Professor at PURDUE UNIVERSITY, United States
vaneet aggarwal is a distinguished professor and university faculty scholar at purdue university, specializing in reinforcement learning, generative AI, quantum machine learning, and LLM alignment 🤖⚛️. With a Ph.D. from Princeton University 🎓 and extensive experience in industry and academia, he has made groundbreaking contributions to networking, robotics, healthcare, and computational biology 🌍🩺. He has served as a visiting professor at KAUST, IIIT Delhi, and IISc Bangalore 📚 and has led major research initiatives at AT&T Labs and Purdue CLAN Labs. His work has been recognized globally through high-impact publications and awards 🏅.
Professional Profile
Education & Experience 🎓💼
📌 Education:
- Ph.D. in Electrical Engineering – Princeton University, 2010 🎓 (GPA 4.0/4.0)
- M.A. in Electrical Engineering – Princeton University, 2007 🏅 (GPA 4.0/4.0)
- B.Tech in Electrical Engineering – IIT Kanpur, 2005 🎓 (GPA 9.6/10)
📌 Experience:
- Purdue University (2015–Present) 🏫 – Professor & University Faculty Scholar
- KAUST, Saudi Arabia (2022–2023) 🏝️ – Visiting Professor
- IIIT Delhi (2022–2023) 🌏 – Adjunct Professor
- Plaksha University (2022–2023) 📡 – Adjunct Professor
- IISc Bangalore (2018–2019) 🏆 – VAJRA Adjunct Faculty
- AT&T Labs Research, NJ (2010–2014) 📡 – Senior Member, Technical Staff
- Columbia University, NY (2013–2014) 📚 – Adjunct Assistant Professor
Professional Development 🚀📚
vaneet aggarwal has consistently contributed to cutting-edge advancements in AI, machine learning, and quantum computing 🧠⚡. As Editor-in-Chief of the ACM Journal of Transportation Systems 🚗📖, he shapes global research trends. He has been a technical lead in Purdue’s AI and security programs, fostering industry collaborations 🤝💡. His leadership in AI decision-making, intelligent infrastructures, and computational biology has driven groundbreaking innovations 🏗️🔬. He frequently mentors Ph.D. students and collaborates with top institutions worldwide, ensuring continuous academic excellence and technological impact 🌍🎯. His work bridges fundamental research with real-world applications, influencing multiple industries 🚀.
Research Focus Areas 🔍💡
🔬 Artificial Intelligence & Machine Learning: Reinforcement learning, generative AI, LLM alignment 🤖
⚛️ Quantum Computing: Quantum machine learning, hidden Markov models 🧠
📡 Networking & Systems: Cloud computing, 5G/6G networks, network virtualization 🌐
🛠️ Optimization & Control: Combinatorial bandits, linear optimization ⚙️
🚗 Transportation & Robotics: AI for intelligent infrastructure and automation 🏎️
🩺 Healthcare & Biomedical AI: Drug discovery, computational biology, medical AI 🧬💊
His research transforms fundamental theories into real-world applications, influencing technology, healthcare, and sustainable infrastructure 🌍.
Awards & Honors 🏅🎖️
🏆 University Faculty Scholar, Purdue University (2024) 🏫
🎖️ Best Paper Award – NeurIPS Workshop on Cooperative AI (2021) 📝
📚 VAJRA Adjunct Faculty, IISc Bangalore (2018–2019) 🔬
🥇 Editor-in-Chief, ACM Journal of Transportation Systems (2022–Present) 🚗📖
🌍 Senior Member, IEEE & ACM ⚡
🏅 Best Research Contributions in AI & Quantum Computing 🤖⚛️
Publication Top Notes
1. Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback
- Authors: Mohammad Pedramfar, Vaneet Aggarwal
- Published in: IEEE Transactions on Artificial Intelligence, 2025
- Summary: This paper addresses the combinatorial multi-armed bandit problem with stochastic submodular rewards and delayed, composite anonymous feedback. The authors analyze three delay models—bounded adversarial, stochastic independent, and stochastic conditionally independent—and derive regret bounds for each. Their findings indicate that delays introduce an additive term in the regret, affecting overall performance.
- Access: The paper is available as open access.
2. FilFL: Client Filtering for Optimized Client Participation in Federated Learning
- Authors: Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini, Marco Canini
- Published in: [No source information available]
- Summary: This conference paper introduces FilFL, a method to enhance federated learning by optimizing client participation through a filtering mechanism. By selecting a subset of clients that maximizes a combinatorial objective function, FilFL aims to improve learning efficiency, accelerate convergence, and boost model accuracy. Empirical evaluations demonstrate benefits such as faster convergence and up to a 10% increase in test accuracy compared to scenarios without client filtering.
- Access: The paper is available as open access.
3. Prism Blockchain Enabled Internet of Things with Deep Reinforcement Learning
- Authors: Divija Swetha Gadiraju, Vaneet Aggarwal
- Published in: Blockchain: Research and Applications, 2024
- Summary: This article explores the integration of Prism blockchain technology with the Internet of Things (IoT) using deep reinforcement learning techniques. The approach aims to enhance security, scalability, and efficiency in IoT networks by leveraging the unique features of Prism blockchain and the adaptive capabilities of deep reinforcement learning.
- Access: The paper is available as open access.
4. GLIDE: Multi-Agent Deep Reinforcement Learning for Coordinated UAV Control in Dynamic Military Environments
- Authors: Divija Swetha Gadiraju, Prasenjit Karmakar, Vijay K. Shah, Vaneet Aggarwal
- Published in: Information (Switzerland), 2024
- Summary: GLIDE presents a multi-agent deep reinforcement learning framework designed for the coordinated control of unmanned aerial vehicles (UAVs) in dynamic military settings. The framework focuses on enhancing mission success rates and operational efficiency by enabling UAVs to adapt to changing environments and collaborate effectively.
- Access: The paper is available as open access.
5. Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN
- Authors: Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Mohamed-Slim Alouini, Vaneet Aggarwal
- Published in: IEEE Networking Letters, 2024
- Summary: This article proposes a method for estimating coverage manifolds in cellular networks using conditional Generative Adversarial Networks (GANs). The approach aims to achieve near-perfect coverage predictions, which are crucial for optimizing network performance and ensuring reliable communication services.
- Access: The paper is available as open access.
Conclusion
vaneet aggarwal is a highly suitable candidate for the Best Researcher Award, given his strong publication record, leadership, and multidisciplinary impact. If he strengthens his global recognition, large-scale funding acquisition, and public engagement, he could be an even stronger contender for such an award.