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Events categorized as (Departments)

• 06
  Nov

  PHYSICS SEMINAR ON MACHINE LEARNING FOR THEORETICAL AND MATHEMATICAL PHYSICS

  Date: November 06, 2025
  Time: 07:00 PM
  Location: Zoom Streaming venue Physics Conference room 9-117

  Speaker: Dr. Fabian Ruehle Assistant Professor at Northeastern University in Boston (US)
  

  Abstract: I will discuss two ways to obtain rigorous results from machine learning, which is needed if we want to apply it to areas like theoretical physics or pure mathematics. I will first introduce neural networks and explain their shortcomings for rigorous applications. Based on this, I will discuss two possible avenues: The first is a new, more interpretable architecture called Kolmogorov-Arnold networks. The other is to formulate the mathematical problem as a (single-player) game and solve it with Reinforcement Learning, a branch of machine learning. I will illustrate both techniques with examples from pure mathematics and theoretical physics. Bio: Fabian Ruehle received two degrees (in Physics and Computer Science) from the University of Heidelberg in Germany. He completed his PhD in Theoretical Physics at the University of Bonn (Germany) under the supervision of Prof. Hans Peter Nilles. After Postdoctoral Research Positions at DESY Hamburg (Germany), the University of Oxford (UK), and CERN (Switzerland), Fabian joined Northeastern University in Boston (US) in 2021, where he is an assistant professor. He is also a senior investigator of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions, a collaborative institute for AI and Physics for the Boston-based Universities Harvard, MIT, Northeastern and Tufts.

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• 27
  Nov

  DEEPIS-IPS: OPTIMIZING WI-FI INDOOR POSITIONING SYSTEM WITH DEEP LEARNING AND INTELLIGENT SURFACES

  Date: November 27, 2025
  Time: 04:00 PM
  Location: EE Reading Room, Maxwell’s Wing, Second Floor, SBASSE, LUMS

  Supervisor: Dr Naveed ul Hassan
  Student(s): Aisha Javed

  Indoor positioning remains challenging due to the limitation of Global Navigation Satellite Systems (GNSS) in indoor environments. Wi-Fi based Indoor Positioning Systems (IPS) presents a practical alternative that leverages the widespread availability of Wi-Fi indoors. However, multipath effects, interference, and minimal control over wireless signals often limit their accuracy. Achieving high accuracy in these systems typically requires Wi-Fi fingerprinting, which involves creating a comprehensive database of Wi-Fi signals and matching real-time data to estimate locations. IPS systems rely on increasing the number of Access Points (APs) to enhance dataset dimensionality, which is costly and challenging to scale. Additionally, acquiring and maintaining this fingerprinting database is labor-intensive, often requiring extensive RF measurements, crowdsourced data, or complex RF propagation models.

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  • Electrical Engineering

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