As a part of its education mission, the Columbia-IBM Center for Blockchain and Data Transparency supports 5 summer internships annually, placing Columbia students from a breadth of backgrounds, from computer science undergraduates to business PhDs, at IBM.
Our students spend their summer tackling one of the biggest technological questions of our time. Data is rapidly becoming more accessible and valuable, permeating into all economic sectors – health, finance, retail, energy, legal, media and entertainment, etc. – but in conjunction the security and privacy of data is becoming increasingly important. How can we benefit from data while respecting the conditions imposed on use of the data?
Fortunately, technologies such as blockchain, homomorphic encryption, secure multi-party computation, zero knowledge proofs, and secure hardware make it possible for mutually distrusting organizations to share data in a secure, privacy-preserving, and tamper-proof manner. In conjunction, policies are emerging on data use and governance. Our summer interns work with leading researchers at IBM to further enhance new technologies, policies, and frameworks to solve this data dilemma.
Students work in the following topic areas:
- Area 1 - Technology, systems and algorithms: Advances in cryptography, game theory, network science, machine learning, and distributed systems that enable and incentivize trusted privacy preserving data sharing & secure distributed computations
- Area 2 - Business model, services and applications: Innovative applications of blockchain and data transparency technologies to create monetization frameworks for data sharing, including building or studying specific industry use cases
- Area 3 - Policy, regulation, law and behavior: Fundamental legal and policy frameworks for distributed data sharing networks, and business models to create, sustain, and evolve these networks and their applications
Explore examples of our Summer 2020 student work below:
Zihe Wang is a 2nd year Data Science student at the Columbia Data Science Institute. Before Columbia, He received his B.S. degree with a double major in Statistics & Computer Science and Applied Math from University of Illinois at Urbana-Champaign in 2019.
Zihe Wang interned at IBM Research during the summer of 2020. He joined the supply chain research team and worked on deep learning time series forecasting methods and their applications.
I am a second-year PhD student in Quantitative Marketing at Columbia University Graduate School of Business. Before my doctoral studies, I completed my masters in Economics at the University of Mannheim in Germany. My current research focuses on the usage of NLP techniques in marketing.