Objective

To work as a problem solver, teacher and researcher across a diverse set of thrust domains pertaining to Computer Science, to the best of my capability and in alignment with the goals of the institute and contribute to nation-building and to be of technological service to the society. 

Prior Work Experience

• Serving as an Assistant Professor (Grade I) in the Department of Computer Science & Engineering, NIT Andhra Pradesh since 9th Aug 2018. 
• Served as an Assistant Professor (Grade II) in the Department of Computer Science and Engineering in Thiagarajar college of Engineering (Anna University), India.
• Served as an IT Associate in Morgan Stanley, Mumbai, India.
• Served as an Assistant System Engineer in TATA Consultancy Services Limited, India.

Administrative Roles in NIT Andhra Pradesh 

• Serving as Dean (Faculty Welfare) since 20.12.2024 till date.  
• Served as the Dean (Students’ Career, Alumni and International Relations) division from 21.09.2021 till 1.01.2025.
  
• Served as the Head of the Department of Computer Science and Engineering for three years from 10.09.2018 to 20.09.2021.

Education

• Doctor of Philosophy (Thesis title: Design and Performance Evaluation of Parallel Algorithms for Hierarchical Clustering of Large Text Collections, Under the mentoring of Prof. Shalinie S.M., AU-Chennai, Prof. Balaraman Ravindran, IIT Madras and Prof. K. Viswanathan Iyer, NIT Trichy. My Ph.D. thesis was classified as “Highly Commended” by Prof. Stephane Bressan of School of Computing, NUS Singapore.
• Master of Technology (Computer Science) – National Institute of Technology, Tiruchirappalli, Tamilnadu, India – CGPA 10 (On a Scale of 10) (Gold Medalist)
• Bachelor of Engineering (Computer Science and Engineering) – Thiagarajar College of Engineering, Madurai (Anna University), India – CGPA 9.11 (On a Scale of 10) (Academic proficiency award for having stood first in my Batch)

Domains of Interest

• Algorithms (Parallel, Approximation and Randomized)
• Data Mining (Web and Text)
• Performance Modeling
  
• Machine Learning
• Deep Learning (Natural Language Processing and Computer Vision)
• Quantum Computing 
• Probabilistic Graphical Models
  
• Cloud Computing
• Reinforcement Learning

Courses Taught

• Data Structures and Algorithms
• Design and Analysis of Algorithms
• Advanced Algorithms
• Problem Solving and Computer Programming
• Theory of Computation
• Compiler Design
• Object Oriented Programming
• Randomized and Approximation Algorithms
• Design and Analysis of Parallel Algorithms
• Performance Modeling of Computer Systems
• Operating Systems 
• Distributed Computing
• Parallel Algorithms
• Machine Learning
• Artificial Intelligence
• Probabilistic Graphical Models
• Computer Networks
• Modeling and Optimization
  
• Blockchains

Projects - in Academia

• As an Assistant Professor in NIT Andhra Pradesh: 
  

1. LinkedIn Sponsored project worth Rs. 15 Lakhs entitled "AI Models for Green Computing" from 2025 to 2026 as PI. (A matching grant of Rs. 5 Lakhs was sanctioned by NIT Andhra Pradesh) (Ongoing).
2. A project entitled “Efficient AI-based CAPEX and OPEX optimizing automatic vitals monitoring and clinical decision support system in hospital wards” worth INR 30 Lakhs was sanctioned by the ATAL National Innovation Mission Mentor in collaboration with Southern Railways Hospital, Perumbakkam, Chennai from 2025 to 2027 as PI (Ongoing)
3. LinkedIn Sponsored project worth Rs. 30 Lakhs entitled "A Scalable Resource Requirement Prediction and Provisioning Framework for Elastic Cloud" from 2021 to 2025 as PI (Ongoing). 
4. NIT AP sponsored seed grant project worth Rs. 2.5 Lakhs entitled "Effective modeling and efficient processing of data provenance and context-aware information retrieval of web-scale documents" from 2019 to 2021 as PI. (Completed)
5. IDRBT Hyderabad sponsored R&D project worth Rs. 32 Lakhs entitled "Intelligent Banking Kiosk" from 2021 to 2022 as one of the two PIs. (Completed) 
6. AI and Robotics Research Park, IISc sponsored R&D project worth Rs. 3.6 Lakhs entitled "Design and Evaluation of a Deep Neural Network Framework for Inferring and Ranking Causes for Sentiments in Product Reviews" from 2021-2022 as PI. (Completed) 
7. MEITY, GoI sponsored project worth Rs. 15 Lakhs on "Quantum enhanced learning and inference in probabilistic graphical models", 2022 to 2024 as PI. (Completed) 
8. A Project on performance evaluation of Enixta's sentiment analysis algorithms, Enixta, Hyderabad worth Rs. 5 Lakhs as PI. (2018-2019 Completed).
   
9. ANRF Sponsored PAIR project under Category B with IIT-H as the Hub, Lead PI from the Department of CSE, NIT Andhra Pradesh for the AI for Energy and Healthcare sub-themes, 2025-2026 (Ongoing)

• As an Assistant Professor in Thiagarajar College of Engineering (Anna University): 

1. Parallel Hierarchical Latent Dirichlet Allocation- Designed and implemented a parallel algorithm to learn a topic hierarchy from a web-scale corpus. This algorithm is a parallel version of the Bayesian extension of the well-known Latent Dirichlet Allocation (LDA). This was carried out in collaboration with Nokia R&D worth Rs. 12 Lakhs. 
2. Multi-word Tagger- Developed and implemented a Natural Language Processing algorithm to extract Multi-words present in a set of documents and this tagger was later integrated into a Sentiment Analyzer component developed in Honeywell worth Rs. 3 Lakhs.
3. Code Validator- A web-based tool to automatically evaluate and grade the code submissions by students. This tool guides the admin to infer test cases to maintain sufficient code coverage and also keeps a tab on the time and space consumed by the code. 
4. Recommender System- Developed and implemented an algorithm to mine huge archives containing profile information of users in a Social Network, to identify communities and to recommend products and activities to users based on the communities identified. This project was carried out in collaboration with Nokia R&D. 
5. Clustering Engine-Designed and implemented a parallel algorithm for clustering web pages in a huge archive. This graph-cut based algorithm is tailored to be run on a distributed system of multi-core workstations. This was done in collaboration with Nokia R&D. 
6. Scalable Classifier – Designed and Implemented an unsupervised algorithm based on graph-theory and Spectral theory, to automatically construct a training set for bootstrapping a classification algorithm. The classification algorithm which was designed is based on Support Vector Machines (SVM) and has a potential to exploit the processing power across all the cores in a multi-core system. 
7. Churn Prediction: Designed a social network analytic model for predicting Churn based on a Telecom Network. The project was sponsored by Idiap Research Institute, Switzerland.

Selected Publications in Journals, Books and Conferences

Journals: 

1. Kumar Anurupam, Karthick Seshadri, Lightweight representation learning for network traffic towards malicious traffic detection in edge devices, Journal of Information Security and Applications, Volume 93, 2025, 104186, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2025.104186. (https://www.sciencedirect.com/science/article/pii/S2214212625002236) (Elsevier Q1)
   
2. Upendar R and Karthick S, Sentiment Analysis of Code-Mixed Telugu-English Text using Transformers, Journal of Computational Social Science, Springer (Accepted for publication in 2025)
3. K Sindhu, Karthick S, A dynamic probabilistic graphical model for mapping tasks to virtual machines in data centers, Engineering Applications of Artificial Intelligence, Elsevier (Q1), Volume 155, 2025, 110963, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2025.110963.
4. Seshadri, K., Yadavilli, V.S. & Ghosh, S. Inference of context and sentiment-aware causal phrase embeddings from product reviews using multi-relational graph neural networks. Multimedia Tools  and Applications, Springer (Q1) (2025). https://doi.org/10.1007/s11042-025-20892-1
5. F Zhang, Karthick S, Shichao Liu, Mattheos Santamouris, The Impact of Indoor Environmental Quality on Tourist Accommodation Ratings Using Guest Reviews, Building and Environment,  Elsevier (Q1) 2025, 113135, ISSN 0360-1323, https://doi.org/10.1016/j.buildenv.2025.113135.
6. Iyer, K.V., Karthick S. and Srinivasulu, K. (2025), An Advancement in Huffman Coding With a Potential for Parallel Decoding. Concurrency Computation: Practice & Experience, Wiley 37: e70096 (Q1). https://doi.org/10.1002/cpe.70096
7. Nayak, P., Seshadri, K. Towards quantum amenable Bayesian networks: classical transformation to facilitate quantum inference. Quantum Mach. Intell. 7, 2 (2025). https://doi.org/10.1007/s42484-024-00227-3 (Q1)
8. K. Seshadri, K. Sindhu, S. N. Bhattu and C. Kollengode, "Design and Evaluation of a Hierarchical Characterization and Adaptive Prediction Model for Cloud Workloads," in IEEE Transactions on Cloud Computing, vol. 12, no. 2, pp. 712-724, 2024, doi: 10.1109/TCC.2024.3393114. (IF: 5.3, Q1).
9. Yadavilli, VRPS Sastry; Seshadri, Karthick, Nagesh Bhattu S, Joint modeling of causal phrases-sentiments-aspects using Hierarchical Pitman Yor Process, Information Processing & Management, Elsevier, vol. 61, no. 4, 2024, DOI: https://doi.org/10.1016/j.ipm.2024.103753. (Impact Factor: 7.4, Q1).
10. Sindhu, Korrapati; Seshadri, Karthick; Kollengode, Chidambaran, Workload characterization and synthesis for cloud using generative stochastic processes, The Journal of Supercomputing, Springer, vol. 78, no. 17, pp. 18825-18855, 2022, DOI: https://doi.org/10.1007/s11227-022-04597-y. (IF: 2.5, Q2).
11. Rao, Rayala Upendar; Seshadri, Karthick; Sristy, Nagesh Bhattu S, Sentiment analysis of code-mixed Telugu-English data leveraging syllable and word embeddings, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 10, 2023, DOI: https://doi.org/10.1145/3620670. (IF: 2, Q2).
12. Zhang, Fan; Seshadri, Karthick; Pattupogula, Vara Prasad Devi; Badrinath, Chandana; Liu, Shichao, Visitors’ satisfaction towards indoor environmental quality in Australian hotels and serviced apartments, Building and Environment, Elsevier, vol. 244, 2023, DOI: https://doi.org/10.1016/j.buildenv.2023.110819. (IF: 7.1, Q1).
13. Yadavilli, VRPS Sastry; Seshadri, Karthick, Explainable sentiment analysis for product reviews using causal graph embeddings, Sadhana, Springer, vol. 47, no. 4, 2022, DOI: https://doi.org/10.1007/s12046-022-01964-6. (IF: 1.4, Q2).
14. Seshadri, Karthick; Iyer, K Viswanathan, Design and evaluation of a parallel document clustering algorithm based on hierarchical latent semantic analysis, Concurrency and Computation: Practice and Experience, Wiley, vol. 31, no. 13, 2019, DOI: https://doi.org/10.1002/cpe.5094. (IF: 1.5, Q2) 
15. Seshadri, Karthick; Iyer, K Viswanathan, Parallelization of a dynamic SVD clustering algorithm and its application in information retrieval, Software: Practice and Experience, Wiley, vol. 40, no. 10, pp. 883-896, 2010, DOI: https://doi.org/10.1002/spe.987. (Impact Factor: 2.6, Q2) 
16. Seshadri, Karthick; Shalinie, S Mercy, Parallelization of a graph?cut based algorithm for hierarchical clustering of web documents, Concurrency and Computation: Practice and Experience, Wiley, vol. 27, no. 17, 2015, DOI: https://doi.org/10.1002/cpe.3545. (Impact Factor: 1.5, Q2)
17. Seshadri, Karthick; Shalinie, S Mercy; Kollengode, Chidambaram, Design and evaluation of a parallel algorithm for inferring topic hierarchies, Information Processing & Management, Elsevier, vol. 51, no. 5, 2015, DOI: https://doi.org/10.1016/j.ipm.2015.06.006. (Impact Factor: 7.4, Q1)
18. Seshadri, Karthick; S. Mercy, Shalinie; Manohar, Sidharth, A distributed parallel algorithm for inferring hierarchical groups from large?scale text corpuses, Concurrency and Computation: Practice and Experience, Wiley, vol. 30, no. 11, 2018, DOI: https://doi.org/10.1002/cpe.4404. (Impact Factor: 1.5, Q2)
19. Seshadri, Karthick; Maruthappan, Aswin; Sundar Raman, Mukunthapriya, A scalable parallel algorithm for building web directories, Concurrency and Computation: Practice and Experience, Wiley, vol. 33, no. 9, 2021, DOI: https://doi.org/10.1002/cpe.6121. (IF: 1.5, Q2).
20. Ramakrishnan, Jegan; Liu, Tingting; Yu, Rongrong; Seshadri, Karthick; Gou, Zhonghua, Towards greener airports: Development of an assessment framework by leveraging sustainability reports and rating tools, Environmental Impact Assessment Review, Elsevier, vol. 93, 2022, DOI: https://doi.org/10.1016/j.eiar.2022.106740. (IF: 9.8, Q1).
21. Ramakrishnan, Jegan; Liu, Tingting; Zhang, Fan; Seshadri, Karthick; Yu, Rongrong; Gou, Zhonghua, A decision tree-based modeling approach for evaluating the green performance of airport buildings, Environmental Impact Assessment Review, Elsevier, vol. 100, 2023, DOI: https://doi.org/10.1016/j.eiar.2023.107070. (IF: 9.8, Q1).
22. Ramakrishnan, Jegan; Seshadri, Karthick; Liu, Tingting; Zhang, Fan; Yu, Rongrong; Gou, Zhonghua, Explainable semi-supervised AI for green performance evaluation of airport buildings, Journal of Building Engineering, Elsevier, vol. 79, 2023, DOI: https://doi.org/10.1016/j.jobe.2023.107788. (IF: 6.7, Q1). 

Conferences/Technical Reports: 

1. Appidi Jaswanth Reddy, Mote Vinay Kumar, Karthick Seshadri, Retrieval Augmented Generation based Explainable Fact Checking using Knowledge Graphs, Accepted for presentation in the IEEE International Conference on Recent Advances in Computing and Systems to be held in IIITM Gwalior, December 2025.
2. A Varshney, Karthick S, Viswa K, Assessment of the Knowledge-Infused Policy Gradient Approach for StarCraft, International Conference on ICT for Intelligent Systems, Cornell University, New York, May 2025. (Springer Proceedings)
3. Sookshma M, NB Sristy, Karthick S, A Dual-Attention Sparsemax Model for Adversarial Neural Topic Modeling, has been accepted for publication in the proceedings of the 11th International Conference on Pattern Recognition and Machine Intelligence (PReMI 2025), IIT Delhi, December 11-14, 2025. 
4. Samriddhee Ghosh, Karthick Seshadri, Harikumar Velayutham, “Assessment of Multi-Resource Characterization Models to Forecast Cloud Workloads for Sustainable Virtual Datacenters” has been accepted for presentation at the International Conference on Energy, Environment, and Green Technology (ICEEGT 2025), Springer Proceedings, organized by NITTTR Kolkata, India, April 2025.
5. Sasi Shrilekhya N, Krishna N, Seshadri K. (2025). Upper Confidence Bound versus Adaptive Posterior Sampling for Multi-Armed Bandit Game Models, IEEE International Conference on Innovative Trends in IT, IIIT Kerala, February 2025. (Best paper award in the AI and ML track). 
6. Aswin K, Suhas K, Seshadri K. (2024). Assessment of the Impact of D-Adaptation on Convolutional Image Classification, International Conference on Computer Vision and Image Processing, Springer, IIIT Kancheepuram, Chennai, December 2024 (Yet to be uploaded to SpringerLink).  
7. Nayak, P., Seshadri, K. (2023). Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks. In: Goyal, V., Kumar, N., Bhowmick, S.S., Goyal, P., Goyal, N., Kumar, D. (eds) Big Data and Artificial Intelligence. BDA 2023. Lecture Notes in Computer Science, vol 14418. Springer, Cham. https://doi.org/10.1007/978-3-031-49601-1_10. 
8. Srinath V Devale, Karthick S, Nagesh Bhattu S, A heuristic for minimizing resource requirements for quantum graph neural networks, International Conference on Deep Learning, AI and Robotics, NIT K, 2024. (Best Paper Award). 
9. Soni, Rajan Kumar; Seshadri, Karthick; Ravindran, Balaraman, Metric Learning for comparison of HMMs using Graph Neural Networks, Asian Conference on Machine Learning, pp. 1365-1380, Proceedings of Machine Learning Research, 2021, DOI: https://proceedings.mlr.press/v157. 
10. Soni, Rajan Kumar; Seshadri, Karthick; Ravindran, Balaraman, Representation Learning using Graph Neural Nets: A case-study in HMMs, Graph Embedding and Mining Workshop, European Conference on ML (ECML), DOI: https://gem-ecmlpkdd.github.io/papers/GEM2021_paper_9. 
11. S. Ghosh, K. Seshadri and C. Kollengode, "Design and Evaluation of Reward Functions for Load Balancing in Cloud Datacenters," 2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, 2024, pp. 1-6, doi: 10.1109/ICITIIT61487.2024.10580611. 
12. V.K. Iyer, K. Seshadri (2021), Adoption of virtual laboratories for Computer Science: deterrents, enablers and modalities, EDULEARN21 Proceedings, Valencia, Spain, pp. 97-102, DOI: 10.21125/edulearn.2021.0041. 
13. V.K. Iyer, K. Seshadri, Mohan R (2023), Leveraging Conventional CS Problems for Blended-Learning in Algorithms Practice: An Experimental Option in Undergraduate Learning, 17th International Technology, Education and Development Conference, March 2023, Valencia, Spain, DOI: 10.21125/inted.2023.0078. 
14. S. Yadavilli VRPS and K. Seshadri, "A Framework for predicting item ratings based on Aspect Level Sentiment Analysis," 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2021, pp. 327-332, doi: 10.1109/ICACITE51222.2021.9404666. 
15. Seshadri, K., Pavana, C., Sindhu, K., Kollengode, C. (2022). Unsupervised Modeling of Workloads as an Enabler for Supervised Ensemble-based Prediction of Resource Demands on a Cloud. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds) Advances in Data Computing, Communication and Security. Lecture Notes on Data Engineering and Communications Technologies, vol 106. Springer, https://doi.org/10.1007/978-981-16-8403-6_10. 
16. C. Charupalli and K. Seshadri, "Fine-tuning the Robust Temporal Feature Magnitude Model for Enhancing the Accuracy of Anomaly Detection," 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR), CA, USA, 2022, pp. 419-424, doi: 10.1109/MIPR54900.2022.00082. 
17. M. Pachava, K. Seshadri and C. Kollengode, "Assessment of Deep Sequence Models for Characterization and Prediction of Cloud Workloads," 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2022, pp. 1-6, doi: 10.1109/GCAT55367.2022.9972080. 
18. Viswanathan Iyer, K., Seshadri, K., Ghosh, S. (2023). Some Observations on Huffman Trees. In: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2358-6_33. 
19. Kammakomati, M., Chowdary, S.D., Varthya, S.N., Seshadri, K. (2023). Anomaly Detection in ATM Vestibules Using Three-Stream Deep Learning Approach. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_1. 
20. Samriddhee Ghosh, Karthick Seshadri; A deep neural model to identify rationale behind sentiments in product reviews. International Conference on Applied Computational Intelligence, AIP Conf. Proc. 16 June 2023; 2705 (1): 030006. https://doi.org/10.1063/5.0133452. 
21. Fan Zhang, Karthick Seshadri, Devi Vara Prasad Pattupogula, Badrinath Chandana, Shichao Liu, Indoor environmental quality satisfaction in Australian hotels and serviced apartments, E3S Web of Conf. 396 01055 (2023), DOI: 10.1051/e3sconf/202339601055. 
22. Subramanian, R.R., Seshadri, K. (2019). Design and Evaluation of a Hybrid Hierarchical Feature Tree Based Authorship Inference Technique. In: Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 39. Springer, Singapore. https://doi.org/10.1007/978-981-13-0277-0_8. 
23. S. Karthick, S. M. Shalinie, S. Umabharathi and S. K. Saroja, "Parallel hierarchical subspace clustering for segmenting large text corpuses," 2017 International Conference on Trends in Electronics and Informatics (ICEI), India, 2017, pp. 1-6, doi: 10.1109/ICOEI.2017.8300713. 
24. Karthick Seshadri and S. Mercy Shalinie. 2015. Parallel algorithms for merging topic trees and their application in meta search engines. In Proceedings of the 2nd ACM IKDD Conference on Data Sciences (CODS '15). Association for Computing Machinery, pp. 114–115. https://doi.org/10.1145/2732587.2732605 (Best Paper Award) 
25. S. Karthick, S. M. Shalinie, C. Kollengode and S. R. M. Priya, "A Sparsification Technique for Faster Hierarchical Community Detection in Social Networks," 2014 3rd International Conference on Eco-friendly Computing and Communication Systems, Mangalore, India, 2014, pp. 29-34, doi: 10.1109/Eco-friendly.2014.81. 
26. S. Karthick, S. M. Shalinie, A. Eswarimeena, P. Madhumitha and T. N. Abhinaya, "Effect of multi-word features on the hierarchical clustering of web documents," 2014 International Conference on Recent Trends in Information Technology, Chennai, India, 2014, pp. 1-6, doi: 10.1109/ICRTIT.2014.6996185. 

Patents

•  A patent entitled “System and Methods for on-the-fly intelligent knowledge acquisition and context aware scribing using IoT” (Ref no: 201841023572) was granted by the controller general of patents, India in 2025.

• A utility model entitled "A Scalable Parallel Web Directory Constructor" has been patented by the German Patent Office (Grant Certificate No: 202022100124) in 2022.

• A patent entitled “System and Method for Computer Vision based Remote Patient Monitoring” was published by the Indian Patent Office in 2025. (Ref. No. 202341039746).
  

  
  

Book Published 

1. K.V. Iyer, Karthick Seshadri, Discrete Mathematics with Applications, First Edition, Cengage Publishers, Feb 2022, ISBN: 9353503175. 

Book Chapters 

1. Karthick Seshadri, Book chapter on “Parallel hierarchical algorithms for clustering big text corpora”, published in the IET handbook of Big Data Analytics, IET, 2020, DOI: https://doi.org/10.1049/PBPC037G_ch15. 

2. K. Sindhu, Karthick S, Book chapter on “Text Summarization: A Technical Overview and Research Perspectives”, to be published in the Handbook of Intelligent Computing and Optimization for Sustainable Development, Wiley, 2021, DOI: https://doi.org/10.1002/9781119792642.ch13. 

3. Sastry Y, Karthick S, Book chapter on “A Survey on Aspect Extraction Approaches for Sentiment Analysis”, Handbook of Research on Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance, IGI Global, 2021, DOI: 10.4018/978-1-7998-7371-6.ch003. 

4. Upendar Rao, Karthick S, Book chapter on “Word Embedding Techniques for Sentiment Analyzers - A Review”, New Opportunities for Sentiment Analysis and Information Processing, IGI Global, 2021, DOI: 10.4018/978-1-7998-8061-5.ch013.

Ph.D. Supervision in NIT Andhra Pradesh

PG Dissertations Supervised in NIT Andhra Pradesh

Faculty Development Programmes Organized

1. A five days FDP on "Machine Learning for Language, Vision and Control" sponsored by the E&ICT Academy NIT Warangal was organized during 25.03.2019 to 30.03.2019. 

2. A five days FDP on "Data Analytics" sponsored by NIT Andhra Pradesh was organized during 15.06.2020 to 19.06.2020. 

3. A five days FDP on "Deep Learning for NLP" sponsored by NIT Andhra Pradesh was organized during 23.11.2020 to 27.11.2020. 

4. A five days FDP on "Machine Learning for Language and Vision" sponsored by AICTE ATAL Academy was organized during 01.02.2021 to 05.02.2021. 

5. A five days Short Term Training Programme on "Design Thinking" sponsored by CRIIC, NIT Andhra Pradesh was organized during September 2021. 

6. A GIAN Course entitled "A Technical Overview of Public and Private Blockchains" is organized during Feb 7 to Feb 11, 2022 with course instructors Prof. Ramki Thurimella, University of Denver, USA and Dr. Karthick S. 

7. A STTP on "Ideation and Design Thinking", co-coordinated under Institute Innovation Council, Nov 2022.

 Miscellaneous Achievements 

• Selected for the prestigious Australian Research Fellowship for Rs. 1.5 Crores to carry out research on data provenance models. 
• Recognized as a research supervisor in Griffith University, Australia.
• Selected for the prestigious TARE fellowship in 2018 by the Science and Engineering Research Board, Govt. of India (Approval No: TAR/2018/000314). 
• Secured the first rank and the gold medal in M.Tech. with a CGPA of 10.00 at NIT Trichy.
• Conferred with the “Outstanding Student” Award at NIT-Trichy.
• Secured the top rank in my college during various semesters in B.E. with a CGPA of 9.11
• Conferred with IEEE Senior Membership in 2023.
• Selected as one of the 25 Engineering faculty members across India for the INAE Fellowship mentoring Programme in 2024. Started undertaking postdoctoral research under the guidance of Prof. Balaraman Ravindran, CeRAI, IIT Madras.
• Received MEITY’s Quantum Computing Applications Lab Research grant in 2021.
• Received AI and Robotics Research Park (IISc)’s innovation grant in 2021.
• My research work on quantum probabilistic graphical models was selected as one of the two best projects for showcase before media at New Delhi by MEITY and Amazon in 2023.
• Best paper award for the research work on "Adaptive Posterior Sampling vs. Upper Confidence Bound for Multi-armed Bandit Game models" in the AI & ML track in ICITIIT, IIIT Kerala, 2025.
• Jointly supervised a M.S. (by research) student Mr. Rajan Kumar Soni with the distinguished Prof. Balaraman Ravindran in the Department of CSE, IIT Madras.
• Secured a Graduate Aptitude Test for Engineering (GATE) score of 98.42%.
• My paper entitled “Parallel Algorithms For merging topic trees” got the best research poster award in the ACM KDD CODS conference in 2015.
• Received the best paper award for the paper entitled “A heuristic for minimizing resource requirements for quantum graph neural networks” in the International Conference on Deep Learning, AI and Robotics organized by NIT Kurushetra, 2024.
• Conferred with the Engineering Excellence Award by Lions Club for the AY 2023-24.
• Obtained an overall IELTS Score of 8.5 out of 9 in the Academic module (Listening: 9, Reading: 8.5, Writing: 7.5 and Speaking: 8.5)
• State Rank: 1 in the Computer Science and Engineering Exam for lecturership conducted by the Govt. of Tamilnadu, India.
• Secured a rating of 5 out of 5 in TCS.
• Secured the “Star of AXA” award for efficient handling of production incidents.
• Cleared the Morgan Stanley’s Technology Analyst program and secured a performance band ‘A’.
• Qualified in UGC National Eligibility Test (NET) in Computer Science in 2016.
• Received a certificate of accomplishment from Prof Peter Norvig and Prof. Sebastian Thrun, for having secured 99.5% in their online Artificial Intelligence class. (I was within top 1% of the world-wide class and received a personal appreciation note from the professors).
• ICPC Coach Award, ACM
• Secured state first in the Sanskrit Higher Secondary exams.
• Ph.D./M.S. (by research) supervisorship granted by Anna University. 

Technology developed, transferred, startups

o   Serving as an Advisor on the Board of Governors of Asta Health Tech, a start-up incubated by Mr. Adyanta Dubey, and Mr. Krishna Kireeti undergraduate students in NIT Andhra Pradesh. This Start-up was selected for a funding support of INR 80 Lakhs under the Samriddh Accelerator Scheme under MEITY and IISER Pune.

o   Designed a validation engine to evaluate Enixta’s Sentiment Analyzer and transferred the technology to Enixta. 

o   Designed and implemented a Remote ICU monitoring framework and transferred the technology to the Director Dr. Suresh K, Southern Railways Hospital, Chennai. (Filed a patent in the Indian Patent Office with the Application number: 202341039746). 

o   Designed a framework for facilitating elastic cloud computing and transferred the design to the AI and Data Platforms division of LinkedIn. (Also culminated as a joint publication in IEEE Transactions on Cloud Computing). 

o   Designed and implemented an E-beat monitoring system for the Police Stations in Tadepalligudem to automatically monitor the patrolling officers and report to the Sub-Inspector. 

o   Designed and implemented an intelligent Placements Automation Application for the T&P section in NIT Andhra Pradesh and the system is in the beta testing phase and will be rolled out shortly. 

o   Collaborated with Dr. Fan Zhang, School of Architecture, Griffith University and Dr. Liu Shichao of Worchester Polytechnic Institute MA, USA, to develop and implement deep neural model for Indoor Air quality analysis from Customer Reviews in Australian hotels and Guest Accommodations. 

o   Collaborated with Dr. Tingting Liu of Griffith University, Dr. Rongrong Yu of South Australian University and Dr. Gou Zhonghua of Wuhan University to develop AI models for green performance analysis of airports.  

Infrastructure established at NIT-ANP:

Outreach Activities: