Dr. Kianoosh Boroojeni | Data Fusion Awards | Best Researcher Award

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Dr. Kianoosh Boroojeni | Data Fusion Awards | Best Researcher Awardย 

Dr. Kianoosh Boroojeni, Florida International University, United States

Dr. Kianoosh Boroojeni is an Associate Teaching Professor at the Knight Foundation School of Computing & Information Sciences, Florida International University (FIU). He earned his Ph.D. and M.S. in Computer Science from FIU and a B.Eng. in Computer Engineering from the University of Tehran. His research interests include cybersecurity, generative AI in computer science education, STEM education, and computer networks. With over 50 scientific publications and more than 1,150 citations, Dr. Boroojeni has made significant contributions to his field. He has played a pivotal role in integrating AI-powered tools into computer science education and has collaborated with Google to enhance programming courses. His leadership extends to overseeing programming gateway courses, developing cybersecurity curricula, and promoting inclusive computing education. He has received multiple teaching recognitions and actively mentors students.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award โ€“ Kianoosh Boroojeni

Dr. Kianoosh Boroojeni is a highly accomplished researcher and educator in cybersecurity, AI in education, and computer networks, making him a strong candidate for the Best Researcher Award. His academic background, extensive research contributions, and leadership in integrating AI into education highlight his impact in the field.

Education ๐Ÿ“š

  • Ph.D., Computer Science โ€“ Florida International University, USA (2017)
  • M.S., Computer Science โ€“ Florida International University, USA (2016)
  • B.Eng., Computer Engineering โ€“ University of Tehran, Iran (2012)

Work Experience ๐Ÿ’ผ

๐Ÿ”น Associate Teaching Professor โ€“ Florida International University (2023 โ€“ Present)

  • Leads Programming Gateway Committee to improve programming course success rates ๐Ÿ“Š
  • Collaborates with Google to integrate Generative AI in CS education ๐Ÿค–
  • Chairs Faculty & Staff Awards Committee in the College of Engineering & Computing ๐Ÿ†
  • Supports intensive programming courses like CS I, II & III, Data Structures, and OS ๐Ÿ’พ

๐Ÿ”น Assistant Teaching Professor โ€“ Florida International University (2017 โ€“ 2023)

  • Taught 16+ undergraduate courses and 4 graduate/Ph.D. courses ๐Ÿซ
  • Developed new cybersecurity courses on Blockchains ๐Ÿ”
  • Led Googleโ€™s Tech-Exchange Program to recruit Hispanic & Black students into Google workforce ๐ŸŒ
  • Designed and improved online courses to achieve Quality Matters (QM) Certifications โœ…
  • Achieved high student evaluations (4.6/5.0 overall) ๐Ÿ“ˆ

๐Ÿ”น Post-Doctoral Fellow โ€“ Florida International University (Spring & Summer 2017)

  • Conducted DoD-funded research on network security & privacy ๐Ÿ”Ž
  • Mentored students in NSF-sponsored Research Experience program ๐Ÿ‘จโ€๐Ÿซ

๐Ÿ”น Graduate Assistant โ€“ Florida International University (2012 โ€“ 2017)

  • Assisted faculty in teaching & grading multiple undergraduate/graduate courses โœ๏ธ
  • Collaborated with researchers from Carnegie Mellon & University of British Columbia ๐Ÿค

Achievements & Awards ๐Ÿ…

๐Ÿ† Published 50+ scientific papers with 1150+ citations (h-index: 19) ๐Ÿ“„
๐Ÿ† Led Google-FIU collaboration to integrate LLM-powered AI tools in CS education ๐Ÿค–
๐Ÿ† Chaired Programming Gateway Committee to improve programming course completion rates ๐ŸŽฏ
๐Ÿ† Successfully developed and taught two new cybersecurity courses on Blockchain ๐Ÿ”
๐Ÿ† Achieved high teaching ratings (4.6/5.0) for multiple CS courses ๐Ÿ“Š
๐Ÿ† Contributed to NSF & DoD research projects on cybersecurity and network security ๐Ÿ›

Publicationย Top Notes:

Fundamentals of brooks-iyengar distributed sensing algorithm: Trends, advances, and future prospects

A Multi-time-scale Time Series Analysis for Click Fraud Forecasting using Binary Labeled Imbalanced Dataset

Dr. Peng Zhi | Deep Learning | Best Researcher Award

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Dr. Peng Zhi | Deep Learning | Best Researcher Awardย 

Dr. Peng Zhi, Lanzhou University, China

Peng Zhi is a Ph.D. candidate in Computer Science at Lanzhou University, China, specializing in computer vision, deep learning, and autonomous driving. He earned his Bachelor’s and Master’s degrees in Computer Science and Technology from Lanzhou University in 2017 and 2020, respectively. His research focuses on LiDAR-camera fusion, 3D object detection, and AI applications in intelligent transportation systems. He has published several high-impact papers in renowned journals and conferences, contributing to advancements in autonomous vehicle perception and artificial intelligence. Additionally, he has co-authored the book Theories and Practices of Self-Driving Vehicles, further solidifying his expertise in the field.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Peng Zhi is a strong candidate for the Best Researcher Award, given his innovative contributions to computer vision, deep learning, and autonomous driving. As a Ph.D. candidate at Lanzhou University, he has been actively involved in research that enhances LiDAR-based 3D object detection, cross-domain generalization, and deep learning applications in autonomous systems.

๐ŸŽ“ Education

  • Ph.D. in Computer Application Technology (2021 โ€“ Present)
    Lanzhou University, Lanzhou, China
  • Masterโ€™s in Computer System Architecture (2017 โ€“ 2020)
    Lanzhou University, Lanzhou, China
  • Bachelorโ€™s in Computer Science and Technology (2013 โ€“ 2017)
    Lanzhou University, Lanzhou, China

๐Ÿ’ผ Work Experience

  • Ph.D. Candidate & Researcher (2021 โ€“ Present)
    Lanzhou University, Lanzhou, China

    • Conducts advanced research in computer vision, deep learning, and autonomous driving
    • Publishes in top-tier journals and conferences
    • Develops LiDAR and camera fusion models for 3D object detection

๐Ÿ† Achievements & Contributions

  • Published Multiple Research Papers ๐Ÿ“„ in top journals and conferences, including Tsinghua Science and Technology, Electronic Research Archive, and IEEE ITSC
  • Author of a Book on Self-Driving Vehicles ๐Ÿ“˜ Theories and Practices of Self-Driving Vehicles (Elsevier, 2022)
  • Developed DefDeN Model ๐Ÿค– A deformable denoising-based LiDAR and camera feature fusion model for 3D object detection
  • Research on Autonomous Driving ๐Ÿš— Focused on boundary distribution estimation and cross-domain generalization for LiDAR-based 3D object detection

๐Ÿ… Awards & Honors

  • Best Paper Award ๐Ÿ† at an International Conference on Intelligent Transportation Systems (ITSC)
  • Outstanding Researcher Award ๐ŸŽ–๏ธ at Lanzhou University for contributions to AI and autonomous driving
  • National Scholarship ๐Ÿ… for academic excellence in computer science and AI research

Publicationย Top Notes:

Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments

Vasyl Martsenyuk | Data Science | Best Researcher Award

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Vasyl Martsenyuk | Data Science | Best Researcher Award

Prof. Vasyl Martsenyuk, University of Bielsko-Biala, Poland.

Prof. Vasyl Martsenyuk is a prominent academic in the field of Computer Science and Automation, currently serving as a Full Professor and Head of the Department at the University of Bielsko-Biaล‚a, Poland. With a strong background in data science, machine learning, and cybernetics, he has significantly contributed to educational reforms and various research projects in Europe. An active member of international academic communities, Prof. Martsenyuk has published extensively and engaged in numerous collaborations, fostering advancements in digital pedagogy and applied artificial intelligence. His dedication to education and research continues to inspire students and colleagues alike.ย ๐ŸŽ“๐ŸŒ๐Ÿ’ป

Publication Profile

Scopus
Orcid

Education and Experience

Education:
  • Ph.D. in Technical Sciencesย (2005)
    Kyiv Taras Shevchenko National University, Faculty of Cybernetics
    Specialty: System Analysis and Decision Makingย ๐Ÿ“š
  • Diploma of Full Professor in Medical Informatics with Biophysicsย (2005)
    Ministry of Education and Sciences of Ukraineย ๐Ÿ‡บ๐Ÿ‡ฆ
  • Candidate of Physical and Mathematical Sciences (Ph.D.)ย (1996)
    Kyiv Taras Shevchenko National University, Faculty of Cybernetics
    Specialty: System Analysis and Decision Makingย ๐ŸŽ“
  • Diploma in Applied Mathematicsย (1993)
    Kyiv Taras Shevchenko National University, Faculty of Cybernetics, Ukraineย ๐Ÿงฎ
  • Diploma in Pharmacyย (2010)
    Kharkiv National Pharmaceutical University, Ukraineย ๐Ÿ’Š
Experience:
  • Full Professor & Head of Departmentย (2020-Present)
    University of Bielsko-Biaล‚a, Department of Computer Science and Automationย ๐Ÿซ
  • Associate Professorย (2015-2020)
    University of Bielsko-Biaล‚a, Department of Computer Science and Automationย ๐Ÿ‘จโ€๐Ÿซ
  • Scientific Supervisorย (2014-2016)
    Institute of Information Technologies and Learning Tools, National Academy of Pedagogical Sciences of Ukraineย ๐Ÿ”ฌ
  • Full Professor & Head of Medical Informatics Departmentย (2005-2016)
    Ternopil Medical University, Ukraineย ๐Ÿฅ
  • Vice-Rectorย (2005-2015)
    Ternopil Medical University, Ukraineย ๐ŸŽ–๏ธ

Suitability For The Award

Prof. Vasyl Martsenyuk is a distinguished academic with a strong educational foundation, extensive professional experience, and significant contributions to computer science, medical informatics, and cybernetics. He has excelled in research areas such as data science, artificial intelligence, and system analysis, alongside leadership roles in academia and international projects. With numerous awards, publications, and innovative initiatives, he has demonstrated exceptional expertise and dedication. His impactful work and commitment to advancing knowledge make him highly deserving of the Best Researcher Award.

Professional Development

Prof. Martsenyuk has engaged in numerous international traineeships, focusing on advancements in digital pedagogy, applied artificial intelligence, and big data innovations. Notably, he participated in the Erasmus+ program and collaborated with institutions such as the University of Montenegro and Charles University in Prague. His efforts have emphasized fostering digital learning environments and enhancing educational methodologies in the fields of medicine and technology. These experiences reflect his commitment to continuous professional growth and adaptation to emerging technological trends in education.ย ๐ŸŒ๐Ÿ“šโœจ

Research Focus

Prof. Martsenyuk’s research centers on data science, encompassing machine learning, artificial intelligence, and big data analysis. He explores the application of reinforcement learning within cybernetics, focusing on control theory and system analysis. His mathematical investigations include functional-differential equations, population dynamics, and stability analysis. By integrating these disciplines, he aims to develop innovative solutions that advance scientific computing and enhance decision-making processes in complex systems. Prof. Martsenyuk’s work significantly contributes to the advancement of knowledge in computer science and its practical applications.ย ๐Ÿ”๐Ÿ“Š๐Ÿค–

Awards and Honors

  • Award of the Supreme Council of Ukraineย (2007)ย ๐Ÿ…
  • Award of the Prime Minister of Ukraineย (2009)ย ๐ŸŽ–๏ธ

Publication Top Notes

  • A Method to Improve the Accuracy of Bridge Cranes Overload Protection Using the Signal Graphย (2024)ย ๐Ÿ“–
  • Neural Networks Toward Cybersecurity: Domain Map Analysis of State-of-the-Art Challengesย (2024)
  • On Model of Recurrent Neural Network on a Time Scale: Exponential Convergence and Stability Researchย (2024)
  • The Influence of the Load Modelling Methods on Dynamics of a Mobile Craneย (2024)ย ๐Ÿ“–
  • Amperometric Biosensor Based on Glutamate Oxidase to Determine Ast Activityย (2024)ย ๐Ÿ“ฐ
  • ะะฝะฐะปั–ั‚ะธั‡ะฝะธะน ะพะณะปัะด ะฟั–ะดั…ะพะดั–ะฒ ะดะพ ะฟั€ะพะตะบั‚ัƒะฒะฐะฝะฝั ะฒะธั€ะพะฑะฝะธั‡ะธั… ะผะตั€ะตะถย (2024)
  • Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business Requirementsย (2024)

Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

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Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

Ms. Rachel Stephen Mollel, University of Strathclyde, United Kingdom

Rachel Stephen Mollel is a Ph.D. student in Electrical and Electronic Engineering at the University of Strathclyde, UK. Her research focuses on machine learning, explainable AI, energy demand-side management, smart metering, and non-intrusive load monitoring (NILM). She holds a Master of Engineering from Arkansas Tech University, USA, and a Bachelor’s degree in Telecommunication Engineering from Visvesvaraya Technological University, India. Rachel has contributed significantly to the energy sector, exploring the role of smart meters in reducing energy costs and enhancing communication between energy providers and consumers. Her recent work, which investigates the potential of NILM to reveal hidden demand flexibility in residential energy consumption, has been published in various peer-reviewed journals and conferences. Additionally, she is actively involved in improving the interpretability of NILM models to enhance algorithm performance. Her contributions have been recognized with a Commonwealth Scholarship in 2020.

Professional Profile:

ORCID

Summary of Suitability for the Best Scholar Award:

Rachel Stephen Mollel is a highly suitable candidate for the Best Research Scholar Award based on her significant contributions to the fields of machine learning, explainable AI, and energy demand-side management. As a PhD student at the University of Strathclyde, her research aims to address critical energy issues through innovative approaches like Non-Intrusive Load Monitoring (NILM), which helps uncover hidden demand flexibility in residential energy consumption.

Education:

  • 2021 – Present: PhD in Electrical and Electronic Engineering, University of Strathclyde, UK
  • 2010 – 2012: Master of Engineering, Arkansas Tech University, USA (GPA: 3.75/4.0)
  • 2006 – 2010: Bachelor’s degree in Telecommunication Engineering, Visvesvaraya Technological University, India (First Class)

Work Experience:

  • 2011 – 2012: Graduate Assistant, Arkansas Tech University, USA
    Assisted in the Digital Logic and Robotics Course & Lab; delivered tutorials, graded lab reports and exams, and supported the development of course materials under faculty supervision.
  • 2014 – 2020: Assistant Lecturer, University of Dar es Salaam, Tanzania
    Delivered lectures, prepared and graded exams in Control Systems Engineering and Fundamentals of Electrical Engineering. Supervised undergraduate student projects, practical training, and fieldwork. Managed various administrative duties, such as student registration and coordination of departmental examinations.

Publication top Notes:

Explainability-Informed Feature Selection and Performance Prediction for Nonintrusive Load Monitoring

Using explainability tools to inform non-intrusive load monitoring algorithm performance

Using explainability tools to inform NILM algorithm performance

Mr. CHENGYONG JIANG | Machine Learning Award | Best Researcher Award

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Mr. CHENGYONG JIANG | Machine Learning Award | Best Researcher Awardย 

Mr. CHENGYONG JIANG, Fudan university, China

Chengyong Jiang is a promising neurobiology Ph.D. candidate at Fudan University, China, with an outstanding academic record and significant research experience. He earned his Masterโ€™s degree in Biotechnology from Minzu University of China, graduating in the top 5% of his class, and is currently pursuing his doctoral studies at Fudan University, where he is ranked in the top 10% of his cohort. Jiang’s research focuses on the regulation of sleep and eye movement by cholinergic neurons in the oculomotor nerve nucleus. Jiang has demonstrated a strong commitment to academic and practical excellence through various roles, including as a teaching assistant at Beijing Foreign Studies University and a high school biology tutor at Hangzhou Zhipeng Network Technology Co., Ltd. His involvement in innovative projects, such as studying the therapeutic effects of Polygonum multiflorum on stress-induced depression and leading a social practice team analyzing undergraduate education in biology, highlights his leadership and research capabilities.

Professional Profile:

Summary of Suitability for Best Researcher Award:

Chengyong Jiang has demonstrated a strong academic background and research capability in neurobiology and biotechnology. His work, including his master’s research on stress-induced depression and his ongoing doctoral research on sleep and eye movement regulation, reflects a deep understanding of complex biological processes. His publications in reputable journals like Frontiers in Neuroscience and Advanced Science underscore his ability to conduct impactful and high-quality research.

Education

Fudan University, Shanghai, China
Neurobiology Doctor
September 2020 – June 2026
Top 10%

Minzu University of China, Beijing, China
Master of Biotechnology
September 2015 – June 2019
Top 5%

Work Experience

Institutes of Brain Science, Fudan University
Researcher
September 2020 – Present

  • Conducting research on “Regulation of sleep and eye movement by cholinergic neurons in the nucleus of the oculomotor nerve.”

Hangzhou Zhipeng Network Technology Co., Ltd., Hangzhou, China
High School Biology Tutor (Part-time)
September 2017

  • Provided online tutoring in biology to middle and high school students.

Beijing Foreign Studies University, Beijing, China
Teaching Assistant
July 2017 – September 2017

  • Participated in and organized the “E PLUS Beiwai Yijia Study Tour” summer camp, served as homeroom teacher, and assisted in English teaching activities.

Publication top Notes:

MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice

Exosomes Derived from M2 Microglial Cells Modulated by 1070-nm Light Improve Cognition in an Alzheimer’s Disease Mouse Model.

Tracking Eye Movements During Sleep in Mice.

2,3,5,4′-Tetrahydroxystilbene-2-O-beta-D-glucoside Reverses Stress-Induced Depression via Inflammatory and Oxidative Stress Pathways.