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+- title slide:
+    - topic: "Ovulation Prediction with Machine Learning"
+    - subtitle: "Comparing Deepl Learning Approaches for Real-World Ovulation Prediction"
+
+- introduction and motivation:
+    - eye and ear catching introduction?
+    - more than 50% of people are directly influenced by the menstrual cycle and its effects
+    - understanding its patterns is essential, especially for those trying to conceive, PCOS patients, menopause, etc.
+    - prediction of ovulation can help in family planning, fertility treatments, and understanding hormonal health
+    - women's health is often uncharted territory in medical research
+
+- OV Basics:
+    - more visuals, less text
+    - little detour -> collection knowledge about the menstrual cycle is limited
+    - explain phases and fertility over cycle, show diagram and actual curves
+    - monophasic vs biphasic cycles
+
+- The Power of Data:
+    - I work for company "VivoSens Medical" -> product "Ovularing"
+    - product is biosensor for intravaginal body core temperature measurement, collect measurements every 5 minutes
+    - extensive database of 60,000+ menstrual cycles with up to 150 days of data -> ~ 100 million data points
+    - extensive context data such as age, weight, height, cycle length, and markers for cycle related events
+    - long histories for many users -> up to 8 years of data
+    - data that is not available to anyone else
+    - makes ML approaches feasible
+
+- The Challenge:
+    - ovulation is a complex biological process influenced by various factors
+    - different goals, depending on use case: getting pregnant vs natural contraception
+    - explain types of cycles, show what an ovulation looks like, fever, stress, etc. -> max 3 plots
+    - traditional methods of prediction often rely on simple statistical algorithms or heuristics
+    - existing machine learning models lack sufficient training data, either in number of cycles or data resolution
+        - Huawei Band, Yu et al. 2022 -> 382 Cycles,
+    - labeling is hard: -> ovulation, even with ultrasound, is not always clear
+    - open research question: is temperature a sufficient predictor for ovulation, or only retrospective?
+    - Cycle variability: cycles can vary significantly in length and pattern, making it difficult to create a one-size-fits-all model
+        - some users have very regular cycles, while others have highly irregular ones (one is 30 days, the next is 70 days)
+
+- Approach:
+    - inputs: show features
+        - static user specific features: age, weight, height, average cycle length, std, num cycles, etc.
+        - time dependent features: day of week, hour of day, month of year -> all sine / cosine encoded
+        - time series data: raw temperature, rolling average, min and max ofer last 24 hours, etc.
+    - targets: time until / since ovulation (linear regression), prob for biphasic cycle (binary classification)
+    - train set of model: lstm, conv lstm, transformer decoder and conv with transformer decoder
+    - use multiple input configurations -> different input window sizes, different sampling rates
+    - evaluation on domain specific metrics: -> some are more important than others, depending on the use case
+        - overall error in days
+        - error before and after ovulation
+        - error at ovulation
+        - error 5 days before ovulation -> start of fertile window, most important for conception
+    - how do errors change over the course of one users cycles?
+    - visually show "training pipeline"
+
+
+- Current Status and Findings:
+    - transformers perform best, especially with larger input windows
+    - Lessons learned:
+        - Multi GPU training is a deep rabbit hole, distributed systems ftw
+        - HPC has its own challenges, slow I/O, misused resources, etc.
+        - trend to overengineer, but usually that pays off in the end, especially configurability
+
+- Conclusion:
+    -
+
+
+- Future Work:
+    - more complex / better suited models for the task
+    - better data preprocessing and feature engineering / frequency analysis
+    - additional context data / biomarkers, i.e., hr, stress, sleep, etc
+    - interpretable models -> Temporal Fusion Transformer derivative, etc.