Trustworthy machine learning
Reliable AI systems whose behavior can be evaluated beyond benchmark accuracy.
Research profile
Research, projects, and grants
I develop AI systems that remain dependable when data shift, unknown categories appear, inputs are corrupted or adversarial, and models must continue learning.
Research vision
My research asks how models can recognize unfamiliar or shifting data and behave reliably under adversarial or corrupted inputs. I study anomaly detection, open-world recognition, and learning over time, with a focus on computer vision and trustworthy machine learning.
I work at New Uzbekistan University and OIST, Japan. Earlier research appointments took me to IPM in Tehran, the University of Oulu, and Troyes. See my academic CV for background, or industry consulting for applied collaborations.
Research interests
The four themes connecting my theoretical work, evaluation methods, and practical AI systems.
Anomaly and OOD detection
Recognizing unfamiliar, rare, shifted, or abnormal inputs.
Continual and lifelong learning
Learning over time while preserving useful knowledge and managing change.
Robustness, safety, and reliability
Studying adversarial, corrupted, backdoored, and failure-prone AI behavior.
Current workplaces
Research and collaboration connecting Uzbekistan and Japan.
New Uzbekistan UniversityAssociate Professor · Tashkent, Uzbekistan
Okinawa Institute of Science and TechnologyVisiting Researcher · Okinawa, JapanCampus photos: New Uzbekistan University · OIST / CC BY 2.0
Featured research
Selected work on dependable perception and recognizing unknown conditions.
CRANE
Context-guided prompt learning and attention refinement for zero-shot anomaly detection.
FrameShield
Adversarially robust video anomaly detection for more dependable visual monitoring.
Universal Novelty Detection
Adaptive contrastive learning for detecting novel data across datasets and tasks.
Research projects and grants
Japanese national research funding and institutional research programs.
- Breaking Boundaries: Robust, Domain-General Anomaly Detection with Vision-Language Models
2026–2030 · ¥18,330,000 · Principal Investigator
Japanese national research funding: JSPS KAKENHI, Grant-in-Aid for Scientific Research (B) · Funded at OIST, Okinawa, Japan · Official KAKEN record (26K02988) - Investigating the Trustworthiness of Deep Pre-trained and Self-Supervised Models
2024–2026 · ¥4,680,000 · Principal Investigator
Japanese national research funding: JSPS KAKENHI, Grant-in-Aid for Early-Career Scientists · Funded at OIST, Okinawa, Japan · Official KAKEN record (24K20806) - AI Safety and Security for Classical AI Models
Institute for Research in Fundamental Sciences (IPM)