Tamjid On The Internet

Curriculum Vitae

Hi, I am Tamjid Hasan Fahim. Computer Science & Engineering ~~freshman~~ ~~sophomore~~ ~~junior-year~~ senior-year student (shocker!) at RUET. Wannabe a real computer scientist someday.

To me, Computer Science feels like just as Terry Pratchett wrote, “It is still magic even if you know how it is doing.” Magic not with wands and robes, but the kind where absurdly simple building blocks stack up through layers of abstraction until they turn into systems of remarkable complexity and elegance. This enduring sense of wonder, of being able to grasp the underlying mechanism yet still be astonished by its form, drives me to explore deeper into the black boxes that make modern computing possible.

My current research focuses on the intersection of NLP methodologies and human cognition, using computational models to better understand and replicate human thought processes. In particular, I am interested in how AI systems can move beyond token-level statistical patterns toward representations that more closely resemble human perception — enabling them to form coherent, unified understanding from language and vision rather than treating information as fragmented signals across modalities.

[July 2026] Best Interdisciplinary Insight Paper Award at the BioNLP Workshop (ACL 2026) PsyDefDetect Shared Task (also secured 3rd place on the official leaderboard) for “Overcoming Extreme Response Bias in LLMs via Rubric-Grounded Retrieval and Supervised Clinical Reasoning Distillation for Fine-Grained Ordinal Classification”. [Paper]
[Jul 2026] Co-authored “AREG: Adversarial Resource Extraction Game for Evaluating Persuasion and Resistance in Large Language Models”. Accepted at the MusIML Workshop, ICML 2026. [Paper]
[Mar 2026] Academic update for the third-year even semester: Achieved an SGPA of 3.90, placing in the top 1.7% of the class among 177 students.
[Nov 2025] Paper accepted at the Bangla Language Processing Workshop (IJCNLP-AACL) Shared Task: “Domain-Adapted BERT for Bangla Hate Speech Detection: Contrasting Single-Shot and Hierarchical Multiclass Classification”.