Hey Berlin Data Folks!
We’re looking forward to welcoming you to our next Data Science Demo Day on 20th October. Our Batch 47 participants have been busy working on their final projects, and they’re ready to share what they’ve built.
This time, we have 7 projects covering a pretty wide mix of topics, from behavioral profiling and healthcare to career transitions, engineering, agriculture, fire detection, and even fermentation. It’ll be a relaxed autumn evening to see some practical data science and AI applications, meet others from Berlin’s data community, and chat about ideas, projects, and what people are working on. And, of course, we’ll have some pizza and drinks to keep everyone going.
Free to Attend!
Agenda: 17:30 - Drinks and Networking 18:00 - Welcome & Introduction Followed by Project Presentations
Project Ideas: 1. ML-Based Data Broker Simulator Project by Sascha Alexeyenko How much can your everyday browsing actually reveal about you? This project explores that question by building an ML-based data broker simulator. It combines browsing metadata with website content to look for patterns around a user’s emotions, activities, and behavior, both in the moment and over time. The project also demonstrates how these insights could be used to target people with certain content or recommendations at just the right moment. The idea is to make behavioral profiling feel less abstract and give people a clearer sense of what can be inferred from their browsing data and how those insights might be used to influence their choices.
2. Personalized Psychiatric Treatment Selection for Depression Project by Aleksandra Garifulina Finding the right antidepressant treatment can be a very individual process. This project explores whether machine learning can help support more personalized treatment selection using patient-level clinical characteristics and treatment-response data. The project looks at supervised classification methods to match treatments more closely to individual patient profiles. It also explores how clinical guidelines, treatment suitability, and contraindications could be incorporated into the process. The goal is to investigate how these tools could support clinical decision-making rather than replace it.
3. Job Transition Workspace Project by Timothy LeBon Changing careers can mean juggling job searches, applications, skills, resumes, advice, and a lot of uncertainty all at once. This project brings those pieces together in one intelligent career transition workspace. Using data from O*NET, the U.S. Current Population Survey, and the Anthropic Economic Index, the system recommends roles based on a person’s skills, experience, career goals, and job-market demand. It also includes an LLM workspace that can work with resumes, chats, and cover letters, along with tools for things like job searches, email, reminders, and calendar management. The goal is to create one place where people can plan and manage a career change with more personalized, data-informed support.
4. AI-Assisted Requirement Change Impact Analysis Project by Lakshmi Swetha Pasupuleti In complex engineering projects, changing one requirement can affect many other things, from software requirements and design decisions to test cases. This project looks at how AI can help engineers understand those connections, especially when they are not already documented. The idea is to use semantic analysis to find related engineering artifacts and, importantly, explain why they might be affected by a change. The project will compare AI-based methods with more traditional traceability approaches across different change scenarios. The end goal is a lightweight prototype that explores where AI can make requirement change analysis easier and more useful for engineers.
5. cropSpectAI: Spectral Sensing and AI-Driven Crop Health Monitoring Project by Sharana Shivanand What if farmers could spot changes in a plant’s health before those changes were visible to the eye? cropSpectAI explores this idea using a low-cost portable device that combines spectral sensing with machine learning. The system captures how leaves reflect light across different wavelengths and uses those patterns to estimate important plant traits such as chlorophyll and nitrogen content. The project experiments with several machine-learning approaches to see how affordable, edge-based technology could help farmers identify problems earlier, make better-informed decisions, and potentially reduce unnecessary use of resources.
6. Residential Fire and Smoke Detection Platform Project by Abhishek Acharya Traditional smoke and heat detectors are essential, but they usually react once smoke or heat has reached the sensor. This project explores whether computer vision can provide an additional layer of information by recognizing signs of fire and smoke visually. The system is designed to understand context too. It looks at situations involving cooking flames, candles, fireplaces, steam, cigarette smoke, pans, stoves, and people to help distinguish an actual hazard from normal household activity. The aim is not to replace certified smoke or heat detectors, but to explore how visual AI could complement them with earlier, more context-aware detection while reducing unnecessary alarms.
7. Predictive Fermentation Assistant Project by Sylas Lau Fermentation sounds simple: give microbes the right ingredients and conditions, then give them time. In reality, factors like temperature, moisture, oxygen, and timing all interact, which makes getting consistent results surprisingly tricky. This project explores how machine learning can help home fermentation enthusiasts better understand and manage that process. From bread and yoghurt to koji, tempeh, and kimchi, the idea is to use data to make fermentation a little more predictable while still keeping the hands-on nature that makes it interesting.
20:10 - Open for Networking 20:30 - Wrap Up Come by, grab some pizza and a drink, meet the people behind the projects, and spend a cosy October evening with Berlin’s data community.
We have limited seats, so please RSVP soon.
