Current Projects


healthcare

Artificial Intelligence and Machine Learning in Healthcare

My research is focused on the application of Artificial Intelligence and Machine Learning in Healthcare. Healthcare offers unique challenges for machine learning as many problems can literally be a matter of life and death. The problems that I am interested in are focused on risk prediction in healthcare e.g., risk of mortality prediction (predicting when a patient is likely to die), risk of readmission prediction (predicting when a patient is likely to be readmitted in a hospital), cost prediction (how much is the cost going to be associated with a patient) etc.

Project Website: N/A ( 2017-Current )

Publications (Research Papers)

  • Eric Liu, Muhammad Aurangzeb Ahmad, Carly Eckert, Anderson Nascimento,  Martine De Cock, Karthik Padthe, Ankur Teredesai, Greg McKelvey Automatic Detection of Excess Healthcare Spending and Cost Variation in ACOs SDM 2018 Workshop on Data Mining for Medicine and Healthcare May 5, 2018 San Diego, USA [Link]
  • Muhammad Aurangzeb Ahmad, Carly Eckert, Greg McKelvey, Kiyana Zolfagar, Anam Zahid, Ankur Teredesai. Death vs. Data Science: Predicting End of Life IAAI February 2-6, 2018 [Link]

Publications (Research Abstracts)

  • Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur Teredesai, Greg McKelvey Machine Learning Based Explanations to Support Risk of Readmission Prediction Models AIMed 2017 Laguna Niguel, CA December 11-14, 2017
  • Muhammad Aurangzeb Ahmad, Carly Eckert Machine Learning in the Prediction of Organ Transplant Processes AIMed 2017 Laguna Niguel, CA December 11-14, 2017
  • Matthew Eckert, Muhammad Aurangzeb Ahmad Deep Learning and Thermal Imaging Technology In the Detection of Surgical Site Infection AIMed 2017 Laguna Niguel, CA December 11-14, 2017
  • Xinran Liu, Carly Eckert, Muhammad Aurangzeb Ahmad Development of Sepsis Prediction Algorithm for Clinical Use AIMed 2017 Laguna Niguel, CA December 11-14, 2017
  • Xinran Liu, Greg McKelvey, Muhammad Aurangzeb Ahmad, Rosemary Grant, David Carlbom Using Machine learning to predict Hospital acquired Sepsis AMIA 2017 Annual Symposium Washington, DC November 4 – 8, 2017
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Explainable/Interpretable Machine Learning

A large number of machine learning algorithms are black boxes i.e., it is not possible to extract the reasons why an algorithm is making a prediction.  In certain domains like healthcare and criminal justice system can be critical to determine why a prediction is being made since a false positive may carry a high penalty e.g., prescribing incorrect diagnosis or recommending a longer sentence for a suspect for the wrong reasons.

Project Website: N/A ( 2017-Current )

Publications:

  • Muhammad Aurangzeb Ahmad, Dr. Carly Eckert M.D., Ankur Teredesai Explainable Models for Healthcare AI KDD 2018 London, United Kingdom August 19-23, 2018
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Reconstruction and Analysis of Massive Historical Social Networks

Historical documents offer a wealth of information about the past including social linkages between people. The aim of this project is to use ancient and medieval texts in various languages to reconstruct social networks. Using medieval and early modern texts in Arabic and English we have been able to reconstruct a social network of more than 20,000 people spanning the globe and all the major civilizations of the world.

Project Website: The Hadith Networks Project  ( 2013-Current )
Publications

  • Muhammad Aurangzeb Ahmad Towards the Analysis of Narrative Networks CSE Technical Report 13-017 Department of Computer Science, University of Minnesota May 23, 2013 [Link]
  • Muhammad Aurangzeb Ahmad Information Network Analysis meets Islamic Studies: Case study from the Analysis of the Hadith Literature Society for the Scientific Study of Religion Annual Conference. Boston, MA November 7, 2013
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Artificial Intelligence for Personality Emulation

The death of my father had a profound effect on me. It made me realize that my children will never have the opportunity to interact with him. This has prompted me to start work on this project where the aim is to create simulations of deceased people so that the living can interact with them. This project is iterative in nature and stands at the intersection of artificial intelligence, machine learning, natural language processing, psychology and sociology.

Project Website: Mushtaq Ahmad Mirza Project (2013 – Current)

Publications

  • Muhammad Aurangzeb Ahmad, After Death: Big Data and the Promise of Resurrection by Proxy Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2016. [Link]

Press


Previous Projects

hajj

Data Mining Pilgrimage Data

Every year millions of Muslims go to the Holy cities of Mecca and Medina to perform the pilgrimage of Hajj. Over the course of just two weeks more than 3 million people are housed within an area less the size of Manhattan. Routing and providing services such a large mass of people is a logistical challenge that can be addressed via the application of machine learning to Big Data. This project was focused on data mining pilgrimage data from Hajj in order to provide provide insights that can greatly improve the logistics of Hajj.

Project Website: N/A ( 2016 )
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Modeling Human Behavior in Massive Online Games

This work was done as part of the Virtual Worlds Observatory project which was a multi-disciplinary collaboration between University of Minnesota, University of Southern California, University of Illinois – Urbana Champaign and Northwestern University. This project was focused on the use of machine learning to model human behavior in massive online games to study phenomenon like team formation, trust formation and evolution, predictive models for demographic characteristics (age, gender etc.) and behavioral characteristics (leadership, political affiliation, deviancy etc).

Project Website: Virtual Worlds Observatory ( 2008-2012)

Publications

  • Muhammad Aurangzeb Ahmad, Cuihua Shen, Jaideep Srivastava, Noshir Contractor (Editors) Predicting Real World Behaviors from Virtual World Data August 2014 Springer Verlog [Link]
  • Zoheb Hassan Borbora, Muhammad Aurangzeb Ahmad, Jehwan Oh, Karen Zita Haigh, Jaideep Srivastava, Zhen Wen Robust Features of Trust in Social Networks Social Network Analysis and Data Mining December 2013, Volume 3, Issue 4, pp 981-999 [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Atanu Roy, Dmitri Williams, Jaideep Srivastava, and Noshir Contractor. Guilt by association?: network based propagation approaches for gold farmer detection. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 121-126. ACM, 2013. [Link]
  • Atanu Roy, Muhammad Aurangzeb Ahmad, Chandrima Sarkar, Brian Keegan and Jaideep Srivastava The Ones That Got Away: False Negative Estimation Based Approaches for Gold Farmer Detection IEEE SocialCom 2012 Amsterdam, Netherlands September 3-5, 2012 [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Sophia Sullivan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor Illicit Bits: Detecting and Analyzing Contraband Networks in Massively Multiplayer Online Games IEEE SocialCom 2011 Boston, MA October 9-11, 2011 [Link]
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. Sic Transit Gloria Mundi Virtuali? Promise and Peril at the Intersection of Computational Social Science and Online Clandestine Organizations The Third ACM WebSci Conference, Koblenz, Germany June 14-17, 2011 (Best Paper Award) [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Trust Amongst Rogues? A Hypergraph Approach for Comparing Clandestine Trust Networks in MMOGs 5th International AAAI Conference on Weblogs and Social Media (ICWSM-11) [Link]
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Mapping Gold Farming Back to Offline Clandestine Organizations: Methodological, Theoretical, and Ethical Challenges. Game Behind the Game. (Best Paper Award)
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor, Dark Gold: Statistical Properties of Clandestine Networks in Massively-Muliplayer Online Games IEEE Social Computing Conference (SocialCom-10) Minneapolis, MN, USA, August 20-22, 2010 [Link]
  • Muhammad Aurangzeb Ahmad, Brain Keegan, Jaideep Srivastava, Dmitri Williams, Noshir Contractor, Mining for Gold Farmers: Automatic Detection of Deviant Players in MMOGS Proceedings of the 2009 IEEE Social Computing (SocialCom-09). Symposium on Social Intelligence and Networking (SIN-09). Vancouver, Canada, August 29-31, 2009 [Link]

Patent

  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor Automatic Detection of Deviant Players in MMORPGs Gold Farming (US20130123003A1)

Press

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Computational Models of Trust in Massive Online Games

Computational trust refers to the mediation of trust via a computational infrastructure. In this project questions related to trust in complex social environments represented by Massively Multiplayer Online Games (MMOGs) are explored. The main emphasis is that trust is a multi-level phenomenon both in terms of how it operates at multiple levels of network granularities and how trust relates to other social phenomenon like homophily, expertise, mentoring, clandestine behaviors etc. Social contexts and social environments affect not just the qualitative aspects of trust but this phenomenon is also manifested with respect to the network and structural signatures of trust network. Additionally trust is also explored in the context of predictive tasks: Previously described prediction tasks like link prediction are studied in the context of trust within the context of the link prediction family of problems: Link formation, link breakage, change in links etc. Additionally we define and explore new trust-related prediction problems i.e., trust propensity prediction, trust prediction across networks which can be generalized to the inter-network link prediction problem and success prediction based on using network measures of a person’s social capital as a proxy.

Project Website: N/A ( 2008-2011 )

PhD Thesis

  • Muhammad Aurangzeb Ahmad (2012). Computational trust in Multiplayer Online Games.. University of Minnesota [Link].

Publications

  • Zoheb Hassan Borbora, Muhammad Aurangzeb Ahmad, Jehwan Oh, Karen Zita Haigh, Jaideep Srivastava, Zhen Wen Robust Features of Trust in Social Networks Social Network Analysis and Data Mining December 2013, Volume 3, Issue 4, pp 981-999 [Link]
  • Zoheb Borbora, Muhammad Aurangzeb Ahmad, Karen Zita Haigh, Jaideep Srivastava, Zhen Wen Exploration of Robust Features of Trust Across Multiple Social Networks. SASO Workshops 2011: 27-32 [Link]
  • Muhammad Aurangzeb Ahmad, Iftekhar Ahmad, Jaideep Srivastava, Marshall Poole Trust me, I ‘m an Expert: Trust, Homophily and Expertise in MMOs IEEE SocialCom 2011 Boston, MA October 9-11, 2011 [Link]
  • Young Ae Kim, Muhammad Aurangzeb Ahmad Trust, distrust and lack of confidence of users in online social media-sharing communities. Knowledge Based Systems 37: 438-450 (2013) [Link]
  • Muhammad Aurangzeb Ahmad, Marshall Scott Poole, Jaideep Srivastava The Trust Propensity Prediction Problem The Third ACM WebSci Conference, Koblenz, Germany June 14-17, 2011 [Presentation Video]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Trust Amongst Rogues? A Hypergraph Approach for Comparing Clandestine Trust Networks in MMOGs 5th International AAAI Conference on Weblogs and Social Media (ICWSM-11) [Link]
  • Muhammad Aurangzeb Ahmad, Marshall Scott Poole, Jaideep Srivastava, Network Exchange in Trust Networks IEEE Social Computing (SocialCom-10). Workshop on Social Intelligence in Applied Gaming. Minneapolis, MN, USA, August 20-22, 2010 [Link]
  • Young Ae Kim, Marla E. Eisenberg, Muhammad Aurangzeb Ahmad, Jaideep Srivastava Modeling Trust in Online Social Networks to Improve Adolescent Health Behavior, CSE Technical Report 10-018 Department of Computer Science, University of Minnesota August 18, 2010 [Link]
  • Young Ae Kim, Muhammad Aurangzeb Ahmad, Jaideep Srivastava, A Technique for Inferring Trust in Recommendation Systems International Sunbelt Social Network Conference (XXIX), San Diego, CA. March 14 2009

Press

clandestine

Clandestine and Deviant Behaviors in Massive Online Games

Gold Farming refers to a set of inter-related activities in online virtual spaces especially in Massively Multiplayer Games where certain players engage in repetitive activities to gain virtual commodities which they sell to other players. Game administrators actively ban Gold Farmers who have to hide their activities from the game admins, who are akin to law enforcers. In our research we compared Gold Farmers with their offline criminal counterparts. It was discovered that the behaviors of Gold Farmers is very similar to real world criminals in that their social networks tend to be quite similar.

Project Website: N/A ( 2009-2011)

Publications

  • Muhammad Aurangzeb Ahmad, Cuihua Shen, Jaideep Srivastava, Noshir Contractor (Editors) Predicting Real World Behaviors from Virtual World Data August 2014 Springer Verlog [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Atanu Roy, Dmitri Williams, Jaideep Srivastava, and Noshir Contractor. Guilt by association?: network based propagation approaches for gold farmer detection. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 121-126. ACM, 2013. [Link]
  • Atanu Roy, Muhammad Aurangzeb Ahmad, Chandrima Sarkar, Brian Keegan and Jaideep Srivastava The Ones That Got Away: False Negative Estimation Based Approaches for Gold Farmer Detection IEEE SocialCom 2012 Amsterdam, Netherlands September 3-5, 2012 [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Sophia Sullivan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor Illicit Bits: Detecting and Analyzing Contraband Networks in Massively Multiplayer Online Games IEEE SocialCom 2011 Boston, MA October 9-11, 2011 [Link]
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. Sic Transit Gloria Mundi Virtuali? Promise and Peril at the Intersection of Computational Social Science and Online Clandestine Organizations The Third ACM WebSci Conference, Koblenz, Germany June 14-17, 2011 (Best Paper Award) [Link]
  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Trust Amongst Rogues? A Hypergraph Approach for Comparing Clandestine Trust Networks in MMOGs 5th International AAAI Conference on Weblogs and Social Media (ICWSM-11) [Link]
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor. (2011). Mapping Gold Farming Back to Offline Clandestine Organizations: Methodological, Theoretical, and Ethical Challenges. Game Behind the Game. (Best Paper Award)
  • Brian Keegan, Muhammad Aurangzeb Ahmad, Dmitri Williams, Jaideep Srivastava, Noshir Contractor, Dark Gold: Statistical Properties of Clandestine Networks in Massively-Muliplayer Online Games IEEE Social Computing Conference (SocialCom-10) Minneapolis, MN, USA, August 20-22, 2010 [Link]
  • Muhammad Aurangzeb Ahmad, Brain Keegan, Jaideep Srivastava, Dmitri Williams, Noshir Contractor, Mining for Gold Farmers: Automatic Detection of Deviant Players in MMOGS Proceedings of the 2009 IEEE Social Computing (SocialCom-09). Symposium on Social Intelligence and Networking (SIN-09). Vancouver, Canada, August 29-31, 2009 [Link]

Patent

  • Muhammad Aurangzeb Ahmad, Brian Keegan, Dmitri Williams, Jaideep Srivastava, Noshir Contractor Automatic Detection of Deviant Players in MMORPGs Gold Farming (US20130123003A1)

Press

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Machine Learning for Historical Census Data

Before the US instituted Social Security numbers as a way to uniquely identify all of its citizens there was no way to identify people across US censuses. The aim of this project was to develop a machine learning model and framework to link people across US historical census data from 1850 to 1910.

Project Website: N/A ( 2006-2008)
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Data Mining ICD Data

The aim of this project is to extract useful insights from ICD (Implantable Cardioverter-Defibrillator). Time series data for tachycardia/tachyarrhythmia and other types of arrhythmia was extracted and different pattern set mining techniques were applied to it. This project hit close to home later on because my father was implanted with the device that I worked on.

Project Website: N/A ( 2008)

Patent

  • Xianting Dong, Deepa Mahajan, Muhammad Aurangzeb Ahmad Systems and Methods for Programming Implantable Medical Devices (Patent #8346369, March 21, 2012)

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