About
Today's AI has no continuity. It forgets who it is the moment a conversation ends, and even within a single conversation, its sense of self can be bent by pressure. I work on a layer that has to come before any of the bigger ambitions in this field: persistent, verifiable identity. Before an AI can reason about its own thinking, or be trusted with real autonomy, it first has to reliably stay itself. That turns out to be one of the hardest and least-solved problems we have.
My research establishes a formal impossibility result about it. Under the stated assumptions, a gate observing only an agent's current output cannot reliably distinguish adversarial pressure from genuine correction. They are indistinguishable, one answer at a time. The only way through is to watch how an agent changes across its whole trajectory. So I build the benchmarks that stress-test agents under sustained adversarial pressure, and the methods that catch identity drift as it happens, not after the damage is done.
I see this as one foundational block in a much larger arc: memory, identity, metacognition, and eventually self-aware reasoning, the pieces that separate a tool that answers from a system that knows who it is. The same instinct runs through the rest of my work. As a data scientist I turn messy, unstructured information into systems people can actually trust, and as a reviewer for the IPCC and the UN Environment Programme I've seen how much depends on making complex systems legible to the people who have to rely on them, whether that system is a climate model or a language agent. The hard part of the coming decade won't be making AI more powerful. It'll be making it stable, accountable, and trustworthy enough to hand real responsibility. That's the gap I want to spend my career closing.
9
Papers & Presentations
4+
Startup Experience
20+
Community & Leadership Roles
50+
Hackathons Participated
100+
Projects
Research
AI Systems
2021–22
→
Carbon Intelligence
2022–25
→
Agent Identity
2025–26
→
Persistent Identity
ongoing
→
Metacognition
horizon
Current research agenda
Having established what single-turn observation cannot achieve, I am now developing the positive counterpart: formal conditions under which persistent agent identity becomes achievable, and the architectural components (external memory, temporal self-models, trajectory-level verification) that could satisfy them. The next horizon is metacognition: agents that observe and reason about their own cognitive states, which requires stable identity as a prerequisite. Both directions connect to formal verification, mechanistic interpretability, and AI governance.
Selective Rigidity: An Impossibility Result and Benchmark for Identity-Preserving Agent Learning
We prove that, under the stated assumptions, a gate observing only an agent's current output cannot reliably distinguish adversarial pressure from genuine correction, making a signal over the agent's whole trajectory necessary. Introduces ICC+TSM, a three-layer identity gate with an online temporal self-model, and the Identity Erosion Chamber, a 70-turn adversarial benchmark for measuring identity drift. Governance implication: accountability schemes relying on single-turn behavioral attestation face a hard ceiling on verification fidelity.
Venues & appearances
Publications & presentations
AI-Enhanced Carbon Simulation Framework: Towards Real-Time Enterprise Sustainability Planning
Invited Talk · ELLIS UnConference, Workshop on AI for Earth and Climate Sciences (EurIPS) · Copenhagen, Denmark · 2025
A real-time simulation framework that lets enterprises model the carbon impact of operational and sourcing decisions before they're made, turning static emissions reporting into forward-looking sustainability planning.
Strengthening Data Consistency for EU Taxonomy Compliance
Conference Presentation · Sustainable and Impact Investments International Conference (SIIIC) · Dublin, Ireland · 2025
A framework for resolving fragmented Scope 3 emissions data under the EU Taxonomy and CSRD, combining data-mapping, IoT ingestion and generative AI to turn ESG compliance from a reporting burden into an actionable decarbonization tool.
Transparency in Carbon Credit by Automating Data-Management Using Blockchain
Peer-Reviewed Publication · IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) · Pune, India · 2022
Blockchain-based auditability for carbon-credit data management, improving trust and reducing double-counting risk across enterprise reporting.
Earlier research
Virtual Office Prototype: A Unity 2-D Work Simulation
Peer-Reviewed Publication · International Conference on Technological Advancements and Innovations (ICTAI) · Tashkent, Uzbekistan · 2021
A Unity-based 2-D simulation prototype exploring remote collaborative workspaces with real-time collaboration and attendance automation.
Development of Information Technology Telecom Chatbot: An Artificial Intelligence and Machine Learning Approach
Peer-Reviewed Publication · IEEE International Conference on Intelligent Engineering and Management (ICIEM) · London, United Kingdom · 2021
Design and evaluation of an AI/ML-driven chatbot for telecom IT support workflows.
