diff --git a/combination-sum/Leo.py b/combination-sum/Leo.py
new file mode 100644
index 000000000..e345049cb
--- /dev/null
+++ b/combination-sum/Leo.py
@@ -0,0 +1,14 @@
+class Solution:
+    def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:
+        dp = [[] for i in range(target + 1)]
+        dp[0] = [[]]
+
+        for num in candidates:
+            for i in range(num, target + 1):
+                for comb in dp[i - num]:
+                    dp[i].append(comb + [num])
+
+        return dp[target]
+
+        ## TC: O(outer loop * 1st inner(target size) * 2nd inner(combination #))
+        ## SC: O(target size * combination #)
diff --git a/construct-binary-tree-from-preorder-and-inorder-traversal/Leo.py b/construct-binary-tree-from-preorder-and-inorder-traversal/Leo.py
new file mode 100644
index 000000000..b0aa8b303
--- /dev/null
+++ b/construct-binary-tree-from-preorder-and-inorder-traversal/Leo.py
@@ -0,0 +1,18 @@
+class Solution:
+    def buildTree(self, preorder: List[int], inorder: List[int]) -> Optional[TreeNode]:
+        if len(inorder) == 0:
+            return None
+
+        if len(preorder) == 1:
+            return TreeNode(preorder[0])
+
+        idx = inorder.index(preorder.pop(0))
+        node = TreeNode(inorder[idx])
+
+        node.left = self.buildTree(preorder, inorder[:idx])
+        node.right = self.buildTree(preorder, inorder[idx + 1:])
+
+        return node
+
+
+        ## TC: O(n^2), SC: O(n)
diff --git a/implement-trie-prefix-tree/Leo.py b/implement-trie-prefix-tree/Leo.py
new file mode 100644
index 000000000..1e24f9fcb
--- /dev/null
+++ b/implement-trie-prefix-tree/Leo.py
@@ -0,0 +1,42 @@
+class TrieNode:
+    def __init__(self):
+        self.children = {}
+        self.isEnd = False
+
+
+class Trie:
+    def __init__(self):
+        self.root = TrieNode()
+
+    def insert(self, word: str) -> None:
+        cur = self.root
+
+        for c in word:
+            if c not in cur.children:
+                cur.children[c] = TrieNode()
+            cur = cur.children[c]
+        cur.isEnd = True
+
+        ## TC: O(len(word)), SC: O(len(word))
+
+    def search(self, word: str) -> bool:
+        cur = self.root
+
+        for c in word:
+            if c not in cur.children:
+                return False
+            cur = cur.children[c]
+        return cur.isEnd
+
+        ## TC: O(len(word)), SC: O(1)
+
+    def startsWith(self, prefix: str) -> bool:
+        cur = self.root
+
+        for c in prefix:
+            if c not in cur.children:
+                return False
+            cur = cur.children[c]
+        return True
+
+        ## TC: O(len(prefix)). SC: O(1)
diff --git a/kth-smallest-element-in-a-bst/Leo.py b/kth-smallest-element-in-a-bst/Leo.py
new file mode 100644
index 000000000..bcdedf02b
--- /dev/null
+++ b/kth-smallest-element-in-a-bst/Leo.py
@@ -0,0 +1,18 @@
+class Solution:
+    def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:
+        ans = []
+
+        def helper(node):
+            if not node: return
+            helper(node.left)
+
+            if len(ans) == k:
+                return
+
+            ans.append(node.val)
+            helper(node.right)
+
+        helper(root)
+        return ans[-1]
+
+        ## TC: O(n), SC: O(h+n) where h == heights(tree) and n == element(tree)
diff --git a/word-search/Leo.py b/word-search/Leo.py
new file mode 100644
index 000000000..0f179c900
--- /dev/null
+++ b/word-search/Leo.py
@@ -0,0 +1,30 @@
+class Solution:
+    def exist(self, board: List[List[str]], word: str) -> bool:
+        rows, cols = len(board), len(board[0])
+
+        def backtrack(r, c, index):
+            if index == len(word):
+                return True
+            if r < 0 or r >= rows or c < 0 or c >= cols or board[r][c] != word[index]:
+                return False
+
+            temp = board[r][c]
+            board[r][c] = "!"
+
+            found = (backtrack(r + 1, c, index + 1) or
+                     backtrack(r - 1, c, index + 1) or
+                     backtrack(r, c + 1, index + 1) or
+                     backtrack(r, c - 1, index + 1))
+
+            board[r][c] = temp
+            return found
+
+        for i in range(rows):
+            for j in range(cols):
+                if backtrack(i, j, 0):
+                    return True
+
+        return False
+
+        ## TC: O(D of row * D of Cols * 4^L), but amortised could be O(DoR * DoC)
+        ## SC: O(L)