Dipendra Jha

Ph.D. Candidate, Northwestern University

Computer scientist with 4+ years of research experience in the field of deep learning, machine learning, and artificial intelligence; designing deep neural network and machine learning models for complex scientific datasets using TensorFlow, Caffe, PyTorch, Theano, and Scikit-Learn since last four years.

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About Me

I am a Ph.D. Candidate in Computer Engineering at Northwestern University under Prof. Alok Choudhary, expecting to graduate around June 2020. My research works mainly focuses on building artificial intelligence (AI) based predictive models for diverse scientific applications by leveraging together big data, machine learning and high performance computing systems. During last four years, I have designed several deep learning based systems which can directly learn the variables of scientific interest from raw inputs from scientific experiments and simulations (chemical formulae, crystal structures, electron diffractions, 1D and 2D X-ray diffractions) using frameworks such as TensorFlow, PyTorch, Caffe and Theano. I have also explored the area of parallelization of deep neural networks, and implemented a hybrid parallel framework for parallelization of recent CNN models such as Inception and ResNets on supercomputers using C++, Caffe and MPI. Recently, I contributed in developing a reinforcement-based neural architecture search framework for diverse scientific applications at Argonne. Currently, I am looking forward to keep learning more about advanced AI techniques such as generative adversial networks and reinforcement learning, and apply my knowledge towards building intelligent AI-based systems to solve real-world and scientific problems.

Prior to machine learning and artificial intelligence, I explored the field of computer networks and distributed systems under Prof. Fabian Bustamante, and obtained a M.S. in Computer Science from Northwestern University. Before joining Northwestern, I completed my Bachelor of Engineering in Computer Science with distinction from Institute of Engineering at Tribhuvan University in Nepal.


Northwestern University, USA.

Graduate Research Assistant

Designing deep-learning based scalable intelligent predictive models for complex scientific datasets from simulations and experiments.

Chefling Inc., USA.

Machine Learning Advisor

Advising the software engineers and data scientists to build AI tools using the state-of-the-art deep learning architectures for real-time image recognition.

Argonne National Lab, USA.

Research Intern

Built a neural architecture search framework for automation of search for deep neural architectures for scientific datasets using reinforcement learning on supercomputers.

Kantipur Engineering College, Tribhuvan University (Kathmandu, Nepal)

Full Time Lecturer

Taught courses such as C, C++, Java, Object Oriented Analysis and Design, Theory of Computation and Software Engineering; supervising students on different kinds of undergraduate course and thesis projects in computer science and engineering.

Kathford International College of Engineering and Management, Tribhuvan University (Kathmandu, Nepal)

Part Time Lecturer

Taught Theory of Computation to undergraduate students in computer science and engineering.

Yomari Pvt. Ltd. (Kathmandu, Nepal)

Java Web Developer Intern

Developed Information and Content Management Systems for educational and medical institutes in Nepal.


Northwestern University

June 2013 - Present

Ph.D., Computer Engineering

Machine Learning, Deep Learning, and Artificial Intelligence.

Northwestern University

June 2013 - June 2015

M.S., Computer Science

Wireless and Cellular Networks.

Tribhuvan University

October 2007 - December 2011

B.E., Computer Science

Graduated with distinction, achieved first rank (University Topper) among 3000 undergraduate student (across all disciplines).


Learning the Chemistry of Materials using AI

Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of ElemNet enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds (published in Nature Scientific Reports).

Optimizing for Minimum Disorientation using CNN

We present a deep learning approach to the indexing of Electron Backscatter Diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent. The deep learning model is trained using 374,852 EBSD patterns of polycrystalline Nickel from simulation and evaluated using 1,000 experimental EBSD patterns of polycrystalline Nickel. The deep learning model results in a mean disorientation error of 0.548°ree compared to 0.652°ree using dictionary based indexing (published in Microscopy and Microanalysis).

Peak Area Detection Network for 2D X-ray Diffraction Patterns

X-ray diffraction (XRD) is a well-known technique used by scientists and engineers to determine the atomic-scale structures as a basis for understanding the composition-structure-property relationship of materials. The current approach for the analysis of XRD data is a multi-stage process requiring several intensive computations such as integration along 2&theta for conversion to 1D patterns (intensity-2&theta), background removal by polynomial fitting, and indexing against a large database of reference peaks. It impacts the decisions about the subsequent experiments of the materials under investigation and delays the overall process. In this paper, we focus on eliminating such multi-stage XRD analysis by directly learning the phase regions from the raw (2D) XRD image. We introduce a peak area detection network (PADNet) that directly learns to predict the phase regions using the raw XRD patterns without any need for explicit preprocessing and background removal. PADNet contains specially designed large symmetrical convolutional filters at the first layer to capture the peaks and automatically remove the background by computing the difference in intensity counts across different symmetries. We evaluate PADNet using two sets of XRD patterns collected from SLAC and Bruker D-8 for the Sn-Ti-Zn-O composition space; each set contains 177 experimental XRD patterns with their phase regions. We find that PADNet can successfully classify the XRD patterns independent of the presence of background noise and perform better than the current approach of extrapolating phase region labels based on 1D XRD patterns (accepted to International Joint Conference on Neural Networks).

Deep Regression Residual Networks

Developed a 48-layered residual DNN model to learn the materials properties from vectors composed of their crystal structures and composition; the DNN model significantly outperformed existing machine learning models on multiple tasks from multiple datasets for prediction modeling without any feature engineering.

Neural Architecture Search using Reinforcement Learning

Automating the search for neural network with dynamic architectures for different types of scientific datasets using reinforcement learning.

Parallel Deep Neural Networks

Scaling up training of the state-of-the-art deep learning models such as Inception and ResNet using a hybrid parallel pipelining approach on supercomputers.


  1. D. Jha, L. Ward, Z. Yang, C. Wolverton, I. Foster, W. Liao, A. Choudhary and A. Agrawal. ``IRNet: A General Purpose Deep Residual Regression Framework For Materials Discovery", 25th ACM Conference on Knowledge Discovery and Data Mining (KDD), 2019.

  2. D. Jha, A. Kusne, R. Al-Bahrani, N. Nguyen, W. Liao, A. Choudhary, C. Wolverton and A. Agrawal. ``Peak Area Detection Network for Directly Learning Phase Regions from Original X-ray Diffraction Patterns", International Joint Conference on Neural Networks, 2019.

  3. D. Jha, A. Paul, W. Liao, A. Choudhary and A. Agrawal. ``Transfer Learning Using Ensemble Neural Nets for Organic Solar Cell Screening", International Joint Conference on Neural Networks, 2019 (pdf).

  4. D. Jha, K. Choudhary, F. Tavazza, W. Liao, A. Choudhary, C. Campbell and A. Agrawal. ``Enhancing Materials Property Prediction by Leveraging Computational and Experimental Data using Deep Transfer Learning", Nature Communications, 2019 (under review).

  5. R. Egele, D. Jha, P. Balaprakash, M. Salim, V. Vishwanath and S. Wild. ``Scalable Reinforcement-Learning-Based Neural Architecture Search for Scientific Applications", ISC High Performance, 2019 (under review).

  6. D. Jha, L. Ward, A. Paul, W. Liao, A. Choudhary, C. Wolverton and A. Agrawal. ``ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition", Nature Scientific Reports, 2018 (pdf).

  7. D. Jha, S. Singh, R. Al-Bahrani, W. Liao, A. Choudhary, M. De Graef, and A. Agrawal. ``Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks", Microscopy and Microanalysis, 2018 (pdf).

  8. A. Paul, D. Jha, R. Al-Bahrani, W. Liao, A. Choudhary and A. Agrawal. ``CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations", NIPS Workshop on Machine Learning for Molecules and Materials, 2018 (pdf).

  9. S. Lee, D. Jha, A. Agrawal, and A. Choudhary and W. Liao, ``Parallel Deep Convolutional Neural Network Training by Exploiting the Overlapping of Computation and Communication", IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC), 2017 (Best Paper Nominee) (pdf).

  10. D. Jha, J. Rula, and F. Bustamante. ``eXploring Xfinity: A first look at provider-enabled community networks", International Conference on Passive and Active Network Measurement, 2016 (pdf).

  11. R. Liu, D. Palsetia, A. Paul, R. Al-Bahrani, D. Jha, W-k. Liao, A. Agrawal, and A. Choudhary, ``PinterNet: A Thematic Label Curation Tool for Large Image Datasets", Open Science in Big Data (OSBD) workshop held in conjunction with IEEE International Conference on Big Data (Big Data), 2016 (pdf).

  12. B. Mishra, D. Jha, P. Shrestha, R. Rijal, ``Image Bandwidth Optimization Algorithm based on Pixel Intensity Difference In Live Video Streaming.", 6th Conference on Software, Knowledge, Information Management and Applications (SKIMA), China, September, 2012 (pdf).


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