Jana's Homepage

Janardhan Rao ( Jana ) Doppa
Huie-Rogers Endowed Chair in Computer Science
Associate Professor
School of EECS, Washington State University

Office: EME 133
Zoom Office hours: None for Summer-2022
Voice: 1-509-335-1846 (Email is the preferred option)
Email: jana.doppa [AT] wsu.edu

LATEST UPDATES

PhD Student Biresh Joardar will start as a Tenure-Track Assistant Professor with University of Houston in Fall-2022!
P hD Student Aryan Deshwal was selected for Heidelberg Laureate Forum (2022) and won the Outstanding RA Award (2022) from the Voiland College of Engineering
Best Paper Award, ACM Transactions on Design Automation of Electronic Systems (2021)
Excited to be part of the newly funded NSF-USDA AI Institute for Agriculture Decision Support and lead the AI Thrust on modeling systems of knowns and unknowns.
Selected for International Joint Conference on Artificial Intelligence, Early Career Spotlight (2021)
PhD Student Syrine Belakaria won IBM PhD Research Fellowship (2021) and was a finalist for Microsoft Research Fellowship (2021)! I'm super proud of her!
PhD Student Biresh Joardar won NSF Computing Innovation Fellowiship (2020)! He is doing superb work to create a virtuous cycle of ML-based hardware design to advance ML
Outstanding Junior Faculty in Research Award (2020) and Reid Miller Teaching Excellence Award (2018) from the Voliland College of Engineering and Architecture
NSF CAREER Award (2019) Thanks NSF!
Outstanding Paper Award at AAAI Conference (2013)

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:

AI-driven Adaptive Experiment Design with applications to engineering and scientific domains
Sequential Decision-making under Uncertainty problems motivated by real-world applications including agriculture and optimization of cyber-physical systems such as smart grid and smart health
Robust Machine learning and Decision-making for high-stakes applications
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 / Monolithic 3D integration, Heterogeneous systems, and Processing-in-Memory cores)
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 the inimitable 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.

Quick Links: [ Research ] [ Publications ] [ Teaching ] [ Students ] [ Awards and Honors ] [ Professional Service ] [ Reading Groups ] [ Personal ]

Education

Ph.D., Computer Science, Oregon State University, 2014
M.Tech., Computer Science, Indian Institute of Technology, Kanpur, India, 2006

Research

I like to work on artificial intelligence and machine learning problems motivated from important real-world applications. A sample of my current and recent research projects include:

AI to Accelerate Science and Engineering

How can we develop AI methods by combining valuable domain knowledge and data to accelerate scientific discovery and engineering design?

We are exploring novel adaptive experiment design algorithms to provide computing and planning support for scientists and engineers in selecting experiments under resource constraints. We have developed novel Bayesian optimization (BO) algorithms to optimize combinatorial spaces (see AAAI-2020, AAAI-2021, NeurIPS-2021, and AAAI-2022 papers); BO for hybrid spaces (ICML-2021 paper); BO for optimizing multiple objectives (see NeurIPS-2019 and AAAI-2020 papers); BO for multi-fidelity optimization (see AAAI-2020 , NeurIPS-2020 workshop, and JAIR-2021 papers); and BO for handling black-box constraints (see NeurIPS-2020 workshop and JAIR-2021 papers). See my IJCAI-2021 Early Career Spotlight paper for a short summary of this research.
AI for electronic design automation and manycore systems design with our collaborators Partha Pande @ WSU, Krish Chakrabarty @ Duke, Helen Li @ Duke, and Deuk Heo @ WSU. See papers in the below section.
AI for electric transportation systems with our power systems collaborator Yue Cao @ Oregon State. See our ECCE-2020 conference paper and TTE-2021 journal paper.
AI for nanoporous materials (NPMs) design and chemical sciences with our chemistry collaborator Cory Simon @ Oregon State. We are looking at sustainability applications of NPMs such as (gas storage) densifying hydrogen - a clean fuel - for compact storage onboard vehicles; (gas separation) capturing carbon dioxide from flue gas of coalfired power plants; subsequently sequester it to prevent global warming; and (gas sensing) detecting toxic compounds and explosives. See our Chemrxiv paper for some initial results.
AI for agricultural decision support to improve water management, farm management, and harvest management by overcoming labor shortage. Excited to be part of the newly funded NSF-USDA AI Institute for Agriculture Decision Support and lead the AI Thrust on modeling systems of knowns and unknowns.

Machine Learning meets Computing Systems Design: ML for Systems and Systems for ML

How can we exploit the synergies between machine learning and computing systems to enable the design of high-performance, energy-efficient, and reliable computing systems spanning from edge devices to servers to cloud, which will empower further advances in ML?

Machine learning methodologies for design space exploration and optimization of manycore systems to significantly reduce the overall engineering cost and design time of application-specific hardware. We developed a theory-guided ML framework by combining the domain knowledge from manycore designers and training data from hardware simulations (see IEEE TCAD 2017, IEEE TC 2019, and ACM TECS 2019 papers)
Machine learning for runtime resource management to save power by maintaining performance and temperature constraints in system-on-chips (SoCs) ranging from mobile to large-scale manycore systems. We developed a theory-guided ML framework by using the learned models of design objectives (e.g., power, performance, and temperature) as a function of the system state to provide strong supervised training data to continuously improve the resource management policy (see IEEE TVLSI 2017, IEEE TVLSI 2019, ACM TODAES 2020 , and DAC-2021 papers)
How can we leverage emerging technologies and machine learning to design high-performance and energy-efficient manycore systems for Big-Data workloads? For example, 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; Processing-in-memory cores enabled by ReRAM will reduce the data movement; and integrating heterogeneous processing cores of different types and functional granularity allows us to bridge the energy efficiency of application-specific hardware with the programmability of general-purpose processors. See IEEE TCAD 2017 for TSV-based 3D manycore systems; ICCD-2017 for benefits of M3D in homogeneous manycore systems; ACM TODAES 2018 for improved thermal and performance trade-offs in M3D based manycore systems; ACM TODAES 2019 for overcoming the challenges due to electrostatic coupling in M3D based manycore systems; IEEE TC 2018 for integrating heterogeneous cores including CPUs and GPUs with conflicting QoS requirements on the same 2D SoC; IEEE TC 2019 for benefits of 3D heterogenous manycore systems ACM JETC 2020 for the benefits of partially connected 3D manycore systems using near-field inductive coupling based interconnect; and ACM TODAES 2021 for benefits of M3D based heterogeneous manycore systems including GPU cores.
How can we design and optimize high-performance, energy-efficient, and reliable manycore systems for training a large class of deep neural networks. See ACM JETC 2018 for 3D manycore architecture with ReRAM based processing-in-memory cores, IEEE TCAD 2021 for training CNNs by synergistically combining the benefits of M3D integration, ReRAM, and GPUs; IEEE TCAD 2021 to improve the 3D ReRAM/GPU architecture by optimising the normalization layers using BO, ACM TECS 2021 for reliable training of CNNs on unreliable ReRAM based manycore accelerators, and DATE-2021, IEEE TVLSI 2021, and ICCAD-2021 papers on processing-in-memory based manycore architectures for high-performance training graph neural networks.
How can we create energy-efficient and secure edge AI? We have developed a general hardware and software co-design framework that is applicable for problems ranging from simple classification to structured output prediction (e.g., 3D object shapes from 2D images in AR/VR applications). See IEEE TCAD 2018, ACM TECS 2019, DAC 2020, ICCAD 2020, and ICCAD 2021 papers for different instantiations. See IEEE TCAD 2020 paper for analyzing and improving the security of Edge AI applications such as smart homes, mobile health, and smart grid.

Collaborators: Partha Pande @ WSU, Krish Chakrabarty @ Duke, Helen Li @ Duke, Deuk Heo @ WSU, Ganapati Bhat @ WSU, Umit Ogras @ UW Madison, Paul Bogdan @ USC, Mike Kishinevsky @ Intel Research, Radu Marculescu @ CMU, and Diana Marculescu @ CMU

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

Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability

Shubhomoy Das, Md Rakibul Islam, Nitthilan Kannappan Jayakodi, and Janardhan Rao Doppa
Comprehensive version: [arXiv 1901.08930] [Code]

Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach

Syrine Belakaria, Janardhan Rao Doppa, Nicolo Fusi, and Rishit Sheth
[arXiv 2206.12708] [Code]

Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach

Taha Belkhouja, Yan Yan, and Janardhan Rao Doppa
[arXiv 2207.04306] [Code]

Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition

Garrett Wilson, Janardhan Rao Doppa, and Diane Cook
8th SIGKDD International Workshop on Mining and Learning from Time Series, 2022
PDF [Dataset]

Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features

Taha Belkhouja and Janardhan Rao Doppa
Journal of Artificial Intelligence Research (JAIR), vol 73, pp 1435-1471, 2022
PDF [Code]

Bayesian Optimization over Permutation Spaces

Aryan Deshwal, Syrine Belakaria, and Janardhan Rao Doppa
Proceedings of Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI), 2022
PDF [Code]

Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis

Taha Belkhouja, Yan Yan, and Janardhan Rao Doppa
Proceedings of Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI), 2022
PDF [Code]

Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health

Dina Hussein, Ganapati Bhat, and Janardhan Rao Doppa
Proceedings of Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI), 2022
PDF [Code]

Bayesian Optimization: From Foundations to Advanced Topics

Jana Doppa, Aryan Deshwal, and Syrine Belakaria
Half-day Tutorial at Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI), 2022
Tutorial website and Slides

Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces

Aryan Deshwal and Janardhan Rao Doppa
To appear in Proceedings of Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021
PDF [Code]

Bayesian Optimization over Hybrid Spaces

Aryan Deshwal, Syrine Belakaria, and Janardhan Rao Doppa
To appear in Proceedings of 38th Internal Conference on Machine Learning (ICML), 2021
PDF [Code]

Mercer Features for Efficient Combinatorial Bayesian Optimization

Aryan Deshwal, Syrine Belakaria, and Janardhan Rao Doppa
Proceedings of 35th AAAI Conference on Artificial Intelligence (AAAI), 2021
PDF [Code]

Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization

Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa
Journal of Artificial Intelligence Research (JAIR), vol 72, pp 667-715, 2021
PDF [Code]

Adaptive Experimental Design for Optimizing Combinatorial Strutures

Janardhan Rao Doppa
To appear in Proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight Track, 2021
PDF

CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning

Garrett Wilson, Janardhan Rao Doppa, and Diane Cook
Under review at IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
PDF [Code]

Bayesian Optimization of Nanoporous Materials

Aryan Deshwal, Cory Simon, and Janardhan Rao Doppa
Molecular Systems Design and Engineering Journal, Royal Society of Chemistry, 2021
PDF [Code]

Machine Learning Enabled Design Automation and Multi-Objective Optimization for Electric Transportation Power Systems

Syrine Belakaria*, Derek Jackson*, Yue Cao, Janardhan Rao Doppa, and Xiaonan Lu (* denotes equal contribution)
To appear in IEEE Transactions on Transportation Electrification (TTE), 2021
PDF

DARe: DropLayer-Aware Manycore ReRAM architecture for Training Graph Neural Networks

Aqeeb Iqbal Arka, Biresh Kumar Joardar, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
To appear in Proceedings of 40th IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2021
PDF

Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise

Xiaoxuan Yang, Syrine Belakari, Biresh Kumar Joardar, Huanrui Yang, Janardhan Rao Doppa, Partha Pratim Pande, Krishnendu Chakrabarty, and Hai (Helen) Li
To appear in Proceedings of 40th IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2021
PDF

Processing-in-Memory Enabled Heterogeneous Manycore Architectures for Deep Learning: From CNNs to GNNs

Biresh Kumar Joardar, Aqeeb Iqbal Arka, Janardhan Rao Doppa, Partha Pratim Pande, Krishnendu Chakrabarty, and Hai (Helen) Li
To appear in Proceedings of 40th IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2021
PDF

A General Hardware and Software Co-Design Framework for Energy-Efficient Edge AI

Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa, and Partha Pratim Pande
To appear in Proceedings of 40th IEEE/ACM International Conference on Computer Aided Design (ICCAD), 2021
PDF

Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach

Aryan Deshwal, Syrine Belakaria, Ganapati Bhat, Janardhan Rao Doppa, and Partha Pratim Pande
To appear in Proceedings of 58th IEEE/ACM Design Automation Conference (DAC), 2021
PDF

Functional Criticality Classification of Structural Faults in AI Accelerators

Arjun Chaudhuri, Biresh Kumar Joardar, Jonti Talukdar, Krishnendu Chakrabarty, Janardhan Rao Doppa, and Partha Pratim Pande
To appear in Proceedings of 58th IEEE/ACM Design Automation Conference (DAC), 2021
PDF

ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks

Aqeeb Iqbal Arka, Biresh Kumar Joardar, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
To appear in Proceedings of 23rd IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2021 Nominated for Best Paper Award
PDF

3D++: Unlocking the Next Generation of High-Performance and Energy-Efficient Architectures Using M3D Integration

Biresh Kumar Joardar, Aqeeb Iqbal Arka, Janardhan Rao Doppa, and Partha Pratim Pande
To appear in Proceedings of 23rd IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2021
PDF

Learning to Train CNNs on Faulty ReRAM based Manycore Accelerators

Biresh Kumar Joardar, Janardhan Rao Doppa, Hai (Helen) Li, Krishnendu Chakrabarty, and Partha Pratim Pande
To appear in ACM Transactions on Embedded Computing Systems (TECS), 2021
PDF

Performance and Accuracy Trade-offs for Training Graph Neural Networks on ReRAM-based Architectures

Aqeeb Iqbal Arka, Biresh Kumar Joardar, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
To appear in IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 2021
PDF

High-Throughput Training of Deep CNNs on ReRAM-based Heterogeneous Architectures via Optimized Normalization Layers

Biresh Kumar Joardar, Aryan Deshwal, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
To appear in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021
PDF

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa
NeurIPS Workshop on Engineering Modeling, Simulation, and Design, 2020
PDF [Code coming soon]

Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints

Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa
NeurIPS Workshop on Machine Learning and the Physical Sciences, 2020
PDF [Code]

Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization with Constraints

Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa
7th ICML Workshop on Automated Machine Learning, 2020
PDF [Code]

GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning

Md Rakibul Islam, Shubhomoy Das, Janardhan Rao Doppa, and Sriraam Natarajan
2nd ICML Workshop on Human in the Loop Learning, 2020
PDF [Code]

Few-Shot Induction of Generalized Logical Concepts via Human Guidance

Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, and Sriraam Natarajan
To appear in Frontiers in Robotics and AI, 2020
PDF

Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Garrett Wilson, Janardhan Rao Doppa, and Diane Cook
Proceedings of 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020
PDF [Code]

Scalable Combinatorial Bayesian Optimization with Tractable Statistical Models

Aryan Deshwal, Syrine Belakaria, and Janardhan Rao Doppa
Proceedings of NeurIPS Workshop on Engineering Modeling, Simulation, and Design, 2020
PDF [Code]

Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework

Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, and Alan Fern
Proceedings of 34th AAAI Conference on Artificial Intelligence (AAAI), 2020
PDF

Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach

Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa
Proceedings of 34th AAAI Conference on Artificial Intelligence (AAAI), 2020
PDF [Code coming soon]

Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization

Syrine Belakaria, Aryan Deshwal, Nitthilan Kannappan Jayakodi, and Janardhan Rao Doppa
Proceedings of 34th AAAI Conference on Artificial Intelligence (AAAI), 2020
PDF [Code]

Machine Learning Enabled Fast Multi-Objective Optimization for Electrified Aviation Power System Design

Syrine Belakaria*, Derek Jackson*, Yue Cao, Janardhan Rao Doppa, and Xiaonan Lu (* denotes equal contribution)
Proceedings of IEEE Energy Conversion Congress and Exposition (ECCE), 2020
PDF

HeM3D: Heterogeneous Manycore Architecture Based on Monolithic 3D Vertical Integration

Aqeeb Iqbal Arka, Biresh Kumar Joardar, Ryan Kim, Daehyun Kim, Janardhan Rao Doppa, and Partha Pratim Pande
To appear in ACM Transactions on Design Automation of Electronic Systems (TODAES), 2020
PDF

An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms

Sumit Mandal, Ganapati Bhatt, Janardhan Rao Doppa, Partha Pratim Pande, and Umit Ogras
ACM Transactions on Design Automation of Electronic Systems (TODAES), 25(3): 28:1-28:26, 2020 Best Paper Award
PDF

AccuReD: High Accuracy Training of CNNs on ReRAM/GPU Heterogeneous 3D Architecture

Biresh Kumar Joardar, Janardhan Rao Doppa, Partha Pratim Pande, Hai (Helen) Li, Krishnendu Chakrabarty
To appear in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020
PDF

3D-ReG: A 3D ReRAM-based Heterogeneous Architecture for Training Deep Neural Networks

Bing Li, Janardhan Rao Doppa, Partha Pratim Pande, Krishnendu Chakrabarty, Joe Qiu, Hai (Helen) Li
ACM Journal on Emerging Technologies in Computing Systems (JETC), 16(2): 20:1-20:24, 2020
PDF

Power Management of Monolithic 3D Manycore Chips with Inter-Tier Process Variations

Anwesha Chatterjee, Shouvik Musavvir, Ryan Kim, Janardhan Rao Doppa, Partha Pratim Pande
To appear in ACM Journal on Emerging Technologies in Computing Systems (JETC), 2020
PDF

Making a Case for Partially Connected 3D NoC: NFIC versus TSV

Aqeeb Iqbal Arka, Srinivasan Gopal, Janardhan Rao Doppa, Deuk Heo, and Partha Pratim Pande
ACM Journal on Emerging Technologies in Computing Systems (JETC), 2020
PDF

Design and Optimization of Energy-Accuracy Trade-off Networks for Mobile platforms via Pretrained Deep Models

Nitthilan Kannappan Jayakodi, Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa
ACM Transactions on Embedded Computing Systems (TECS), 19(1): 4:1-4:24, 2020
PDF

SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms

Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa, and Partha Pratim Pande
Proceedings of 39th IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2020
PDF

PETNet: Polycount and Energy Trade-off Deep Networks for Producing 3D Objects from Images

Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa, and Partha Pratim Pande
Proceedings of 57th IEEE/ACM Design Automation Conference (DAC), 2020
PDF

Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs

Sumit Mandal, Umit Ogras, Janardhan Rao Doppa, Raid Ayoub, Mike Kishinivesky, and Partha Pratim Pande
To appear in Proceedings of 57th IEEE/ACM Design Automation Conference (DAC), 2020
PDF

Power, Performance, and Thermal Trade-offs in M3D-enabled Manycore Chips

Shouvik Musavvir, Anwesha Chatterjee, Ryan Kim, Daehyun Kim, Janardhan Rao Doppa, and Partha Pratim Pande
Proceedings of 23rd IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2020
PDF

GRAMARCH: A GPU-ReRAM based Heterogeneous Architecture for Neural Image Segmentation

Biresh Kumar Joardar, Nitthilan Kannappan Jayakodi, Janardhan Rao Doppa, Partha Pratim Pande, Helen Li, and Krishnendu Chakrabarty
Proceedings of 23rd IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2020 Nominated for Best Paper Award
PDF

Design of Multi-Output Switched-Capacitor Voltage Regulator via Machine Learning

Syrine Belakaria*, Zhiyuan Zhou*, Aryan Deshwal, Wookpyo Hong, Janardhan Rao Doppa, Partha Pratim Pande, and Deukhyoun Heo (* denotes equal contribution)
Proceedings of 23rd IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2020
PDF

Max-value Entropy Search for Multi-Objective Bayesian Optimization

Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa
Proceedings of 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019
PDF Appendix [Code]

Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning

Chao Ma*, F A Rezaur Rahman Chowdhury*, Aryan Deshwal, Md Rakibul Islam, Janardhan Rao Doppa, and Dan Roth (* denotes equal contribution)
Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019
PDF Appendix [Code]

Learning and Inference for Structured Prediction: A Unifying Perspective

Aryan Deshwal, Janardhan Rao Doppa, and Dan Roth
Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019
PDF

Taming Extreme Heterogeneity via Machine Learning based Design of Autonomous Manycore Systems

Paul Bogdan, Fan Chen, Aryan Deshwal, Janardhan Rao Doppa, Biresh Kumar Joardar, Hai (Helen) Li, Shahin Nazarian, Linghao Song, and Yao Xiao
Proceedings of International Conference on Hardware/Software Codesign and System Synthesis (CODES), 2019
PDF

Autonomous Design Space Exploration of Computing Systems for Sustainability: Opportunities and Challenges

Janardhan Rao Doppa, Paul Bogdan, and Justinian Rosca
IEEE Design and Test of Computers, 36(5): 35-43, 2019
PDF

Dynamic Resource Management of Heterogeneous Mobile Platforms via Imitation Learning

Sumit Mandal, Ganapati Bhatt, Chetan Arvid Patel, Janardhan Rao Doppa, Partha Pratim Pande, and Umit Ogras
IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 27(12): 2842-2854, 2019
PDF

MOOS: A Multi-Objective Design Space Exploration and Optimization Framework for NoC Enabled Manycore Systems

Aryan Deshwal, Nitthilan Kannappan Jayakodi, Biresh Kumar Joardar, Janardhan Rao Doppa, and Partha Pratim Pande
ACM Transactions on Embedded Computing Systems (TECS), 18(5s): 77:1-77:23, 2019
PDF

Impact of Electrostatic Coupling on Monolithic 3D-enabled Network on Chip

Dongjin Lee, Sourav Das, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
ACM Transactions on Design Automation of Electronic Systems (TODAES), 24(6): 62:1-62:22, 2019
PDF

Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems

Biresh Kumar Joardar, Ryan Kim, Janardhan Rao Doppa, Partha Pratim Pande, Radu Marculescu, and Diana Marculescu
IEEE Transactions on Computers (TC), 68(6): 852-866, 2019
PDF

Design and Optimization of Heterogeneous Manycore Systems enabled by Emerging Interconnect Technologies: Promises and Challenges

Biresh Kumar Joardar, Ryan Kim, Janardhan Rao Doppa, and Partha Pratim Pande
Proceedings of 22nd IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2019
PDF

REGENT: A Heterogeneous ReRAM/GPU-based Architecture Enabled by NoC for Training CNNs

Biresh Kumar Joardar, Bing Li, Janardhan Rao Doppa, Partha Pratim Pande, Helen Li, Krishnendu Chakrabarty
Proceedings of 22nd IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2019
PDF

Recent Advances in Structured Prediction

Janardhan Rao Doppa, Liping Liu, and Chao Ma
Tutorial at AAAI Conference on Artificial Intelligence (AAAI), 2018
Tutorial website and Slides

Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis

Ryan Kim, Janardhan Rao Doppa, Partha Pratim Pande, Diana Marculescu, and Radu Marculescu
IEEE Computer, 51(7): 66-77, 2018
PDF

Machine Learning for Design Space Exploration and Optimization of Manycore Systems

Ryan Kim, Janardhan Rao Doppa, and Partha Pratim Pande
Proceedings of 37th IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2018
PDF

Hybrid On-Chip Communication Architectures for Heterogeneous Manycore Systems

Biresh Kumar Joardar, Janardhan Rao Doppa, Partha Pratim Pande, Radu Marculescu, and Diana Marculescu
Proceedings of 37th IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2018
PDF

On-Chip Communication Network for Efficient Training of Deep Convolutional Networks on Heterogeneous Manycore Systems

Wonje Choi, Karthi Duraisamy, Ryan Kim, Janardhan Rao Doppa, Partha Pratim Pande, Radu Marculescu, and Diana Marculescu
IEEE Transactions on Computers (TC), 67(5): 672-686, 2018 Spotlight Paper for May Issue
PDF

Trading-Off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach

Nitthilan Kannappan Jayakodi, Anwesha Chatterjee, Wonje Choi, Janardhan Rao Doppa, and Partha Pratim Pande
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 37(11): 2881-2893, 2018
PDF

Performance and Thermal Trade-Offs for Energy Efficient Monolithic 3D Network-on-Chip

Dongjin Lee, Sourav Das, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
ACM Transactions on Design Automation of Electronic Systems (TODAES), 23(5): 60:1-60:25, 2018
PDF

Abetting Planned Obsolescence by Aging 3D Networks-on-Chip

Sourav Das, Kanad Basu, Janardhan Rao Doppa, Partha Pratim Pande, Ramesh Karri, and Krishnendu Chakrabarti
Proceedings of IEEE/ACM International Symposium on Networks-on-Chip (NOCS), Embedded Systems Week, 2018
PDF

Design Space Exploration of 3D Network-on-Chip: A Sensitivity-based Optimization Approach

Dongjin Lee, Sourav Das, DaeHyun Kim, Janardhan Rao Doppa, and Partha Pratim Pande
ACM Journal on Emerging Technologies in Computing Systems (JETC), 14(3): 32:1-32:26, 2018
PDF

Scalable Network-on-Chip Architectures for Brain-Machine-Interface Applications

Xian Li, Karthi Duraisamy, Paul Bogdan, Janardhan Rao Doppa, and Partha Pratim Pande
IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 26(10): 1895-1907, 2018
PDF

Towards Problem Solving Agents that Communicate and Learn

Anjali Narayan-Chen, Colin Graber, Mayukh Das, Md Rakibul Islam, Soham Dan, Sriraam Natarajan, Janardhan Rao Doppa, Julia Hockenmaier, Martha Palmer, Dan Roth
To appear in Proceedings of ACL Workshop on Language Grounding for Robotics, 2017
PDF

Multi-Task Structured Prediction for Entity Analysis: Search-based Learning Algorithms

Chao Ma, Janardhan Rao Doppa, Prasad Tadepalli, Hamed Shahbazh, and Xiaoli Fern
Proceedings of Machine Learning Research (PMLR), Vol 77, pp 514-529, 2017
PDF

Learning to Speed Up Query Planning in Graph Databases

Mohammad Hossein Namaki*, F A Rezaur Rahman Chowdhury*, Md Rakibul Islam, Janardhan Rao Doppa, and Yinghui Wu (* denotes equal contribution)
Proceedings of 27th International Conference on Automated Planning and Scheduling (ICAPS-2017) Nominated for Best Student Paper Award
PDF

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
PDF

Data Analytics Enables Energy-Efficiency and Robustness: From Mobile to Manycores, Datacenters, and Networks

Sudeep Pasricha, Janardhan Rao Doppa, Krishnendu Chakrabarty, Saideep Tiku, Daniel Dauwe, Shi Jin, Partha Pratim Pande
Proceedings of International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Embedded Systems Week, 2017
PDF

Adaptive Manycore Architectures for Big Data Computing

Janardhan Rao Doppa, Ryan Kim, Mihailo Isakov, Michel Kinsy, HyoukJun Kwon, and Tushar Krishna
Proceedings of IEEE/ACM International Symposium on Networks-on-Chip (NOCS), Embedded Systems Week, 2017
PDF

Imitation Learning for Dynamic VFI Control in Large-Scale Manycore Systems

Ryan Kim, Wonje Choi, Zhou Chen, Janardhan Rao Doppa, Partha Pratim Pande, Diana Marculescu, and Radu Marculescu.
IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 25(9), pp 2458-2471, 2017
PDF

Monolithic 3D-enabled High-Performance and Energy Efficient Network-on-Chip

Sourav Das, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
Proceedings of 35th IEEE International Conference on Computer Design (ICCD), 2017
PDF

3D Heterogeneous NoC-enabled Manycore Architectures for Accelerating CNN Training: Performance and Thermal Trade-offs

Biresh Kumar Joardar, Wonje Choi, Ryan Kim, Janardhan Rao Doppa, Partha Pratim Pande, Radu Marculescu, and Diana Marculescu
Proceedings of IEEE/ACM International Symposium on Networks-on-Chip (NOCS), Embedded Systems Week, 2017
PDF

VFI-based Power Management to Enhance the Lifetime of High-Performance 3D NoCs

Sourav Das, Dongjin Lee, Wonje Choi, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
ACM Transactions on Design Automation of Electronic Systems (TODAES), 23(1), pp 7:1-7:26, 2017
PDF

Design-Space Exploration and Optimization of an Energy-Efficient and Reliable 3D Small-world Network-on-Chip

Sourav Das, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 36(5), pp 719-732, 2017
online link and Longer ArXiv version

Robust TSV-based 3D NoC Design to Counteract Electromigration and Crosstalk Noise

Sourav Das, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
Proceedings of 20th IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2017
PDF

Hybrid Network-on-Chip Architectures for Accelerating Deep Learning Kernels on Heterogeneous Manycore Platforms

Wonje Choi, Karthi Duraisamy, Ryan Kim, Janardhan Rao Doppa, Partha Pratim Pande, Radu Marculescu, and Diana Marculescu
Proceedings of IEEE International Conference on Compilers, Architectures and Synthesis for Embedded Systems, Embedded Systems Week, 2016
PDF

Energy-Efficient and Reliable 3D Network-on-Chip (NoC): Architectures and Optimization Algorithms

Sourav Das, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
Proceedings of 35th IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2016
PDF

Reliability and Performance Trade-offs for 3D NoC-Enabled Multicore Chips

Sourav Das, Janardhan Rao Doppa, Partha Pratim Pande, and Krishnendu Chakrabarty
Proceedings of 19th IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2016
PDF

Integrating Learning and Search for Structured Prediction

Janardhan Rao Doppa
Half-day Tutorial at International Joint Conference on Artificial Intelligence (IJCAI), 2016
Tutorial website and Slides

Optimizing 3D NoC Design for Energy Efficiency: A Machine Learning Approach

Sourav Das, Janardhan Rao Doppa, Dae Hyun Kim, Partha Pratim Pande, and Krishnendu Chakrabarty
Proceedings of 34th IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2015
PDF

Data-Driven Activity Prediction: Algorithms, Evaluation Methodology, and Applications

Bryan Minor, Janardhan Rao Doppa, and Diane Cook
Proceedingss of 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015
PDF

HC-Search for Structured Prediction in Computer Vision

Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, and Tom Dietterich
Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2015
PDF

Learning Greedy Policies for the Easy-First Framework

Jun Xie, Chao Ma, Janardhan Rao Doppa, Prashanth Mannem, Xiaoli Fern, Tom Dietterich, and Prasad Tadepalli
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2015
PDF

Prune-and-Score: Learning for Greedy Coreference Resolution

Chao Ma, Janardhan Rao Doppa, Walker Orr, Prashanth Mannem, Xiaoli Fern, Tom Dietterich, and Prasad Tadepalli
Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014
PDF

Integrating Learning and Search for Structured Prediction

Janardhan Rao Doppa
Ph.D. Thesis, Oregon State University, 2014 Nominated for ACM Doctoral Dissertation Award
PDF

HC-Search: A Learning Framework for Search-based Structured Prediction

Janardhan Rao Doppa, Alan Fern, and Prasad Tadepalli
Journal of Artificial Intelligence Research (JAIR), vol 50, pages 369-407, 2014
jair link

Structured Prediction via Output Space Search

Janardhan Rao Doppa, Alan Fern, and Prasad Tadepalli
Journal of Machine Learning Research (JMLR), 15(Apr):1317−1350, 2014
jmlr link

HC-Search for Multi-Label Prediction: An Empirical Study

Janardhan Rao Doppa, Jun Yu, Chao Ma, Alan Fern, and Prasad Tadepalli
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2014
PDF

Learning Scripts as Hidden Markov Models

J. Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Fern, and Thomas Dietterich
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2014
PDF

Learning to Detect Basal Tubules of Nematocysts in SEM Images

Michael Lam, Janardhan Rao Doppa, Xu Hu, Sinisa Todorovic, Thomas Dietterich, Abigail Reft, and Marymegan Daly
Proceedings of ICCV 2013 Workshop on Computer Vision for Accelerated Biosciences.
PDF

HC-Search: Learning Heuristics and Cost Functions for Structured Prediction

Janardhan Rao Doppa, Alan Fern, and Prasad Tadepalli
Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2013 Outstanding Paper Award
PDF

Output Space Search for Structured Prediction

Janardhan Rao Doppa, Alan Fern, and Prasad Tadepalli
Proceedings of International Conference on Machine Learning (ICML), 2012
PDF

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)
PDF

Learning Rules from Incomplete Examples via Implicit Mention Models
Janardhan Rao Doppa, Shahed Sorower, Mohammad Nasresfahani, Jed Irvine, Walker Orr, Thomas G. Dietterich, Xiaoli Fern, and Prasad Tadepalli

Journal of Machine Learning Research (JMLR) Proceedings Track, vol 20, pages 197-212, 2011

PDF
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions
Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa, Walker Orr, Xiaoli Fern, and Prasad Tadepalli

Proceedings of Advances in Neural Information Processing Systems (NIPS), 2011

PDF

Learning Rules from Incomplete Examples via Observation Models
Janardhan Rao Doppa, Mohammad Nasresfahani, Shahed Sorower, Jed Irvine, Thomas G. Dietterich, Xiaoli Fern, and Prasad Tadepalli

Proceedings of IJCAI 2011 Workshop on Learning by Reading and its Applications in Intelligent Question-Answering.

PDF

A Structured Prediction Approach for Entity Coreference Resolution

Janardhan Rao Doppa, Walker J. Orr, Mohammad Nasresfahani, Thomas G. Dietterich, Xiaoli Fern, and Prasad Tadepalli
Machine Reading Symposium, DARPA's Machine Reading Program, 2011.
PDF

Learning Rules from Incomplete Examples via a Probabilistic Mention Model
Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa, Xiaoli Fern, and Prasad Tadepalli

Proceedings of IJCAI 2011 Workshop on Learning by Reading and its Applications in Intelligent Question-Answering.

PDF

Learning Algorithms for Link Prediction based on Chance-Constraints

Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, and Lise Getoor

Proceedings of European Conference on Machine Learning (ECML), 2010

PDF

Towards Learning Rules from Natural Texts

Janardhan Rao Doppa, Mohammad Nasresfahani, Mohammad S. Sorower, Thomas G. Dietterich, Xiaoli Fern, and Prasad Tadepalli

Proceedings of NAACL 2010 Workshop on Formalisms and Methodologies in Learning by Reading.

PDF

Chance-Constrained Programs for Link Prediction

Janardhan Rao Doppa, Jun Yu, Prasad Tadepalli, and Lise Getoor

Proceedings of NIPS 2009 Workshop on Analyzing Networks and Learning with Graphs.

PDF

An Ensemble Learning and Problem Solving Architecture for Airspace Management

Xiaoqin Zhang, Sung Wook Yoon, Phillip DiBona, Darren Scott Appling, Li Ding, Janardhan Rao Doppa, Derek T. Green, Jinhong K. Guo, Ugur Kuter, Geoffrey Levine, Reid MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontanon, Charles Parker, Jainarayan Radhakrishnan, Antons Rebguns, Bhavesh Shrestha, Zhexuan Song, Ethan Trewhitt, Huzaifa Zafar, Chongjie Zhang, Daniel D. Corkill, Gerald DeJong, Thomas G. Dietterich, Subbarao Kambhampati, Victor R. Lesser, Deborah L. McGuinness, Ashwin Ram, Diana F. Spears, Prasad Tadepalli, Elizabeth T. Whitaker, Weng-Keen Wong, James A. Hendler, Martin O. Hofmann, and Kenneth R. Whitebread

Proceedings of AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI), 2009

PDF

Teaching

Courses at WSU:

CptS 570 Machine Learning (Fall 2014; Fall 2015; Fall 2016; Fall 2017; Fall 2018; Fall 2019; Fall 2020; Fall 2021)
CptS 577 Structured Prediction and Intelligent Decision-Making (Spring 2015; Spring 2016; Spring 2017; Spring 2018; 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; Spring 2020; Spring 2021; Spring 2022)

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)

Current Research Group

I'm fortunate to work with the below group of students.

Alaleh Ahmadian, Computer Science, PhD Student (Fall 2019-)

Outstanding Graduate Teaching Assistant Award, Voiland College of Engineering, 2022

Syrine Belakaria, Computer Science, PhD Student (Fall 2018-)

Alfred Suksdorf Fellowship, 2018
Outstanding Teaching Assistant in Computer Science Award, Voiland College of Engineering, 2019
Top Reviewer Award, ICML Conference, 2020
Finalist for Open Philanthropy AI Fellowship, 2020
Finalist for Microsoft Research Fellowship, 2021
IBM PhD Research Fellowship, 2021-2023
Outstanding Graduate Research Assistant Award from School of EECS, Voiland College of Engineering, 2021
Selected for EECS Rising Stars @ MIT, 2021

Taha Belkhouja, Computer Science, PhD Student (Fall 2019-)

Alfred Suksdorf Fellowship, 2019
Mahmoud Dillsi Graduate Fellowship, 2020
Outstanding Graduate Teaching Assistant Award from EECS, Voiland College of Engineering, 2021

Yassine Chemngui, Computer Science, PhD Student (Fall 2022-)

Alfred Suksdorf Fellowship, 2022

Aryan Deshwal, Computer Science, PhD Student (Fall 2018-)

Alfred Suksdorf Fellowship, 2018
Outstanding Teaching Assistant in Computer Science Award, Voiland College of Engineering, 2020
GPSA Graduate Research Assistant Excellence Award, 2020
Outstanding Innovation in Technology Award (for his MS dissertation), Washington State University, 2020
Outstanding Reviewer Award, ICML (2020, 2021) and ICLR (2021)
Outstanding Research Assistant Award, Voiland College of Engineering, 2022
Selected for Heidelberg Laureate Forum, 2022

Pratyush Dhingra, Computer Engineering (co-advised w/ Partha Pratim Pande), PhD Student (Fall 2022-)
Chibuike Emmanuel, Computer Science, PhD Student (Fall 2022-)
Azza Fadhel, Computer Science, PhD Student (Fall 2022-)

Nakhara Scholarship, 2022

Mohammed Amine Gharsallaoui, Computer Science, PhD Student (Spring 2022-)

Alfred Suksdorf Fellowship, 2022

Subhankar Ghosh, Computer Science (co-advised by Yan Yan), PhD Student (Spring 2022-)
Nitthilan Kannappan Jayakodi, Computer Engineering, PhD Student

Alfred Suksdorf Fellowship, 2017

Harold and Dianna Frank Electrical Engineering Fellowship, 2019
Harold and Dianna Frank Electrical Engineering Fellowship, 2020
Richard Newton Young Fellow, ACM Special Interest Group on Design Automation, 2020

Gaurav Narang, Computer Engineering (co-advised w/ Partha Pratim Pande), PhD Student (Fall 2021-)

Alfred Suksdorf Fellowship, 2021

Seyed Hooman Shahrokhi:, Computer Science (Fall 2022-)

Alfred Suksdorf Fellowship, 2022

Harsh Sharma, Computer Engineering (co-advised w/ Partha Pratim Pande), PhD Student (Fall 2021-)
Chukwufumnanya Ogbogu, Computer Engineering (co-advised w/ Partha Pratim Pande), PhD Student (Spring 2021-)
Stephany Lamas, Team Mentoring Program / Undergraduate Researcher

Boeing Research Scholar, 2020
McNair Scholar, 2019

William Foster, Undergraduate Researcher

McNair Scholar, 2022

Marisa Mendoza, Team Mentoring Program / Undergraduate Researcher
Wyatt Croucher, Undergraduate Researcher

Former Students and Postdocs

Garrett Wilson, Computer Science, PhD 2021 (Co-advised w/ Diane Cook), First position: Research Scientist @ Facebook

PhD Thesis: Multi-Source Unsupervised Domain Adaptation for Time-Series Sensor Data

Md Rakibul Islam, Computer Science, PhD 2021, First position: Research Scientist @ Facebook

PhD Thesis: Human-in-the-Loop Learning for Anomaly Detection: Novel Insights, Algorithms, and Applications

Biresh Kumar Joardar, Computer Engineering, PhD 2020 (co-advised w/ Partha Pratim Pande), First position: NSF Computing Innovation Postdoc Fellow @ Duke University => Assistant Professor @ University of Houston

PhD Thesis: Machine Learning enabled Vertically Integrated Heterogeneous Manycore Systems for Big-Data Analytics
Harold and Dianna Frank Electrical Engineering Fellowship, 2018
Outstanding Graduate Student Researcher in Electrical and Computer Engineering Award, 2019
NSF Computing Innovation Fellow, 2020

F A Rezaur Rahman Chowdhury, Computer Science, PhD 2020, First position: Research Scientist @ Facebook

PhD Thesis: Machine Learning to Scale Up Combinatorial Applications
Alfred Suksdorf Fellowship, 2015

Chao Ma, Computer Science, PhD 2019 (co-advised w/ Prasad Tadepalli), First position: Machine Learning Engineer @ Uber AI Labs

PhD Thesis: New Directions in Search-based Structured Prediction: Multi-Task Learning and Integration of Deep Models

Sourav Das, Computer Engineering, PhD 2018 (co-advised w/ Partha Pratim Pande), First position: Researcher @ Intel Labs, Santa Clara

PhD Thesis: Towards High-Performance, Energy-Efficient, and Reliable 3D Manycore Chips Enabled by Machine Learning

Outstanding Graduate Student Researcher in Electrical and Computer Engineering Award, 2017

Shubhomoy Das, Postdoctoral Researcher (2017-2019), First position: Senior Machine Learning Scientist @ Astound.AI
Sriyandass Adidass, Computer Science, MS Thesis (2020), First position: Data Scientist @ IBM

MS Thesis: Time-Series Anomaly Detection via Representation Learning: A Latent Space Approach

Aryan Deshwal, Computer Science, MS Thesis (2020), First position: Continuing PhD Student

MS Thesis: MOOS: A Learning to Search Framework for Multi-Objective Optimization of Manycore Systems Design

Kalvin Hallesy, Computer Engineering, MS Thesis (2018-2019), First position: Micro-architecture Engineer @ Apple
Mohammad Omar Faruk, Computer Science, MS Thesis (2018), First position: Dell EMC

MS Thesis: Multi-Fidelity Blackbox Optimization of Continuous Spaces: An Empirical Study

Tyler Cleveland, Undergraduate Researcher (2020-2021), First position: Microsoft
Marcus Blaisdell, Undergraduate Researcher (2019), First position: N/A
Albert Wu, Undergraduate Researcher (2019), First position: N/A
Maddy Brockmeyer, Undergraduate Researcher (2018), First position: Nordstrom
Srikar Murali, Undergraduate Researcher (2018), First position: Graduate Student @ University of Washington
Shusanta Bhattarai, Undergraduate Researcher (2017), First position: Microsoft

Boeing Research Scholar, 2017

Shrunga Malavalli, Undergraduate Researcher (2017), First position: Microsoft
Ellis Hoag, Undergraduate Researcher (2017-18), First position: Graduate Student in CS @ Stanford University

Best Student Abstract Award, AAAI Conference, 2018

Luke Weber, Undergraduate Researcher (2016-17), First position: Co-founder of AI-based startup company BreatheFIT → Allen Institute of Brain Science
Vanessa Putnam, Undergraduate Researcher (2016), First position: Graduent Student in CS @ Univeristy of British Columbia

CRA-W Grace Hopper Conference (GHC) Research Scholar, 2016

Xinheng Gong, Undergraduate Researcher (2016), First position: LinkedIn
Nicholaus Jackson, Undergraduate Researcher (2015), First position: Google

Professional Service

Tutorials, Invited Talks, and Special Sessions:

Advances in Bayesian Optimization Tutorial at NeurIPS 2022
Processing-in-Memory enabled Heterogeneous Manycore Systems for Deep Learning Workloads at Embedded Systems Week 2022 (w/ Biresh Kumar Joardar)
Bayesian Optimization: From Foundations to Advanced Topics at AAAI 2022 (w/ Aryan Deshwal, Syrine Belakaria)
Data science for physical sciences: Data Science driven approaches to advance catalysis, separations and materials, Energy Sciences Center, PNNL
Plenary Talk, Harnessing Machine Learning to Accelerate Electronic Design Automation @ MLCAD 2021
Overcoming Moore's Law via Technology and Machine Learning-driven Manycore Systems at Embedded Systems Week Conference 2021 (w/ Biresh Kumar Joardar)
Cross Layer Design Solutions for Energy-Efficient and Secure Edge AI at ICCAD Conference 2021 (w/ Mohammad Shafique, Aviv Barkai, Evgeni Gousev, and Partha Pande)
Harnessing the Power of Machine Learning: EDA to Accelerator Design ICCAD Conference 2021 (w/ Partha Pande, Krishnendu Chakrabarty, Sachin Sapatnekar, Haoxing (Mark) Ren, and Helen Li)
Data Analytics for Scalable Computing Systems Design: Challenges, Opportunities, and Solutions at DATE Conference 2020, Grenoble, France (w/ Partha Pratim Pande, Helen Li, Krishnendu Chakrabarty, and Rob Aitken)
Machine Learning for Embedded Systems Design at Embedded Systems Week Conference 2019, New York (w/ Philip Brisk)
Taming Extreme Heterogeneity: Novel Design Methodologies for Autonomous Heterogeneous Computing Architectures at Embedded Systems Week Conference 2019, New York (w/ Paul Bogdan and Helen Li)
Machine Learning for Electronic Design Automation: Modeling, Optimization, and Resiliency at ICCAD 2018, San Diego. (w/ Joseph Greathouse, Gabriel Loh, and Krishnendu Chakrabarty)
Recent Advances in Structured Prediction at AAAI 2018, New Orleans. (w/ Liping Liu and Chao Ma)
Design of Energy-Efficient and Reliable VLSI Systems: A Data-Driven Perspective at Embedded and VLSI Design Conference 2018, Pune, India. (w/ Sudeep Pasricha, Partha Pratim Pande, and Krishnendu Chakrabarty)
Adaptive Manycore Architectures for Big Data Computing at Embedded Systems Week 2017, Seoul, South Korea. (w/ Tushar Krishna and Michel Kinsy)
Data Analytics Enables Energy-Efficiency and Robustness: From Mobile to Manycores, Datacenters, and Networks at Embedded Systems Week Conference 2017, Seoul, South Korea. (w/ Sudeep Pasricha and Krishnendu Chakrabarty)
Integrating Learning and Search for Structured Prediction at IJCAI 2016, New York City.
Energy-Efficient and Reliable 3D Network-on-Chip: Architectures and Optimization Algorithms at ICCAD 2016, Austin, TX. (w/ Partha Pratim Pande and Krishnendu Chakrabarty)

Conference Organization:

Co-Chair, Panels, International Conference on AI-ML Systems 2022.
Co-Chair, 1st Annual Workshop on AI to Accelerate Science and Engineering (this year's theme is AI for Chemistry), AAAI 2022, Vacouver, Canada (w/ Aryan Deshwal, Syrine Belakaria, Cory Simon, and Yolanda Gil)
Co-Chair, AAAI Student Abstracts Program, AAAI 2021, Vancouver, Canada
Co-Chair, Workshop on BeyondLabeller -- Human is More than a Labeller at IJCAI 2016, New York City.
Co-Chair, Workshop on Interactions between Inference and Learning (Inferning 2013) at ICML 2013, Atlanta, GA.

Journal Editorial Service:

Editorial Board Member (Elected), Machine Learning Journal (2021 - present)
Editorial Board Member (Elected), Journal of Artificial Intelligence Research (2016 - present)
Associate Editor (Elected), IEEE Design and Test of Computers (2022 - present)
Guest Editor, IEEE Design and Test of Computers, Special issue on Smart and Autonomous Systems for Sustainability: Sustainable Computing and Computing for Sustainability (2018 - 2019)

Track Chair, Area Chair, and Senior Program Committee Member:

Area Chair: International Conference on Learning Representations (ICLR), 2023
Area Chair: Main Track, AAAI National Conference on Artificial Intelligence (AAAI), 2023
Area Chair: Annual Conference on Neural Information Processing Systems (NeurIPS), 2022
Area Chair: Asian Conference on Machine Learning (ACML), 2022
Track Chair for AI Systems and Applications of AI at Edge: International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES), 2022
Track Chair for AI and ML Systems: Design Automation Conference (DAC), 2022
Area Chair: International Conference on Learning Representations (ICLR), 2021
Area Chair: International Conference on Machine Learning (ICML), 2020
SPC: International Joint Conference on Artificial Intelligence (IJCAI), 2022
SPC: AI for Social Impact Track, AAAI National Conference on Artificial Intelligence (AAAI), 2023
SPC: Main Track, AAAI National Conference on Artificial Intelligence (AAAI), 2022
SPC: AI for Social Impact Track, AAAI National Conference on Artificial Intelligence (AAAI), 2022
SPC: International Joint Conference on Artificial Intelligence (IJCAI), 2021
SPC: AAAI National Conference on Artificial Intelligence (AAAI), 2021
SPC: International Joint Conference on Artificial Intelligence (IJCAI), 2020
SPC: AAAI National Conference on Artificial Intelligence (AAAI), 2019
SPC: AAAI National Conference on Artificial Intelligence (AAAI), 2018
SPC: International Joint Conference on Artificial Intelligence (IJCAI), 2016

Program Committee Member:

International Conference on Machine Learning (ICML), 2022
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
International Conference on Machine Learning (ICML), 2021
Annual Conference on Neural Information Processing Systems (NeurIPS), 2021
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
International Conference on Uncertainty in Artificial Intelligence (UAI), 2021
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
International Conference on Automated Planning and Scheduling (ICAPS), 2021
58th Design Automation Conference (DAC), 2021
24th IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2021
ACM/IEEE International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), 2021
IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021
Annual Conference on Neural Information Processing Systems (NeurIPS), 2020
AAAI National Conference on Artificial Intelligence (AAAI), 2020
International Conference on Uncertainty in Artificial Intelligence (UAI), 2020
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
International Conference on Automated Planning and Scheduling (ICAPS), 2020
SIAM International Conference on Data Mining (SDM), 2020
57th Design Automation Conference (DAC), 2020
ACM/IEEE International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), 2020
ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis (CODES), 2020
International Conference on Machine Learning (ICML), 2019
Annual Conference on Neural Information Processing Systems (NeurIPS), 2019
International Joint Conference on Artificial Intelligence (IJCAI), 2019
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
International Conference on Uncertainty in Artificial Intelligence (UAI), 2019
International Conference on Automated Planning and Scheduling (ICAPS), 2019
ACM/IEEE International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), 2019
ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis (CODES), 2019
ACM/IEEE International Network-on-Chip Symposium (NOCS), 2019
International Workshop on Graphs, Architectures, Programming, and Learning (GrAPL), held in conjunction with IPDPS Conference, 2019
International Conference on Machine Learning (ICML), 2018
Annual Conference on Neural Information Processing Systems (NeurIPS), 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
ACM/IEEE International Network-on-Chip Symposium (NOCS), 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

Reviewer:

Conferences

ICML 2008, IJCAI 2009, KDD 2010, AISTATS 2010, KDD 2012, ECML 2012, ACML 2012, AAAI 2014, ICDM 2015, DAC 2016, DAC 2018

Journals

Machine Learning Journal (MLJ)
Journal of Artificial Intelligence Research (JAIR)
Journal of Machine Learning Research (JMLR)
ACM Transacations on KDD (TKDD)
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Knowledge and Data Engineering (TKDE)
IEEE Transactions on Computers (TC)
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
IEEE Transactions on Very Large-Scale Integrated Circuits and Systems (TVLSI)
ACM Transactions on Design Automation of Electronic Systems (TODAES)
ACM Transactions on Embedded Computing Systems (TECS)
IEEE Computer Architecture Letters (CAL)
IEEE Transactions on Multi-Scale Computing Systems (TMCS)
IEEE Transactions on Mobile Computing (TMC)

Machine Learning Reading Group (MLRG)

At WSU, I often run focused reading groups on topics related to my current projects.

Machine Learning meets System-on-Chip Design, Control, and Test (Fall 2016)
Large-Scale Planning under Uncertainty (Spring 2017)
Bandit Algorithms (Fall 2017)
Anomaly Detection (Spring 2018)
Deep Learning (Fall 2018)

At OSU, I organized and led several reading groups on a wide variety of topics (2009-2014). Some of them include:

Integrating Learning and Search (2009, 2010)
Machine Reading: Rule learning, Coreference Resolution, and Learning from Incomplete Examples (Fall 2010, Winter 2011)
Knowledge-Guided Structure Learning (Winter 2012, Spring 2012)
Generic MLRG (Fall 2010, Fall 2011, Fall 2012)

Awards and Honors

Huie-Rogers Endowed Chair in Computer Science, 2022
Outstanding Researcher Award, School of EECS, Washington State University, 2022
Best Paper Award, ACM Transactions on Design Automation of Electronic Systems (TODAES), 2021
Selected for International Joint Conference on Artificial Intelligence (IJCAI) Early Career Spotlight, 2021
Outstanding Reviewer Award, NeurIPS Conference, 2021
Nomination for Best Paper Award, IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2021
Nomination for Best Paper Award, IEEE/ACM International Conference on Design Automation and Test in Europe (DATE), 2020
Outstanding Junior Faculty in Research Award, Voiland College of Engineering and Architecture, Washington State University, 2020
NSF CAREER Award, 2019
George and Joan Berry Distinguished Professor in Engineering, 2019-2022
Early Career Research Excellence Award, School of EECS, Washington State University, 2019
Outstanding Reviewer Award, NeurIPS Conference, 2018
Reid Miller Teaching Excellence Award, Voiland College of Engineering and Architecture, Washington State University, 2018
Outstanding Teacher Award, School of EECS, Washington State University, 2018
Nomination for Best Student Paper Award, International Conference on Automated Planning and Scheduling (ICAPS), 2017
Oregon State University, Outstanding Innovation in Technology Award, 2015
Google Faculty Research Award, 2015
Nominee for ACM Doctoral Dissertation Award, 2014
Outstanding Paper Award, AAAI Conference, 2013
Oregon State University, College of Engineering Outstanding Graduate Research Assistant Award , 2013
All India Rank 75 (99.8 percentile) in Graduate Aptitude Test for Engineers (GATE) Exam conducted by IITs, 2004
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? :)