The IEEE Big Data 2024 Ph.D. Forum provides an exceptional opportunity for doctoral students to present their ongoing research and dissertation plans in the field of big data analytics and related technologies. This forum aims to create a supportive environment where students at various stages of their Ph.D. journey can share their ideas, methodologies, and preliminary findings with experts in the field. Participants will benefit from constructive feedback from experienced researchers, engage in thought-provoking discussions with their peers, and potentially discover avenues for future collaboration within the big data community.

The forum is open exclusively to currently enrolled Ph.D. Students. Submissions encompass a wide spectrum of topics within big data research, reflecting the broad scope of the IEEE BigData 2024 Conference. This includes topics such as big data science and foundations, infrastructure, management, search and mining, learning and analytics, data ecosystems, foundation models, and applications across various domains like science, engineering, medicine, finance, business, and more.

Accepted papers will be published in the conference proceedings. Top submissions will be chosen for oral presentations during the Forum, while other accepted works will be showcased in interactive poster sessions. Moreover, attendees can apply for the IEEE BigData travel award.

In addition to research presentations, the Ph.D. Forum will include a panel discussion featuring experts from both academia and industry. This session will explore diverse career paths and research opportunities in the field of big data, allowing students to ask questions and gain insights into navigating their doctoral studies and future careers.

Please note that in-person attendance is required for all participants in the Ph.D. Forum; remote participation is not available for this event.

Program Schedule

December 17, 2024

Time Activity
10:00 AM – 10:30 AM Poster Presentations
10:30 AM – 11:30 AM Oral Presentations
11:30 AM – 12:30 PM Panel: Beyond the Dissertation: Insights into Academic and Industry Careers
12:30 PM – 2:00 PM Lunch
2:00 PM – 4:00 PM Oral Presentations
4:00 PM – 4:30 PM Poster Presentations
4:30 PM – 5:30 PM Oral Presentations
5:30 PM – 5:35 PM Award Announcement

PhD Forum Panel – Beyond the Dissertation: Insights into Academic and Industry Careers

Bios of Panelists

  • Dr. Cornelia Caragea

Dr. Cornelia Caragea is a Professor of Computer Science and the Director of the Information Retrieval Research Laboratory at the University of Illinois Chicago (UIC). Caragea currently serves as Program Director at the National Science Foundation. Her research interests are in natural language processing, artificial intelligence, deep learning, machine learning, and information retrieval. Caragea’s work has been recognized with several National Science Foundation (NSF) research awards, including the prestigious NSF CAREER award. She has published many research papers in top venues such as ACL, EMNLP, NAACL, ICML, AAAI, and IJCAI and was a program committee member for many such conferences. She reviewed many journals, including Nature, ACM TIST, JAIR, and TACL, served on many NSF review panels, and organized several workshops on scholarly big data. In 2020-21, she received the College of Engineering (COE) Research Award, which is awarded to faculty in the College of Engineering at UIC for excellent research contributions. Caragea was included on an Elsevier list of the top 2% of scientists in their fields for her single-year impact in 2020.

  • Dr. Huzefa Rangwala

At AWS AI/ML, Dr. Huzefa Rangawala spearheads a team of scientists and engineers, revolutionizing AWS services through advancements in graph machine learning, reinforcement learning, AutoML, low-code/no-code generative AI, and personalized AI solutions. His passion extends to transforming analytical sciences with the power of generative AI. He is a Professor of Computer Science and the Lawrence Cranberg Faculty Fellow at George Mason University, where he also served as interim Chair from 2019-2020. He is the recipient of the National Science Foundation (NSF) Career Award, the 2014 University-wide Teaching Award, Emerging Researcher/Creator/Scholar Award, the 2018 Undergraduate Research Mentor Award. In 2022, Dr. Huzefa co-chaired the ACM SIGKDD conference in Washington, DC. His research interests include structured learning, federated learning, and ML fairness inter-twinned with applying ML to problems in biology, biomedical engineering, and learning sciences.

  • Dr. Kesheng (John) Wu

Dr. Kesheng (John) Wu leads multiple R&D endeavors focused on advanced technologies and testbeds at the Scientific Networking Division of Lawrence Berkeley National Laboratory. These projects aim to expedite data transfer among DOE user facilities, implement in-network storage and computational resources for intricate scientific workflows, and explore algorithms, strategies, and practices to enhance the efficiency of network operations. Additionally, Dr. Wu’s team is tasked with developing and managing networking testbeds, providing the broader research community with platforms to explore future generations of networking technologies and optimize their utilization. These testbeds encompass conventional optical networking alongside cutting-edge quantum communication capabilities.

  • Dr. Jianping Zhang

Dr. Jianping Zhang earned his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1990. He is currently a Senior Managing Director at Ankura, based in Washington, DC, where he applies over 25 years of leadership and technical expertise to address complex data analytics challenges. His work encompasses artificial intelligence, machine learning, predictive analytics, big data, and text analytics, with a proven track record of developing advanced analytics solutions across industries such as finance, legal, and high-tech. Dr. Zhang’s impactful contributions include two U.S. patents and over 100 peer-reviewed technical publications. Prior to Ankura, Dr. Zhang held prominent roles in both academia and industry. He served as the principal AI scientist at the MITRE Corporation, spearheading R&D in machine learning and data mining for U.S. government applications. At AOL, he was the chief architect, leading the development of innovative platforms for web content categorization and user profiling. Earlier in his career, he was a lead AI scientist at MITRE, focusing on data mining projects for government agencies, and spent nearly a decade as an assistant and associate professor at Utah State University, mentoring future innovators.

Accepted Papers

  • Knowledge Transfer Predictive Models for Power Outage Caused by Various Types of Extreme Weather Events * Video
    • Jangjae Lee and Stephanie Paal (Texas A&M University, USA)
  • Leveraging Big Data Technologies for Practical Radio Frequency Fingerprinting Applications
    • Stefan Kunze and Wolfgang Dorner (Institute for Applied Informatics Deggendorf Institute of Technology, Germany)
  • BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection
    • He Cheng (Utah State University, USA)
  • Towards Trustworthy Graph Neural Networks and Their Applications in Recommender Systems * Video
    • Longfeng Wu (Virginia Tech, USA)
  • Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization
    • Haoliang Wang (The University of Texas at Dallas, USA), Chen Zhao, and Feng Chen
  • Enhancing Customer Behavior Prediction and Interpretability Video
    • Yu-Chung Wang, Lars Arne Jordanger, and Jerry Chun-Wei Lin (Western Norway University of Applied Sciences, Norway)
  • Forensic Intelligence Derived from Crime Scene Evidence Using Text Embeddings Video
    • Vinicius Lima and Umit Karabiyik (Purdue University, USA)
  • Optimizing Deployment of Homomorphic Encryption and SQL using Reinforcement Learning Video
    • Ryan Marinelli (University of Oslo, Department of Informatics, Norway), Avald Sommervoll, and Laszlo Erdodi
  • Accounting for Cancer Patients with Severe Outcomes: An Anomaly Detection Perspective Video
    • Yang Yan (Southern Illinois University, USA), Christopher Lominska, Gregory Gan, Hao Gao, and Zhong Chen
  • Domain-Aware LLM Routing During Generation *
    • Josef Pichlmeier (Ludwig Maximilians University, Germany), Philipp Ross, and Andre Luckow
  • Robust Hate Speech Detection Without Predefined Spurious Words
    • Xingyi Zhao (Utah State University, USA)
  • Time Series Causal Discovery Using a Hybrid Method *
    • Saima Absar and Lu Zhang (University of Arkansas, USA)
  • A Methodology for Analysing Code Anomalies in Open-Source Software Using Big Data Analytics Video
    • Jimmy Campbell (University of Portsmouth, UK)
  • Responsible AI for Government Program Evaluation and Performance Audits Video
    • Daniel Fonner and Frank Coyle (Southern Methodist University, USA)
  • User Privacy in Skeleton-based Motion Data Video
    • Thomas Carr and Depeng Xu (University of North Carolina at Charlotte, USA)
  • TIFG: Text-Informed Feature Generation with Large Language Models *
    • Xinhao Zhang and Kunpeng Liu (Portland State University, USA)
  • Thought Space Explorer: Navigating and Expanding Thought Space for Large Language Model Reasoning
    • Jinghan Zhang and Kunpeng Liu (Portland State University, USA)
  • Enhanced Deepfake Detection Leveraging Multi-Resolution Wavelet Convolutional Networks Video
    • Supriyo Sadhya and Xiaojun Qi (Utah State University, USA)
  • Global and Local Structure Learning for Sparse Tensor Completion
    • Dawon Ahn and Evangelos Papalexakis (University of California, Riverside, USA)
  • Towards a Supporting Framework for Neuro-Developmental Disorder: Considering Artificial Intelligence, Serious Games and Eye Tracking * Video
    • Abdul Rehman (Western Norway University of Applied Sciences, Norway), Ilona Heldal, Diana Stilwell, and Jerry Chun-Wei Lin

* accepted for long presentations during the PhD Forum (18min + 2min Q&A)

All the other papers are accepted for short presentations during the PhD Forum (10min + 2min Q&A)

All papers will be presented during the poster sessions

Important Dates

  • Submission deadline: Oct 30, 2024 🔴Nov 3,2024🔴
  • Notification of acceptance: Nov 10, 2024
  • Camera-ready of Accepted Papers: Nov 17, 2024
  • Forum date: TBD

Application Submission

Applications must be submitted using the following submission portal: https://wi-lab.com/cyberchair/2024/bigdata24/index.php

Application Material

Papers

Submitted papers should not exceed 2 pages for the main content, with an optional third page reserved exclusively for references. All submissions must adhere to the official IEEE Conference Proceedings template in a two-column format, accessible via IEEE Templates. Your submission should not be anonymized and should include a complete list of all authors and their affiliations, with the Ph.D. student as the primary author. Submissions should present preliminary findings in big data research that align with the broad spectrum of topics covered by the IEEE Big Data 2024 Conference.

Each Ph.D. student is limited to a single submission to this Ph.D. Forum. Once a paper is submitted, no alterations to the author list will be permitted after the submission is due, so please ensure all contributors are properly credited before submission. We are specifically seeking works-in-progress that demonstrate innovative approaches or novel insights in the field of big data. Submissions that do not meet these criteria or fall outside the realm of big data research may not be considered for the forum. We strongly encourage all applicants to carefully review these guidelines to ensure their submissions meet our requirements.

CV

Please include a CV limited to 1 page. List your background, research publications, and other experiences (education, employment, open-source projects, etc.). The list of publications must include venue, year, and author list.

Personal Statement

Please submit a 1-page personal statement. This document should outline your current research direction and potential thesis topics, summarize your academic achievements (such as publications, internships, and teaching experience), and explain what specific advice, insights, or ideas you hope to gain from the Ph.D. Forum. This personal statement helps us understand your research journey and allows us to tailor the forum experience to best support your growth as a doctoral student in the field of big data.

Submissions must be in a single PDF file.

Additional Information

Please refer to this page for more updates regarding the PhD Forum at IEEE BigData 2024. For additional questions, please email PhD Forum co-chairs

  • Feng Chen (Feng.Chen@UTDallas.edu)
  • Shuhan Yuan (Shuhan.Yuan@usu.edu)