Saem Hasan

Email: saemhasan027@gmail.com

Links: [Curriculum Vitae] [LinkedIn] [GitHub]

Publications

Pre-prints:

1. Terraces in Species Tree Inference from Gene Trees
   Mursalin Habib*, Kowshic Roy*, Saem Hasan*, Atif Hasan Rahman, Md. Shamsuzzoha Bayzid
   *: Equal contribution
   bioXiv Pre-print, 2023
   [bib] [abstract]

   A terrace in a phylogenetic tree space is a region where all trees contain the same set of subtrees, due to certain patterns of missing data among the taxa sampled, resulting in an identical optimality score for a given data set. This was first investigated in the context of phylogenetic tree estimation from sequence alignments using maximum likelihood (ML) and maximum parsimony (MP). The concept of terraces was later extended to the species tree inference problem from a collection of gene trees, where a set of equally optimal species trees was referred to as a “pseudo” species tree terrace. Pseudo terraces do not consider the topological proximity of the trees in terms of the induced subtrees resulting from certain patterns of missing data. In this study, we mathematically characterize species tree terraces and investigate the mathematical properties and conditions that lead multiple species trees to induce/display an identical set of locus-specific subtrees owing to missing data. We report that species tree terraces are agnostic to gene tree topologies and the discordance therein. Therefore, we introduce and characterize a special type of gene tree topology-aware terrace which we call “peak terrace”, and investigate conditions on the patterns of missing data that give rise to peak terraces. In addition to the theoretical and analytical results, we empirically investigated different challenges as well as various opportunities pertaining to the multiplicity of equally good species trees in terraced landscapes. Based on an extensive experimental study involving both simulated and real biological datasets, we present the prevalence of species tree terraces and the resulting ambiguity created for tree search algorithms. Remarkably, our findings indicate that the identification of terraces and the trees within them can substantially enhance the accuracy of summary methods. Furthermore, we demonstrate that reasonably accurate branch support can be computed by leveraging trees sourced from these terraces.

                                       @@article {Habib2022.11.21.517454,
                                           author = {Mursalin Habib and Kowshic Roy and Saem Hasan and Atif Hasan Rahman and Md. Shamsuzzoha Bayzid},
                                           title = {Terraces in Species Tree Inference from Gene Trees},
                                           elocation-id = {2022.11.21.517454},
                                           year = {2023},
                                           doi = {10.1101/2022.11.21.517454},
                                           publisher = {Cold Spring Harbor Laboratory},
                                           abstract = {A terrace in a phylogenetic tree space is a region where all trees contain the same set of subtrees, due to certain patterns of missing data among the taxa sampled, resulting in an identical optimality score for a given data set. This was first investigated in the context of phylogenetic tree estimation from sequence alignments using maximum likelihood (ML) and maximum parsimony (MP). The concept of terraces was later extended to the species tree inference problem from a collection of gene trees, where a set of equally optimal species trees was referred to as a {\textquotedblleft}pseudo{\textquotedblright} species tree terrace. Pseudo terraces do not consider the topological proximity of the trees in terms of the induced subtrees resulting from certain patterns of missing data. In this study, we mathematically characterize species tree terraces and investigate the mathematical properties and conditions that lead multiple species trees to induce/display an identical set of locus-specific subtrees owing to missing data. We report that species tree terraces are agnostic to gene tree topologies and the discordance therein. Therefore, we introduce and characterize a special type of gene tree topology-aware terrace which we call {\textquotedblleft}peak terrace{\textquotedblright}, and investigate conditions on the patterns of missing data that give rise to peak terraces. In addition to the theoretical and analytical results, we empirically investigated different challenges as well as various opportunities pertaining to the multiplicity of equally good species trees in terraced landscapes. Based on an extensive experimental study involving both simulated and real biological datasets, we present the prevalence of species tree terraces and the resulting ambiguity created for tree search algorithms. Remarkably, our findings indicate that the identification of terraces and the trees within them can substantially enhance the accuracy of summary methods. Furthermore, we demonstrate that reasonably accurate branch support can be computed by leveraging trees sourced from these terraces.Competing Interest StatementThe authors have declared no competing interest.},
                                           URL = {https://www.biorxiv.org/content/early/2023/08/07/2022.11.21.517454},
                                           eprint = {https://www.biorxiv.org/content/early/2023/08/07/2022.11.21.517454.full.pdf},
                                           journal = {bioRxiv}
                                       }
                                   

Ongoing Projects

1. STELAR: Beyond Rooted Gene Trees [statement]

   Extending the STELAR algorithm to handle unrooted gene trees.

   
   Supervisor: Dr. Md. Shamsuzzoha Bayzid
   
   
2. Iterative ScribbleDom [statement]

   Iteratively guiding the model to learn from the user's scribbles.

   
   Supervisor: Dr. Mohammad Saifur Rahman
   
   
3. Extension of PMAO using Evolutionary Algorithms [statement]

   Using Evolutionary Algorithms to extend the PMAO algorithm.

   
   Supervisor: Dr. Muhammad Ali Nayeem
   
   

Education

• (June 2023 - Present) Master of Science in Computer Science and Engineering
  Department of Computer Science and Engineering
  Bangladesh University of Engineering and Technology (BUET)
  Supervisor: Dr. Md. Shamsuzzoha Bayzid
  
  
• (March 2018 - May 2023) Bachelor of Science in Computer Science and Engineering
  Department of Computer Science and Engineering
  Bangladesh University of Engineering and Technology (BUET)
  CGPA: 3.96/4.00
  Thesis Supervisor: Dr. Md. Shamsuzzoha Bayzid
  

Professional Experience

• (October 2023 - Present) Lecturer
  Department of Computer Science and Engineering
  Bangladesh University of Engineering and Technology (BUET)
  
  
• (June 2023 - October 2023) Adjunct Lecturer
  Department of Computer Science and Engineering
  Bangladesh University of Engineering and Technology (BUET)
  
  

Honors & Awards

• (2018 - 2022) Dean's Award in each academic year for excellent undergraduate results
  
• (2018 - 2022) University Merit Scholarships in each semester for excellent undergraduate results
  

Teaching Experience

• July 2023

  • Course Instructor @ Computer Architecture (CSE 305)
  • Course Instructor @ Computer Architecture Sessional (CSE 306)
  • Course Instructor @ Digital Logic Design Sessional (CSE 206)
  • Course Instructor @ Computer Programming Sessional (CSE 282)
  • Course Instructor @ Structured Programming Language Sessional (CSE 102)

• January 2023

  • Course Instructor @ Data Structures and Algorithms I Sessional (CSE 106)
  • Course Instructor @ Object Oriented Programming Language Sessional (CSE 108)
  • Course Instructor @ Database Sessional (CSE 216)
  • Course Instructor @ Computer Security Sessional (CSE 406)
  • Course Instructor @ Software Development Sessional (CSE 408)

• Others

  • Course Instructor @ C Programming Virtual Classes for BUET 2022 Batch. [Link]
  • Course Instructor @ Business Analytics [Link]