diff --git a/src/fdconversion.jl b/src/fdconversion.jl
new file mode 100644
index 000000000..bf5bb3a6f
--- /dev/null
+++ b/src/fdconversion.jl
@@ -0,0 +1,179 @@
+module FDConversion
+
+import Symbolics
+import SymbolicUtils
+import FastDifferentiation as FD
+import Random
+
+#=
+try implementing these
+    https://github.com/JuliaSymbolics/SymbolicUtils.jl/blob/master/src/interface.jl
+=#
+
+"""converts from Node to Symbolics expression"""
+function _to_symbolics!(a::T, cache::IdDict, variable_map::IdDict) where {T<:FD.Node}
+    tmp = get(cache, a, nothing)
+    if tmp !== nothing
+        return tmp
+    else
+        if FD.arity(a) === 0
+            if FD.is_constant(a)
+                cache[a] = Symbolics.Num(FD.value(a))
+            elseif FD.is_variable(a)
+                tmp = Symbolics.variable(FD.value(a))
+                cache[a] = tmp
+                variable_map[a] = tmp
+            else
+                throw(ErrorException("Node with 0 children was neither constant nor variable. This should never happen."))
+            end
+        else
+            if FD.arity(a) === 1
+                cache[a] = a.node_value(_to_symbolics!(a.children[1], cache, variable_map))
+            else
+                cache[a] = foldl(a.node_value, _to_symbolics!.(a.children, Ref(cache), Ref(variable_map)))
+            end
+        end
+    end
+end
+
+function to_symbolics(a::T, cache::IdDict=IdDict(), variable_map::IdDict=IdDict()) where {T<:FD.Node}
+    _to_symbolics!(a, cache, variable_map)
+    return cache[a], variable_map
+end
+export to_symbolics
+
+function to_symbolics(a::AbstractArray{T}, cache::IdDict=IdDict(), variable_map::IdDict=IdDict()) where {T<:FD.Node}
+    _to_symbolics!.(a, Ref(cache), Ref(variable_map))
+    return map(x -> cache[x], a), variable_map
+end
+
+"""Converts expression x from Symbolics to FastDifferentiation form. Returns a tuple of the **FD** form of the expression and a Dict that maps Symbolics variables to **FD** variables"""
+function to_fd(x::Real, cache=IdDict(), substitutions=IdDict())
+    result = _to_FD!(x, cache, substitutions)
+    syms = collect(filter(x -> SymbolicUtils.issym(x), keys(cache)))
+    sym_map = Dict(zip(syms, map(x -> cache[x], syms)))
+
+    return result, sym_map
+end
+export to_fd
+
+function to_fd(x::AbstractArray{<:Real})
+    cache = IdDict()
+    substitutions = IdDict()
+    result = _to_FD!.(x, Ref(cache), Ref(substitutions))
+    syms = collect(filter(x -> SymbolicUtils.issym(x), keys(cache)))
+    sym_map = Dict(zip(syms, map(x -> cache[x], syms))) #map from symbolics variables to FD variables
+    return result, sym_map
+end
+
+function _to_FD!(sym_node, cache::IdDict, visited::IdDict)
+    # Substitutions are done on a Node graph, not a SymbolicsUtils.Sym graph. As a consequence the values
+    # in substitutions Dict are Node not Sym type. cache has keys (op,args...) where op is generally a function type but sometimes a Sym, 
+    # and args are all Node types.
+
+    @assert !(typeof(sym_node) <: Symbolics.Arr) "Differentiation of expressions involving arrays and array variables is not yet supported."
+    if SymbolicUtils.istree(Symbolics.unwrap(sym_node))
+        @assert !SymbolicUtils.issym(SymbolicUtils.operation(Symbolics.unwrap(sym_node))) "expressions of the form `q(t)` not yet supported."
+    end
+
+    symx = isa(sym_node, Symbolics.Num) ? sym_node.val : sym_node
+    @assert typeof(symx) != Symbolics.Num
+
+
+    tmpsub = get(visited, symx, nothing)
+    if tmpsub !== nothing
+        return visited[symx] #substitute Node object for symbolic object
+    end
+
+    tmp = get(cache, symx, nothing)
+
+    if tmp !== nothing
+        return tmp
+    elseif !SymbolicUtils.istree(symx)
+        if SymbolicUtils.issym(symx)
+            tmpnode = FD.Node(Symbol(symx))
+        else #must be a number of some kind
+            tmpnode = FD.Node(symx)
+        end
+
+        cache[symx] = tmpnode
+
+        return tmpnode
+    else
+        numargs = length(SymbolicUtils.arguments(symx))
+        symargs = SymbolicUtils.arguments(symx)
+
+        args = _to_FD!.(symargs, Ref(cache), Ref(visited))
+
+        key = (SymbolicUtils.operation(symx), args...)
+        tmp = get(cache, key, nothing)
+
+        if tmp !== nothing
+            return tmp
+        else
+            tmpnode = FD.Node(SymbolicUtils.operation(symx), args...)
+            cache[key] = tmpnode
+
+            return tmpnode
+        end
+    end
+end
+
+
+function remap(fd_function_to_call, symbolics_function, differentiation_variables::AbstractVector{Symbolics.Num}, fast_differentiation::Bool)
+    fd_func, variable_map = to_fd(symbolics_function)
+    partial_vars = map(x -> variable_map[x], differentiation_variables)
+    tmp = fd_function_to_call(fd_func, partial_vars)
+    if fast_differentiation
+        return fd_func, variable_map #return FastDifferentiation expression to be passed to make_function for efficient evaluation
+    else
+        reverse_map = IdDict{Any,Any}(map(x -> variable_map[x] => x, differentiation_variables))
+        reverse_variable_map = deepcopy(reverse_map)
+        return to_symbolics(tmp, reverse_map, reverse_variable_map) #return Symbolics expression for further evaluation
+    end
+end
+
+
+"""
+Converts from `Symbolics` form to `FastDifferentiation` form and computes Jacobian with respect to `diff_variables`.
+If `fast_differentiation=false` returns result in Symbolics form. If `fast_differentiation=true` the result will be a 2-tuple. The first tuple entry will be the jacobian of `symbolics_function` converted to `FastDifferentiation` form. 
+The second tuple term will be `differentiation_variables` converted to `FastDifferentiation` form.
+This tuple can be passed to `fd_make_function` to create an efficient executable."""
+fd_jacobian(symbolics_function::AbstractArray{Symbolics.Num}, differentiation_variables::AbstractVector{Symbolics.Num}; fast_differentiation=false) = remap(FD.jacobian, symbolics_function, differentiation_variables, fast_differentiation)
+export fd_jacobian
+
+"""
+Converts from `Symbolics` form to `FastDifferentiation` form and computes sparse Jacobian with respect to `diff_variables`.
+If `fast_differentiation=false` returns result in Symbolics form. If `fast_differentiation=true` the result will be a 2-tuple. The first tuple entry will be the sparse jacobian of `symbolics_function` converted to `FastDifferentiation` form. 
+The second tuple term will be `differentiation_variables` converted to `FastDifferentiation` form.
+This tuple can be passed to `fd_make_function` to create an efficient executable."""
+fd_sparse_jacobian(symbolics_function::AbstractArray{Symbolics.Num}, differentiation_variables::AbstractVector{Symbolics.Num}; fast_differentiation=false) = remap(FD.sparse_jacobian, symbolics_function, differentiation_variables, fast_differentiation)
+export fd_sparse_jacobian
+
+"""
+Converts from `Symbolics` form to `FastDifferentiation` form and computes Hessian with respect to `diff_variables`.
+If `fast_differentiation=false` returns result in Symbolics form. If `fast_differentiation=true` the result will be a 2-tuple. The first tuple entry will be the hessian of `symbolics_function` converted to `FastDifferentiation` form. 
+The second tuple term will be `differentiation_variables` converted to `FastDifferentiation` form.
+This tuple can be passed to `fd_make_function` to create an efficient executable."""
+fd_hessian(symbolics_function::Symbolics.Num, differentiation_variables::AbstractVector{Symbolics.Num}; fast_differentiation=false) = remap(FD.hessian, symbolics_function, differentiation_variables, fast_differentiation)
+export fd_sparse_hessian
+
+"""
+Converts from `Symbolics` form to `FastDifferentiation` form and computes sparse Hessian with respect to `diff_variables`.
+If `fast_differentiation=false` returns result in Symbolics form. If `fast_differentiation=true` the result will be a 2-tuple. The first tuple entry will be the sparse Hessian of `symbolics_function` converted to `FastDifferentiation` form. 
+The second tuple term will be `differentiation_variables` converted to `FastDifferentiation` form.
+This tuple can be passed to `fd_make_function` to create an efficient executable."""
+fd_sparse_hessian(symbolics_function::Symbolics.Num, differentiation_variables::AbstractVector{Symbolics.Num}; fast_differentiation=false) = remap(FD.sparse_hessian, symbolics_function, differentiation_variables, fast_differentiation)
+export fd_sparse_hessian
+
+"""
+Creates an efficient runtime generated function to evaluate the function defined in `a[1]` with arguments given by `a[2]`. Used in conjuction with fd_jacobian,fd_sparse_jacobian,fd_hessian,fd_sparse_hessian.
+"""
+fd_make_function(a::Tuple{T,S}; in_place=false) where {T<:AbstractArray{<:FD.Node},S<:AbstractVector{<:FD.Node}} = FD.make_function(a[1], a[2], in_place=in_place)
+export fd_make_function
+#etc. for Jv Jᵀv Hv
+
+# export FastDifferentiation.make_function
+
+end # module FSDConvert
+
diff --git a/test/fdconversion.jl b/test/fdconversion.jl
new file mode 100644
index 000000000..16714a544
--- /dev/null
+++ b/test/fdconversion.jl
@@ -0,0 +1,143 @@
+
+import Symbolics
+import FastDifferentiation as FD
+using FDConversion
+using Random
+using Test
+
+#Function used in tests for FDConversion
+function P(::Type{T}, l, m, z::T) where {T}
+    if l == 0 && m == 0
+        return T(1)
+    elseif l == m
+        return (1 - 2m) * P(T, m - 1, m - 1, z)
+    elseif l == m + 1
+        return (2m + 1) * z * P(T, m, m, z)
+    else
+        return ((2l - 1) / (l - m) * z * P(T, l - 1, m, z) - (l + m - 1) / (l - m) * P(T, l - 2, m, z))
+    end
+end
+
+
+function S(::Type{T}, m, x::T, y::T) where {T}
+    if m == 0
+        return T(0)
+    else
+        return x * C(T, m - 1, x, y) - y * S(T, m - 1, x, y)
+    end
+end
+
+
+function C(::Type{T}, m, x::T, y::T) where {T}
+    if m == 0
+        return T(1)
+    else
+        return x * S(T, m - 1, x, y) + y * C(T, m - 1, x, y)
+    end
+end
+
+function factorial_approximation(::Type{T}, x) where {T}
+    local n1 = x
+    sqrt(2 * T(π) * n1) * (n1 / T(ℯ) * sqrt(n1 * sinh(1 / T(n1)) + 1 / (810 * T(n1)^6)))^n1
+end
+
+
+function compare_factorial_approximation()
+    for n in 1:30
+        println("n $n relative error $((factorial(big(n))-factorial_approximation(BigFloat,n))/factorial(big(n)))")
+    end
+end
+
+
+function N(::Type{T}, l, m) where {T}
+    @assert m >= 0
+    if m == 0
+        return sqrt((2l + 1 / (4 * T(π))))
+    else
+        # return sqrt((2l+1)/2π * factorial(big(l-m))/factorial(big(l+m)))
+        #use factorial_approximation instead of factorial because the latter does not use Stirlings approximation for large n. Get error for n > 2 unless using BigInt but if use BigInt get lots of rational numbers in symbolic result.
+        return sqrt((2l + 1) / 2 * T(π) * factorial_approximation(T, l - m) / factorial_approximation(T, l + m))
+    end
+end
+
+
+"""l is the order of the spherical harmonic"""
+function Y(::Type{T}, l, m, x::T, y::T, z::T) where {T}
+    @assert l >= 0
+    @assert abs(m) <= l
+    if m < 0
+        return N(T, l, abs(m)) * P(T, l, abs(m), z) * S(T, abs(m), x, y)
+    else
+        return N(T, l, m) * P(T, l, m, z) * C(T, m, x, y)
+    end
+end
+
+Y(l, m, x::T, y::T, z::T) where {T<:FD.Node} = Y(FD.Node, l, m, x, y, z)
+Y(l, m, x::T, y::T, z::T) where {T<:Number} = Y(T, l, m, x, y, z)
+
+
+function SHFunctions(max_l, x, y, z)
+    @assert typeof(x) == typeof(y) == typeof(z)
+    result = Vector(undef, 0)
+
+    for l in 0:max_l-1
+        for m in -l:l
+            push!(result, Y(l, m, x, y, z))
+        end
+    end
+
+    return result
+end
+
+
+
+@testset "conversion from Symbolics to FD" begin
+    Symbolics.@variables x y
+
+    symbolics_expr = x^2 + y * (x^2)
+    dag, tmp = to_fd(symbolics_expr)
+    vars = collect(values(tmp))
+    fdx, fdy = FD.value(vars[1]) == :x ? (vars[1], vars[2]) : (vars[2], vars[1]) #need to find the variables since they can be in any order
+
+    correct_fun = FD.make_function([dag], [fdx, fdy])
+
+
+    #verify that all the node expressions exist in the dag. Can't rely on them being in a particular order because Symbolics can
+    #arbitrarily choose how to reorder trees.
+    num_tests = 100
+    rng = Random.Xoshiro(8392)
+    for _ in 1:num_tests
+        (xval, yval) = rand(rng, 2)
+        FDval = correct_fun([xval, yval])[1]
+        Syval = Symbolics.substitute(symbolics_expr, Dict([(x, xval), (y, yval)]))
+
+        @test isapprox(FDval, Syval.val)
+
+    end
+end
+
+
+@testset "conversion from FD to Symbolics" begin
+    order = 8
+    FD.@variables x y z
+
+    FD_funcs = FD.Node.(SHFunctions(order, x, y, z))
+    Sym_funcs, variables = to_symbolics(FD_funcs)
+
+    sx, sy, sz = map(p -> variables[p], [x, y, z])
+
+    FD_eval = FD.make_function(FD_funcs, [x, y, z])
+    rng = Random.Xoshiro(8392)
+    num_tests = 100
+    for _ in 1:num_tests
+        tx, ty, tz = rand(rng, BigFloat, 3)
+        subs = Dict([sx => tx, sy => ty, sz => tz])
+        res = Symbolics.substitute.(Sym_funcs, Ref(subs))
+
+        FD_res = FD_eval([tx, ty, tz])
+
+        @test isapprox(FD_res, res, atol=1e-12)
+    end
+end
+
+