CV

Education

• B.S. in Github, Github University, 2012
• M.S. in Jekyll, Github University, 2014
• Ph.D in Version Control Theory, Github University, 2018 (expected)

Work experience

• Summer 2015: Research Assistant
  • Github University
  • Duties included: Tagging issues
  • Supervisor: Professor Git
• Fall 2015: Research Assistant
  • Github University
  • Duties included: Merging pull requests
  • Supervisor: Professor Hub

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

Model Selection Via Bilevel Programming

K. P. Bennett, X. Ji, J. Hu, G. Kunapuli and J.-S. Pang. Model Selection Via Bilevel Programming Proceedings of the 2006 IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, July 16-21, 2006.

Bilevel Model Selection for Support Vector Machines

G. Kunapuli, K. P. Bennett, J. Hu and J.-S. Pang. Bilevel Model Selection for Support Vector Machines CRM Proceedings and Lecture Notes. Volume 45 (2008), pp. 129-158. American Mathematical Society. Pierre Hansen and Panos Pardolos, Editors.

Classification Model Selection via Bilevel Programming

G. Kunapuli, K. P. Bennett, J. Hu and J.-S. Pang. Classification Model Selection via Bilevel Programming Optimization Methods and Software , Volume 23, Issue 4 (2008), pp. 475-489, Special Issue on Mathematical Programming in Data Mining and Machine Learning, Guest Editors: Katya Scheinberg and Jiming Peng.

On the Global Solution of Linear Programs with Linear Complementarity Constraints

J. Hu, J. E. Mitchell, J.-S. Pang, K. P. Bennett and G. Kunapuli. On the Global Solution of Linear Programs with Linear Complementarity Constraints . SIAM Journal on Optimization , Volume 19, Number 1 (2008), pp. 445-471.

Bilevel Optimization and Machine Learning

K. P. Bennett, G. Kunapuli, J. Hu and J.-S. Pang. Bilevel Optimization and Machine Learning Computational Intelligence: Research Frontiers. Lecture Notes in Computer Science. Volume 5050 (2008), pp. 25-47. IEEE World Congress on Computational Intelligence, WCCI 2008, Hong Kong, China, June 1-6, 2008, Plenary/Invited Lectures.

Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule

S. Natarajan, P. Tadepalli, G. Kunapuli and J. W. Shavlik. Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. Eighth International Conference on Machine Learning and Applications (ICMLA'09), Miami Beach, FL, December 13-15 2009.

ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem

S. Natarajan, G. Kunapuli, C. O' Reilly, R. Maclin, T. Walker, D. Page and J. W. Shavlik. ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem. Nineteenth International Conference on Inductive Logic Programming (ILP'09), Leuven, Belgium, July 2-4, 2009.

Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge

T. Walker, C. Reilly, G. Kunapuli, S. Natarajan, R. Maclin, D. Page and J. W. Shavlik. Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. Twentieth International Conference on Inductive Logic Programming (ILP'10), Firenze, Italy, June 27-30, 2010.

Multi-Agent Inverse Reinforcement Learning

S. Natarajan, G. Kunapuli, K. Judah, P. Tadepalli, K. Kersting and J. W. Shavlik. Multi-Agent Inverse Reinforcement Learning. Ninth International Conference on Machine Learning and Applications (ICMLA'10), Washington D. C., USA, December 12-14, 2010.

Online Knowledge-Based Support Vector Machines

G. Kunapuli, K. P. Bennett, A. Shabbeer, R. Maclin and J. W. Shavlik. Online Knowledge-Based Support Vector Machines. Twenty-first European Conference on Machine Learning (ECML'10), Barcelona, Spain, September 20-24, 2010.

The Adviceptron: Giving Advice To The Perceptron

G. Kunapuli, K. P. Bennett, R. Maclin and J. W. Shavlik. The Adviceptron: Giving Advice To The Perceptron. Twentieth Conference on Artificial Neural Networks In Engineering (ANNIE'10), St. Louis, MO , November 1-3, 2010.

Integrating Knowledge Capture and Supervised Learning Through a Human-Computer Interface

T. Walker, G. Kunapuli, N. Larsen, D. Page and J. W. Shavlik. Integrating Knowledge Capture and Supervised Learning Through a Human-Computer Interface. Sixth International Conference on Knowledge Capture (KCAP'11), Banff, Canada, June 25-29, 2011.

Advice Refinement in Knowledge-Based Support Vector Machines

G. Kunapuli, R. Maclin and J. W. Shavlik. Advice Refinement in Knowledge-Based Support Vector Machines. Twenty-Fifth Neural Information Processing Systems (NIPS'11), Granada, Spain, December 12-16, 2011.

Mirror Descent for Metric Learning: A Unified Approach

G. Kunapuli and J. W. Shavlik. Mirror Descent for Metric Learning: A Unified Approach. Twenty-Third European Conference on Machine Learning (ECML'12), Bristol, United Kingdom, September 24-29, 2012.

AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy

B. Saha, G. Kunapuli, N. Ray, J. Maldjian and S. Natarajan. AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy . Twenty-Fourth European Conference on Machine Learning (ECML'13), Prague, Czech Republic, September 23-27, 2012.

Guiding Autonomous Agents to Better Behaviors through Human Advice

G. Kunapuli, P. Odom, J. W. Shavlik and S. Natarajan. Guiding Autonomous Agents to Better Behaviors through Human Advice . Thirteenth IEEE International Conference on Data Mining (ICDM'13), Dallas, TX, December 7-10, 2013.

A Graphical Model Approach to ATLAS-free Mining of MRI Images

C. S. Magnano, A. Soni, S. Natarajan and G. Kunapuli A Graphical Model Approach to ATLAS-free Mining of MRI Images . 2014 SIAM International Conference on Data Mining (SDM'13), Philadelphia, PA, April 24-26, 2014.

Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach

S. Yang, T. Khot, K. Kersting, G. Kunapuli, K. Hauser and S. Natarajan. Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach . Fourteenth IEEE International Conference on Data Mining (ICDM'14), Shenzhen, China, December 14-17, 2014.

Neurocognitive Correlates of Learning in a Visual Object Recognition Task

I. Juvina, P. Ganapathy, M. Sherwood, M. S. Usmani, G. Kunapuli, T. Tamminedi and N. Kashou. Neurocognitive Correlates of Learning in a Visual Object Recognition Task . Ninth International Conference on Augmented Cognition, Part of HCI International (HCI'15), Los Angeles, CA, August 2-7, 2015.

Development of a Smart Tutor for a Visual-Aircraft Recognition Task

P. Ganapathy, I. Juvina, T. Tamminedi, G. Kunapuli, M. Sherwood and M. S. Usmani. Development of a Smart Tutor for a Visual-Aircraft Recognition Task . Ninth International Conference on Augmented Cognition, Part of HCI International (HCI'15), Los Angeles, CA, August 2-7, 2015.

Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach

N. Kaur, G. Kunapuli, T. Khot, K. Kersting, W. W. Cohen and S. Natarajan. Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach . Twenty-Seventh International Conference on Inductive Logic Programming (ILP'17), Orleans, France.

A Decision-Support Tool for Renal Mass Classification

G. Kunapuli, B. A. Varghese, P. Ganapathy, B. Desai, S. Cen, M. Aron, I. Gill and V. Duddalwar. A Decision-Support Tool for Renal Mass Classification . Journal of Digital Imaging , Volume 1, Number 11 (2018).

On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach

S. Natarajan, S. Das, N. Ramanan, G. Kunapuli and P. Radivojac. On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach . Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden.

Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities

D. Dhami, G. Kunapuli, M. Das, D. Page and S. Natarajan. Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities. Third IEEE/ACM Conference on Connected Health: Applications, Systems, Engineering and Technologies (CHASE'18), Washington D.C.

Classification of Burn Injury using Raman Spectroscopy and Optical Coherence Tomography: An Ex-Vivo Study on Porcine Skin

L. P. Rangaraju, G. Kunapuli, D. Every, O. D. Ayala, P. Ganapathy, A. Mahadevan-Jansen. Classification of Burn Injury using Raman Spectroscopy and Optical Coherence Tomography: An Ex-Vivo Study on Porcine Skin. Burns, (2018, in press).

Structure Learning for Relational Logistic Regression: An Ensemble Approach

N. Ramanan, G. Kunapuli, T. Khot, B. Fatemi, S. M. Kazemi, D. Poole, K. Kersting and S. Natarajan. Structure Learning for Relational Logistic Regression: An Ensemble Approach. Proc. International Conference on Principles of Knowledge Representation and Reasoning (2018).

Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs

M. Das, D. S. Dhami, G. Kunapuli, K. Kersting and S. Natarajan. Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs. Proc. Thirty-third AAAI Conference on Artificial Intelligence (2019).

Talks

Teaching

CS6375: Machine Learning

CS6301: Machine Learning for Engineers and Scientists

CS6375: Machine Learning

Service and leadership

• Currently signed in to 43 different slack teams