Resume

• 2012

  IEEE Journal of Translational Engineering in Health and Medicine

  Machine Learning

  Image Processing

  C

  Embedded Systems

  LaTeX

  Signal Processing

  Bioinformatics

  Robotics

  C++

  Science

  Python

  Matlab

  Artificial Intelligence

  Computer Science

  Software Development

  Linux

  ASP+POMDP: Integrating Non-monotonic Logical Reasoning and Probabilistic Planning on Robots

  Mohan Sridharan

  Paper of Excellence Award

  ASP+POMDP: Integrating Non-monotonic Logical Reasoning and Probabilistic Planning on Robots

  Predation is the ultimate survival game between the predator and prey. In this study

  we use game theory as a modeling framework to demonstrate why and how different strategies in predation for both predator and prey are chosen based on body size and energetics. Two distinct and mutually exclusive strategies

  active and passive

  are considered for both players; hence the corresponding predation can be formulated as a 2*2 game. The payoffs are defined using energetics (energy gain and loss)

  with functional response to predator/prey body size. The game is formulated as a realistic general sum model and the numerical results of Nash equilibrium for different body sized predators and preys are calculated: in general

  smaller sized predators and preys tend to use active strategy more often (mixed strategy equilibrium)

  and larger sized tend to choose active strategy exclusively (pure strategy equilibrium). The long-term evolutionary stability of the predator-prey system is also investigated

  and the Nash equilibrium derived from these games are shown evolutionarily unstable. In summary

  this study provides a unified modeling framework to study how animal body size and energetics determine predation strategies

  and can easily extend to more complicated conditions

  such as across multiple trophic levels.

  Linking Body Size and Energetics with Predation Strategies: A Game Theoretic Modeling Framework

  Xin Liu

  Christina Zhang

  Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series

  e.g.

  MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works

  we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing

  an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper

  we introduce PyEEG

  an open source Python module for EEG feature extraction.

  PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

  My expertise spans over two areas: artificial intelligence (knowledge engineering + natural language processing) and biomedical signal processing (EEG/MRI). I regularly publish papers at or help organize top-notch AI/NLP venues

  such as ACL

  EACL

  NAACL

  WWW

  and AAAI. My research on medical signal processing was covered by MIT Technology Review and Lancet Neurology. \n\nFor my research outcomes

  visit my homepage http://fsbao.net (hosted at Google

  censored at various parts of the world.)\n\nMedia coverage on research: \n* MIT Technology Review: \n http://www.technologyreview.com/blog/arxiv/23465/\n* Epilepsy Foundation of American: \n http://www.epilepsyfoundation.org/epilepsyusa/magazine/Issue5-2009/New_Tool.cfm\n* Lancet Neurology\n http://thelancet.com/journals/laneur/article/PIIS1474-4422%2815%2900311-7/fulltext?rss=yes

  Forrest Sheng

  Bao

  Texas Tech University

  The University of Akron

  a unicorn

  Iowa State University

  Stony Brook University Medical Center

  As a tenure-track professor whose main responsibility is advancing and passing knowledge

  my main research effort is on artificial intelligence (AI) and signal/image processing

  with extended interest on bioinformatics and wireless communication. I had been awarded almost $500k research grants

  including those from National Science Foundation (NSF)

  U.S. Air Force Research Lab (AFRL)

  Microsoft and Redfin

  on projects covering machine learning and augmented reality (AR).

  The University of Akron

  K-12 Engineering Outreach Mentor

  It's awesome to spread my passion on science and engineering to kids. When I was in high school

  I was effected by similar projects

  like NSF/NASA GLOBE program. So

  now it's the time for me to feed back.

  Texas Tech University

  Iowa State University

  Des Moines

  Iowa Area

  Continuing my journey at Iowa State University. \n\nhttps://www.cs.iastate.edu/people/forrest-sheng-bao

  Assistant Professor of Computer Science

  California

  Taking a small detour from my main research areas to help out a unicorn startup. Responsibilities include leading a group of interns to work on computer vision problems for ADAS.

  Autonomous Driving Engineer

  a unicorn

  I wrote computer programs for the computational neuroscience project MindBoggle http://mindboggle.info/

  Stony Brook University Medical Center

  Chinese

  English

  German

  NSF Research Grant MCB-1616216

  Collaborative Research: Productivity Prediction of Microbial Cell Factories using Machine Learning and Knowledge Engineering

  PI

  50% of total $470

  000 budget

  2016-2019.\nSee https://www.nsf.gov/awardsearch/showAward?AWD_ID=1616216

  National Science Foundation

  FAA Center of Excellence for Technical Training and Human Performance

  Opportunity No. 15-C-TTHP-100PM71515

  Center of Excellence for Technical Training and Human Performance

  a multi-institution multi-PI center of $5M minimum budget 2016-2021 (Phase I) and possible 2021-2026 (Phase II). I am one of the original proposers. PI in 2 projects to date ($80k annual budget): \n* Curriculum Architecture Gap Analysis (initial annual support $45

  000)

  a project using Natural Language Processing to analyze the redundancy and inconsistency between training documents;\n* Technical Training Knowledge Architecture (initial semi-annual support $35

  000)

  Federal Aviation Administration

• 2007

  Ph. D.

  minor in Electrical Engineering

  Computer Science

  Texas Tech University

• 2002

  Bachelor of Engineering (B.Eng.)

  Electronics and Information Engineering

  Nanjing University of Posts and Telecommunications

  Introduction to Artificial Intelligence

  Udacity