Deepa Mulimani | Computer Science | Best Researcher Award

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Mrs. Deepa Mulimani | Computer Science | Best Researcher Award

Assistant Professor of KLE Technological University, Hubballi, India

Deepa Mulimani is a dedicated and highly experienced Assistant Professor in Computer Science and Applications with over 19 years of expertise 🌟. Renowned for her higher cognitive training methodologies, exceptional communication skills, and technical documentation prowess, she has consistently provided stellar support to both professors and students 🎓. Proficient in data management, machine learning, big data analytics, and programming languages such as Python, Java, C, C++, and C#, Deepa’s versatile skill set is complemented by her impressive academic and administrative capabilities 📊. Her commitment to lifelong learning is evident through her numerous Coursera certifications, including scalable machine learning and deep learning 🧠. Deepa’s innovative teaching methods, curriculum development, and student research guidance at KLE Technological University have significantly impacted her students’ academic progress 🌱. Her robust publication record, featuring research on concept drift adaptation, deep learning, and streaming data mining, showcases her active contribution to the scientific community 📚.

Professional profile

Education📚

Deepa holds a Master of Science in Computer Science from Karnatak University, Dharwad, where she was the University Rank II, and a Bachelor of Computer Applications from Karnatak Science College, Dharwad, also with University Rank II. Her academic achievements underscore her strong foundational knowledge and academic excellence.

Professional Experience🏛️

As an Assistant Professor at KLE Technological University, Hubli, Karnataka, since 2008, Deepa has applied innovative teaching methods, revised curricula, and designed courses for MCA students. She has also created blended learning materials and collaborated with industry leaders for student training in robotic process automation (RPA). Her previous role as a lecturer at KLES’s College of Business Administration involved educating BBA students and coordinating cultural activities.

Research🏆

Currently, Deepa is pursuing research in Big Data Analytics with a focus on streaming data mining. Her ongoing research endeavors align well with contemporary challenges in data science and analytics, demonstrating her commitment to advancing knowledge in this field.

Publications top noted📜
  • Impact analysis of real and virtual concept drifts on the predictive performance of classifiers 🧠
    • Authors: Benni, R., Totad, S., Mulimani, D., Kg, K.
    • Year: 2024
    • Citations: 0
  • Online Detection and Adaptation of Concept Drift in Streaming Data Classification 🔄
    • Authors: Mulimani, D., Patil, P., Totad, S., Benni, R.
    • Year: 2024
    • Citations: 0
  • Heuristic Approach for Detecting and Neutralizing Black Hole Attacks in Wireless Sensor Networks 🌐
    • Authors: Benni, R., Kittur, M.M., Patil, P., Mulimani, D.
    • Year: 2023
    • Citations: 0
  • Adaptive Classifier to Address Concept Drift in Imbalanced Data Streams ⚖️
    • Authors: Mulimani, D., Patil, P.R., Totad, S.G.
    • Year: 2023
    • Citations: 0
  • Weighted Averaging Ensemble Model for Concept Drift Adaptation in Streaming Data ⚙️
    • Authors: Mulimani, D., Kanakaraddi, S.G., Totad, S.G., Patil, P.R.
    • Year: 2022
    • Citations: 3
  • Experiential Learning Enhancing User Interface Design Skills through Cognitive Action 💡
    • Authors: Mulimani, D., Seeri, S.V., Patil, P., Kulkarni, S.
    • Year: 2017
    • Citations: 0

Rithish S V | Computer Science | Best Researcher Award

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Mr. Rithish S V | Computer Science | Best Researcher Award

Student of Amrita University, India

Rithish S V is a passionate Computer Science student at Amrita Viswa Vidyapeetham specializing in Cloud Computing and Machine Learning 🌐📊. With a strong foundation in Microservices Architecture and cloud-native application design and deployment ☁️💻, he is actively enhancing his skills in Salesforce development 🚀. Rithish’s projects, such as developing a cloud monitoring app on Kubernetes and an emergency assistance app called “Emergify” 🚨📱, showcase his innovative approach and technical prowess. Fluent in multiple programming languages and proficient in various cloud and DevOps tools, Rithish exemplifies a blend of technical expertise and effective communication skills 🌟🗣️. His awards and certifications further validate his dedication and excellence in the field of computer science 🏆🎖️.

Professional profile

Education📚

Rithish has consistently excelled academically, currently pursuing a B.Tech in Computer Science and Engineering at Amrita Viswa Vidyapeetham with a GPA of 8.2. He completed his higher secondary education at Sri Lathangi Vidya Mandir with an impressive percentage of 91.6 and his secondary school education with a percentage of 93.6.

Professional Experience🏛️

Rithish S V is a dedicated Computer Science student with a strong focus on Cloud Computing and Machine Learning, demonstrating a profound understanding of Microservices Architecture and cloud-native application design and deployment. He is actively honing his skills in Salesforce development and showcases proficiency in problem-solving, communication, and team management, ensuring efficient project execution.

Skills🏆

Rithish is proficient in multiple programming languages, including Python, C++, and Java, and possesses expertise in cloud and DevOps tools such as AWS, Google Cloud Platform, Kubernetes, and Docker. He also has knowledge in networking, blockchain (solidity), and various development languages like SQL, JS, HTML, React.js, and CSS. His soft skills include team management, analytical thinking, problem-solving, and effective communication.

Publications top noted📜
  • Title: Echoes of Truth: Unraveling Homophily in Attributed Networks for Rumor DetectionAuthors: Rithish S.V., Prabu C.R., Anuush M.B., Deepthi L.R.

    Journal: Procedia Computer Science, 2024, Volume 233, Pages 184–193

    Abstract: The paper “Echoes of Truth: Unraveling Homophily in Attributed Networks for Rumor Detection” presents innovative research on identifying and mitigating the spread of rumors in social networks. By leveraging homophily—the tendency of individuals to associate and bond with similar others—the authors developed algorithms that effectively detect rumor sources within attributed networks. This study provides significant insights into network dynamics and offers practical solutions for improving information reliability in digital communication platforms.

Kalpa Subbaiah | Computer Science | Women Researcher Award

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Mrs. Kalpa Subbaiah | Computer Science | Women Researcher Award

VP-Lead Data Scientist of JP Morgan Chase, India

👩‍💼 Mrs. Kalpa Subbaiah is a seasoned Data Scientist with 16 years of experience, including 8 in Data Science. She holds advanced degrees in Machine Learning and AI. Certified in AWS, Azure, and Microsoft technologies, Kalpa excels in Azure Databricks, Machine Learning, and AI Cognitive Services. She is proficient in processing streaming and batch data and building cloud deployment pipelines. A published researcher in sentiment analysis, she is recognized for her strong analytical and project management skills.

Professional profile

Education📚

🎓 Mrs. Kalpa Subbaiah holds a Master of Science in Machine Learning and Artificial Intelligence from Liverpool John Moores University, UK. She also earned a Post-Graduation Diploma in Machine Learning and AI from the International Institute of Information Technology, Bangalore, and a Post-Graduation Certificate in Big Data Analytics & Optimization from Insofe (International School of Engineering). Additionally, Kalpa has a Bachelor’s degree in Computer Science and Engineering from Vishweshwaraiah Technological University, completed in 2006. 📚

Professional Experience🏛️

👩‍💼 Mrs. Kalpa Subbaiah boasts a robust professional journey with 16 years of experience, including 8 in Data Science. She has held roles at HP 🖥️, Bosch 🛠️, Insofe 🏫, Microsoft 💼, and JP Morgan Chase 🏦. As a Microsoft Open Hack Coach and Lead, she specializes in serverless, AI knowledge mining, and Modern Data Warehouse. Her expertise spans Azure Databricks, Azure Machine Learning, and AI Cognitive Services, where she excels in processing streaming and batch data, building models, and creating cloud deployment pipelines.

Research Interest🌐

🔍 Mrs. Kalpa Subbaiah is deeply interested in advancing the fields of Machine Learning, Deep Learning, and AI. Her research focuses on Natural Language Processing (NLP) 🤖, Computer Vision 🖼️, and GenAI technologies like lang chain, transformers, and OpenAI. She has a keen interest in Aspect-Based Sentiment Analysis, particularly using Weakly Supervised Learning. Kalpa is also passionate about developing end-to-end machine learning pipelines, integrating Big Data components, and leveraging frameworks such as TensorFlow, sklearn, and NLTK to solve complex data science problems. 📊📈

Awards and Honors🏆

🏆 Mrs. Kalpa Subbaiah has received numerous awards and honors throughout her career. She has been recognized for her exceptional contributions to Data Science and AI, including publishing a research paper on Aspect-Based Sentiment Analysis using Weakly Supervised Learning. As a certified professional in AWS Machine Learning Specialty, Microsoft Azure Data Scientist, Azure AI Engineer Associate, and more, she has consistently demonstrated her expertise and leadership. Additionally, she has earned accolades for her roles as a Microsoft Open Hack Coach and Lead in serverless, AI knowledge mining, and Modern Data Warehouse.

Achievements🏅
  • 📜 Published a research paper on Aspect-Based Sentiment Analysis using Weakly Supervised Learning.
  • 👩‍💻 Successfully led and completed large, complex data science projects across various industries.
  • 💼 Served as a Microsoft Open Hack Coach and Lead for serverless, AI knowledge mining, and Modern Data Warehouse.
  • 📊 Developed end-to-end machine learning pipelines and integrated Big Data components like Azure Event Hubs, Synapse, Stream Analytics, Spark Structured Streaming, Hadoop, Kafka, and Spark.
  • 🎓 Certified in multiple prestigious certifications, including AWS Machine Learning Specialty and Microsoft Azure Data Scientist.
  • 🤖 Expert in leveraging advanced machine learning frameworks such as TensorFlow, sklearn, OpenCV, and NLTK.
  • 🧠 Recognized for strong analytical and team player skills, consistently delivering impactful data science solutions.
  • 🌟 Created and shared knowledge through blogs on Medium and machine learning videos on the Microsoft community channel.
Certificates🛠️
  • 🏆 AWS Certified: Machine Learning Specialty
  • 🎓 Microsoft Certified: Azure Data Scientist
  • 💼 Microsoft Certified: Azure AI Engineer Associate
  • 🚀 Microsoft: Open Hack Serverless Tech Lead
  • 📊 Microsoft Certified: Data Engineer Associate
  • 🏫 Post-Graduation Certificate in “Big Data Analytics & Optimization” from Insofe (International School of Engineering)
  • 🤖 Microsoft Certified: Azure AI Fundamentals
  • 📈 Microsoft Certified: Azure Data Fundamentals
Publications top noted📜
  • Author: Kalpa Subbaiah, Bolla B.K.
  • Title: Aspect Category Learning and Sentimental Analysis Using Weakly Supervised Learning
  • Journal: Procedia Computer Science
  • Year: 2024
  • Volume: 235
  • Pages: 1246–1257
  • Citations: 0 📉