I'm a computer scientist with experience in distributed graph analytics,
authentication and authorization platforms, and AI systems (both symbolic,
deep learning, and LLMs).
My journey spans academic research at ETH Zurich and Imperial College
London, co-founding and scaling a graph analytics startup (acquired by PwC),
and leading enterprise-scale data and AI initiatives within PwC.
Currently, I am the CTO at Futurae,
working on strong customer authentication and identity for fintech, and the
Chief Scientist (board advisor) at MadSense,
working on agentic workforce in adtech. In both roles I lead the deployment of
AI agents as part of the everyday engineering and operational workforce.
Current
--cto futurae
Lead the Backend, SDK, QA, and Data teams developing Futurae's MFA and
Strong Customer Authentication (SCA) products for fintech, along with trust
signals and digital wallet products. Current work focuses on two problems
raised by AI agents:
Strong human authentication: proving that a real human is behind a
transaction, to prevent agent-driven fraud.
Agentic authorization and identity: access points, built on top of
OAuth2, through which humans delegate scoped and auditable authority to
agents.
Also works closely with product and sales on the anti-phishing offering
and risk-based authentication built on passkeys and ML models.
--chief-scientist madsense
Serve as Chief Scientist and board advisor, helping the company build an
agentic workforce for the adtech industry. Advise on the agent architecture,
on how agents are evaluated and monitored in production, and on how they fit
into the company's teams and workflows.
--agents
Lead the rollout of an agent workforce across engineering, putting agents
to work on development, code review, testing, and operations, while keeping
the security and reliability bar expected in fintech.
--continuous-delivery
Build and mentor engineering teams that ship small changes often and
recover quickly when things go wrong:
Continuous delivery: trunk-based development, automated testing,
and frequent, low-risk deployments.
Monitoring: services instrumented from day one, with alerts tied
to what customers actually experience.
Resilience: fail-safe defaults, rehearsed rollbacks and recovery
procedures, and blameless post-mortems.
Agents as teammates: AI agents working inside the same delivery,
review, and monitoring loops as engineers.
--research
See HISTORY below.
History
PwC US, Tech-and-Innovation Director
Integrated the Signal Graph
technology from SignalFrame into various PwC projects, including
Smart Venues
for IoT-driven venue management, and an organizational network analysis
platform based on graph ML and LLMs. Also built tools to measure and improve
ROI on LLMs and agents in PE companies.
SignalFrame, co-founder and CTO (2015-2021)
Technical co-founder and CTO of SignalFrame
(acquired by PwC in 2021). Led a team of 20 engineers in building a streaming
temporal graph platform that processed data from ~1 billion IoT devices
monthly. The daily stream consisted of ~100 million vertices with ~1 billion
edges. On top of this knowledge graph we implemented advanced graph
embeddings for temporal and spatial analytics, for behavioral anomaly
detection and structural forecasting.