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.
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
Projects:
Smart Detection of Track Irregularities and Key Equipment Performance (founded RMB 150K, by State Key Laboratory of Rail Transit Vehicle System )
Epiconductor, collaborate with Harvard Medical School and Dr. Robert Gentleman
Intelligent System for Mobile Edge Computing (founded RMB300K from SWJU and Research Foundation for Advanced Talents)
Professional Development
Generative AI Nanodegree, Udacity, May 2024- July 2024
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, SA. King, D. Chen, " Empowering generative AI through mobile edge computing"(2024), Nature Reviews Electrical Engineering 1, 478–486
[2] L. Ale, L. Ale, R. Gentleman, T. Filshtein Sonmez, and C. Endres, “nhanesA: Achieving Transparency and Reproducibility in NHANES” (2024), Database: The Journal of Biological Databases and Curation
[3] S. Li, L. Ale, H .Chen, F. Tan, TQS. Quek, N. Zhang, M. Dong, K. Ota, "Joint Computation Offloading and Multi-Dimensional Resource Allocation in Air-Ground Integrated Vehicular Edge Computing Network" (2024), IEEE Internet of Things Journal (Impact Factor: 10.6)
[4] 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: 10.6)
[5] L. Ale, N. Zhang, and L. Li, "Road Damage Detection Using RetinaNet" (2018), IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 5197-5200.
[6] 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
[7] 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
[8] 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
[9] 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 (Impact Factor: 7.6)
[10] 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)
[11] L. Ale, N. Zhang, S. King, and A. Sattar, “Deep Reinforcement Learning Aided Task Partitioning and Computation Offloading in Mobile Edge Computing” (2021), IEEE/CIC International Conference on Communications in China (ICCC), Xiamen, China, July 2021
[12] 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
[13] 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 (Impact Factor: 10.6)
[14] H. Sun, Y. Zhou, H. Zhang, L Ale, HN. Dai, N. Zhang, “Joint Optimization of Caching, Computing and Trajectory Planning in Aerial Mobile Edge Computing Networks: A MADDPG Approach” (2024), IEEE Internet of Things Journal (Impact Factor: 10.6)