Shubham Aggarwal
Scholar

Shubham Aggarwal

Google Scholar ID: h5evShEAAAAJ
University of Illinois, Urbana Champaign
Deep Reinforcement learningEdge intelligenceGame theoryOptimal control
Citations & Impact
All-time
Citations
89
 
H-index
6
 
i10-index
3
 
Publications
20
 
Co-authors
10
list available
Contact
No contact links provided.
Resume (English only)
Academic Achievements
  • Multiple papers accepted to top conferences and journals such as IEEE L-CSS, IEEE CDC, IEEE TAC, IEEE INFOCOM, and IEEE GLOBECOM.
Research Experience
  • Conducting research at the Coordinated Science Laboratory and the Department of Mechanical Science & Engineering at UIUC, focusing on reinforcement learning, deep learning, control theory, stochastic processes, and neuroscience.
Education
  • Ph.D. student: University of Illinois, Urbana Champaign (UIUC), Department of Mechanical Science & Engineering, advised by Prof. Tamer Basar and Prof. Prashant Mehta; M.S. in Mathematics from the Department of Mathematics at UIUC; B.Tech. in Electrical Engineering and M.Tech. in Power Electronics from the Indian Institute of Technology (BHU) Varanasi, India, in 2020.
Background
  • Fifth-year Ph.D. student in the Department of Mechanical Science & Engineering and the Coordinated Science Laboratory (CSL) at the University of Illinois, Urbana Champaign. Research interests include developing principled frameworks for distributed and/or sequential decision making under uncertainty, with applications spanning remote estimation, human-robot interaction (HRI), healthcare decision systems, and edge AI.