Tutorials
/ Special Sessions: Data-Driven Design Optimization of Computing
Systems; Adaptive Manycore Architectures for Big Data Computing @ Embedded Systems Week 2017; Design of Energy-Efficient and Reliable VLSI Systems: A Data-Driven Perspective @ VLSI Design 2018; Recent Advances in Structured Prediction @ AAAI 2018;
My general research interests are in the broad field of
artificial intelligence (AI), where I mainly focus on sub-fields of
machine learning, and data-driven science and engineering. Current focus of my work is:
Structured Prediction for natural language processing,
computer vision, bioinformatics, smart home environments, and health
informatics
Machine
Learning to improve Electronic Design Automation for
designing high-performance, energy-efficient, and reliable hardware for large-scale data analysis applications
Optimized Computer
Architectures for Big Data Computing using Emerging Technologies (e.g.,
Through-Silicon-Via and Monolithic 3D Integration)
Machine Learning for Sustainable Computing and Computational Sustainability
I did my PhD with the Artificial Intelligence group at Oregon State
University, where I was
wisely advised by Prof. Prasad
Tadepalli and Prof.
Alan Fern . I'm fortunate to work with Prof. Alan Fern and I enjoy working with him a lot.
Note for Prospective Students:
I'm always looking for strong, self-motivated, and ambitious PhD
students. You can find more details here. For undergrad students at WSU:
If you are interested in working with me for research experience and/or
honors thesis, please take my data mining class (CptS 315) and we can discuss
the details along the way. Please read this article for some useful advice.
Undergrad / MS Non-Thesis Advising Meetings: Please drop by my office hours. If my office hours don't work for you, please send me an email for appointment.
I like to work on machine learning problems motivated from
important real-world applications. A sample of my current and
recent research projects include:
Design Space Exploration of Large-Scale Heterogeneous Computing Systems
Heterogeneous
System-on-Chip (SoC) architectures integrate processing cores of
different types and functional granularity using an appropriate interconnect
technology. In this project, we
are investigating computationally-efficient methodologies to explore
this architecture design space to achieve the desired trade-offs in
terms of performance, energy, thermal, and reliability metrics.
Chip Goes Vertical: High-Performance, Energy-Efficient, and Reliable Systems for Big Data Computing via 3D Integration
Three-dimensional
(3D) integration is a breakthrough technology to achieve ``More Moore
and More Than Moore,'' and provides numerous benefits (better
performance, lower power consumption, and higher bandwidth) by
utilizing vertical interconnects and 3D stacking. In this project, we
are developing scalable machine learning based methodologies
for design, control, and test to convert the potential of 3D
integration into reality for Big Data computing. We are exploring both
Through-Silicon-Via (TSV) and Monolithic 3D (M3D) technologies for
integration.
Towards Efficient Human-Computer Communication for Collaborative Problem Solving
How
can we build AI technology to enable efficient human-computer
communication for collaboratively solving complex problems? We are
working towards developing this technology for communication via
natural language. [Funded by DARPA as part of the Communicating with
Computers (CwC) program]
Structured
Prediction: Algorithms and Applications
How can we learn to predict structured outputs (e.g., sequences,
trees, and graphs)? Structured prediction tasks arise in a variety of
domains including natural language processing (e.g., POS tagging,
dependency parsing, coreference resolution) and computer vision (e.g.,
object detection, semantic segmentation).
HC-Search framework (see JAIR-2014
paper) unifies the cost function and control knowledge learning
frameworks for structured prediction. The effectiveness of this
framework depends on the quality of the search space (the depth at
which target outputs can be located). We have designed the limited
discrepancy search (LDS) space, which is parameterized by a greedy
recurrent classifier or policy, and also its sparse variant to improve
efficiency (see JMLR-2014
paper). We have also designed the randomized segmentation space
for computer vision tasks, where we probabilistically sample likely
object configurations in the image from a hierarchical segmentation
tree (see Michael's CVPR-2015 paper).
Easy-first
framework learns to make easy prediction decisions first to constrain
the harder decisions akin to constraint satisfaction algorithms. We
have developed a principled optimization-based learning approach for
easy-first framework (see Jun's AAAI-2015 paper).
We have solved a variety of structured prediction applications including multi-label prediction (see AAAI-2014 paper); coreference resolution within document (see ChaoMa's EMNLP-2014 paper); joint entity and even coreference across documents (see Jun's AAAI-2015
paper); object detection in challenging biological images,
semantic segmentation of images, and monocular depth estimation from images (see
Michael's ICCV-2013 workshop paper and CVPR-2015 paper); and activity prediction from sensor data (see Bryan's KDD-2015 paper)
Other researchers' have employed HC-Search to solve various structured prediction applications: dependency parsing (see IJCAI-2016 and ACL-2016 papers); predicting DDoS attacks (see MLJ-2016 paper)
Deep Language
Understanding (with Chao Ma, Jun Xie, Shahed Sorower,
Walker Orr, Prashanth Mannem, Tom Dietterich, Xiaoli Fern and Prasad
Tadepalli)
How can we build intelligent computer systems that can achieve deep
language understanding? In the Deep
Reading and Learning project, we are trying to learn a
high-level representation called event
graphs (a
form of Abstract Meaning Representation) from raw text. Towards this
goal, we are working on several sub-problems: 1) Entity co-reference
resolution within a document; 2) Joint entity and event co-reference
resolution across documents; 3) Joint models for entity linking and discovery; and 4) Learning general scripts of events.
See our AAAI2014
paper on script learning, EMNLP2014 paper on
co-reference resolution, and AAAI2015 paper on learning for Easy-first framework. [Funded by DARPA as part of
the DEFT program]
Machine
Reading (with Shahed Sorower, Mohammad NasrEsfahani, Tom
Dietterich, Xiaoli Fern and Prasad Tadepalli)
How can we learn relational world knowledge rules (e.g., Horn clauses)
from natural texts to support textual inference? Natural texts are
radically incomplete (writers don't mention redundant information) and
systematically biased (writers mention exceptions to avoid the readers
from making incorrect inferences), which makes the rule learning very
hard. We solve this problem by modeling the pragmatic relationship
between what rules exist and what things will be mentioned (e.g.,
Gricean maxims). We worked with BBN and other
researchers from CMU, University of Washington and ISI. See
our NIPS2011
and ACML2011
papers
for details. [Funded by DARPA as part of the Machine Reading program]
Integrated Learning (with Tom
Dietterich and Prasad
Tadepalli)
How can we integrate information from multiple sources to learn better
? In the past, we worked on DARPA's Integrated learning
project,
where the goal was to learn a complex problem solving task from
a single demonstration of the expert. We learned the cost function that
the expert is minimizing while producing the demonstration by
formulating it as an inverse optimization problem. Our component's name
was
DTLR (Decision Theoretic Learner and Reasoner). We worked with other
researchers
from
Lockheed-Martin, ASU, RPI, UMD, UMASS, UIUC and Georgia Tech. See our TIST2012
paper for details. [Funded by DARPA]
Publications
Recent Advances in Structured Prediction
Janardhan Rao Doppa, Liping Liu, and Chao Ma
Tutorial at AAAI Conference on Artificial Intelligence (AAAI), 2018
Select-and-Evaluate: A Learning Framework for Large-Scale Knowledge Graph Search
F A Rezaur Rahman Chowdhury*, Chao Ma*, Md Rakibul Islam,
Mohammad Hossein Namaki, Mohammad Omar Faruk, and Janardhan Rao Doppa (* denotes equal contribution)
Proceedings of Machine Learning Research (PMLR), Vol 77, pp 129-144, 2017
PDF
Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications
Bryan Minor, Janardhan Rao Doppa, and Diane Cook
IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(12), pp 2744-2757, 2017
An Ensemble Architecture for Learning
Complex Problem Solving Techniques from Demonstration
Xiaoqin Zhang, Bhavesh Shrestha, Sung Wook Yoon, Subbarao
Kambhampati, Phillip DiBona, Jinhong K. Guo, Daniel McFarlane, Martin
O. Hofmann, Kenneth R. Whitebread, Darren Scott Appling, Elizabeth T.
Whitaker, Ethan Trewhitt, Li Ding, James Michaelis, Deborah L.
McGuinness, James A. Hendler, Janardhan Rao Doppa, Charles Parker,
Thomas G. Dietterich, Prasad Tadepalli, Weng-Keen Wong, Derek T. Green,
Antons Rebguns, Diana F. Spears, Ugur Kuter, Geoffrey Levine, Gerald
DeJong, Reid MacTavish, Santiago Ontanon, Jainarayan Radhakrishnan,
Ashwin Ram, Hala Mostafa, Huzaifa Zafar, Chongjie Zhang, Daniel D.
Corkill, Victor R. Lesser, and Zhexuan Song
ACM
Transactions on
Intelligent Systems and Technology , vol 3, issue 4, article 75, pp
1-38 (TIST-2012)
Learning Rules from Incomplete Examples via a
Probabilistic
Mention Model
Teaching
Courses at WSU:
CptS 570 Machine Learning (Fall 2014; Fall 2015; Fall 2016; Fall 2017; Fall 2018)
CptS 580 Structured Prediction: Algorithms and Applications
(Spring 2015; Spring 2016; Spring 2017; Spring 2018) --
strong focus on Electronic Design Automation domain in addition to
NLP and Vision
CptS 577 Structured Prediction and Intelligent Decision-Making (Spring 2019) --
strong focus on Electronic Design Automation domain in addition to
NLP and Vision
CptS 315 Introduction to Data Mining (Spring 2018; Spring 2019)
In the past, I was Instructor for the following courses:
CS/ECE 507:
Introduction to Graduate School (aka Research Methods in Computer Science,
partly based on Prof. Tom Dietterich's Fall
1996 course),
Oregon State University. (Fall 2010, Fall 2011, Fall 2012, Fall 2013)
CS 261: Data
Structures,
Oregon State University. (Summer
2007)
Summer
School on Data
structures and
Algorithms, IIT Kanpur. (Summer 2006)
Object
Oriented Programming
with C++, IIT Kanpur.
(Winter 2006)
C101:
Introduction to Computing, IIT
Kanpur. (Fall
2005)
Guest Editor, IEEE Design and Test, Special issue on Smart and Autonomous Systems for Sustainability: Sustainable Computing and Computing for Sustainability
Senior Program Committee Member:
AAAI National Conference on Artificial Intelligence (AAAI),
2019
AAAI National Conference on Artificial Intelligence (AAAI),
2018
International Joint Conference on Artificial Intelligence (IJCAI), 2016
Program Committee Member:
ACM/IEEE International Network-on-Chip Symposium (NOCS), 2018
International Conference on Machine Learning (ICML), 2018
Annual Conference on Neural Information Processing Systems (NIPS), 2018
International Joint Conference on Artificial Intelligence (IJCAI), 2018
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018
International Conference on Artificial Intelligence and Statistics (AISTATS), 2018
International Conference on Automated Planning and Scheduling (ICAPS), 2018
AAAI Student Abstract Program, 2018
International Conference on Machine Learning (ICML), 2017
Annual Conference on Neural Information Processing Systems (NIPS), 2017
AAAI National Conference on Artificial Intelligence (AAAI),
2017
International Conference on Uncertainty in Artificial Intelligence (UAI), 2017
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
International Conference on Automated Planning and Scheduling (ICAPS), 2017
EMNLP Workshop on Structured Prediction for Natural Language Processing, 2017
AAAI Student Abstract Program, 2017
International Conference on Machine Learning (ICML), 2016
Annual Conference on Neural Information Processing Systems (NIPS), 2016
AAAI National Conference on Artificial Intelligence (AAAI),
2016
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016
International Conference on Artificial Intelligence and Statistics (AISTATS), 2016
International Conference on Automated Planning and Scheduling (ICAPS), 2016
International Conference on Computational Linguistics (COLING), 2016
EMNLP Workshop on Structured Prediction for Natural Language Processing, 2016
AAAI Doctoral Consortium, 2016
AAAI Student Abstract Program, 2016
Annual Conference on Neural Information Processing Systems (NIPS), 2015
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015
International Joint Conference on Artificial Intelligence (IJCAI), 2015
AAAI National Conference on Artificial Intelligence (AAAI),
2015
International Conference on Artificial Intelligence and Statistics (AISTATS), 2015
AAAI Student Abstract Program, 2015
International Conference on Machine Learning (ICML), 2014
European Conference on Machine Learning (ECML), 2014
ECML Workshop on Multi-Target Prediction, 2014
CVPR Workshop on Computational Models of Social Interactions and
Behavior: Scientific Grounding, Sensing, and Applications, 2014
International Conference on Machine Learning (ICML), 2013
International Joint Conference on Artificial Intelligence
(IJCAI), 2013
AAAI National Conference on Artificial Intelligence (AAAI),
2013
European Conference on Machine Learning (ECML), 2013
ICCV Workshop on Understanding Human Activities: Context
and Interactions, 2013
AAAI National Conference on Artificial Intelligence (AAAI),
2012
ECML Workshop on Collective Learning and Inference on
Structured
Data (CoLISD), 2012
International Joint Conference on Artificial Intelligence
(IJCAI), 2011
AAAI National Conference on Artificial Intelligence (AAAI),
2011
ECML Workshop on Collective Learning and Inference on
Structured
Data (CoLISD), 2011
Sir. C.V. Raman Educational Award, awarded by the State
Govt. of Andhra Pradesh, India, 1998
Personal
I'm passionate about cricket.
Playing cricket helps me remain sane amidst the hectic research
life. I try to play in the nearby cricket leagues during the
summers. I played for OSU Cricket club in 2007, 2008 and 2009.
Our
team Chak De
Oregon won
the 2009 NWCL cricket championship. In 2010, I played
for Chak De
Oregon in NWCL (Div I) and for Portland
club in
OCL. We won the 2010
OCL T20 championship. In 2011, I played for only Portland club
in OCL as part of the budget cut on cricket. After 2011 season I became
very busy and could not justify my time spent on cricket, so I stopped playing. I used to maintain my cricket
scores
here.
I like to cook, but I don't like to spend too much time on it.
So I follow an engineering methodology for cooking, which provides a
good trade off between preparation time and quality of the
food! Does this remind you of my research work on trading off
computation time and quality of the predictions? :)
