Laha Ale
I am an Associate Professor at the SWJTU-Leeds Joint School, Southwest Jiaotong University. Previously, I held the position of Postdoctoral Research Fellow at Harvard Medical School's Center for Computational Biomedicine. I earned my Ph.D. from Texas A&M University-Corpus Christi (TAMUCC) with a perfect GPA of 4.0/4.0.
With six years of invaluable experience in esteemed organizations such as Tieto, Symantec, and Veritas, I bring a wealth of industry knowledge to my academic pursuits.
My current areas of research intrigue encompass a wide range of topics, including Mobile Edge Computing, Deep Reinforcement Learning, Deep Learning, Bayesian Inference, and Deep Universal Probabilistic Programming.
Deep Learning
Deep Reinforcement Learning
Mobile Edge Computing
Deep Probabilistic Programming
Education
Texas A &M University-Corpus Christi, USA January 2018 - December 2021
Geospatial Computer Science, Ph.D.
Department of Computing Sciences
Webster University, US June 2014 - June 2016
Master of Business Administration (MBA)
George Herbert Walker School of Business & Technology
Southwest University of Science and Technology, China June 2007 - July 2011
Bachelor in Computer Science
School of Computer Science
Professional Development
Center of Brains, Minds and Machines Summer Course, Harvard and MIT, Summer 2020
Deep Reinforcement Learning Nanodegree, Udacity, Jan 2019 - Mar 2019
Deep Learning Specialization (5 Courses), Coursera (by Andrew Ng), Jan 2018 - Jun 2018
Publications
[1] L. Ale, N. Zhang, H. Wu, D. Chen and T. Han, "Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network" (2019), IEEE Internet of Things Journal (Impact Factor: 9.5)
[2] L. Ale, N. Zhang and L. Li, "Road Damage Detection Using RetinaNet" (2018), 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 5197-5200.
[3] L. Ale, X. Fang, D. Chen,Y. Wang and N. Zhang, "Lightweight Deep Learning Model For Facial Expression Recognition" (2019), IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2019), Rotorua, New Zealand, 2019
[4] L. Ale, A.Sheta, L.Li, Y. Wang and N. Zhang, "Deep Learning based Plant Disease Detection for Smart Agriculture" (2019), IEEE GLOBECOM 2019 Workshop on Computational Intelligence and Big Data Towards Trustworthy IoT, Waikoloa, Hawaii, USA, 2019
[5] L. Ale, L. Li, D. Kar, N. Zhang, A. Palikhe, "Few-Shot Learning to Classify Android Malwares" (2020), IEEE 5th International Conference on Signal and Image Processing (ICSIP 2020), 2020
[6] L. Ale, N. Zhang, X. Feng, X. Cheng, S. Wu and L. Li, "Delay-aware and Energy-Efficient Computation Offloading in Mobile Edge Computing Using Deep Reinforcement Learning" (2021), IEEE Transactions on Cognitive Communications and Networking
[7] L. Ale, N. Zhang, S. King, and J. Guardiola, "Spatio-Temporal Bayesian Learning for Mobile Edge Computing Resource Plan in Smart Cities" (2021), ACM Transactions on Internet Technology (TOIT)
[8] L. Ale, N. Zhang, S. King, and A. Sattar, “Deep Reinforcement Learning Aided Task Partition-ing and Computation Offloading in Mobile Edge Computing” (2021),2021 IEEE/CIC InternationalConference on Communications in China (ICCC), Xiamen, China, July 2021
[9] L. Ale, S. A King, and N. Zhang, “Deep Bayesian Learning for Car Hacking Detection” (2021), Bayesian Deep Learning Workshop, 35th Conference on Neural Information Processing Systems
[10] L. Ale, N. Zhang, S. King, and A. Sattar, “D3PG: Dirichlet DDGP for Task Partitioning and Offloading with Constrained Hybrid Action Space in Mobile Edge Computing” (2022), IEEE Internet of Things Journal