Without easy access to SIMD or value types (and JNI does not count as easy), the JVM's performance for regular computations on large datasets is still pretty lacking. Quoting http://cr.openjdk.java.net/~jrose/values/values-0.html, "Numeric types like complex numbers, extended-precision or unsigned integers, and decimal types are widely useful but can only be approximated (to the detriment of type safety and/or performance) by primitives or object classes."

And the JVM's costs in terms of deployment and startup times aren't very attractive for scientific workloads. You wouldn't gain as much in a migration from Python or Matlab to Java (or any other JVM language) as you would to C++ (or increasingly Julia), since you'll likely still need access to the same native libraries and JNI is kind of a pain.

Having done both Java and scientific programming I'd mostly say that the verbosity of Java really distracts away from the task of writing scientific code. An academic really doesn't usually care to learn about singletons and classes to write really fast for-loops. Also while the speed of Java now is awesome, floating point performance is key for those scientists who do know more systems level programming (memory management is a big issues). Actually GC in Julia can be a pain also.