Artifical Intelligence. Machine Learning. Neuro-Symbolic AI. Statistical Learning. Healthcare. Relational Learning. Human in the Loop. Deep Learning. Probabilistic Deep Models. Causality

I am a DEPTH research group leader on Causality And neUro-Symbolic artificial intElligence (CAUSE), under The Hessian Center for Artificial Intelligence (hessian.AI) and TU Darmstadt.

I am also a Postdoctoral Researcher under Prof. Dr. Kristian Kersting at TU Darmstadt in the Artificial Intelligence and Machine Learning Lab.

Ph.D. in Computer Science from University of Texas, Dallas (Advisor : Professor Sriraam Natarajan -- StARLinG lab). My PhD thesis focused on learning effective models from noisy, heterogeneous and multi-relational healthcare data.

Master of Science (MS) in Computer Science from Indiana University, Bloomington. (My master's thesis focused on creating novel features from galaxy images for spiral shape detection under Prof. David Crandall.)

Email: devendra.dhami [at] cs.tu-darmstadt [dot] de ; Phone: +49 1523 7919 263

Research interests: Artificial Intelligence ● Machine Learning ● Data Science ● ML in healthcare ● Probabilistic Deep Models ● Human-in-the-loop Deep learning ● Graphical Models ● Knowledge Graphs ● Scaling in Probabilistic Logic models.

My Bachelors was completed from Sir MVIT Bangalore (@Visvesvaraya Technological University) with a gold medal and an academic excellence award. Prior to pursuing graduate studies, I have worked with Hewlett-Packard. I also write short stories as a hobby and am also an avid reader.

Currently Reading: The Master Algorithm by Pedro Domingos

Books read recently:

1. Thinking, Fast and Slow, by Daniel Kahneman

2. The Book of Why by Judea Pearl

3. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

4. The Black Swan by Nassim Nicholas Taleb

Professional Services

1. Electronics Publishing Editor: Journal of Artificial Intelligence Research (JAIR)

2. Proceedings Chair: SDM 2020

3. Editorial Board: Frontiers in Artificial Intelligence

4. PC member: Pacific Symposium on Biocomputing 2019,2020,2021; IJCAI 2020, 2022; SDM 2020,2021; AAAI 2021 (top 25%),2022; ICML 2021,2022; UAI 2021,2022; NeurIPS 2021,2022; ACM Multimedia 2021, 2022; ICMLA 2021; ICLR 2022; AISTATS 2022; IJCAI (Demo Track) 2022, IJCAI (Survey Track) 2022

5. Reviewer (Journal): Big Data Journal, Data Mining and Knowledge Discovery (DAMI), Journal of Artificial Intelligence Research (JAIR), Machine Learning Journal (MLJ), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Frontiers in Artificial Intelligence, iScience: Cell Press; NeuroImage

6. Reviewer (Conference): CoDS-COMADS 2020

7. Volunteer: ICDE 2020

8. REU Mentor: Mentored 2 undergraduate researchers for Indiana University's Proactive Health Informatics "Research experiences for Undergraduates (REU) program"

News/Updates:

  • Our paper 'Predictive Whittle Networks for Time Series' is accepted to UAI 2022.

  • 2 papers on probabilistic logic languages accepted to International Conference on Principles of Knowledge Representation and Reasoning (KR).

  • Our paper on training CTGANs in a differentially private way is accepted to Artificial Intelligence in Medicine (AIME) 2022.

  • Our team '51 Billion Shades of Carbon' won the Climathon, Darmstadt. It is an yearly hackathon that seeks submissions to combat climate change.

  • Our paper 'Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models' is accepted to NeurIPS 2021.

  • Our paper 'A Statistical Relational Approach to Learning Distance-based GCNs' is accepted to Tenth International Workshop on Statistical Relational AI.

  • 2 papers accepted to the International Conference on Inductive Logic Programming, ILP2020-21 @ 1st International Joint Conference on Learning & Reasoning (IJCLR).

  • Our paper 'Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models' is accepted to the Tractable Probabilistic Modeling (TPM) workshop @UAI.

  • Our paper 'Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation' is accepted to International Conference on Principles of Knowledge Representation and Reasoning (KR).

  • I gave a talk on 'Distance based Graph Convolutional Networks' at the Cyberinfrastructure for Network Science Center @ Indiana University Bloomington research symposium.

  • Our paper 'Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach' is accepted to Artificial Intelligence in Medicine (AIME).

  • I am the new Electronics Publishing Editor for Journal of Artificial Intelligence Research (JAIR).

  • Our paper 'The Curious Case of Stacking Boosted Relational Dependency Networks' is accepted to I Can't Believe It's Not Better (ICBINB) workshop @NeurIPS as a spotlight presentation.

  • Our paper 'Human-Guided Learning of Column Networks: Knowledge Injection for Relational Deep Learning' is accepted to ACM India CODS-COMAD 2021.

  • Our paper 'Knowledge Intensive Learning of Generative Adversarial Networks' received the best paper award at KiML workshop @KDD.

  • The paper 'Knowledge Intensive Learning of Generative Adversarial Networks' is accepted to the KiML workshop @KDD.

  • I am in the editorial board for 'Frontiers in Artificial Intelligence'.

  • I have graduated with a PhD and will be joining TU Darmstdat as a Postdoc in September 2020.