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Academic Experts
Dr. Akanksha Mehndiratta

Biography

Akanksha Mehndiratta is an accomplished academic and researcher currently serving as an Assistant Professor at Jaypee Institute of Information Technology (JIIT), Noida. With a strong foundation in computer science, she holds a Master of Technology (M.Tech) degree in Computer Science and Engineering, complemented by a Bachelor of Technology (B.Tech) in the same domain. She has completed her Ph.D. with a focus on discourse modeling and deep learning techniques for natural language understanding.

With over 12 years of teaching and research experience, Akanksha has developed a deep expertise in machine learning, natural language processing (NLP), and data analytics. Her research interests include retrieval-based dialogue systems, discourse structure analysis, and multimodal representation learning using Canonical Correlation Analysis (CCA) and its deep variants.

At JIIT, she is actively involved in mentoring undergraduate and postgraduate students, delivering lectures on core computing subjects, and guiding student projects in AI and data science. She has contributed to multiple conferences and journals and remains engaged with the academic community through workshops, peer reviews, and interdisciplinary collaborations.

Ms. Akanksha is passionate about bridging the gap between theoretical concepts and practical applications, aiming to equip her students with both strong academic grounding and industry-relevant skills. Her commitment to continuous learning and innovation positions her as a dedicated educator and an emerging voice in computational linguistics and AI research.

She continues to work towards creating impactful, data-driven solutions to real-world problems through her academic and scholarly contributions.

Educational Qualifications

Ph.D. (Computer Science & Engineering) M.Tech. (Computer Science and Engineering) B.Tech. (Computer Science and Engineering)

Research Highlights

Akanksha Mehndiratta’s research primarily focuses on natural language processing, discourse modeling, and machine learning, with a special interest in retrieval-based dialogue systems and deep learning architectures. Her work explores how discourse relations can be effectively modeled using advanced representation learning techniques, such as Canonical Correlation Analysis (CCA), Multiview CCA, and Deep CCA, to improve context understanding in dialogue systems.

She has contributed to the development of discourse-aware retrieval models, which incorporate structured representations of utterances and relations for enhanced response relevance. Akanksha has also conducted comparative analyses of annotation frameworks like PDTB and CCR, shedding light on their applicability in both written and spoken discourse. She is particularly interested in leveraging multiview learning and spectral methods to align linguistic features across different levels of discourse structure.

Her contributions include conference presentations, peer-reviewed publications, and collaborative projects that bridge theory with practical AI solutions. Through her work, Akanksha aims to advance the understanding of discourse in computational systems, making strides in both research and realworld NLP applications.

Areas of Interest
  • Natural Language Processing
  • Machine Learning
  • Deep Learning
Publications
  • A. Mehndiratta and K. Asawa, “Performance Evaluation and Analysis in Complex Systems,” International Journal of Performability Engineering, vol. 21, no. 2, pp. 6573, Feb. 2025. doi: 10.23940/ijpe.25.02.p1.657.
  • A. Mehndiratta and K. Asawa, “Discovering Elementary Discourse Units in Textual Data using Canonical Correlation Analysis,” International Journal of Performability Engineering, vol. 20, no. 12, pp. 723–732, Dec. 2024.
  • A. Mehndiratta and P. Mehndiratta, “Factors Affecting Student’s Academic Performance in Programming Using Association Rule Mining,” in Proc. 2023 Fifteenth Int. Conf. on Contemporary Computing (IC3), Noida, India, 2023, pp. 371–374. doi: 10.1145/3607947.3608030
  • A. Mehndiratta and K. Asawa, “Non-goal oriented dialogue agents: state of the art, dataset, and evaluation,” Artificial Intelligence Review, vol. 54, no. 1, pp. 329–357, Jan. 2021. doi: 10.1007/s10462-020-09848-z
  • A. Mehndiratta and K. Asawa, “Spectral Learning of Semantic Units in a Sentence Pair to Evaluate Semantic Textual Similarity,” in Big Data Analytics (BDA 2020), L. Bellatreche, V. Goyal, H. Fujita, A. Mondal, and P. K. Reddy, Eds., Lecture Notes in Computer Science, vol. 12581, Cham, Switzerland: Springer, 2020, pp. 41–56. doi: 10.1007/978-3-030-66665-1_4