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Ms. Tanya Liyaqat

Ms. Tanya Liyaqat

Assistant Professor, Computer Science & Applications (CSA)

Tanya.liyaqat@sharda.ac.in

About

Tanya Liyaqat is a dedicated professional deeply committed to both teaching and research, particularly in the fields of artificial intelligence and machine learning. She holds a B.Tech in Computer Science and Information Technology from MJP Rohilkhand University, Bareilly, and an M.Tech in Computer Engineering from Jamia Millia Islamia University, Delhi. Currently pursuing her Ph.D. at Jamia Millia Islamia University, her research focuses on the application of machine learning in drug discovery. With over five years of research experience, Tanya has made significant contributions to her field, evident through her multiple research publications and presentations at international conferences. Her academic journey is complemented by certifications including GATE and NET qualifications, a JRF award, and several professional certifications in data science and machine learning fundamentals. Tanya's areas of expertise and interest encompass Artificial Intelligence (AI), Natural Language Processing (NLP), and Bioinformatics, reflecting her passion for leveraging advanced technologies to drive innovation in scientific research and education.


 

Experience

5 years experience as teaching assistant in Ph.D.

Qualification
  • B. Tech
  • M. Tech
  • Ph.D.(pursuing)
Award & Recognition

  • Successfully qualified the Graduate Aptitude Test in Engineering (GATE) in 2017 and 2019. 
  • Attained the National Eligibility Test (NET) certification for Teaching in 2017. 
  • Awarded the NET-Junior Research Fellowship (JRF) in 2018. 

Research

Total Research - 

  • Tanya Liyaqat and Tanvir Ahmad. ”A machine learning strategy with clustering under sampling of majority instances for predicting drug target inter- actions.” Molecular Informatics 42, no. 5 (2023): 2200102. (SCIE indexed) Impact factor - 3.6. Publisher: Wiley Online Library. 
  • Tanya Liyaqat, Tanvir Ahmad, and Chandni Saxena. ”TeM-DTBA: Time- efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection.” Journal of Computer-Aided Molecular Design 37, no. 12 (2023): 573-584. (SCIE indexed) Impact factor - 3.5. Publisher: Springer Nature. 
  • Tanya Liyaqat, Tanvir Ahmad, and Chandni Saxena. ”A Methodology for the Prediction of Drug Target Interaction Using CDK Descriptors.” In 29th International Conference on Neural Information Processing (ICONIP), pp. 408-419, 2022, Singapore: Springer Nature Singapore. (Scopus indexed) 
  • Tanya Liyaqat and Tanvir Ahmad. ”A brief review on Artificial Intelligence based Drug Target Interaction Prediction.” In 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), pp. 544-549. 2022, IEEE. (Scopus indexed) 

Certifications

Total - 8

  • Earned a Professional Certificate in Data Analysis from Coursera. 
  • Completed the 'Deep Learning Fundamentals' certification from Cognitive Class. 
  • Received an NPTEL ‘ELITE certificate for achieving a score of 71% in the course 'Bioinformatics: Algorithms and Applications' in 2024. 
  • Participated in an online Program on “Medical Image Processing” jointly organized by the Electronics and ICT Academies at IIT Roorkee, MNIT Jaipur, NIT Patna, PDPM IIITDM Jabalpur and NIT Warangal. 
  • Participated in the online Faculty Development Programme on "PYTHON PROGRAMMING LANGUAGE" organized by the Department of Computer Engineering, Jamia Millia Islamia, New Delhi in association with Spoken Tutorial, IIT- Bombay. 

Area of Interest

  • Machine Learning, Bioinformatics, Drug Discovery, Health informatics