Faculty Profile

Taraka Kasicheyanula

Title
Assistant Professor
Department
Linguistics
College
College of Information

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, University of Gothenburg, 2015. 
Major: Natural Language Processing
Degree Specialization: Computational Historical Linguistics
Dissertation Title: Studies in computational historical linguistics





          

        
                

          
Current Scheduled Teaching*


          
No current or future courses scheduled.


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  


    

    

      


        


          


            
Previous Scheduled Teaching*


            
                    

                    LING 5412.001, NLP in Linguistics, Fall 2021  SPOT
                    

                    LING 4135.001, Python Programming for Text, Fall 2021 Syllabus SPOT
                    

                    LING 5405.001, Python Programming for Text, Fall 2021  SPOT
                    

                    CSCE 6290.001, Advanced Topics in Human/Machine Intelligence, Spring 2021 Syllabus SPOT
                    

                    LING 4140.001, Computational Linguistics, Spring 2021 Syllabus SPOT
                    

                    LING 5410.001, Computational Linguistics 1, Spring 2021 Syllabus SPOT
                    

                    LING 5410.600, Computational Linguistics 1, Spring 2021 Syllabus SPOT
                    

                    LING 5415.001, Computational Linguistics II, Spring 2021  SPOT
                    

                    LING 6040.001, Introduction to Computational Linguistics, Spring 2021  SPOT
                    

                    LING 5412.001, NLP in Linguistics, Fall 2020 Syllabus SPOT
                    

                    LING 5020.001, Studies in Historical Linguistics, Fall 2020  SPOT
                    

                    CSCE 6290.001, Advanced Topics in Human/Machine Intelligence, Spring 2020 
                    

                    LING 5410.001, Computational Linguistics 1, Spring 2020 
                    

                    LING 5410.600, Computational Linguistics 1, Spring 2020 
                    

                    LING 5415.001, Computational Linguistics II, Spring 2020 
                    

                    LING 2040.001, Endangered Languages, Fall 2019 Syllabus SPOT

          


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  

  
  	

	

	  

		

		   
Published Publications



  

  
Published Intellectual Contributions


  
Conference Proceeding
Kasicheyanula, T. R., Beinborn, L., Eger, S.  (2020). Probing Multilingual BERT for Genetic and Typological Signals. Proceedings of the 28th International Conference on Computational Linguistics. 1214--1228.  Barcelona, Spain (Online): International Committee on Computational Linguistics. https://www.aclweb.org/anthology/2020.coling-main.105
Cathcart, C., Kasicheyanula, T. R. (2020). Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping. Proceedings of the 24th Conference on Computational Natural Language Learning. 620--630.  Online: Association for Computational Linguistics. https://www.aclweb.org/anthology/2020.conll-1.50
Aiken, B., Kelly, J., Palmer, A. M., Polat, S. O., Kasicheyanula, T. R., Nielsen, R. D. (2019). Sigmorphon 2019 Task 2 system description paper: Morphological analysis in context for many languages, with supervision from only a few. https://www.aclweb.org/anthology/W19-4211.pdf
Kasicheyanula, T., List, J.  (2019). An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics. 11. Association for Computational Linguistics. https://www.aclweb.org/anthology/P19-1627.pdf
Journal Article
Kasicheyanula, T. R., Wichmann, S.  (2020). A test of Generalized Bayesian dating: A new linguistic dating method. PLOS One. 15(8), 1-18. Public Library of Science. https://doi.org/10.1371/journal.pone.0236522



	
		
		
	  

	

	

  
    
	

	

	  

		

		   
Awarded Grants



  
  
  	  
          
Contracts, Grants and Sponsored Research  

 	 
	 	 
  
Grant - Research   
Palmer, A. M., Kasicheyanula, T. R., "nVidia Accelerated Data Science GPU Grant," Sponsored by nVidia, International, $12000 Funded. (2019 – 2019).



	
		
		
	  

	

	

	
	

  
  
  

  	

		

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					Overall
Summative Rating		
					Challenge and
Engagement Index		
					Response Rate
					
				

			
			
				

					
					
out of 5		
					
out of 7		
					%
 of 
students responded		
					
					
				

			
		


		
    
			
  • Overall Summative Rating (median):
    
			This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
				
      
					
    • The course as a whole was
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    • The course content was
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    • The instructor’s contribution to the course was
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    • The instructor’s effectiveness in teaching the subject matter was
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  • Challenge and Engagement Index:
    
			This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
				
      
					
    • Do you expect your grade in this course to be
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    • The intellectual challenge presented was
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    • The amount of effort you put into this course was
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    • The amount of effort to succeed in this course was
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    • Your involvement in course (doing assignments, attending classes, etc.) was
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