Mr.Supriya Manna | Operating Systems | Best Researcher Award

Supriya Manna is a curious researcher exploring the intricacies of opaque AI models with a focus on explainability, causal learning, adversarial robustness, and the ethical implications of AI. With a strong foundation in optimization problems and scheduling, Supriya actively contributes to advancing AI research.

Publication Profile

Orcid

Scholar

Assessment of Supriya Manna for the Best Researcher Award

🔍 Strengths for the Award:

1. Research Focus and Innovation: Supriya Manna has a strong focus on explainable AI, causal learning, adversarial robustness, and the ethical dimensions of AI. These are emerging areas in AI research, and his contributions reflect a keen understanding of critical challenges within the field, particularly with regard to model transparency and security.
2. Academic Excellence: Supriya has maintained a stellar GPA of 9.02/10.00 during his B.Sc. in Computer Science and Engineering at SRM University. Additionally, his consistent high grades in relevant coursework such as Design and Analysis of Algorithms, Artificial Intelligence, and Machine Learning demonstrate both his academic capability and understanding of core concepts.
3. Research Experience: Supriya has undertaken research internships at both SRM University and Deakin University, focusing on cutting-edge areas like adversarial attacks, model explainability, and optimization. This experience has helped him develop a deep understanding of key research methodologies.
4. Publications and Patents: Supriya has authored several significant publications, including articles in Scopus-indexed journals and a book chapter accepted by CRC Press. His research on topics like post-hoc explainability in NLP and the need for AI in education indicates his contribution to both theoretical and applied AI. Additionally, his patent on job batch processing showcases his innovation and technical skills.
5. Technical Skills: Supriya is proficient in multiple programming languages and tools relevant to his research areas, including Python, MATLAB, and machine learning libraries like PyTorch. His projects, such as the scheduling algorithm simulator and the TSP solver using Ant Colony Optimization, further exemplify his practical problem-solving skills.

🚀 Areas for Improvement:

1. Broader Impact and Collaboration: While Supriya has made notable strides in his research, further engagement in collaborative projects across different domains or with industry stakeholders could enhance the broader applicability and impact of his work.
2. Diversity of Research Topics: His research is primarily focused on explainable AI and adversarial robustness. While these are vital areas, expanding into related topics like fairness in AI, AI ethics, or other optimization problems could broaden his research portfolio and make his work more comprehensive.
3. Grant Acquisition and Leadership Roles: Although Supriya’s academic and research background is strong, gaining experience in securing research grants or taking on leadership roles in collaborative projects or conferences could further strengthen his profile.

Education:

B.Sc. Computer Science and Engineering (Hons), SRM University, AP (Sept 2021 – Sept 2025) Current GPA: 9.02/10.00 Relevant Coursework: AI, Machine Learning, Algorithms, Operating Systems, Discrete Mathematics. MOOCs: Ethics of AI, AI in Society, Philosophy and Critical Thinking.
Academic scholarship holder, 3 consecutive times.

Research Experience:

Research Intern, Deakin University (June 2024 – Present) Focused on security aspects of SOTA models and adversarial attacks.. Undergraduate Researcher, SRM University, AP (Sept 2023 – Present) Investigating explainability of deep models and adversarial robustness.. Research Intern, SRM University, AP (June 2023 – Sept 2023) Worked on machine learning and information retrieval projects.

Key Skills:

Languages: C/C++, Python, Java, MATLAB Tools: PyTorch, LIME, SHAP, scikit-learn, Git/GitHub, Gurobi Frameworks: React, Node.js, Flask, FastAPI

Publications:

• Evaluating Post-hoc Explainers with Adversarial Sensitivity in NLP (under review)

• Revitalizing the Single Batch Environment for Fairness and Efficiency, Int. J. of Computers and Applications (2024).

• Need of AI in Modern Education: in the Eyes of Explainable AI (xAI), CRC Press, Taylor & Francis Group (2024).

• Patent: A Computing System and a Method for Managing Batch Processing of Jobs (Indian Patent, 2024).