diff --git a/include/matx/core/file_io.h b/include/matx/core/file_io.h
index 3c919344..fb4f337b 100644
--- a/include/matx/core/file_io.h
+++ b/include/matx/core/file_io.h
@@ -118,16 +118,27 @@ using namespace pybind11::literals;
 
 
 /**
- * Read a CSV file into a tensor view
+ * @brief Read a CSV file into a tensor view
  *
  * CSVs are currently read in using the Python interpreter through pybind11.
- *This has a startup performance hit, but CSV reading is intended to be a
- *slow-path function, so this is not a critical component to speed up. Currently
- *1D and 2D tensors are supported only.
+ * This has a startup performance hit, but CSV reading is intended to be a
+ * slow-path function, so this is not a critical component to speed up. Currently
+ * 1D and 2D tensors are supported only.
+ * 
+ * @tparam TensorType
+ *   Data type of tensor
+ * @param t
+ *   Tensor to read data into
+ * @param fname
+ *   File path of .csv file
+ * @param delimiter
+ *   Delimiter to use for CSV file
+ * @param skip_header
+ *   Skip the header row of the CSV file, default as `true`.
  **/
 template <typename TensorType>
 void read_csv(TensorType &t, const std::string fname,
-             const std::string delimiter, bool header = true)
+             const std::string delimiter, bool skip_header = true)
 {
   MATX_NVTX_START("", matx::MATX_NVTX_LOG_API)
   
@@ -146,18 +157,27 @@ void read_csv(TensorType &t, const std::string fname,
 
   auto np = pybind11::module_::import("numpy");
   auto obj = np.attr("genfromtxt")("fname"_a = fname.c_str(), "delimiter"_a = delimiter,
-                                   "skip_header"_a = header ? 1 : 0,
+                                   "skip_header"_a = skip_header,
                                    "dtype"_a = detail::MatXPybind::GetNumpyDtype<typename TensorType::scalar_type>());
   pb->NumpyToTensorView(t, obj);
 }
 
 /**
- * Read a CSV file into a tensor view
+ * Write a CSV file from a tensor view
  *
- * CSVs are currently read in using the Python interpreter through pybind11.
- *This has a startup performance hit, but CSV reading is intended to be a
- *slow-path function, so this is not a critical component to speed up. Currently
- *1D and 2D tensors are supported only.
+ * CSVs are currently written using the Python interpreter through pybind11.
+ * This has a startup performance hit, but CSV writing is intended to be a
+ * slow-path function, so this is not a critical component to speed up. Currently
+ * 1D and 2D tensors are supported only.
+ * 
+ * @tparam TensorType
+ *   Data type of tensor
+ * @param t
+ *   Tensor to write data from
+ * @param fname
+ *   File path of .csv file
+ * @param delimiter
+ *   Delimiter to use for CSV file
  **/
 template <typename TensorType>
 void write_csv(const TensorType &t, const std::string fname,
@@ -220,7 +240,7 @@ void read_mat(TensorType &t, const std::string fname,
 }
 
 /**
- * @brief Read a MAT file into a tensor view
+ * @brief Read a MAT file and return a tensor view
  *
  * MAT files use SciPy's loadmat() function to read various MATLAB file
  * types in. MAT files are supersets of HDF5 files, and are allowed to
@@ -232,7 +252,8 @@ void read_mat(TensorType &t, const std::string fname,
  *   File name of .mat file
  * @param var
  *   Variable name inside of .mat to read
- *
+ * @return
+ *  Tensor view of data read from file
  **/
 template <typename TensorType>
 auto read_mat(const std::string fname,