Delhi SP/ML Pre-Conference Workshop

Crossroads of Machine Learning and Signal Processing

ABSTRACT: Traditional signal processing techniques often rely on models that accurately depict the system under consideration. However, the complexity and dynamic nature of modern systems limit the effectiveness of these model-based approaches. In this context, machine learning techniques offer data-driven alternatives that do not require explicit prior model information. The proposed pre-ICASSP workshop aims to explore various aspects of learning-based signal processing. Topics will include high-dimensional statistics, quickest detection, sampling theory, online learning, tensor signal processing, stochastic filtering, and multi-agent signal processing and learning.

VENUE AND DATES: This two-day workshop, scheduled for April 03 – 04, 2025 will be held in a luxury hotel in Delhi, preceding the IEEE ICASSP 2025 conference. The technical program will feature 1-2 tutorials and approximately 10-15 research talks by leading researchers from around the world. The workshop is designed to disseminate knowledge, foster idea exchanges, and stimulate new collaborations within the research community. It will differ from ICASSP satellite workshops in Hyderabad in its extended duration and broader scope, promoting cross-domain collaboration.

General Chairs:

Arpan Chattopadhyay
Indian Institute of Technology, Delhi

Kumar Vijay Mishra
US Army Research Laboratory

Steering Committee:

Muralidhar Rangaswamy
US Air Force Research Laboratory

Ananthram Swami
US Army Research Laboratory

Raghu Raj
US Naval Research Laboratory

KVS Hari
Indian Institute of Science, Bengaluru

Finance Chair:

Shobha Sundar Ram
Indraprastha Institute of Information Technology, Delhi

SPS Liaison & Local Arrangements Chair:

Monika Aggarwal
Indian Institute of Technology, Delhi

Registration Chair:

Chandrashekhar Rai
Indian Institute of Technology, Delhi

  • Tutorial:  “Adversarial inference and inverse cognition” – Muralidhar Rangaswamy (US Air Force Research Laboratory) and Vikram Krishnamurthy (Cornell)
  • Technical Talk #1: “Domain-informed ML for Networks” – Ananthram Swami (US DEVCOM Army Research Laboratory)
  • Technical Talk #2: “Optimized recalibration for pretrained machine learning models” – Alfred Hero (US National Science Foundation)
  • Technical Talk #3: “Interactive Acoustic Simulation: From signal processing to scientific solvers to machine learning” – Dinesh Manocha (University of Maryland, College Park)
  • Technical Talk #4: “Distributed signal processing and learning for heterogeneous multi-agent systems“ – Zhi Tian (George Mason University)
  • Technical Talk #5: “Copula: From Signal Processing to AI/M” – Pramod Varshney (Syracuse University)
  • Technical Talk #6: “Targeted Applications of Machine Learning in Wireless Communications” – Lee Swindlehurst (University of California, Irvine)
  • Technical Talk #7: “Analysis of overfitting and modeling error tradeoff in learning systems using misspecified performance bounds” – Joseph Tabrikian (Ben Gurion University)
  • Technical Talk #8: “Multiview signal processing using linear and nonlinear canonical correlation analysis” – Nicholas Sidiropolous
    (University of Virginia)
  • Technical Talk #9: “Graph Constructions for Machine Learning Applications: New Insights and Algorithms“ – Antonio Ortega (University of Southern California)
  • Technical Talk #10: “Neuromorphic Radar” – Chandra Sekhar Seelamantula (Indian Institute of Science)
  • Technical Talk #11: “Byzantine-Resilient Federated Few Shot Learning for Phase Retrieval” – Namrata Vaswani (Iowa State University)
  • Technical Talk #12: “Kernel-driven Self-Supervision for Learning over Large-scale Graphs” – Georgios B. Giannakis (University of Minnesota)
  • Technical Talk #13: “ONRG’s Perspectives on AI/ML: Roadmap and Grand Challenges” – Joel Goodman (ONRG)
  • Technical Talk #14: “Hierarchical Reinforcement Learning” – Brian M. Sadler (University of Texas, Austin)