Afloresep

PhD in Chemoinformatics at the University of Bern, working with Reymond’s group. My research focuses on Big Data and AI. Trying to advance the field of chemistry and computer-aided drug discovery. I am particularly interested in employing machine learning and deep learning techniques to address complex problems in these areas. My work involves using tools like TMAP

In addition to my primary research, I am also exploring algorithms, data structures, and computer vision, aiming to expand my skill set and apply these concepts to various challenges in my field. This site serves as a platform to document my coursework, projects, and reflections as I navigate my academic and professional journey

Also, someone once told me that in order to think, you have to write. So here’s my attempt.

Coursework

This section tracks the classes I’m currently taking or have completed, both through formal University courses and self-study. I’ll create subpages for individual courses or topics.

• HS2024-0: Machine Learning
  According to the course description, this class covers fundamental topics in machine learning and pattern recognition. It provides an introduction to:
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

  These topics are explored through a probabilistic framework, with a strong emphasis on optimization techniques throughout the course.

• HS2024-0: Computer Vision
  This course covers fundamental topics in computer vision. The course will provide an introduction to image formation, image processing, feature detection, segmentation, multiple view geometry and 3D reconstruction, motion, object recognition and classification.

  Learning outcome:

  • Understand how cameras capture images of a scene Implement
  • Implement and use:
    • algorithms for image processing such as image filtering and image segmentation
    • algorithms for object detection (such as faces) and recognition
    • algorithms for 3D reconstruction (e.g., from stereo systems) Describe the mathematics underpinning each method and know how to adapt it to new scenarios.
• HS2024-0: Applied Optimization This course offers an applied introduction, covering a broad range of practically important topics, as for instance:
  • Mathematical modeling of real-world problems
  • Theory of convexity
  • Lagrange dualism
  • Algorithms for unconstrained and constrained optimization with inequalities (e.g. gradient descent, Newton’s method, trust-region methods, active set approaches, interior point methods, …). A major goal of the course is to train students in appropriately modelling optimization problems, and identifying suitable optimization algorithms, based on the understanding of their specific strengths and weaknesses.

  Learning outcome:

  • Understand which classes of optimization problems are easy/hard to solve.
  • Model or re-formulate problems in a way that they become easier (e.g. convex).
  • Understand the fundamental ideas behind unconstrained, constrained and mixed-integer optimization.
  • Implement and use various optimization algorithms (programming exercises are in C++).
  • Understand and tune the parameters and output statistics that are exposed by optimization packages.

Note: All material posted here reflects my own work and thoughts. Naturally, I won’t share any restricted resources or information meant solely for enrolled students.

Portfolio

This section will document any projects I undertake during my PhD or on my own. It serves two main purposes:

1. To showcase my skills and past experiences to others.
2. To maintain a personal record or diary of everything I’ve done, allowing me to reflect on my progress, mistakes, and reasoning. Plus, I simply enjoy taking notes, so there’s that too.

Blog Posts

Here, I’ll store subpages with additional insights, updates, or thoughts related to the coursework or projects from my portfolio.

Additional Info