diff --git a/CHANGELOG.md b/CHANGELOG.md
index f8f9a25e64..d54b06c055 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,9 @@
-# Pending additions
+# v1.1.0
 
 ## Highlights
+- Slicing/indexing overhaul for a more NumPy-like user experience. Warning for distributed arrays: [breaking change!](#breaking-changes) Indexing one element along the distribution axis now implies the indexed element is communicated to all processes.
+- More flexibility in handling non-load-balanced distributed arrays.
+- More distributed operations, incl. [meshgrid](https://github.com/helmholtz-analytics/heat/pull/794).
 
 ## Breaking Changes
 - [#758](https://github.com/helmholtz-analytics/heat/pull/758) Indexing a distributed `DNDarray` along the `DNDarray.split` dimension now returns a non-distributed `DNDarray`, i.e. the indexed element is MPI-broadcasted.
@@ -36,13 +39,13 @@ Example on 2 processes:
   ```
 
 ## Bug Fixes
-- [#796](https://github.com/helmholtz-analytics/heat/pull/796) `heat.reshape(a, shape, new_split)` now always returns a distributed `DNDarray` if `new_split is not None` (inlcuding when the original input `a` is not distributed)
 - [#758](https://github.com/helmholtz-analytics/heat/pull/758) Fix indexing inconsistencies in `DNDarray.__getitem__()`
 - [#768](https://github.com/helmholtz-analytics/heat/pull/768) Fixed an issue where `deg2rad` and `rad2deg`are not working with the 'out' parameter.
 - [#785](https://github.com/helmholtz-analytics/heat/pull/785) Removed `storage_offset` when finding the mpi buffer (`communication. MPICommunication.as_mpi_memory()`).
 - [#785](https://github.com/helmholtz-analytics/heat/pull/785) added allowance for 1 dimensional non-contiguous local tensors in `communication. MPICommunication.mpi_type_and_elements_of()`
 - [#787](https://github.com/helmholtz-analytics/heat/pull/787) Fixed an issue where Heat cannot be imported when some optional dependencies are not available.
 - [#790](https://github.com/helmholtz-analytics/heat/pull/790) catch incorrect device after `bcast` in `DNDarray.__getitem__`
+- [#796](https://github.com/helmholtz-analytics/heat/pull/796) `heat.reshape(a, shape, new_split)` now always returns a distributed `DNDarray` if `new_split is not None` (inlcuding when the original input `a` is not distributed)
 - [#811](https://github.com/helmholtz-analytics/heat/pull/811) Fixed memory leak in `DNDarray.larray`
 - [#820](https://github.com/helmholtz-analytics/heat/pull/820) `randn` values are pushed away from 0 by the minimum value the given dtype before being transformed into the Gaussian shape
 - [#821](https://github.com/helmholtz-analytics/heat/pull/821) Fixed `__getitem__` handling of distributed `DNDarray` key element
@@ -55,7 +58,7 @@ Example on 2 processes:
 ### Linear Algebra
 - [#718](https://github.com/helmholtz-analytics/heat/pull/718) New feature: `trace()`
 - [#768](https://github.com/helmholtz-analytics/heat/pull/768) New feature: unary positive and negative operations
-- [#820](https://github.com/helmholtz-analytics/heat/pull/820) `dot` can handle matrix vector operation now
+- [#820](https://github.com/helmholtz-analytics/heat/pull/820) `dot` can handle matrix-vector operation now
 
 ### Manipulations
 - [#796](https://github.com/helmholtz-analytics/heat/pull/796) `DNDarray.reshape(shape)`: method now allows shape elements to be passed in as single arguments.
diff --git a/README.md b/README.md
index 381916fc21..4fc90614dc 100644
--- a/README.md
+++ b/README.md
@@ -26,7 +26,7 @@ analytics and machine learning. It provides highly optimized algorithms and data
 structures for tensor computations using CPUs, GPUs and distributed cluster
 systems on top of MPI. The goal of Heat is to fill the gap between data
 analytics and machine learning libraries with a strong focus on single-node
-performance, and traditional high-performance computing (HPC). Heat's dtype
+performance, and traditional high-performance computing (HPC). Heat's generic
 Python-first programming interface integrates seamlessly with the existing data
 science ecosystem and makes it as effortless as using numpy to write scalable
 scientific and data science applications.
diff --git a/heat/core/version.py b/heat/core/version.py
index 6a2b361063..9de7bd1335 100644
--- a/heat/core/version.py
+++ b/heat/core/version.py
@@ -2,7 +2,7 @@
 
 major: int = 1
 """Indicates HeAT's main version."""
-minor: int = 0
+minor: int = 1
 """Indicates feature extension."""
 micro: int = 0
 """Indicates revisions for bugfixes."""
diff --git a/setup.py b/setup.py
index 86462e5ddb..740f6660f7 100644
--- a/setup.py
+++ b/setup.py
@@ -33,7 +33,7 @@
     install_requires=[
         "mpi4py>=3.0.0",
         "numpy>=1.13.0",
-        "torch>=1.7.0",
+        "torch>=1.7.0, <1.9",
         "scipy>=0.14.0",
         "pillow>=6.0.0",
         "torchvision>=0.8.0",