Mohit Kataria | Machine Learning | Best Researcher Award

Mr. Mohit Kataria | Machine Learning | Best Researcher Award

Professor at IIT-Delhi

๐Ÿ“Œย ย Mohit Kataria is a 4th-year Ph.D. scholar at the School of Artificial Intelligence, IIT Delhi, India, specializing in Graph Machine Learning. His research focuses on scalability of graph algorithms, including graph coarsening, structure learning, federated learning, and large-scale applications. He has published in top venues like NeurIPS, MICAAI, and CBME. Mohit holds a Masterโ€™s in Computer Applications (80.1%) and has expertise in Python, PyTorch, TensorFlow, CUDA, and C/C++. His skill set spans deep learning (GNNs, CNNs, RNNs), machine learning (SVM, XGBoost), and mathematical optimization.

Publication Profile

Google Scholar

Academic Background ๐ŸŽ“๐Ÿ”ฌ

๐Ÿ“Œย Mohit Kataria is a Ph.D. scholar in Graph Machine Learning at the MISN Lab, IIT Delhi, maintaining an 8.0 CGPA since August 2021. He holds a Masterโ€™s in Computer Applications (80.1%) from May 2020. His technical expertise spans Python, PyTorch, TensorFlow, CUDA, MPI, C/C++, Java, MySQL, and Erlang. ๐Ÿ–ฅ๏ธ He specializes in Machine Learning (SVM, Random Forest, XGBoost, Decision Trees) and Deep Learning (ANNs, GNNs, CNNs, RNNs, LSTM, VAE, GANs). ๐Ÿ“Š His strong foundation in Linear Algebra, Probability, and Optimization fuels his research in scalable graph algorithms and AI applications. ๐Ÿš€

๐Ÿ’ผ Professional Experience of Mohit Kataria

๐Ÿ“Œ Mohit Kataria has been actively involved in AI/ML training at IIT Delhi (2021-Present), where he has helped train 260+ industry experts in a six-month AI/ML program, covering fundamentals to advanced ML models. ๐ŸŽ“ He also conducted 5-day ML training programs for CAG and CRIS, Government of India. As a WebMaster (2022-Present), he manages the Yardi-ScAI and MISN group websites. ๐ŸŒ Previously, as a Member of Technical Staff at Octro.Inc (2020-2021), he led a team of four and contributed to the backend architecture of multiplayer games like Poker3D and Soccer Battles. ๐ŸŽฎ๐Ÿš€

๐Ÿ”ฌ Research Focus of Mohit Kataria

๐Ÿ“Œ Mohit Kataria specializes in Graph Machine Learning, focusing on graph coarsening, structure learning, and scalable AI applications. His work enhances GNN performance on heterophilic datasets ๐Ÿง , improves large-scale single-cell data analysis ๐Ÿงฌ, and optimizes histopathological image processing ๐Ÿ”. His research, published in NeurIPS, MICAAI, and CBME, develops efficient graph-based frameworks for biomedical and computational applications. ๐Ÿฅ His expertise spans AI-driven healthcare, graph-based AI models, and machine learning scalability, making significant contributions to bioinformatics, medical imaging, and large-scale data processing. ๐Ÿš€

Publication Top Notesย 

 

 

 

Deepali Bhamare | Deep Learning | Best Researcher Award

Ms. Deepali Bhamare | Deep Learning | Best Researcher Award

Ms. Deepali Bhamare, S.S.V.P.S.B.D’s COE Dhule, India

Deepali Bhamare is an accomplished educator and engineer with over two decades of experience in electronics and telecommunication. She holds a B.E. in Electronics and Telecommunication from NMU Jalgaon (2002), an M.E. in Digital Communication from R.G.P.V. Bhopal (2012), and is pursuing a PhD in Electronics Engineering. Deepali has worked in industry as a QC and Testing Engineer before transitioning into academia, where she currently serves as Assistant Professor at S.S.V.P.S. College of Engineering Dhule. She has actively contributed to various institutional committees and has attended numerous FDPs and workshops related to AI, machine learning, and research methodologies. ๐ŸŽ“๐Ÿ“Š๐Ÿ“ก

 

Publication Profile

Scopus

Educational Qualification

Ms. Deepali Bhamare has a robust educational background in Electronics and Telecommunications. She completed her H.S.C. in Science with 74.08%, followed by a B.E. in Electronics and Telecommunication Engineering from N.M.U. Jalgaon (64%) in 2002. She also holds a master’s degree in Digital Communication from R.G.P.V. Bhopal (75.03%), and is currently pursuing her PhD in Electronics Engineering from N.M.U., which showcases her commitment to advancing her expertise in the field.

Professional Experience

Her extensive work experience spans across both industry and academia. She worked as a Quality Control Engineer at renowned firms like Satronix India Pvt Ltd and Penguin Audio Products Ltd. Since 2008, she has transitioned into academia, holding roles such as Lecturer and Assistant Professor at S.S.V.P.S. College of Engineering, Dhule. Her academic career, coupled with her technical experience, demonstrates her comprehensive understanding of engineering principles and practical applications.

Training and Development

Ms. Bhamare has actively participated in various Faculty Development Programs (FDPs), Short-Term Training Programs (STTPs), and workshops. Notable topics include “Next Generation Artificial Intelligence,” “Python Programming with Django,” “Machine Learning and Deep Learning,” “Neural Networks and Fuzzy Logic,” and “Artificial Intelligence in Healthcare.” These programs indicate her continuous efforts to stay updated with emerging technologies, particularly in AI, machine learning, and data science.

Academic Involvement

In addition to teaching, she has held several key positions in her college, such as Member of the Anti-Ragging Committee, BC Cell, and Extra Curricular Cell, as well as Lab In-Charge. These roles highlight her dedication to both student welfare and the efficient management of college resources.

Conclusion

Ms. Deepali Bhamareโ€™s well-rounded qualifications, research pursuits in electronics, and ongoing professional development through numerous FDPs and workshops position her as a strong candidate for the Best Researcher Award. Her blend of academic knowledge, research focus, and involvement in emerging technologies such as AI and machine learning, make her a notable contributor to the field of electronics engineering.

 

Publication Top Notes ย 

A Review on Person Identification Using Periocular Biometrics

Person Identification System Using Periocular Biometrics Based on Hybrid Optimal Dense Capsule Network

Dharmapuri Siri | Deep Learning Award | Best Researcher Award

Dr. Dharmapuri Siri | Deep Learning Award | Best Researcher Award

Dr. Dharmapuri Siri, Gokaraju Rangaraju Institute of Engineering and Technology, India

Based on Dr. Dharmapuri Siri’s resume, here is a conclusion on his suitability for the Research for Best Researcher Award:

Publication profile

Scopus

Career Experience

Dr. Dharmapuri Siri has extensive teaching experience spanning over 11 years across various institutions, including TRR Engineering College, TRR College of Engineering, and Malla Reddy Engineering College for Women. His role as an Assistant Professor in Computer Science and Engineering highlights a solid foundation in academic and practical knowledge.

Educational Background

Dr. Siri’s educational qualifications are robust, with a Ph.D. in Computer Science and Engineering from JJT University, an M.Tech from JNTU Hyderabad, and a B.Tech from JNTU Hyderabad. His academic background demonstrates a strong commitment to his field and a continual pursuit of advanced knowledge.

Researchย 

Dr. Siri has made significant contributions to research, particularly in the areas of software quality, machine learning, and image analysis. His journal publications and conference presentations reflect a broad range of research interests, from bug prediction models to sentiment analysis and cancer diagnosis. Notable papers include his work in IEEE Access and various webE3S conferences.

Workshops and Training

He has actively participated in multiple workshops and refresher courses, focusing on cloud computing, innovative teaching methods, and problem-solving techniques. This engagement in continuous professional development underscores his dedication to staying current in his field.

Patent and Projects

Dr. Siri holds a patent for a “Vehicle with Smart Biometric Device,” showcasing his ability to apply theoretical knowledge to practical solutions. His Ph.D. thesis on “Bug Prediction Model For Software Quality Using Machine Learning Techniques” further emphasizes his research focus and expertise.

Conclusion

Dr. Dharmapuri Siri is a strong candidate for the Research for Best Researcher Award due to his comprehensive academic background, extensive teaching experience, substantial research contributions, and practical innovations. His work in improving software quality through machine learning and his active involvement in professional development make him a suitable candidate for this accolade.

 

Publications Top Notes

Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

Dharmapuri Siri | Deep Learning | Best Researcher Award

Dharmapuri Siri | Deep Learning | Best Researcher Award

Assistant Prof Dharmapuri Siri, Gokaraju Rangaraju Institute of Engineering and Technology, india.

Dr. D. Siri is a dedicated academic professional with over 11 years of experience in teaching and research in the field of Computer Science and Engineering. She has served as an Assistant Professor in various prestigious institutions, including TRR Engineering College and Malla Reddy Engineering College for Women. Her work is focused on the intersection of software quality, machine learning, and data analysis.

Profile:

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Education๐Ÿ“š:

Dr. D. Siri holds a Ph.D. in Computer Science and Engineering from JJT University, obtained in 2022. She completed her M.Tech in Computer Science and Engineering from JNTU Hyderabad in 2010, and her B.Tech in Information Technology from the same university in 2006.

Professional Experience๐Ÿ‘ฉโ€๐Ÿซ:

Dr. Siri began her teaching career in 2008 as an Assistant Professor in the Department of Information Technology at TRR Engineering College, where she worked until 2013. She then transitioned to the Department of Computer Science and Engineering at TRR College of Engineering, continuing her role until 2017. From 2017 to 2019, she served as an Assistant Professor at Malla Reddy Engineering College for Women. Throughout her career, she has gained extensive experience in teaching core subjects like C, C++, Java, DBMS, Software Testing Methodologies, and Software Engineering.

Research Interests๐Ÿ”:

Dr. Siri’s research interests are primarily focused on software quality improvement, bug prediction using machine learning techniques, and the application of deep learning methods to various domains such as sentiment analysis, medical imaging, and automated systems. Her work in these areas has been presented at numerous national and international conferences.

Awards and Recognitions๐Ÿ†:

Dr. Siri’s innovation and contributions to the field have been recognized through a published patent titled “A Vehicle with Smart Biometric Device” (Application No. 201841019142-A), which was published in The Patent Office Journal No. 22/2018 on June 1, 2018.

Research Contributions ๐Ÿ“š:

Dr. Siriโ€™s research is primarily centered around software quality, machine learning, and data analysis. Her notable works include studies on bug prediction models, software engineering methodologies, and the application of machine learning techniques in software quality improvement. Her Ph.D. thesis on “Bug Prediction Model for Software Quality Using Machine Learning Techniques” reflects her deep commitment to enhancing software reliability and performance.

Publication:

Publications ๐Ÿ“:
Dr. Siri has published several research articles in reputed journals. Here are some of her notable publications:

  1. A Study on Bug Prediction in Determining The Software Quality | History Research Journal | 2019.
  2. Machine Learning Techniques on Historical Software Bugs for Prediction of Software Bugs | Think India Journal | 2019.
  3. Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis | IEEE Access | 2024.
  4. Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism | IEEE Access | 2024.