Rajat Rajbhandari, PhD

Rajat Rajbhandari, PhD

CIO and Co-founder at dexFreight

Recent posts by Rajat Rajbhandari, PhD

5 min read

Future of Logistics Workflow Automation Using AI and Smart Contracts

By Rajat Rajbhandari, PhD on Sep 10, 2021

dexFreight has embarked on a journey to build an AI-powered collaborative system to automate logistics workflow.

Topics: smart contracts artificial intelligence
7 min read

Smart contracts and wallets will turn trucks to next-generation logistics workhorse

By Rajat Rajbhandari, PhD on Aug 12, 2021

In this blog post, we argue that automated trucks (especially Levels 4 and 5) will be embedded with digital wallets connected with smart contracts to interact with blockchain to:

Topics: Use case Blockchain Trucking
3 min read

dexFreight Omnichannel Data Marketplace Powered by Ocean Protocol

By Rajat Rajbhandari, PhD on Feb 13, 2020

The dexFreight omnichannel data marketplace, powered by Ocean Protocol, enables transportation and logistics companies to aggregate and monetize operational data.

Topics: Data Silos Data Marketplace Ocean Protocol
5 min read

Introducing Blockchain Powered Marketplace to Monetize Logistics Data

By Rajat Rajbhandari, PhD on Feb 13, 2020

dexFreight and Ocean Protocol are building the first Web3 marketplace for logistics industry to unlock and monetize data.

Topics: Logistics Data Marketplace Ocean Protocol
5 min read

Is the blockchain community overlooking interoperability?

By Rajat Rajbhandari, PhD on Jan 14, 2020

In this blog, we explain why interoperability is critical in the logistics industry for the widespread adoption of blockchain.

Topics: Use case Blockchain
4 min read

Data Science and Machine Learning at dexFreight

By Rajat Rajbhandari, PhD on Dec 23, 2019

How dexFreight is utilizing machine learning to improve logistics operations and reduce data silos?

Topics: Machine learning Data Silos
1 min read

Use Cases of Machine Learning in dexFreight’s  Logistics Platform

By Rajat Rajbhandari, PhD on Jul 30, 2018

In this document, we describe in high-level use cases and strategies to implement machine learning algorithms.

Topics: Use case Machine learning Logistics