AI for Scientific Data Analysis

Location: Kollektorn, MC2, Chalmers campus Johanneberg,
Gothenburg, Sweden
31 May – 1 June 2023

Website: AI for Scientific Data Analysis: Mini-conference

Summary: The mini-conference will feature a diverse range of speakers from both academia and industry, who will present their cutting-edge research and share their insights on the current state of the field. There will also be ample time to network with fellow professionals.

Some of the topics that will be covered include:

  • Machine learning algorithms for complex data analysis
  • Applications of deep learning in scientific research
  • Real-world examples of AI in action in various scientific fields
  • Tools and techniques for incorporating AI into data analysis workflows

Whether you are a researcher or data analyst using AI, or simply someone interested in the intersection of AI and scientific data analysis, this mini-conference is for you. The event is free of charge.


This event is organized by CHAIR – AI for Scientific Data Analisys.

Contact for the miniconference:
Henrik Klein Moberg, Doctoral Student, Chemical Physics, Physics

Schedule overview

Day 1: [31 May]
Day 2: [1 June]


31 May
[Back to schedule overview]

9:20-9:30: Welcome by the organizers
9:30-10:15: Daniel Freedman; Molecular Design via Semi-Equivariant Normalizing Flows
10:15-10:30: Yu-Wei Chang; Training of neural networks with incomplete datasets
10:30-10:45: Caroline Adiels; Dynamic virtual live/apoptotic/dead cell assay using deep learning
10:45-11:00: Agnese Callegari; Faster and More Accurate Geometrical-Optics Optical Force Calculation Using Neural Networks
11:00-11:15: Mattias Geilhufe; AI design of organic quantum matter using the Organic Materials Database
11:15-12:00: Balpreet Singh Ahluwalia; Development of high-throughput on-chip optical nanoscopy and label-free quantitative phase microscopy for life science


14:00 -14:15: Adel Daoud; The AI and Global Development Lab
14:15-14:30: Smaragda-Maria Argyri ; Acoustic levitation and machine learning for the determination of surface properties
14:30-14:45: Edoardo Grasso; Discovering far astrophysical sources: a bizarre application of generative models
14:45-15:00: Viktor Martvall; Deep Ensemble Modelling for Acceleration of Nanoplasmonic Gas Sensing
15:00-15:15: Valentina Matovic; Revealing the complex nano porous structure of a polymer hybrid electrolyte aided by deep learning


15:45-16:00: Greger Hammarin; Classification of diffraction patterns in single particle X-ray experiments
16:00-16:15: Christopher Kolloff; Resolving Disparities between Experiments and Simulations of Biomolecular Systems using Dynamic Augmented Markov Models
16:15-17:00: Ramin Bostanabad; Materials design under multiple coexisting uncertainties
17:00– Light dinner & Posters

1 June
[Back to schedule overview]

9:20-9:30: Welcome again (by the organizers)
9:30-10:15: Elias Nehme; Towards intelligent microscopes with deep learned optics
10:15-10:30: Jesús Pineda; Geometric deep learning reveals the spatiotemporal features of microscopic motion
10:30-10:45: Basudev Roy; Towards detection of pitch rotation in birefringent microspheres from video microscopy
10:45-11:00: Henrik Klein Moberg; Deep Learning Microscopy for label-free sub-10 kDa molecule characterization
11:00-11:45: Lars Tornberg; Evaluation of generative models for cytometry applications in drug development


13:45-14:30: Carlo Manzo; Data-driven approaches for the analysis of molecular motion
14:30-14:45: Joel Jonsson; Towards efficient large-scale microscopy image analysis using content-adaptive deep learning
14:45-15:00: Daniel Midtvedt ; Optical characterization of biological matter across scales
15:00-15:15: Harshith Bachimanchi; Microplankton life histories revealed by holographic microscopy and deep learning
15:15-15:30: Benjamin Midtvedt; Single-shot self-supervised object detection in microscopy


16:00-16:45: Cynthia Rudin; Scoring systems: at the extreme of interpretable machine learning
16:45– Posters