diff --git a/hf_cohort/aces_task.sh b/hf_cohort/aces_task.sh
new file mode 100644
index 0000000..c8dcf5d
--- /dev/null
+++ b/hf_cohort/aces_task.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+MEDS_DIR=/storage/shared/meds_tabular_ml/ebcl_dataset/processed/final_cohort
+OUTPUT_DIR=/storage/shared/meds_tabular_ml/ebcl_dataset/processed/tabularize
+# N_PARALLEL_WORKERS="$1"
+WINDOW_SIZES="window_sizes=[1d]"
+AGGS="aggs=[code/count,value/sum]"
+
+python /home/nassim/projects/MEDS_Tabular_AutoML/hf_cohort/aces_task_extraction.py \
+    MEDS_cohort_dir=$MEDS_DIR \
+    tabularized_data_dir=$OUTPUT_DIR \
+    min_code_inclusion_frequency=1 do_overwrite=False \
+    "$WINDOW_SIZES" "$AGGS"
diff --git a/hf_cohort/aces_task_extraction.py b/hf_cohort/aces_task_extraction.py
new file mode 100644
index 0000000..6b86af1
--- /dev/null
+++ b/hf_cohort/aces_task_extraction.py
@@ -0,0 +1,51 @@
+"""
+Setup Conda environment as described here: https://github.com/justin13601/ACES
+"""
+from pathlib import Path
+
+import hydra
+import polars as pl
+from aces import config, predicates, query
+from tqdm import tqdm
+
+
+@hydra.main(version_base=None, config_path="../configs", config_name="tabularize")
+def main(cfg):
+    # create task configuration object
+    task_cfg = config.TaskExtractorConfig.load(config_path="hf_cohort/task.yaml")
+
+    # setup directories
+    med_dir = Path(cfg.tabularized_data_dir)
+
+    # location of MEDS format Data
+    cohort_dir = med_dir.parent / "final_cohort"
+    # output directory for tables with event_ids and labels
+    output_dir = med_dir / "task"
+
+    shard_fps = list(cohort_dir.glob("*/*.parquet"))
+
+    for in_fp in tqdm(shard_fps):
+        out_fp = output_dir / "/".join(in_fp.parts[-2:])
+        out_fp.parent.mkdir(parents=True, exist_ok=True)
+        # one of the following
+        predicates_df = predicates.generate_predicates_df(task_cfg, in_fp, "meds")
+
+        # execute query
+        df_result = query.query(task_cfg, predicates_df)
+        label_df = (
+            df_result.select(pl.col(["subject_id", "trigger", "label"]))
+            .rename({"trigger": "timestamp", "subject_id": "patient_id"})
+            .sort(by=["patient_id", "timestamp"])
+        )
+        data_df = pl.scan_parquet(in_fp)
+        data_df = data_df.unique(subset=["patient_id", "timestamp"]).sort(by=["patient_id", "timestamp"])
+        data_df = data_df.with_row_index("event_id")
+        data_df = data_df.drop(["code", "numerical_value"])
+        output_df = label_df.lazy().join_asof(other=data_df, by="patient_id", on="timestamp")
+
+        # store it
+        output_df.collect().write_parquet(out_fp)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/hf_cohort/config.yaml b/hf_cohort/config.yaml
new file mode 100644
index 0000000..a9911fb
--- /dev/null
+++ b/hf_cohort/config.yaml
@@ -0,0 +1,21 @@
+# Path to the task configuration file
+config_path: task.yaml
+
+# Raw Data
+data:
+  # Path to the data file or directory
+  path: /storage/shared/meds_tabular_ml/ebcl_dataset/processed/final_cohort/train/0.parquet
+
+  # Data standard, one of (csv, meds, esgpt)
+  standard: meds
+
+# Output Directory (saves as .parquet file)
+output_dir: results/
+
+# Hydra
+hydra:
+  job:
+    name: ACES_${now:%Y-%m-%d_%H-%M-%S}
+  run:
+    dir: ${ACES_dir}/.logs/${hydra.job.name}
+# aces-cli --config-dir='./' --config-name='config.yaml'
diff --git a/scripts/hf_cohort_e2e.sh b/hf_cohort/hf_cohort_e2e.sh
similarity index 100%
rename from scripts/hf_cohort_e2e.sh
rename to hf_cohort/hf_cohort_e2e.sh
diff --git a/scripts/hf_cohort_shard.sh b/hf_cohort/hf_cohort_shard.sh
similarity index 100%
rename from scripts/hf_cohort_shard.sh
rename to hf_cohort/hf_cohort_shard.sh
diff --git a/hf_cohort/task.yaml b/hf_cohort/task.yaml
new file mode 100644
index 0000000..19ff7f0
--- /dev/null
+++ b/hf_cohort/task.yaml
@@ -0,0 +1,21 @@
+# Task: 30-day Readmission Risk Prediction
+predicates:
+  admission:
+    code: ADMIT_DATE
+  discharge:
+    code: DISCHARGE_DATE
+
+trigger: admission
+
+windows:
+  input:
+    start: trigger
+    end: start -> discharge
+    start_inclusive: False
+    end_inclusive: True
+  target:
+    start: input.end
+    end: start + 30 days
+    start_inclusive: False
+    end_inclusive: True
+    label: admission
diff --git a/scripts/e2e.sh b/scripts/e2e.sh
deleted file mode 100644
index fd1ff60..0000000
--- a/scripts/e2e.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env bash
-
-MEDS_DIR=/storage/shared/meds_tabular_ml/ebcl_dataset/processed/final_cohort
-OUTPUT_DIR=/storage/shared/meds_tabular_ml/ebcl_dataset/processed/tabularize
-N_PARALLEL_WORKERS="2" #"$3"
-
-# echo "Running identify_columns.py: Caching feature names and frequencies."
-# POLARS_MAX_THREADS=32 python scripts/identify_columns.py \
-#     MEDS_cohort_dir=$MEDS_DIR \
-#     tabularized_data_dir=$OUTPUT_DIR \
-#     min_code_inclusion_frequency=1 "window_sizes=[1d, 7d, 30d, 365d, full]" do_overwrite=True
-
-# echo "Running tabularize_static.py: tabularizing static data"
-# POLARS_MAX_THREADS=32 python scripts/tabularize_static.py \
-#     MEDS_cohort_dir=$MEDS_DIR \
-#     tabularized_data_dir=$OUTPUT_DIR \
-#     min_code_inclusion_frequency=1 "window_sizes=[1d, 7d, 30d, 365d, full]" do_overwrite=True
-
-echo "Running summarize_over_windows.py with $N_PARALLEL_WORKERS workers in parallel"
-POLARS_MAX_THREADS=1 python scripts/summarize_over_windows.py \
-    --multirun \
-    worker="range(1,$N_PARALLEL_WORKERS)" \
-    hydra/launcher=joblib \
-    MEDS_cohort_dir=$MEDS_DIR \
-    tabularized_data_dir=$OUTPUT_DIR \
-    min_code_inclusion_frequency=1 "window_sizes=[1d, 7d, 30d, 365d, full]" do_overwrite=True