Tirtharaj Dash

Research Associate, University of Cambridge, UK
Postdoctoral Affiliate of Trinity College, Cambridge



University of Cambridge

Cambridge CB2 0RE, UK

I am a Research Associate at the University of Cambridge, UK. I am also a Postdoctoral Affiliate of Trinity College, Cambridge. I am currently working with Susanne Bornelöv. We work on several interesting research problems concerning codon usage, optimality and in general, knowledge discovery from large-scale genomic data. I am focusing on developing novel ML approaches, such as Neuro-Symbolic Models and Deep Neural Networks to learn from genomic sequences. Prior to this position at Cambridge, I was a Postdoctoral Researcher at the University of California, San Diego, where I worked with Professor Debashis Sahoo in the Boolean Lab. In the Boolean Lab, my research focused on using AI and Boolean analysis for analysing several biological datasets (RNA-seq and Single-cell RNA-seq) in the domain of cancer, inflammatory bowel disease and macrophages. In general, my research areas of interest are Neuro-Symbolic AI, Deep Learning, Graph Representation Learning, Machine Learning and Computational Biology. You may want to visit this page for more details on my (research) interests. Prior to my current position at UCSD, I was working as an Assistant Professor (Grade-II) at BITS Pilani Goa Campus in the Department of Computer Science and was also affiliated with its AI Research Centre (APPCAIR).

I completed my PhD in Computer Science in July 2022 from BITS Pilani. I completed my doctoral research during Jan 2017 to April 2022 under the supervision of Senior Professor Ashwin Srinivasan. The title of my PhD thesis is “Inclusion of Symbolic Domain-Knowledge into Deep Neural Networks”. In my doctoral research, I focused on constructing deep neural networks from relational data and symbolic domain-knowledge—this resulted in beautiful combinations of neural computation with logical representation. The real-world applications of my doctoral research are in the broad area of drug discovery. Further, it has enormous potential to be adopted in many other real-world problems such as health science, social networks, robotics, etc. See this page for more details about my PhD.

I received a Master of Technology (M.Tech) degree in Computer Science from VSSUT, Burla (one of the oldest engineering institutes in India) in the year 2014 and a Bachelor of Technology (B.Tech) degree in Information Technology from NIST Berhampur in the year 2012. I am a Silver Medalist in both M.Tech and B.Tech for my academic performances.

I have worked as an Assistant Professor in the School of Computer Science at NIST Berhampur for over a year during 2014 - 2015. I have also worked as IASc-INSA-NASI Summer Research Fellow at ISI Kolkata. I have qualified national-level competitive examinations such as GATE (twice) and UGC-NET.

Here is my semi-updated CV. References are available upon request.

latest news

Jan 8, 2024 I am in the program committee for IJCAI 2024.
Jan 1, 2024 I am now a Postdoctoral Affiliate of Trinity College, Cambridge.
Dec 9, 2023 Shreyas Bhat got a paper (preprint) accepted at AAAI-24 (Main Track).
Oct 10, 2023 I am joining the University of Cambridge, UK as a Research Associate starting Oct 2023. I will be working with Susanne Bornelöv and team.
Jul 14, 2023 Our paper on Compositional Relational Machines (CRMs) got accepted at MLJ.
Jun 21, 2023 Shreyas and Rohit got a paper accepted at ICIP 2023: [Preprint].
Apr 11, 2023 Soham got a paper accepted at EMBC 2023: [Preprint].
Mar 21, 2023 I won the Best PhD Thesis Award from my university!
Jan 4, 2023 I am now a member of the editorial board for PLOS One. Looking forward to new responsibilities in science.
Dec 12, 2022 I am in the program committee for IJCAI 2023.

selected publications

  1. AAAI
    Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback
    Brahmavar, Shreyas Bhat, Srinivasan, AshwinDash, Tirtharaj, Krishnan, Sowmya Ramaswamy, Vig, Lovekesh, Roy, Arijit, and Aduri, Raviprasad
    Proceedings of the AAAI Conference on Artificial Intelligence Mar 2024
  2. MLJ
    Composition of relational features with an application to explaining black-box predictors
    Srinivasan, Ashwin, Baskar, A,  Dash, Tirtharaj, and Shah, Devanshu
    Machine Learning Mar 2023
  3. WACV
    Calibrating Deep Neural Networks Using Explicit Regularisation and Dynamic Data Pruning
    Patra, Rishabh, Hebbalaguppe, RamyaDash, TirtharajShroff, Gautam, and Vig, Lovekesh
    In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Jan 2023