diff --git a/torch2trt/converters/__init__.py b/torch2trt/converters/__init__.py
index ffce493f..88a80059 100644
--- a/torch2trt/converters/__init__.py
+++ b/torch2trt/converters/__init__.py
@@ -32,6 +32,7 @@
 from .div import *
 from .einsum import *
 from .expand import *
+from .expand_as import *
 from .example_plugin import *
 from .floordiv import *
 from .gelu import *
diff --git a/torch2trt/converters/expand_as.py b/torch2trt/converters/expand_as.py
new file mode 100644
index 00000000..fea5bd06
--- /dev/null
+++ b/torch2trt/converters/expand_as.py
@@ -0,0 +1,52 @@
+from torch2trt.torch2trt import *
+from torch2trt.module_test import add_module_test
+
+
+@tensorrt_converter('torch.Tensor.expand_as')
+def convert_expand_as(ctx):
+    input = ctx.method_args[0]
+    output = ctx.method_return
+    
+    inshape = tuple(input.shape)[1:] # exclude batch
+    shape = tuple(output.shape)[1:]
+    ndim = len(shape)
+    start = tuple([0]*ndim)
+    stride = tuple([int(i == o) for i, o in zip(inshape, shape)])  # stride == 1 if dimensions match, 0 otherwise
+    
+    layer = ctx.network.add_slice(input._trt, start, shape, stride)
+    
+    output._trt = layer.get_output(0)
+    
+    
+class ExpandAsModule(torch.nn.Module):
+    def __init__(self, other: torch.Tensor):
+        super(ExpandAsModule, self).__init__()
+        self.other = other
+    
+    def forward(self, x: torch.Tensor):
+        return x.expand_as(self.other)
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1),])
+def test_tensor_expand_as_scalar():
+    return ExpandAsModule(torch.randn(3))
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 1, 3, 3),])
+def test_tensor_expand_as_singledim():
+    return ExpandAsModule(torch.randn((1, 3, 3, 3)))
+
+
+@add_module_test(torch.float32, torch.device('cuda'), [(1, 1, 1, 3),])
+def test_tensor_expand_as_multidim():
+    return ExpandAsModule(torch.randn((1, 3, 3, 3)))
+
+
+@add_module_test(torch.float16, torch.device('cuda'), [(1, 1, 3, 3),])
+def test_tensor_expand_as_singledim_half():
+    return ExpandAsModule(torch.randn((1, 3, 3, 3)))
+
+
+@add_module_test(torch.float16, torch.device('cuda'), [(1, 1, 1, 3),])
+def test_tensor_expand_as_multidim_half():
+    return ExpandAsModule(torch.randn((1, 3, 3, 3)))