Dr. Michael Meehan Image

Dr. Michael Meehan

General Counsel and Chief Legal Officer

Diveplane Corporation

Dr. Michael Meehan is the General Counsel and Chief Legal Officer of Diveplane Corporation. He has shepherded numerous companies to strong positions, both in terms of ongoing operations and in preparation for funding and acquisition. In addition to advising numerous startups as an attorney and board member, his career includes driving the IP position of the maps and geo business unit at Google, as well as leading and dramatically growing Uber’s IP legal team. Before becoming an attorney, Dr. Meehan completed his PhD in computer science at UNC concentrating on virtual reality, and he has held research positions at Stanford (where he later attended law school), an Italian hospital, Eastman Kodak, and a Swiss government research lab. He also worked extensively in the tech industry on the engineering side, working his way from computer programmer to CTO and director of engineering at various dot.coms. When he is not working on closing deals or positioning Diveplane, you will find him training for half marathons and triathlons, and chasing his two toddlers around.

Recent Articles by Dr. Michael Meehan

Machine Learning Models and the Legal Need for Editability: Surveying the Pitfalls (Part II)

In Part I of this series, we discussed the Federal Trade Commission’s (FTC’s) case against Everalbum as just one example where companies may be required to remove data from their machine learning models (or shut down if unable to do so). Following are some additional pitfalls to note. A. Evolving privacy and data usage restrictions Legislators at the international, federal,…

Machine Learning Models: The Legal Need for Editability (Part I)

A widespread concern with many machine learning models is the inability to remove the traces of training data that are legally tainted. That is, after training a machine learning model, it may be determined that some of the underlying data that was used to develop the model may have been wrongfully obtained or processed. The ingested data may include files that an employee took from a former company, thus tainted with misappropriated trade secrets. Or the data may have been lawfully obtained, but without the adequate permissions to process the data. With the constantly and rapidly evolving landscape of data usage restrictions at the international, federal, state, and even municipal levels, companies having troves of lawfully-obtained data may find that the usage of that data in their machine learning models becomes illegal.