Ananda Mohan Mondal

Tenure & Promotion Materials

  1. Biographical Summary
  2. Curriculum Vitae
  3. Research Statement
  4. Teaching Statement
  5. Service Statement
  6. Five Significant Publications
  7. Teaching: List of Courses Taught (9 courses with 8 different preparations)
    1. Undergraduate Courses:
      1. COP 3530 Data Structures (Fall 2020)
      2. COP 4534 Algorithm Techniques (Spring 2020)
      3. CAP 4612 Introduction to Machine Learning (Fall 2021, Fall 2022)
    2. Graduate Courses:
      1. COT 5407 Introduction to Algorithms (Spring 2024)
      2. CAP 5510C Bioinformatics (Spring 2023)
      3. CAP 5610 Machine Learning (Fall 2021, Fall 2022, Fall 2023)
      4. CAP 5738 Data Visualization (Spring 2019)
      5. CAP 6619 Advanced Machine Learning (Spring 2021, Spring 2022, Spring 2023)
      6. CAP 6778 Advanced Data Mining (Fall 2018, Fall 2019)
  8. Supervised Graduate Students
    1. Ph.D. Major Advisor [2 graduated; 3 left; 2 advising].
      1.  Abdullah Al Mamun (Fall 2018 – Spring 2022: Graduated). Dissertation: A Machine Learning Framework for Identifying Molecular Biomarkers from Transcriptomic Cancer Data. Year of Graduation: Spring 2022. Support: Fall 2020 through Spring 2021 from NSF RAPID and Spring 2022 from NSF CAREER. Employment: Machine Learning Engineer at Meta (Facebook)
      2.  Raihanul Bari Tanvir (Fall 2018 – Summer 2023: Graduated). Dissertation: Graph-Theoretic and Machine Learning-Based Frameworks for Cancer Biomarker Discovery. Year of Graduation: Summer 2023. Support: Fall 2020 through Summer 2023 (NSF CAREER). Employment: Postdoctoral associate at Boehringer Ingelheim
      3. Mona Maharjan (Fall 2018 – Summer 2020: Left with non-thesis Master’s). Project: Computational Identification of Biomarker Genes for Lung Cancer Considering Treatment and Non-Treatment Studies. Support: Fall 2018 through Summer 2020 (NSF CAREER)
      4. Tasmia Aqila: (Fall 2018 – Fall 2020: Left with non-thesis Master’s). Project: Pseudotime Based Discovery of Breast Cancer Heterogeneity. Support: Fall 2018 through Fall 2020 (FIU Startup)
      5. Emam Hossain (Spring 2022 – Summer 2022: Left after one semester)
      6. Masrur Sobhan (Fall 2020 – Current). PhD Candidate (Passed qualifying, working on dissertation proposal). Support: NIH Pilot, NIH R01, and NSF CAREER
      7. Md Mezbahul Islam (Fall 2022 – Current). PhD Candidate (Passed qualifying, working on dissertation proposal). Support: NSF CAREER
    2. Master’s Major Advisor (5 graduated).
      1. Genevieve Ferguson (Fall 2023). Project: Discovering Potential Biomarkers for Uterine and Cervical Cancers with Machine Learning.
      2. Jaya Gudipalli (Summer 2023). Project: Autoencoder-based Approach for Cancer Subtype Prediction and Intratumor Heterogeneity Level Estimation Using Multi-Omics Data.
      3. Ravi Chandra Madamanchi (Spring 2023). Project: Analyzing Skin Tone Bias in Deep Neural Networks for Skin Condition Diagnosis.
      4. Santhosh Gadipelly (Spring 2023). Project: Analyzing Skin Tone Bias in Deep Neural Networks for Skin Condition Diagnosis.
      5. Sai Borelly (Fall 2022). Project: Computer Aided Diagnosis of Chest X-ray Images.
    3. Master’s Major Advisor (5 Current Students).
      1. Vamshidhar Sai Donekal. Project: AutoML for Multi-Omics Based Cancer Subtype Prediction.
      2. Sri Sai Teja Chanthati. Project: AutoML for Multi-Omics Based Cancer Subtype Prediction.
      3. Sai Siva Prabhu. Project: Graph Neural Network-Based Analysis of Multi-Omics Data for Cancer Subtype Prediction.
      4. Jhansi Lakshmi. Project: Graph Neural Network-Based Analysis of Multi-Omics Data for Cancer Subtype Prediction.
      5. Erick Gonzalez-Vega. Project: Machine Learning-Based Analysis of Drug Response for Breast Cancer Treatment.
  9. Research Activities: Representative Journal Publications: (Graduate student advisee; Undergraduate# student advisee, Only Publications at FIU are included) 
    1. R. B. Tanvir, M. M. Islam, M. Sobhan, D. Luo, and A. M. Mondal (2024). MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction. International Journal of Molecular Sciences, 25(5): 1-16.  https://doi.org/10.3390/ijms25052788 [PDF]
    2. C. A. Balbin, J. Nunez-Castilla, V. Stebliankin, P. Baral, M. Sobhan, T. Cickovski, A. M. Mondal, G. Narasimhan, P. Chapagain, K. Mathee, and J. Siltberg-Liberles (2023). Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes. ImmunoInformatics, 9: 1-9. https://doi.org/10.1016/j.immuno.2023.100023 [PDF]
    3. J. Nunez-Castilla, V. Stebliankin, P. Baral, C. A. Balbin, M. Sobhan, T. Cickovski, A. M. Mondal, G. Narasimhan, P. Chapagain, K. Mathee, and J. Siltberg-Liberles (2022). Potential Autoimmunity Resulting from Molecular Mimicry between SARS-CoV-2 Spike and Human Proteins. Viruses, 14(7): 1-20.  https://doi.org/10.3390/v14071415 [PDF]
    4. A. A. Mamun, R. B. Tanvir, M. Sobhan, K. Mathee, G. Narasimhan, G. E. Holt, A. M. Mondal (2021). Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers. International Journal of Molecular Sciences, 22 (21): 1-13.  https://doi.org/10.3390/ijms222111919 [PDF]
    5. M. Maharjan, R. B. Tanvir, K. Chowdhury, W. Duan, and A. M. Mondal (2020). Computational Identification of Biomarker Genes for Lung Cancer Considering Treatment and Non-Treatment Studies. BMC Bioinformatics, 21(9):1-19. DOI: 10.1186/s12859-020-3524-8 [PDF]
    6. R. B. Tanvir, T. Aqila, M. Maharjan, A. A. Mamun, and A. M. Mondal (2019). Graph Theoretic and Pearson Correlation Based Discovery of Network Biomarkers for Cancer. Data, 4(2): 1-12. https://doi.org/10.3390/data4020081 [PDF]
  10. Sample of Peer-Reviewed Conference Proceeding Publications
    1. Z. Chen, J. Zhang, J. Ni, X. Li, Y. Bian, M. M. Isam, A. M. Mondal, H. Wei and D. Luo (2024). Interpreting Graph Neural Networks with In-Distributed Proxies. The 2nd Workshop on Trustworthy Learning on Graphs (TrustLOG). Singapore, May 13-17. Accepted. [PDF]
    2. R. B. Tanvir, R. Ruiz, S. Ebert, M. Sobhan, A. A. Mamun, and A. M. Mondal (2023). Quantifying Intratumor Heterogeneity by Key Genes Selected Using Concrete Autoencoder. 10th International Conference on Pattern Recognition and Machine Intelligence (PReMI 2023), Kolkata, India, December 12-15, pages 844–852. https://doi.org/10.1007/978-3-031-45170-6_88 [PDF]
    3. M. Sobhan and A. M. Mondal (2023). Evaluating SHAP’s Robustness in Precision Medicine: Effect of Filtering and Normalization. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of Workshop on Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM 2023). Istanbul, Turkey, December 5-8, pages 3157-64. DOI: 10.1109/BIBM58861.2023.10385704 [PDF]
    4. D. Leizaola, M. Sobhan, K Kaile, A. M. Mondal, and A. Godavarty (2023). Deep Learning Algorithms to Classify Fitzpatrick Skin Types for Smartphone-Based NIRS Imaging Device. Next-Generation Spectroscopic Technologies XV, Vol. 12516. SPIE Defense + Commercial Sensing, Orlando, USA, June 15. https://doi.org/10.1117/12.2665179 [LINK]
    5. M. Sobhan, D. Leizaola, A. Godavarty, and A. M. Mondal (2022) Subject Skin Tone Classification with Implications in Wound Imaging using Deep Learning. 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, USA, December 14-16, pages 1648-53. [PDF]
    6. M. Sobhan and A. M. Mondal (2022). Explainable Machine Learning to Identify Patient-specific Biomarkers for Lung Cancer. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of Workshop on Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM 2022). Las Vegas, USA, December 06-08, pages 3152-59. DOI: 10.1109/BIBM55620.2022.9995516 [PDF]
    7. B. Tanvir, M. Sobhan, and A. M. Mondal (2022). An Autoencoder Based Bioinformatics Framework for Predicting Prognosis of Breast Cancer Patients. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of Workshop on Machine Learning and Artificial Intelligence in Bioinformatics and Medical Informatics (MABM 2022). Las Vegas, USA, December 06-08, pages 3160-66. DOI: 10.1109/BIBM55620.2022.9995632 [PDF]
    8. M. Sobhan, K. Kaile, A. Godavarty, and A. M. Mondal (2022). Skin Tone Benchmark Dataset for Diabetic Foot Ulcers and Machine Learning to Discover the Salient Features. Proceedings of The 26th International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV). Las Vegas, USA, July 25-28, pages 1-10. [PDF]
    9. K. Kaile, M. Sobhan, A. M. Mondal, and A. Godavarty (2022). Machine learning algorithms to classify Fitzpatrick skin types during tissue oxygenation mapping. Proceedings of Biophotonics Congress: Biomedical Optics, OSA Technical Digest. Fort Lauderdale, USA, April 24-27.  https://doi.org/10.1364/TRANSLATIONAL.2022.JM3A.4 [LINK]
    10. R. B. Tanvir and A. M. Mondal (2020). Stage-Specific Co-expression Network Analysis for Cancer Biomarker Discovery. Proceedings of 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of International Workshop on Biological Network Analysis and Integrative Graph-Based Approaches (IWBNA). Seoul, South Korea (Virtual), December 16-19, pages 1661–1667. DOI: 10.1109/BIBM49941.2020.9313242 [PDF]
    11. A. A. Mamun, W. Duan, and A. M. Mondal (2020). Pan-cancer Feature Selection and Classification Reveals Important Long Non-coding RNAs. Proceedings of 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Health Informatics (DLB2H). Seoul, South Korea (Virtual), December 16-19, pages 1853–60. DOI: 10.1109/BIBM49941.2020.9313332 [PDF]
    12. A. A. Mamun, M. Sobhan, R. B. Tanvir, C. J. Dimitroff, and A. M. Mondal (2020). Deep Learning to Discover Cancer Glycome Genes Signifying the Origins of Cancer. Proceedings of 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Health Informatics (DLB2H). Seoul, South Korea (Virtual), December 16-19, pages 1861–67. DOI: 10.1109/BIBM49941.2020.9313450 [PDF]
    13. M. Sobhan, A. Al Mamun, R. B. Tanvir, M. J. Alfonso, P. Valle, and A. M. Mondal (2020). Deep Learning to Discover Genomic Signatures for Racial Disparity in Lung Cancer. Proceedings of 2020 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). Seoul, South Korea (Virtual), December 16-19, pages 2147–49. DOI: 10.1109/BIBM49941.2020.9313426 [PDF]
    14. R. B. Tanvir and A. M. Mondal (2019). Cancer Biomarker Discovery from Gene Co-expression Networks Using Community Detection Methods. Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of International Workshop on Biological Network Analysis and Integrative Graph-Based Approaches (IWBNA). San Diego, USA, November 18-21, pages 2097–2104. DOI: 10.1109/BIBM47256.2019.8982960 [PDF]
    15. N. Z. Tsaku, S. C. Kosaraju, T. Aqila, M. Masum, D. H. Song, A. M. Mondal, H. M. Koh, M. Kang (2019). Texture-based Deep Learning for Effective Histopathological Cancer Image Classification. Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). San Diego, USA, November 18-21, pages 973–977. DOI: 10.1109/BIBM47256.2019.8983226 [PDF]
    16. T. Aqila, A. A. Mamun, and A. M. Mondal (2019). Pseudotime Based Discovery of Breast Cancer Heterogeneity. Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of International Workshop on Biological Network Analysis and Integrative Graph-Based Approaches (IWBNA), San Diego, USA, November 18-21, pages 2049–54. DOI:10.1109/BIBM47256.2019.8983300 [PDF]
    17. A. A. Mamun and A. M. Mondal (2019). Feature Selection and Classification Reveal Key lncRNAs for Multiple Cancers. Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of Workshop on Long Non-Coding RNAs: Mechanism, Function, and Computational Analysis. San Diego, USA, November 18-21, pages 2825–31. DOI: 10.1109/BIBM47256.2019.8983413 [PDF]
    18. M. Maharjan, R. B. Tanvir, K. Chowdhury, and A. M. Mondal (2019). Determination of Biomarkers for Diagnosis of Lung Cancer Using Cytoscape-based GO and Pathway Analysis. Proceedings of The 20th International Conference on Bioinformatics & Computational Biology (BIOCOMP’19). Las Vegas, USA, July 29 – Aug 01, pages 17-23. [PDF]
    19. A. M. Mondal, C. A. Schultz#, M. Sheppard#, J. Carson#, R. B. Tanvir, and T. Aqila (2018). Graph Theoretic Concepts as the Building Blocks for Disease Initiation and Progression at Protein Network Level: Identification and Challenges. Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) as part of Workshop BigDataNetAnalysis. Madrid, Spain, December 3-6, pages 2713-19. DOI: 10.1109/BIBM.2018.8621417 [PDF]