Anindya Bijoy Das
Computer Science · Purdue University West Lafayette
Publications
102
Citations
854
Est. group size
~3
Recurring co-author estimate
Active years
23
Publishing since 2004
Anindya Bijoy Das works in computer science, with recent research focused on the reliability and security of large language models (LLMs), including their tendency to produce false or misleading content (hallucinations), vulnerability to adversarial manipulation, and use in domains like medical imaging and agriculture. Earlier work included topics such as coded distributed computing, privacy-preserving data techniques, and signal processing methods like blind source separation. The overall trajectory reflects a shift toward evaluating trustworthiness, robustness, and fairness of modern AI systems.
Publication output has grown substantially over the last decade, rising from just a few papers per year before 2022 to over a dozen annually since 2023.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- A Survey on Coded Matrix Computations and Gradient Coding
IEEE BITS the Information Theory Magazine · 2026
- Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework
arXiv (Cornell University) · 2026
- Adversarial Reframing: A Framework for Targeted Generation in Language Models
arXiv (Cornell University) · 2026
- Classification of Single and Mixed Partial Discharges under Switching Voltage Using an AWA-CNN Framework
arXiv (Cornell University) · 2026
- Adversarial Reframing: A Framework for Targeted Generation in Language Models
arXiv (Cornell University) · 2026
- Memory as an Attack Surface in LLM Agents: A Study on Multiple-Choice Question Answering
arXiv (Cornell University) · 2026
- Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
arXiv (Cornell University) · 2026
- Memory as an Attack Surface in LLM Agents: A Study on Multiple-Choice Question Answering
arXiv (Cornell University) · 2026
- Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
arXiv (Cornell University) · 2026
- Battling Misinformation: An Empirical Study on Adversarial Factuality in Open-Source Large Language Models
2025
- Can Large Language Models Challenge CNNs in Medical Image Analysis?
2025
- Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models
2025
- Can Large Language Models Challenge CNNs in Medical Image Analysis?
arXiv (Cornell University) · 2025
- Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models
arXiv (Cornell University) · 2025
- Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities
2025
- arXiv (Cornell University)×33
- PLoS ONE×3
- Biomedical Signal Processing and Control×2
- IEEE Transactions on Information Theory×2
- IEEE Journal on Selected Areas in Communications×2
- Sahil Tyagi
Computer Science · Indiana University
- Haoyu Wang
Computer Science · Purdue University West Lafayette
- Abolfazl Hashemi
Computer Science · Purdue University West Lafayette
- Feijie Wu
Computer Science · Purdue University West Lafayette
- Dong-Jun Han
Computer Science · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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