Crawford Allan is an international expert on wildlife trafficking and trade with 25 years’ experience in wildlife conservation. Working with TRAFFIC, the wildlife trade program of WWF and IUCN, Allan has led black market investigations internationally in dozens of countries and has directly supported strong enforcement action, including major seizures, arrests and prosecutions internationally. He is the TRAFFIC leader globally of the joint WWF and TRAFFIC Wildlife Crime Initiative and a member of the US President's Federal Advisory Panel on Wildlife Trafficking.

He was the leader of phase 1 of WWF’s Wildlife Crime Technology project – the recipient of a $5m Google Global Impact Award Grant. Current Chief of Party on the USAID ROUTES Partnership, a $10m investment reducing opportunities for unlawful transport of endangered species with the transport sector. A technical Judge on the USAID Wildlife Crime Tech Challenge.

The author of several publications and numerous papers on wildlife trafficking, species conservation, improving wildlife law enforcement and building strong enforcement partnerships, he guides the development of innovative approaches to wildlife trade challenges. He helped to pioneer regional wildlife enforcement networks, including the ASEAN WEN and the Central American WEN (ROAVIS).

Featured in hundreds of media and communications pieces including documentaries like HBOs “VICE”; live network television interviews like CNN, NBC, ABC, France24; major press outlets including New York Times, Washington Post and specialist media including Newsweek, Engadget, Wired, MotherBoard etc. Speaker on wildlife trafficking and technology at dozens of seminars, events and panels and fundraisers.

Ravi holds a Bachelor of Engineering and has more than 14 years of experience in the IT industry. He is the co-founder of Binomial Solutions, an IT solution and service provider and one of the Wildlife Crime Tech Challenge’s 16 Prize Winners. Binomial Solutions won the Challenge with the e-Eye® application, an innovative, self-learning wildlife surveillance system.