Welcome!
I am currently co-founder and CTO of CoVent. At CoVent, our mission is to redefine sponsorship discovery and evaluation through an AI-enabled marketplace. Before, I worked as a Staff Research Scientist in Blue Prism AI Labs, London and also worked as Principal Researcher (Machine Learning) in Bell Labs, Cambridge (UK).I finished my Ph.D. in 2012 at the University of Leeds under Prof. Tony Cohn and Prof. David Hogg in the areas of Machine Learning and Cognitive Vision and was funded by the European Commission's Co-Friend, RACE, STRANDS projects, and DARPA's Mind's Eye project.
News
CoVent is backed by Founders Factory!Other Updates
Employment
Blue Prism (London, UK)
Staff Research Scientist
Bell Labs (Cambridge, UK)
Principal Researcher, Machine Learning
Advanced Research Lab, Nokia (Cambridge, UK)
Principal Researcher, Machine Learning
University of Leeds, UK
Research Fellow
D.E.Shaw & Co, India
Member - Information Technology
Education
University of Leeds, UK
PhD (Machine Learning)
University of Hyderabad, India
Master of Technology (Artificial Intelligence)
J.N.T. University, India
Bachelor of Technology (Computer Science and Engineering)
Patents
1) An Apparatus and Associated Methods for Determining User Activity Profiles 
2)
An Apparatus, Method and Computer Program for Obtaining Images From an Image Capturing Device
3) Apparatus, Method and System For Identifying a Target Object From a Plurality of Objects
4)
Method of remote access
6) Method, Device and System For Validating Sensitive User Data Transactions Within Trusted Circle
7) Profile Information Sharing
8) System and Methods for Robotic Process Automation
Publications
Journals
1) Krishna S.R. Dubba, Anthony G. Cohn, David C. Hogg, Mehul Bhatt, Frank Dylla: Learning Relational Event Models from Video, In Journal of Aritificial Intelligence Research, 2015.
2) Krishna S.R. Dubba, Arun K. Pujari: N-gram Analysis for
Computer Virus Detection, Journal in Computer Virology, Vol-2, Number-3, Dec-2006, Springer-France.
Conference Proceedings
1) Joachim Hertzberg, Jianwei Zhang, Liwei Zhang, Sebastian Rockel, Bernd Neumann, Jos Lehmann, Krishna S.R. Dubba et.al. The RACE Project. In KI-Künstliche Intelligenz, 2014.
2) Krishna S.R. Dubba; Miguel R. de Oliveira; Gi Hyun Lim; Hamidreza Kasaei; Luis Seabra Lopes; Ana Tome and Anthony G. Cohn: Grounding Language in Perception for Scene Conceptualization in Autonomous Robots. In AAAI Spring Symposium Series, 2014.
3) Rockel, S.; Neumann, B.; Zhang, J.; Dubba, K.; Cohn, A.; Konecny, S.; Mansouri, M.; Pecora, F.; Saffiotti, A.; Gunther, M.; Stock, S.; Hertzberg, J.; Tome, A.; Pinho, A.; Seabra Lopes, L.; von Riegen, S.; and Hotz, L: An Ontology-based Multi-Level Robot Architecture for Learning from Experiences. In AAAI Spring Symposium Series, 2013.
4) Krishna S.R. Dubba, Mehul Bhatt, Frank Dylla, Anthony G. Cohn, David C. Hogg: Interleaved Inductive-Abductive Reasoning for Learning Event-Based Activity Models, Proc. of ILP, 2011. Berkshire, UK.
5) Krishna S.R. Dubba, Anthony G. Cohn, David C. Hogg: Event Model Learning from Complex Videos using ILP, European Conference on Artificial Intelligence - 2010, Portugal.
6) Krishna S.R. Dubba, Subrat K. Dash, Arun K. Pujari: New Malicious Code Detection Using Variable Length n-grams, LNCS-4332/2006, Springer-Berlin / Heidelberg.
Book Chapters
1) Subrat K. Dash, Krishna S.R. Dubba, Arun K. Pujari: New Malicious Code Detection Using Variable Length n-grams, Algorithms, Architectures and Information Systems Security, Statistical Science and Interdisciplinary Research - VOL3, World Scientific, 2008.
PhD Thesis
Learning Relational Event Models from Videos, 2012.
Research in Industry
My work in Blue Prism AI Labs, London was in deep learning, computer vision and program analysis to help make tools for improving business process automations.
In Bell Labs, Cambridge, UK, I worked in Social Dynamics Group focused on the topic "quantifying the unquantifiable". I worked on explainable emotion detection in images using explainable AI (XAI) fusing knowledge graphs and deep learning.
Past Academic Research
My academic research in general falls under the areas of Machine Learning (in particular Statistical Relational Learning, Deep Learning), Cognitive Vision, Knowledge Representation, Graph Analysis and Spatio-Temporal Reasoning. I worked in European Commission's RACE, STRANDS and Co-Friend projects. I also worked in DARPA's Mind's Eye project where we developed systems that learn relational event models from videos.
Grounding Language in Perception for Scene Conceptualization in Autonomous Robots
In order to behave autonomously, it is desirable for robots to have the ability to use human supervision and learn from different input sources (perception, gestures, verbal and textual descriptions etc). In this work, we present a cognitive architecture and learning framework for robot learning through natural human supervision and using multiple input sources by grounding language in perception.
An Ontology-based Multi-Level Robot Architecture for Learning from Experiences
One way to improve the robustness and flexibility of robot performance is to let the robot learn from its experiences. In this work, we propose an architecture and knowledge-representation framework for a service robot being developed in the EU project RACE. As a unique innovative feature, the framework combines memory records of low-level robot activities with ontology-based high-level semantic descriptions.
Learning Relational Event Models from Video
In this paper, we present a novel framework REMIND (Relational Event Model INDuction) for supervised relational learning of event models from large video datasets. The learned models can be used for recognizing events from previously unseen videos. The experimental results on several hours of video data from two challenging real world domains (an airport domain and a physical action verbs domain) suggest that the techniques are suitable to real world scenarios.
Event model learning from complex videos
In this paper, we have proposed and successfully applied a novel supervised framework for learning relational event models from a huge, complex and noisy video dataset. The experimental results on video data from an airport apron where events such as Loading, Unloading, Jet-Bridge Parking etc. are learned suggests that the techniques are suitable to real world scenarios.
N-gram analysis for computer virus detection
Motivated by the success of Machine Learning techniques in intrusion detection systems, recent research in detecting malicious executables is directed towards devising efficient non-signature based computer virus detection techniques that can profile the program characteristics from a set of training examples. In this paper, we describe a new feature selection measure, class-wise document frequency of byte n-grams. We empirically demonstrate that the proposed method is a better method for feature selection. For detection, we combine several classifiers using Dempster Shafer Theory for better classification accuracy instead of using a single classifier. Our experimental results show that such a scheme detects virus program far more efficiently than the earlier known methods.
Updates
Program Committees
I’m honored to serve as a program committee member for MICA 2024, contributing to peer review and scientific discussions.
View MICA 2024 Conference
I’m honored to serve as a program committee member for IPA-2020, contributing to peer review and scientific discussions.
IPA-2020
I’m honored to serve as a program committee member for EPIC-2016, contributing to peer review and scientific discussions.
EPIC@ECCV2016I’m honored to serve as a program committee member for PAIR-2013, contributing to peer review and scientific discussions.
PAIR-2013Judging & Events
In July 2021, the Institution of Mechanical Engineers hosted the Formula Student event at the iconic Silverstone race track. Formula Student is Europe's most established educational engineering competition, held every year. The competition aims to develop enterprising and innovative young engineers and encourage more young people to pursue a career in engineering.
Event HighlightsDagstuhl Seminars
Dagstuhl seminars are the flagship of the event program at Schloss Dagstuhl. They are often praised by participants as the most productive academic events they have ever experienced. Consequently participation is highly coveted. In order to maintain the constant high standard of the Dagstuhl Seminars, proposals must also include a list of intended participants which is checked by the Scientific Directorate as part of the review process. Consequently, participation in a Dagstuhl seminar is by invitation only.
Event LinkReport
Dagstuhl seminars are the flagship of the event program at Schloss Dagstuhl. They are often praised by participants as the most productive academic events they have ever experienced. Consequently participation is highly coveted. In order to maintain the constant high standard of the Dagstuhl Seminars, proposals must also include a list of intended participants which is checked by the Scientific Directorate as part of the review process. Consequently, participation in a Dagstuhl seminar is by invitation only.
Event LinkReport
Invited Talk / Speaker
Video of my talk published by Microsoft (starts after 24 mins)
View the video on Youtube
Awards and Invited Participation
