Dr. Chih Lai

Research interests in Machine Learning, Deep Learning, Machine Vision, Healthcare Analytic

Education

Ph.D in Computer Science, Oregon State University, June 1999. (Honor Student)

M.S. in Computer Science, Oregon State University.

B.A. Fu-Jen Catholic University, Taiwan.

Experience

Professor, Graduate Program in Software, University of St. Thomas, St. Paul, MN

Research Consultant, Research Group, Neuromodulation Division, Medtronic Co., St. Paul, MN

Visiting Professor, Dept. of Informatics, Trier University of Applied Science, Germany

Senior Software Engineer, United Parcel Service Aviation Technologies Co., Oregon

Operating Systems Instructor, Dept. of Computer Science, Oregon State University, Oregon

Patents

“Motion Analysis for Behavior Identification”, Inventor: Chih Lai, et al. with Medtronic Co.,

Pending U.S. Patents 14-104-057 December 2013.

“Motion-Based Behavior Identification for Controlling Therapy”, Inventor: Chih Lai, et al. with Medtronic Co.,

Pending U.S. Patents 14-104-078 December 2013.

“Detection and removal of self-alerts in an airborne tracking system”, Inventor: Chih Lai,

U.S. Patent 6-594-578, July 15 2003. Europe Patent EP1299747, March 4 2003.

“Method for determining conflicting paths between mobile airborne vehicles and associated system and computer software program product”, Inventor: Chih Lai,

U.S. Patent 6-564-149, May 13 2003. Europe Patent EP1299742, March 4 2003.

“Multisource target correlation”, Inventor: Chih Lai,

U.S. Patent 6-542-810, April 1 2003. Europe Patent EP1374205, January 2 2004.

Selected Refereed and Peer-Reviewed Publications

Yuyu He, Chih Lai, Dalma Martinovic-Weigelt, Zezheng Long, “A Pipeline Approach in Selecting Important Features from Neural Nnetwork”, 2019 IEEE International Conference on System Engineering, Anchorage, Alaska, May, 2019.

Xinyue Sui, Chih Lai, Dalma Martinović-Weigelt, “Advancing Machine Learning in Environmental Toxicology via Information Sharing and Transfer Learning”, Europe SETAC meeting, May, 2019, Helsinki.

Chih Lai, Dalma Martinovic-Weigelt, Xinyue Sui, “Predicting Fish Embryo Development in Videos using Convolutional Encoder and Decoder Neural Network”, North America SETAC meeting, November, 2018, CA.

Chih Lai, Dalma Martinovic-Weigelt, Xinyue Sui, “Predicting Potential Adverse Outcome Pathways using Long-Short Term Memory (LSTM) Neural Network”. North America SETAC meeting, November, 2018, CA.

K. Kantipudi, C. Lai, et. al. “Identifying Weeds in Images using Convolutional Neural Network”. 2018 International Conference on Precision Agriculture, Quebec, Canada, June, 2018. (abstract submit for review)

K. Zhao, C. Klaue, C. Lai, “Predicting Concept Drift via Dynamic Naïve Bayes”. IEEE International Conference on Big Data, Workshop of Real-Time and Stream Analytics in Big Data, London, Boston, December 2017.

K. Zhao, B. Imamura, C. Lai, E. Curran, D. Martinovic-Weigelt, “Integrating chemical monitoring and high-content bioeffects data to prioritize contaminants of emerging concern in MN”. Society of Environmental Toxicology and Chemistry, SETAC 38th Annual Meeting, November 2017

J. Ommen, C. Lai. “Identifying Distinguishing Factors in Predicting Brain Activities - An Inclusive Machine Learning Approach”. International Conference on Brain Informatics and Health (BIH), London, UK, September, 2015.

J. Ommen, C. Lai, Y. Yang. “Big Graph Analytics of Human Connectome Networks”, Poster presented at the SIAM Conference on Computational Science and Engineering (CSE), Salt Lake City, UT, March 2015.

C. Lai, C. Belsky, B. Holub, J. Ellingson, C. Greene, and S. Morgan, “Building Proactive Predictive Models with Big Data Technology for Precision Agriculture”, 12^th International Conference on Precision Agriculture, Sacramento, CA., July, 2014.

Jianping Wu, Chih Lai, Matthew Beckman, Robert Raike, Rahul Gupta, Aviva Abosch, Dwight Nelson, "Video-Motion Detection for Objectively Quantifying Movements in Patients with Parkinson’s Disease", 17^th International Congress of Parkinson’s Disease and Movement Disorders, Syndey, Australia, June 2013.

C. Lai, T. Rafa, D. Nelson, “Approximate Minimum Spanning Tree Clustering in High Dimensional Space”, Journal of Intelligent Data Analysis, Volume 13(4), pp575-597, August, 2009.

C. Lai, E. A. Heuer, “Efficiently Maintaining Moving Micro Clusters for Clustering Moving Objects”, IEEE International Conference on System Engineering, Monterey, CA. 2008.

C. Lai, T. Rafa, D. Nelson, “Mining Motion Patterns using Color Motion Map Clustering”, ACM SIGKDD Explorations, Vol. 8, No. 2, pp3-10, December 2006.

C. Lai, T. Rafa, D. Nelson, “Mining Motion Patterns using Color Motion Map Clustering”, ACM Multimedia Data Mining, Philadelphia, August, 2006, (Best Paper, and was invited to publish in 2006 ACM SIGKDD Explorations).

C. Lai, N. Nguyen, D. Nelson, “Mining Periodic Patterns from Floating and Ambiguous Time Segments”, IEEE International Conference on Systems, Man and Cybernetics, Hawaii, 2005.

C. Lai, N. Nguyen, “Predicting Density-Based Spatial Clusters Over Time”, 4^th IEEE International Conference on Data Mining (IEEE ICDM’04), Brighton, UK, November, 2004.

C. Lai, L. Stephanie, M. Fang, Real-Time Mining of Partial Periodic Patterns, 15^th International Conference on Software Engineering and Knowledge Engineering (SEKE’03), San Francisco, CA, July 2003.

C. Lai, Szara Loring, Joe Breuer, Mining Access Patterns for Enhancing Navigational Access in Object-Oriented Databases, International Workshop on Data Mining for Software Engineering and Knowledge Engineering, San Francisco, CA, July, 2003.

Joe Breuer, Szara Loring, Chih Lai, Implementing FOIL into WEKA: A Methodology and Analysis of the Problems and Benefits, International Conference on Information and Knowledge Engineering, Las Vegas, NV, June 2003.

C. Lai, Optimistic Similarity-Based Real-Time Concurrency Control, IEEE Real-Time Technology and Applications Symposium, Denver, Colorado, June 1998.

C. Lai, H. R. Callison, A Framework for Simulation of Concurrency Control Policy in Real-Time Systems, IEEE Real-Time Technology and Applications Symposium, Brookline, Massachusetts, June 1996.

C. Lai, T. Lee, T. Minoura, C. Park, Distributed Structural Active-Object System (DSAOS) for Groupware Implementation, International Workshop on Multimedia Systems, Hawaii, March 1995.

C. Lai, T. Minoura, Object Behavior Composition by Transition and State Composition, IEEE Workshop on Object-Oriented Real-Time Dependable Systems, Dana Point, California, October 1994.

Research Funding and Proposals

Total Granted: $918,912.27

Grant Proposals under Review

1. NSF, CISE Research Infrastructure for 2018-2020, Co-PI with 2 faculty in School of Engineering, PROJECT TITLE: Intelligent Robotics and Sensing for Sustainable Agriculture, $300,000.00. (pass 1^st phase abstract review, full proposal under development).
2. Seagate Technology Co., PI. 2017-2018. PROJECT TITLE: Deep Learning in Detecting Wafer Defects from Microscope Images, $18,988.20. (under review).

Granted Proposals

1. NSF, Co-PI, PROJECT TITLE: Network Design and Implementation for Small Institutions: The Tommie Science Network- A dedicated research network for the University of St. Thomas, 2018 to 2020. $390,671.00. (Granted)
2. Deutsche Academic Exchange Scientist Fund, in collaboration with the Helmholtz-Centre for Environmental Research, PROJECT: Apply Deep Learning in Environmental Research, $14,100.00. (Granted)
3. German UFZ (Helmholtz Centre for Environmental Research), Co-PI, PROJECT TITLE: Identifying Associations and Implementation of Suitable Data Analysis Pipelines, Doctoral Student Grant. (Granted).
4. STELAR Center, Information Technology, University St. Thomas, PI, PROJECT TITLE: Artificial Intelligence for Posture Evaluation and Creative Writing Recommendation, 2017. $15,000.00 equipment grant. (Granted).
5. Vertical Circle Co., PI, PROJECT TITLE: Hydro Energy Prediction, 2017. $43,741.27 (Granted).
6. Minnesota State Environment and Natural Resources Trust Fund (ENRTF) 2017—2019, Co-PI with Biology Department, PROJECT TITLE: Reassessing Toxicity of Petroleum Spills on Groundwater and Surface Water, $300,000.00 (Granted).
7. YourData Co., PI, PROJECT TITLE: Wind Power Component Failure Prediction, July 2016– February 2017, $230,000.00 (Granted $60,000.00).
8. Microsoft Azure Cloud Research Grant on Machine Learning, PI, Data Mining and Machine Learning on Azure Cloud, 2015- 2016. Funding amount: $20,000.00. (Granted).
9. Amazon Teaching Grant for 2015-2016, PI, Multiple Data Analytics Courses on Amazon Cloud, Funding amount: $18,000.00. (Granted).
10. Amazon Cloud Research Grant for 2015-2017, PI, In-Memory Analysis of Brain Connectivity Graphs, Funding amount: $23,600.00. (Granted).
11. Amazon Cloud Teaching Grant for spring 2015, PI, Mining Big Data on Amazon Cloud, Funding amount: $7,000.00. (Granted).
12. UST Research Grant for one course release in spring 2016, PI, Analyzing and Predicting Brain Connectome via Cloud Computing, Funding amount: $4,800.00. (Granted)
13. University Graduate Research Team Grant, PI, University of St. Thomas, Big Graph Analysis of Brain Neural Connectome via Cloud Computing, May—August 2014, $10,000. (Granted)
14. Medtronic Neuromodulation, PI, Stomach Motility Analysis, May 2014, $12,000. (Granted).

Submitted Proposals

1. NSF, Co-PI with 3 faculty in School of Engineering, PROJECT TITLE: Risk Reduction for Transitioning from Current Industrial Framing Machinery and Methods to Modern Enterprise Based Precision Agriculture Robotics and Protocols, 2016, $497,458.00.
2. NSF, Co-PI with UST Information Technology Department, PROJECT TITLE: Campus Cyberinfrastrc, 2016, $386,970.00.
3. Minnesota State Environment and Natural Resources Trust Fund (ENRTF), Co-PI with 3 faculty in School of Engineering, PROJECT TITLE: Measuring reductions in nitrate pollution from precision agriculture, 2016, $159,833.00.
4. University Graduate Research Team Grant, PI, University of St. Thomas, Building Predictive Models for Precision Agriculture, 2016, $10,000.

HONORS / Awards

Medtronic Idea Disclosure Contributor Award for quarter 3 and 4, 2013.

Elected IEEE Senior member. 2010.

Best Paper Award in ACM Multimedia Data Mining Conference, 2006.

Aerospace Industry Pioneer Award, (Project Award), February 2000.

Elected member of Upsilon Pi Epsilon Honor Society of Computing. 1997.

Teaching

Due to the huge demand of data science education, University of St. Thomas is going through fast expansion of its School Engineering program.

As a result, Dr. Lai teaches 9 courses per year. Normal faculty teaching load at University of St. Thomas is 6 courses per year.

Hence, Dr. Lai's teaching is 1.5 times overload than the regular university teaching load.

Average teaching evaluation scores to be higher than 4.2 in both the excellent course category and the excellent instructor category. (with 4 = "Very Good")