Prof Dr. Vaneet Aggarwal | Machine Learning | Best Researcher Award

Prof Dr. Vaneet Aggarwal | Machine Learning | Best Researcher Award

Prof Dr. Vaneet Aggarwal, Purdue University, United States

Prof. Dr. Vaneet Aggarwal, an accomplished scholar with a Ph.D. in Electrical Engineering from Princeton University, is currently a distinguished faculty member at Purdue University. With a diverse academic background spanning machine learning, computational perception, and computer science, he has garnered recognition for his impactful contributions. 🌟 His research interests encompass a wide array of cutting-edge fields, including reinforcement learning, generative AI, quantum machine learning, and federated learning. 🧠 Through leadership roles in prestigious journals and institutions, such as ACM Journal of Transportation Systems and Purdue-TVS Advanced AI Program, he continues to drive innovation at the intersection of AI and various domains, ranging from networking to healthcare. πŸš€ Honored with accolades like the IEEE Communications Society William R. Bennett Prize and featured in esteemed publications like Nature and Axios News, Prof. Aggarwal’s work exemplifies excellence in advancing the frontiers of artificial intelligence. πŸ†

🌐 Professional Profile:

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Education

  • Ph.D. in Electrical Engineering, Princeton University, Princeton, New Jersey, July 2010
    • Thesis: Decisions in Distributed Wireless Networks with Imprecise Information
    • Minors: Machine Learning and Computational Perception, Computer Science
    • Advisor: Prof. A. Robert Calderbank
  • M.A. in Electrical Engineering, Princeton University, Princeton, New Jersey, June 2007
  • Bachelor of Technology in Electrical Engineering, Indian Institute of Technology, Kanpur, May 2005

Work Experience

  • Purdue University, West Lafayette, Jan. 2015 – Current: Faculty in the School of Industrial Engineering and Elmore Family School of Electrical and Computer Engineering
  • KAUST, Saudi Arabia, May 2022 – Aug 2023: Visiting Professor
  • IIIT Delhi, Jan 2022 – Mar 2023: Adjunct Professor
  • Plaksha University, Nov 2022 – Jan 2023: Adjunct Professor
  • Indian Institute of Science (IISc) Bangalore, May 2018 – Apr 2019: VAJRA Adjunct Faculty
  • AT&T Labs Research, NJ, Aug. 2010 – Dec. 2014: Senior Member of Technical Staff-Research
  • Columbia University, New York, NY, Aug. 2013 – Dec. 2014: Adjunct Assistant Professor

Key Leadership Experience

  • ACM Journal of Transportation Systems, co-Editor-in-Chief, 2022-Current
  • Director of Potential NSF AI Institute on Human-AI Decision Making at Scale, 2021-Aug 2022
  • Founding Technical Lead Purdue-TVS Advanced AI Program, 2021-Current
  • AI Thrust Lead in Purdue Center of Intelligent Infrastructures, 2019-Current

Honors & Awards

  • Purdue University Faculty Scholar Professor, 2024-Current
  • IEEE ComSoc Distinguished Lecturer for the class of 2024-2025
  • 2024 IEEE Communications Society William R. Bennett Prize
  • Featured on Axios News for paper [J176] in 2023
  • Featured on Cover of Nature for paper [J139] in 2023
  • NeurIPS Workshop Best Paper Award in 2021
  • Most Impactful Faculty Innovator, Purdue University in 2020
  • Infocom Workshop Best Paper Award in 2018

Research Interests:

Reinforcement Learning; Generative AI; Quantum Machine Learning; Federated Learning; Applications of ML in Networking, Transportation, Robotics, Manufacturing, Healthcare, and Biomedical.

Publication Top Notes:

  1. Title: Design and characterization of a full-duplex multiantenna system for WiFi networks
    • Journal: IEEE Transactions on Vehicular Technology
    • Citations: 665
    • Year: 2013
  2. Title: Efficient low rank tensor ring completion
    • Proceedings: Proceedings of the IEEE International Conference on Computer Vision
    • Citations: 192
    • Year: 2017
  3. Title: Wide compression: Tensor ring nets
    • Proceedings: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
    • Citations: 180
    • Year: 2018
  4. Title: Prometheus: Toward quality-of-experience estimation for mobile apps from passive network measurements
    • Proceedings: Proceedings of the 15th Workshop on Mobile Computing Systems and Applications
    • Citations: 177
    • Year: 2014
  5. Title: Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
    • Journal: IEEE Transactions on Intelligent Transportation Systems
    • Citations: 159
    • Year: 2019

 

 

 

 

 

Dr. Fatemeh Aghagoli | Machine Learning Awards | Best Researcher Award

Dr. Fatemeh Aghagoli | Machine Learning Awards | Best Researcher Award

Dr. Fatemeh Aghagoli, Iran University of Science and Technology, IranΒ 

πŸŽ“ Fatemeh Aghagoli is a Ph.D. student at the Iran University of Science and Technology, specializing in Mathematics and Computer Science. She is affiliated with the Faculty of Mathematics and Computer Science and is a member of the National Elite Foundation of Iran, recognized for her academic excellence. πŸ” Her primary research interests encompass machine learning, image processing, artificial intelligence, cluster computing, and statistical modeling, particularly focusing on applications in medical science. 🌐 Actively contributing to cutting-edge research initiatives, Fatemeh leverages her expertise to address complex challenges in medical science, aiming to advance diagnostics and treatment methodologies. πŸ“Š With a commitment to interdisciplinary applications, she has demonstrated exceptional academic prowess and a dedication to pushing the boundaries of knowledge in her field.

Professional Profile:

Google Scholar

πŸŽ“ Education:

Fatemeh Aghagoli is a Ph.D. student at the Iran University of Science and Technology, specializing in Mathematics and Computer Science. She is also a member of the National Elite Foundation of Iran, recognizing her academic excellence.

πŸ” Research Interests:

Fatemeh’s primary research interests revolve around machine learning, image processing, artificial intelligence, cluster computing, and statistical modeling, particularly in the field of medical science.

πŸ“Š Expertise:

With a focus on interdisciplinary applications, Fatemeh leverages her expertise in machine learning and image processing to address complex challenges in medical science, aiming to advance diagnostics and treatment methodologies.

🌟 Achievements:

As a member of the National Elite Foundation, Fatemeh has demonstrated exceptional academic prowess and a commitment to pushing the boundaries of knowledge in her field.

Publications Top Note :

A novel approach for automatic tumor detection and localization in mammography images via mixture of factor analyzers based on co-clustering

  • Published in Biomedical Signal Processing and Control in 2024.

 

 

 

 

 

Dr. Bechoo Lal | Machine Learning Award | Best Faculty Award

Dr. Bechoo Lal | Machine Learning Award | Best Faculty Award

Dr. Bechoo Lal, KL University Vijayawada Campus Andhra Pradesh, India

Dr. Bechoo Lal πŸŽ“ is an accomplished academic with expertise in Data Science, Machine Learning, and Big Data Analytics. He holds a Ph.D. in Computer Science and Information Systems from prestigious universities in India and the USA. Currently serving as an Associate Professor at KLEF-KL Deemed University, he has extensive teaching experience spanning over two decades across various institutions. Dr. Lal is deeply involved in research, with a focus on predictive modeling using Machine Learning and Data Science. He has received numerous certifications and training in Data Science-related fields, including from Stanford University and IBM. With a commitment to academic excellence, Dr. Lal has contributed significantly to the field through publications, projects, and memberships in professional organizations. πŸ“šπŸ’»

Professional Profile:

Scopus

Orcid

Google Scholar

πŸŽ“ Education:

Dr. Bechoo Lal holds a PhD in Information Systems from the University of Mumbai, specializing in Data Analytics. He also completed a PhD in Computer Science from SJJT University, focusing on Machine Learning, and a Master of Technology in Computer Science and Engineering from AAI-Deemed University.

πŸ‘¨β€πŸ’Ό Experience:

With over two decades of experience in academia, Dr. Lal has served as an Associate Professor at KLEF University, Vijayawada Campus, and as an Assistant Professor at various institutions including the University of Mumbai and King Khalid University in Saudi Arabia. He has expertise in teaching Data Science, Machine Learning, Database, and Programming Languages.

πŸ”¬ Research:

Dr. Lal’s research interests lie in Machine Learning and Data Science, with a focus on predictive modeling and big data analytics. He has supervised numerous PhD and master’s dissertations and has contributed significantly to research with over 60 publications, including patents, books, journals, and conference papers.

🌐 Skills:

Dr. Lal possesses strong technical skills in machine learning, data visualization, and predictive modeling, along with proficiency in programming languages such as Python, C/C++, and Java. He is well-versed in DBMS/RDBMS systems and statistical analysis tools like SPSS and R.

πŸ‘¨β€πŸ’» Teaching:

As a dedicated educator, Dr. Lal has taught courses in Computer Science, Information Technology, and Software Engineering. He has also held administrative roles such as coordinator and examination chairperson, demonstrating leadership and organizational abilities.

πŸ“š Memberships:

Dr. Lal is a member of several prestigious organizations including the International Association of Engineers (IAENG) and the Indian Society for Technical Education (ISTE). He also serves as a research supervisor and adjunct faculty for international universities.

Scopus Metrics:

  • πŸ“Β Publications: 20 documents indexed inΒ Scopus.
  • πŸ“ŠΒ Citations: A total of 06 citations for his publications, reflecting the widespread impact and recognition of Dr. Bechoo Lal’s research within the academic community.

Publications Top Notes :

  1. A road map: e-commerce to world wide web growth of business world
    • Published in Global Journal of Management and Business Research in 2019.
    • Cited by 6 articles.
  2. Analysis of Business Processes and Modeling Approach to Business Process Re-Engineering
    • Published in International Journal of Computer Science and Information Technology in 2012.
    • Cited by 5 articles.
  3. Analysis Report on Attacks and Defence Modeling Approach to Cyber Security
    • Published in International Journal of Scientific Research in Science and Technology in 2019.
    • Cited by 2 articles.
  4. Critical Review of Success Factors of Knowledge Management System (KMS) on Competency Building on IT Based Organization
    • Published in British Journal of Research in 2014.
    • Cited by 1 article.
  5. An optimization approach to analysis of success factors and significance of IT enabled services in business process re-engineering
    • Published in International Journal of Computer Applications in Engineering Sciences in 2012.
    • Cited by 1 article.