Jiangling Wu | Transportation | Best Researcher Award

Dr. Jiangling Wu | Transportation | Best Researcher Award

Doctorate at Henan University, China

Summary:

Dr. Jiangling Wu is a distinguished researcher and educator in the field of transportation engineering. With a PhD from Chang’an University and postdoctoral research experience at Henan University, he has developed significant expertise in urban air mobility, ITS, travel behavior, and traffic safety. His international experience includes a visiting scholar tenure at the University of Texas at Austin. Dr. Wu’s research has led to multiple patents and publications, reflecting his innovative approach to addressing contemporary transportation challenges. As a dedicated educator, he has been teaching various undergraduate and graduate courses, contributing to the development of future engineers. Dr. Wu continues to push the boundaries of transportation research, focusing on improving safety, efficiency, and sustainability in urban and highway environments.

Professional Profile:

πŸ‘©β€πŸŽ“Education:

Postdoctoral Research (2020-2023) in Geography at Henan University, supervised by Professor Yun-feng Kong.

PhD in Transportation Engineering (2012-2017) from Chang’an University, supervised by Professor Sheng-rui Zhang and Professor Bao-jie Yan.

Visiting Scholar (2014-2015) in Transportation Engineering at the University of Texas at Austin, supervised by Professor Randy Machemehl and Professor Sheng-rui Zhang.

MS in Transportation Planning and Management (2010-2013) from Chang’an University, supervised by Professor Sheng-rui Zhang and Professor Bao-jie Yan.

BS in Traffic Engineering (2006-2010) from Henan Polytechnic University.

🏒 Professional Experience:

Dr. Jiangling Wu has a robust academic and research background in transportation engineering, with a focus on urban air mobility, intelligent transportation systems (ITS), travel behavior, traffic safety, innovative roadway design, and traffic operations in highway work zones. His postdoctoral research at Henan University, combined with his PhD from Chang’an University and experience as a visiting scholar at the University of Texas at Austin, has equipped him with a deep understanding of transportation systems and their optimization. Dr. Wu has been involved in teaching both undergraduate and graduate courses, such as Technical English in Civil Engineering, Fundamentals of Traffic Engineering, Roadway Design, and Urban Road and Interchanges.

Research Interests:

Author Metrics:

Dr. Wu is a prolific researcher with numerous publications under peer review and forthcoming in high-impact journals. His work covers a wide range of topics, including bike-sharing demand forecasting, travel mode choices, urban traffic flow prediction, and the effects of gasoline prices on travel behavior. He has authored and co-authored several patents related to roadway intersection designs, lane-changing behavior, and public rental bicycle signs. Dr. Wu’s research outputs have been published in journals such as the Journal of Applied Science and Engineering, PloS ONE, and the International Journal of Pavement Engineering.

Top Noted Publication:

What drives users to accept flying cars for urban air mobility? Findings from an empirical study

  • Authors: Wu, J., He, Q., Singh, A.K., Tian, L.
  • Journal: Journal of Air Transport Management
  • Year: 2024
  • Volume: 119
  • Article Number: 102645

Bond Slip Effect of Composite Trough Beam with Corrugated Steel Web Wrapped with Steel Plate and its Influence on Displacement Under Different Loading Conditions

  • Authors: Tian, L., Dong, Z., Xiong, Y., Wu, J.
  • Journal: International Journal of Civil Engineering
  • Year: 2024
  • Status: Article in Press

Data mining and spatio-temporal characteristics of urban road traffic emissions: A case study in Shijiazhuang, China

  • Authors: Ren, L., Guo, X., Wu, J., Singh, A.K.
  • Journal: PLoS ONE
  • Year: 2023
  • Volume: 18
  • Issue: 12 (December)
  • Article Number: e0295664

Application of ALINEA Ramp Control Algorithm for Type-C Weaving Sections

  • Authors: Wu, J., Wang, H., Singh, A.K., Yuan, Y.
  • Conference: CICTP 2020: Transportation Evolution Impacting Future Mobility – Selected Papers from the 20th COTA International Conference of Transportation Professionals
  • Year: 2020
  • Pages: 2596–2609

Elastic analysis of tradable credits program considering multiple commuting methods

  • Authors: Sun, Z., Zhang, S., Wu, J., Li, Y., Zhao, W.
  • Journal: Xi’an Jianzhu Keji Daxue Xuebao/Journal of Xi’an University of Architecture and Technology
  • Year: 2018
  • Volume: 50
  • Issue: 3
  • Pages: 381–388

 

Gayan KIncy Kulatilleke | Machine learning

Dr. Gayan KIncy Kulatilleke: Leading Researcher in Machine learning, AI

Dr. Gayan Kincy Kulatilleke is a distinguished researcher and expert in the field of Artificial Intelligence and Machine Learning. Holding a Ph.D. in AI, M.Sc. degrees in Big Data and Networking, and a B.Sc. in Engineering, he has garnered recognition for his outstanding contributions to the field.

As a leading academic at the University of Queensland, Brisbane, Dr. Kulatilleke serves as a Sessional Academic and Research Assistant, specializing in Computer Networks and High-Performance Computing. He also plays a pivotal role as an AI/ML Mentor for Summer and Internship Projects, showcasing his dedication to nurturing the next generation of innovators.

πŸŽ“ Education

  • PhD(AI), MSc(Big Data), MSc(Networking), BSc(Eng.), ACMA(UK), CGMA(USA), AMIE(SL)

πŸ† Awards

  • Best Paper: Microsoft Award (Cybersecurity, HFES 2020)
  • Best Paper: CSIRO Award (AJCAI 2022)
  • “Global Knowledge Sharing” Award for Best Technical Publication (Virtusa 2003)
  • Runner Up: cmbHack.js 2014 (48-hour JavaScript Hackathon)
  • Multiple Australian Scholarships: UQ RTP, ARC LP for PhD

Professional Profiles:

Publication Top Noted

  • XG-BoT: An explainable deep graph neural network for botnet detection and forensics
  • HFE and cybercrime: Using systems ergonomics to design darknet marketplace interventions
  • Efficient block contrastive learning via parameter-free meta-node approximation
  • Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin
  • DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection

Research FocusΒ 

Dr. Gayan Kincy Kulatilleke’s research encompasses a broad spectrum within the realms of Artificial Intelligence (AI) and Machine Learning (ML). While specific details of his research focus might require more recent information, the provided details indicate several areas of interest and expertise:

  1. Botnet Detection and Forensics
  2. Human Factors Engineering (HFE) and Cybercrime
  3. Contrastive Learning for Efficient Block Analysis
  4. Self-Supervised Learning for Money Laundering Detection
  5. Hybrid Deep Learning for Network Anomaly Detection

πŸ’Ό Work Experience

University of Queensland, Brisbane

  • Sessional Academic & Research Assistant (01/2019 – Present)
    • Lead Tutor: Computer Networks, High-Performance Computing
    • AI/ML Mentor for Summer and Internship Projects
    • Presenter: High-Performance Compute GPU Clusters, ML Pipelines

Freelance AI ML and Software Consultant/Developer, Remote (01/2006 – Present)

  • Advanced skills in C++, OS, Automation, Integration, DevOps, Cloud Management, Google Technologies, Data Engineering, Software Development, AI/ML.

Mailot – Founder CTO, Brisbane (01/2023 – 06/2023)

  • GPT-3 Productivity SaaS Startup
  • NLP and Human Inputs for Augmented Responses
  • Integration of OpenAI, Langchain, Vector Databases with Node.js on Vercel

AMZ Consulting Pty Ltd, Sydney – Consultant/Trainer (05/2023 – Present)

  • PowerBI, Modelling, DAX, Integrations

TeleMARS, Brisbane – Lead AI ML Engineer and Researcher (04/2023 – Present)

  • AI Architecture Design, AI Analytics Solution Design
  • Development and Solution Delivery Management

Space Platform, Brisbane – Head ML (06/2019 – 06/2020)

  • Privacy Preserving Query and Intent Identification Algorithms
  • Research on IOS and Android’s Randomized MAC Addresses
  • Data Engineering, ETL Operations, ML Model Design and Implementation

Capital Staffing, London – Software Developer (10/2017 – 07/2018)

  • C#, .NET MSSQL Server, DevOps, Cloud Services

Central Bank of Sri Lanka – Senior Assistant Director (05/2005 – 10/2022)

  • AI ML Big Data Initiatives
  • Data Science and Analysis Teams Coaching
  • Implementation of Strategic IT Needs
  • Surveillance, Authentication, Access Control Systems Oversight

Virtusa Corporation, Colombo – Software Engineer (R&D) (05/2004 – 08/2004)

  • Managed R&D Portal, Global Technology Center Consultation
  • “Global Knowledge Sharing” Award for Best Technical Whitepaper

MillenniumIT (London Stock Exchange Group) – Application Engineer (08/2003 – 05/2004)

  • Global Support, Technical Advice to Capital Market Clients
  • Performance Benchmarking, Real-time Multithreaded C/C++ Code Development

πŸŽ“ Educational/Professional Qualifications

  • PhD Candidate, Tutor, Research Assistant, University of Queensland (2019 – 2023)
  • MSc Big Data Science, Queen Mary, University of London (2017 – 2018)
  • MSc Computer Science, Networking, University of Moratuwa, Sri Lanka (2004 – 2006)
  • BSc Computer Science & Engineering, University of Moratuwa, Sri Lanka (1999 – 2003)

πŸ† Special Achievements/Publications

  • Google Research Profile: Link
  • “Microsoft Best Paper Award: Cybersecurity Technical Group” (2020)
  • “Global Knowledge Sharing” Award for Best Technical Publication (Virtusa 2003)

🀝 Positions of Responsibility

  • International Reviewer – Multiple AI ML Journals (2022 – Onwards)
  • Operational Committee Member – WiT Cyber Review Committee, Queensland (2023 – 2024)
  • Coordinator – Chess & Drafts Games, Central Bank Recreation Club (2010 – 2017)
  • Chief Examiner – Institute of Bankers, Sri Lanka (2009 – 2017)
  • Volunteer Work with ADB to Create Computer-Aided Educational Software for Schools (2004)

πŸ“š Lateral Courses Completed

  • Macro Econometric Forecasting, IMF, Institute for Capacity Development, 2016
  • Financial Programming and Policies, Part 2: Program Design, IMF, 2012

πŸ† Skills

  • Data, AI, Machine Learning Engineering
  • Software Development (Python, C++, C#)
  • AI ML (GPTx, Langchain Models, TensorFlow)
  • DevOps and Cloud Management (Azure, AWS)
  • Google Technologies (Data Studio, Firebase, Cloud Firestore)
  • Data Engineering (PowerBi, Tableau, DAX, Python-based Web Scraping)

πŸŽ‰ Awards and Recognitions

  • Best Paper Awards (Microsoft, CSIRO)
  • Global Knowledge Sharing Award (Virtusa)
  • Australian Scholarships (UQ RTP, ARC LP)