in the Data Analytics and Information Systems (DAIS) Division of the Institute of Industrial and Systems Engineers (IISE) starting in June 2021. A great pleasure to serve the DAIS community.
- Our research paper about Attention Mechanism in Convolutional Neural Network has been accepted by Expert Systems with Applications.
- Our poster presentations Artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis and Retinal disease diagnosis through an adaptive deep learning model are available online in the Clinical Cancer Research journal.
- Our research paper about Wavelet-based Recurrent Neural Network has been accepted by Robotics and Computer-Integrated Manufacturing journal.
2020
- Congrats! Our research "Leveraging Immersive Virtual Reality Technology to Perform Nurse Training in the State of Mississippi" is funded by the Mississippi Department of Employment Security.
- Congrats! Our research paper entitled "Brain Functional Connectivity Pattern Recognition for Attention-deficit/hyperactivity Disorder Diagnosis" has been accepted and invited to the Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM 2020) workshop at IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2020).
- Good news! Our research paper "A Bayesian Optimization-Based Augmented Attention Neural Network for Efficient Rice Diseases Detection" has been invited for the presentation at the 15th INFORMS Virtual Workshop on Data Mining and Decision Analytics.
- Congrats! Our conference paper (first author Yibin Wang, Ph.D. student) entitled "An Unsupervised Domain Adaptation Model for Cell Detection on Stimulated Ramen Scattering Images" is selected as one of the four finalists for the Data Analytics and Information Systems (DAIS) Division Best Paper Competition at the IISE annual conference in November at New Orleans.
- Congrats! Our conference paper (first author Christian Zamiela, Master's student) was awarded 2nd place for oral presentation at the 36th Southern Biomedical Engineering Conference (SBEC). The paper titled “A Comparative Study on Data Mining Techniques for Breast Cancer Survivability Prediction.”
- Congrats! Our journal paper was accepted by Neurocomputing. Title: A 1-norm regularized linear programming nonparallel hyperplane support vector machine for binary classification problems.
2019
- Dr. Wang is selected as one of the six panelists for the 2019 INFORMS Academic Job Search panel. The panel aims to provide prospective job-seekers with advice to help them make an informed career choice, avoid pitfalls, and prepare for the next step in their academic job search.
- Congrats! Our journal paper related to deep learning-based 3D medical image analysis is published online in IISE Transactions on Healthcare Systems Engineering in September 2019. Title: A deep separable neural network for human tissue identification in three-dimensional optical coherence tomography images.
- Congrats! Our journal paper related to real-time stencil printing process optimization is published online in IEEE Transactions on Components, Packaging and Manufacturing Technology in August 2019. Title: Real-time stencil printing process optimization using a hybrid multi-layer online sequential extreme learning machine and evolutionary search approach.
- Dr. Wang joined the Department of Industrial and Systems Engineering as an assistant professor at Mississippi State University in August, 2019. A great pleasure to be part of MSU-ISE family. Check us: https://www.ise.msstate.edu
☑ Lab Members
- Amirhossein Eskorouchi, Ph.D. student (2024.01- )
- Abdur Rahman, Ph.D. student (2021.08- )
- Michael Carter, Ph.D. student (2022.09- )
- Julius Phillips, Ph.D. student (2021.08- )
- Jolly Ehiabhi, Ph.D. student (2020.09- )
- Swarup Bhattarai, Undergraduate research assistant (2024.02- )
- Niraj Ghimire, Undergraduate research assistant (2023.10- )
Former Members
- Yibin Wang, Ph.D. 2023, "Distributionally Robust Unsupervised Domain Adaptation and Its Applications in 2D and 3D Image Analysis"
- Wesley Chorney, Ph.D. 2023, "Vertical Federated Learning using Autoencoders with Applications in Electrocardiograms"
- Fatine Elakramine, Ph.D. 2023, "Developing systems engineering and machine learning frameworks for the improvement of aviation maintenance," co-Chair with M. Marufuzzaman.
- Zachariah Douglas, MS 2022, "Improving deep neural network training with batch size and learning rate optimization for head and neck tumor segmentation on 2D and 3D medical images, " committee chair. Current position: Apprentice Engineer - Data Science/Machine Learning at LinkedIn
- Manideep Kolla, Master student (2023.08- 2023.12) Research Area: Genome-wide association studies
- Meng Chen, Undergraduate Research Assistant (2021.10- 2023.05) Topic: Embedded system development
- Ethan Kang, Undergraduate Research Assistant (2023.01- 05) Topic: Biometric signal processing
- Darnell Nunn, Undergraduate Research Assistant (2022.04- 2022.12) Topic: Embedded system analysis
- Jinhee Yu, Undergraduate Research Assistant (2021.06- 2022.09) Topic: Speech emotion recognition
- Rakeen Zaman, Undergraduate Research Assistant (2021.05- 2021.07). Topic: Data visualizations
- Timothy Wunrow, Undergraduate Research Assistant (2020.08- 2021.05). Topic: Anomaly detection
- Sunny Gurung, Undergraduate Research Assistant (2020.08- 2021.05). Topic: Hyperspectral image correction
Current Members
☑ Publications
See also my Google Scholar profile.-
A. Rahman*, M. Marufuzzaman, J. Street, J. Wooten, V. Gude, R. Buchanan, and H. Wang
A comprehensive review on wood chip moisture content assessment and prediction
Renewable and Sustainable Energy Reviews, 189, 2024.
(Impact Factor: 15.9 (2023))
-
Y. Wang*, and H. Wang
Distributionally robust unsupervised domain adaptation
Journal of Computational and Applied Mathematics, 436, 2024.
(Impact Factor: 2.4 (2023))
-
W. Chorney*, H. Wang, L. He, S. Lee, and LW Fan
Convolutional block attention autoencoder for denoising electrocardiograms
Biomedical Signal Processing and Control, 86, 2023.
(Impact Factor: 5.1 (2023))
-
Y. Lu, S. Young, and H. Wang
Robust plant segmentation of color images based on image contrast optimization
Computers and Electronics in Agriculture, 193, 2022.
(Impact Factor: 5.65 (2020))
-
Y. Wang*, H. Wang, and Z. Peng
Rice diseases detection and classification using attention based neural network
Expert Systems with Applications, 178, 2021.
(Impact Factor: 6.95 (2020))
-
H. Wang, S. Alelaumi, H. Lu, and S. W. Yoon
A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction
Robotics and Computer Integrated Manufacturing, 71, 2021.
(Impact Factor: 5.66 (2020))
- Y. Wang*, W. Duggar, T. Thomas, P. Robert, L. Bian, and H. Wang
Extracapsular extension identification for head and neck cancer using multi-scale 3D deep neural network
Proceedings of the 2021 ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), 2021.
(Acceptance rate: 30%)
-
H. Wang, D. Won, and S.W. Yoon
An adaptive neural architecture optimization model for medical image analysis
Applied Soft Computing, 111, 2021.
(Impact Factor: 6.72 (2020))
-
Y. Wang*, C. Zamiela, T. Thomas,W. Duggar, P. Robert, L. Bian, and H. Wang
3D texture features-based lymph node automated detection in head and neck cancer analysis
Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), 2020.
(Acceptance rate: 19%)
-
H. Pirim, M. Fan, and H.Wang
Brain functional connectivity pattern recognition for attention-deficit/ hyperactivity disorder diagnosis
Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), 2020.
(Acceptance rate: 19%)
- Y. Wang, and H. Wang
Unsupervised domain adaptation for cell detection on SRS images
Proceedings of the 2020 IISE Annual Conference, 2020.
(Best student paper finalist. Acceptance rate: 9%) [Poster]
-
Q. Zhang, H. Wang, and S. Yoon
A 1-norm linear programming nonparallel hyperplane support vector machine for pattern classification
Neurocomputing, 376 (1), 2020
(Impact Factor: 5.719 (2020))
- H. Dauod, D.MSerhan, H.Wang, N. Khader, S.W. Yoon, and K. Srihari
Robust optimization and receding horizon control strategy to enhance replenishment planning of pharmacy robotic dispensing systems
Robotics and Computer Integrated Manufacturing, 59, 2019.
(Impact Factor: 5.66 (2020))
-
H. Lu, H. Wang, Q. Zhang, D. Won, and S. Yoon
A dual-tree complex wavelet transform based convolutional neural network for human thyroid OCT image segmentation
Proceedings of the 6th IEEE International Conference on Healthcare Informatics (IEEE ICHI), 2018.
(Acceptance rate: 28%)
-
Q. Zhang, H. Wang, H. Lu, D. Won, and S. Yoon
Medical image synthesis with generative adversarial networks for tissue recognition
Proceedings of the 6th IEEE International Conference on Healthcare Informatics (IEEE ICHI), 2018.
(Acceptance rate: 28%)
- H. Wang, B Zheng, SW Yoon, and HS Ko
A support vector machine-based ensemble algorithm for breast cancer diagnosis
European Journal of Operational Research (EJOR), 267(2), 2018.
(Impact Factor: 5.33 (2020))
- H. Wang, H. Dauod, N. Khader, S. Yoon, and K. Srihari
Multi-objective planogram optimization using association rule mining and evolutionary algorithms for robotic dispensing system
International Journal of Computer Integrated Manufacturing, 31 (8), 2018.
(Impact Factor: 3.20 (2020))
Selected Presentation and Peer Reviewed Abstracts:
- Y. Wang*, H. Wang, H. Pirim, Z. Chen, L. Fan, and N. Ojeda
Attention deficit/hyperactivity disorder (ADHD) diagnosis using diffusion convolutional recurrent neural networks with temporal data
Mississippi Academy of Sciences 86th Annual Meeting, 2022.
[Poster]
- H. Pirim, H. Wang, and M. Fan
Dynamic Network Connectivity Analysis for Understanding Attention Deficit Hyperactivity Disorder
Networks in Biology and Medicine Conference, 2021.
- Y. Wang*, W. Duggar, P. Robert, T. Thomas, R. Gatewood, L. Bian, and H. Wang
Interpretable artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis
American Association of Physicists in Medicine 63rd Annual Meeting, 2021.
[Poster]
- Y. Wang*, W. Duggar, P. Robert, T. Thomas, R. Gatewood, L. Bian, and H. Wang
Automatic extracapsular extension identification for head and neck cancer using deep neural network with local-global information
International Journal of Radiation Oncology*Biology*Physics, 111(3), 2021.
- Y.Wang*, T. Thomas,W. Duggar, P. Robert, L. Bian, and H.Wang
Artificial intelligence-based extracapsular extension prediction in head and neck cancer analysis
Clinical Cancer Research, 27, 2021.
[Poster]
Selected Publications:
☑ Grants/Projects
- Mississippi Soybean Promotion Board (2024-2025): Role: Co-PI, "Developing an Intelligent Real-Time Monitoring Platform for Rapid Diagnosis of Soybean Diseases"
- NIH-R03 (2023-2025): Role: PI, "Optimization and Validation of a Cost-effective Image-Guided Automated Extracapsular Extension Detection Framework through Interpretable Machine Learning in Head and Neck Cancer"
- USDA-NIFA (2022-2025): Role: Co-PI, "Image-Based Assessment of Woodchip Qualities: Transfer Learning Model Development to Field Validation"
- Mississippi Department of Employment Security (MDES) (2020-2022): Role: Co-PI, "Leveraging Immersive Virtual Reality Technology to Perform Nurse Training in the State of Mississippi"
- Mississippi State University, ORED, Undergraduate Research Support Program (2020-2021; 2021-2022; 2023-2024)
- MSU-MAFES Special Research Initiative (2022-2023)
- Mississippi State University, Strategic Research Initiative (SRI) Faculty Seed Funding Program Track I, College of Arts & Sciences (2021)
☑ Teaching
- IE 4683/6683: Machine Learning with Industrial Engineering Applications (Fall 2024)
Previous versions: Spring 2022
This course is an entry-level machine learning course with an emphasis on the basic approaches of machine learning and the coding. The topics include the foundation of Python computational tools, data preprocessing, data descriptive analysis, regression, classification, and model evaluation.
- IE 2990: Fundamentals and Engineering Applications of Programming with Python (Spring 2024)
Previous versions: Spring 2024
An introduction to Python programming for use in engineering applications.
- IE 4613/6613: Engineering Statistics I (Spring 2024)
Previous versions: Winter 2022, Spring 2023
Introduction to statistical analysis. Topics include probability, probability distributions, data analysis, parameter estimation, statistical intervals, and statistical inferences.
- IE 8743: Nonlinear Programming (Fall 2023)
Previous versions: Spring 2021
This course introduces the fundamentals of nonlinear programming theory and its applications with a focus on understanding the convexity and convex optimization theory. The course includes convex theory, unconstrained optimization, gradient methods, line search methods, theory of constrained optimization, KKT Conditions, Lagrangian duality, and support vector machine.
- IE 8990: Large-Scale Optimization for Deep Learning (Spring 2022)
This course provides an introduction to deep neural networks and related optimization algorithms with an emphasis on developing intuitions for understanding how to optimize computationally intensive machine learning models. The course will include: bias–variance tradeoff, backpropagation, convolutional neural networks, recurrent neural networks, gradient methods (gradient descent, subgradient, stohastic gradient, accelerated gradient, projected gradient), convergence analysis, loss landscape, proximal gradient methods, and mirror descent.
- IE 4933/6933: Information System for Industrial Engineering (Spring 2023)
Previous versions: Fall 2022
This course provides an introduction to computer-based information systems. The course focuses on Excel and Access with topics including Excel functions, Visual Basic for Applications (VBA) fundamentals (Procedures, Functions, IF, Loop, Forms), Pivot Tables/Charts (Grouping, Sorting, and Filtering), and Relational Database Management System (RDBMS) (Queries, Tables, Forms).
- IE 4934/6934: Information System for Industrial Engineering
Previous versions: Fall 2021, Fall 2020, Spring 2020, Fall 2019
This course provides an introduction to computer-based information systems. The course focuses on Excel and Access with topics including Excel functions, Visual Basic for Applications (VBA) fundamentals (Procedures, Functions, IF, Loop, Forms), Pivot Tables/Charts (Grouping, Sorting, and Filtering), and Relational Database Management System (RDBMS) (Queries, Tables, Forms).
- Directed Individual Study Courses
- Fall 2022: Advanced Machine Learning in Radiotherapy
- Fall 2022: Interpretable Machine Learning
- Spring 2022: Advances in Domain Adaptation Theory
Binghamton University
- SSIE 650: Systems Optimization (Teaching Assistant, Spring 2019)
This course provides a broad spectrum of models and methods for systems optimization. The course includes motivating examples; classical constrained and unconstrained methods; search techniques; linear programming; network and transportation systems; introduction to integer programming.
Mississippi State University
☑ Services
Professional events:- 2023-present: Guest Editor, Mathematics Journal, Special Issue ``Computational Intelligence in Addressing Data Heterogeneity."
- 2023-present: Guest Editor, Information Journal, Special Issue ``From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics."
- 2021-present: Underrepresented Minority Student Mentor, LS-PAC MODELS Center, Louisiana State University.
- 2021-present: Reviewer Board Member, Symmetry Journal.
- 2020-present: Academic Committee Member, Society for Health Systems.
- 2023: Program and Organizing Committee, The 39th Southern Biomedical Engineering Conference (SBEC).
- 2023: Competition Chair, 2023 IISE Student Data Analytics Competition in the DAIS Division.
- 2023: Competition Chair, 2023 IISE Best Track Paper Competition in the DAIS Division.
- 2022: Co-chair for the Health Systems Track, 2022 IISE Annual Conference.
- 2021-2023: Director, DAIS Division, IISE.
- 2022: Competition Chair, 2022 IISE Student Data Analytics Competition in the DAIS Division.
- 2022: Competition Chair, 2022 IISE Best Student Paper Competition in the DAIS (Data Analytics and Information Systems) Division.
- 2021: Panelist of IISE DAIS Academic Job Search panel.
- 2020-2022: Chair, Healthcare Systems Research Working Group, MSU.
- 2019: Panelist of INFORMS Academic Job Search panel - MC92, Seattle, WA.
- 2023: IISE Annual Conference, INFORMS Annual Conference, The Southern Biomedical Engineering Conference (SBEC)
- 2022: IISE Annual Conference, ICCMAE 2022: The Second International Conference on Computational Methods and Applications in Engineering
- 2021: INFORMS Annual Conference
- 2020: IISE Annual Conference
- 2018: FAIM Conference
- 2017: IISE Annual Conference
- 2015: IISE Annual Conference
-
IEEE Transactions on Neural Networks and Learning Systems;
Information Sciences;
SIAM Review;
Expert Systems With Applications;
Information Systems;
Annals of Operations Research;
Journal of Imaging;
Symmetry;
Cancers;
Operations Research Forum;
Artificial Intelligence Review;
Soft Computing;
Computers and Operations Research.
☑ GoodReads
- Conferences in the area of Machine Learning.
- Reading: Research 101 for Engineers.
- Reading: Writing An Academic Biography.
- Tutorial: Deep Implicit Layers.
- Tutorial: Optimization Cheat Sheet from Prof. Pokutta.
- Tutorial: Robust Optimization from Prof. Aharon Ben-Tal.
- Seminars: Online Seminar on Mathematical Foundations of Data Science.
- Seminars: Simons Institute.
- Course: Dimitri P. Bertsekas Reinforcement Learning Course.
- National Weather Digital Forecast Database.