Prof Dr. Gulnihal Ozbay | Machine Learning Award | Best Researcher Award

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Prof Dr. Gulnihal Ozbay | Machine Learning Award | Best Researcher Award 

Prof Dr. Gulnihal Ozbay, Delaware State University, United States

Dr. Gulnihal Ozbay is a distinguished Professor and Extension Specialist in Natural Resources at Delaware State University, where she also serves as Director of the Environmental Health & Seafood Safety Lab and the Integrative Ph.D. Program in Agriculture, Food, and Environmental Sciences. Her career is marked by significant achievements in diverse fields, including aquaculture, fisheries, water chemistry, and aquatic ecology. Dr. Ozbay is highly regarded for her expertise in program development, grant writing, and student mentorship. She has built and managed several research labs, including the Mariculture Lab and GIS Lab, and has a strong record of collaboration with various institutions and agencies. Dr. Ozbay holds multiple degrees in relevant fields, including a Ph.D. in Fisheries & Allied Aquacultures from Auburn University and an M.Sc. in Food Science & Biotechnology from Delaware State University. Her leadership extends beyond teaching and research to include roles such as Vice President of DSU AAUP and Chair of the DSU Faculty Research Committee. Her commitment to environmental science is evident in her active participation in programs addressing sustainability, climate change, and seafood safety.

Professional Profile:

Suitability for the Best Researcher Award

Dr. Gulnihal Ozbay’s extensive career demonstrates exceptional proficiency in various fields related to natural resources, including aquaculture, fisheries, water chemistry, aquatic ecology, climate science, seafood chemistry, and microbiology. His role as a Professor and Extension Specialist, combined with his leadership positions, showcases his strong research background and administrative capabilities.

🎓 Professional Preparation

  • Ph.D., Fisheries & Allied Aquacultures (Water Quality)
    Auburn University, 2002
  • Ph.D. Credits, Food Science & Technology
    Dalhousie University, 1999
  • M.Sc., Bio-Resource Engineering (Marine Bio-Resources)
    University of Maine, 1996
  • M.Sc., Food Science & Biotechnology
    Delaware State University, 2016
  • B.Sc., Fisheries & Aquaculture Engineering
    University of Ondokuzmayis, 1991

🏆 Professional Appointments

  • Professor & Extension Specialist, Natural Resources
    Delaware State University, 2012 – Present
  • Adjunct Faculty, Food Science & Biotechnology Graduate Program
    DSU, 2008 – Present
  • Adjunct Faculty, Applied Chemistry Graduate Program
    DSU, 2018 – Present
  • Director, Environmental Health & Seafood Safety Lab
    DSU, 2009 – Present
  • Director, Integrative Ph.D. Program in Agriculture, Food and Environmental Sciences (IAFES)
    DSU, 2021 – Present
  • Vice President, DSU AAUP
    2021 – Present

đź“š Teaching Experience

  • Environmental Toxicology
    DSU, 2020-Present
  • Climatology
    DSU, 2012-Present
  • Introduction to Environmental Science
    DSU, 2011-Present
  • Special Problems (Sustainability & Climate Change)
    DSU, 2004-Present
  • Graduate Seminar
    DSU, 2010

Publication top Notes:

CITED: 78
CITED: 74
CITED: 68
CITED:56
CITED: 53
CITED: 51

Prof Dr. Ersin Elbasi | Machine learning Award | Excellence in Research

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Prof Dr. Ersin Elbasi | Machine learning Award | Excellence in Research

Prof Dr. Ersin Elbasi, American University of the Middle East, Kuwait 

Ersin Elbasi, Ph.D., is a distinguished professor specializing in Computer Science and Engineering, currently serving at the American University of the Middle East in Kuwait. He earned his Ph.D. in Computer Science from the Graduate Center, CUNY, with a dissertation on robust video watermarking schemes, following a Master of Philosophy in the same field from the same institution. He holds a Master of Science in Electrical Engineering and Computer Science from Syracuse University and a Bachelor of Science in Industrial Engineering from Sakarya University, Turkey. Dr. Elbasi’s extensive academic career includes previous positions as Associate Professor at the American University of the Middle East and faculty roles at the Higher Colleges of Technology in the UAE and Çankaya University in Turkey. His research focuses on machine learning, multimedia security, and data mining, with notable projects in digital image and video watermarking and event mining in video sequences. He has also held significant roles in research and development at TĂśBÄ°TAK, contributed to SQL application development in New York City, and engaged in various international research activities. Dr. Elbasi’s technical expertise spans Visual C++, SQL programming, and Java, with a notable scholarship record and recognition for his contributions to the field.

Professional Profile:

Summary of Suitability for Excellence in Research 

Dr. Ersin Elbasi holds a Ph.D. in Computer Science from the Graduate Center, CUNY, with a specialization in robust video watermarking schemes in transform domains. His advanced degrees in computer science, electrical engineering, and industrial engineering demonstrate a strong interdisciplinary foundation.

Education

  • Ph.D. in Computer Science
    Graduate Center, CUNY, New York City, NY
    Graduated: April 2007
    Dissertation: “Robust Video Watermarking Scheme in Transform Domains”
  • Master of Philosophy in Computer Science
    Graduate Center, CUNY, New York City, NY
    Graduated: May 2006
  • Master of Science in Electrical Engineering & Computer Science
    Syracuse University, Syracuse, NY
    Graduated: May 2001
  • Bachelor of Science in Industrial Engineering
    Sakarya University, Sakarya, Turkey
    Graduated: June 1997

Work Experience

  • Professor
    American University of the Middle East (QS ranking 500-600), Kuwait
    June 2022 – Current
  • Associate Professor
    American University of the Middle East (QS ranking 500-600), Kuwait
    October 2016 – June 2022

    • Taught courses including CNIT 180, CNIT 280, CNIT 380, CNIT 315, CS 159, CNIT 480, CNIT 372, CNIT 399/499, TECH 330, and TECH 320.
  • Faculty Member
    Computer and Information Science, Higher Colleges of Technology, Al Ain, Abu Dhabi, UAE
    August 2015 – July 2016

    • Taught courses including Introduction to Multimedia, Research Methods in Emerging Technologies, Statistics and Probability, and Information Systems in Organizations and Society.
  • Instructor/Associate Professor
    Çankaya University (400-500 by Times ranking), Department of Computer Engineering, Ankara
    September 2007 – June 2015

    • Taught courses including Data Mining, Multimedia Security, Object-Oriented Languages, Database Management, Multimedia and Internet, Data Management and File Structures, and Formal Languages and Automata.
  • Expert/Chief Expert of Scientific Programs
    TĂśBÄ°TAK, Ankara, Turkey
    August 2007 – July 2014

    • Served as Executive Secretary to the Electrical, Electronics, and Informatics Research Grant Committee, National Scientific Expert in COST Information and Communication Domain, and National Delegate in COST (FP 7) Trans Domain Proposals.
  • SQL Application Developer
    Bureau of Revenue Enhancement and Automation, Finance Office, New York City Government
    November 2004 – July 2007

    • Developed SQL applications, performed ad-hoc queries, and managed staff training in SQL and related software tools.
  • Instructor
    The City University of New York (CUNY)
    September 2004 – May 2007

    • Taught courses at Brooklyn College, Borough Manhattan Community College, and Lehman College, including Operations Management, Introduction to Computer Applications, Database Management, Discrete Structures, and GMAT Math.
  • Research Assistant
    Electrical Engineering and Computer Science, Syracuse University
    January 2003 – May 2004

    • Worked on Automated Scenario Recognition in Video Sequences and implemented data mining and machine learning techniques.
  • Engineer
    Calik Textile, Istanbul, Turkey
    January 1999 – August 1999

    • Focused on Production Planning.
  • Engineer
    HES Machine, Kayseri, Turkey
    June 1997 – March 1998

    • Focused on Production Planning and Quality Control.

Publication top Notes:

Transformer Based Hierarchical Model for Non-Small Cell Lung Cancer Detection and Classification

Anticipate Movie Theme from Subtitle: A Deep Learning Approach

Robust and Secure Watermarking Algorithm Based on High Frequencies of Integer Wavelet Transform

Fortifying Integrity and Privacy in Medical Imaging: Discrete Shearlet and Radon Transform-Based Watermarking Approach

Machine Learning-Based Analysis and Prediction of Liver Cirrhosis

 

 

 

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

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

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 Master’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.

Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award

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Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award 

Mr. Omer Tariq, Korea Advanced Institute of Science and Technology, KAIST, South Korea

Omer Tariq is a Ph.D. candidate at the Korea Advanced Institute of Science and Technology (KAIST), specializing in efficient and privacy-preserving deep learning for AIoT and autonomous systems. With a strong foundation in digital ASIC design, embedded systems, and hardware design, Omer has over seven years of experience in developing and deploying innovative machine learning solutions using TensorFlow, TensorRT, and PyTorch. His research includes advanced robotics software systems, autonomous navigation, and state-of-the-art motion planning algorithms. He has led teams in high-performance SoC/RTL design and verification at the National Electronics Complex, Pakistan, and contributed to satellite imaging systems at SUPARCO. Omer holds a BSc in Electrical Engineering from the University of Engineering and Technology, Taxila, and has published several papers in prominent journals. His technical skills are complemented by a range of certifications in machine learning, data science, and digital signal processing.

Professional Profile:

Summary of Suitability for Best Researcher Award

Omer Tariq is a Ph.D. candidate specializing in efficient and privacy-preserving deep learning for AIoT and Autonomous Systems. His work is highly relevant to current technological advancements and addresses significant challenges in machine learning, robotics, and autonomous systems. His research includes:

Education

Korea Advanced Institute of Science and Technology (KAIST)
Doctor of Philosophy (Ph.D.) in Computer Science
May 2021 – July 2025

  • Majors: Machine Learning & AI
  • CGPA: 3.74/4.3
  • Coursework: Programming for AI, Introduction to Artificial Intelligence, Design and Analysis of Algorithms, Intelligent Robotics, Human-Computer Interaction, Artificial Intelligence and Machine Learning, Technical Writing for Computer Science, Advanced Machine Learning, IoT Datascience

University of Engineering and Technology (UET), Taxila
Bachelor of Science in Electrical Engineering
Nov 2010 – July 2014

  • CGPA: 3.25/4.0
  • Thesis: Computer Vision-Assisted Object Detection and Control Framework for 3-DoF Robotic Arm
  • Area: Microelectronics, Control Systems, and Advanced Computer Architecture

Work Experience

Department of Industrial & Systems Engineering (ISysE), KAIST
Research Assistant
Nov. 2023 – March 2024

  • Designed and developed the electronics and power management module for the DAIM-Autonomous Mobile Robot, enhancing operational efficiency and reliability.
  • Engineered advanced robotics software systems for autonomous navigation and task execution.
  • Implemented state-of-the-art robot motion planning, mapping, and localization (SLAM) algorithms to improve real-time navigation accuracy.

National Electronics Complex, Pakistan (NECOP)
Engineering Manager & Team Lead
Apr. 2019 – Sep. 2022

  • Led verification and validation of high-performance SoC/RTL designs, ensuring system performance and reliability.
  • Spearheaded RTL development and optimization for high-performance IC designs, including logic synthesis, DFT, scan chain insertion, formal verification, and static timing analysis.
  • Managed the use of Synopsys and Cadence EDA tools for front-end and back-end digital IC design processes.

National Space Agency, Pakistan (SUPARCO)
Assistant Manager
Oct. 2014 – Apr. 2019

  • Designed and developed satellite imaging payload systems for national satellite missions.
  • Engineered high-speed, multi-layer PCB designs and conducted signal/power integrity simulations for satellite systems.
  • Developed embedded systems for the Satellite Ku-Band Positioning Unit, enhancing communication and positioning capabilities.

Publication top Notes:

2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation

TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection

Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC Implementations

DeepIOD: Towards A Context-Aware Indoor–Outdoor Detection Framework Using Smartphone Sensors

 

 

Mrs. Nazli Kazemi | Machine Learning Award | Best Researcher Award

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Mrs. Nazli Kazemi | Machine Learning Award | Best Researcher Award 

Mrs. Nazli Kazemi, Polytechnique Montr´eal, Canada

Nazli Kazemi, designated as Mrs., is a distinguished figure in the field of Electrical Engineering, currently serving as a Postdoctoral Fellow at Polytechnique Montreal. Born on August 18, 1986, in Athens, Greece, Nazli’s journey in academia and research spans over a decade, marked by groundbreaking contributions to microwave sensors enhanced with machine learning techniques. Nazli holds a Ph.D. in Software Engineering and Intelligent Systems from the University of Alberta, complemented by an M.Sc. in Electromagnetics and Microwaves and a B.Sc. in Electrical Engineering from Iran University of Science and Technology. Her academic prowess and practical acumen shine through her role as an RF/Antenna Consultant at Polaris RF Antenna Solutions Inc., where she pioneers innovative wireless power transfer systems for electric vehicles. Her research focuses on advancing microwave planar sensors, leveraging machine learning algorithms to enhance accuracy and sensitivity across diverse applications. Notably, Nazli’s work includes pioneering contributions to energy harvesting using metasurfaces, anomaly detection in smart grids, and the development of predictive glucose sensors. She has authored numerous papers in esteemed journals and is recognized for her teaching excellence at Humber College.

Professional Profile:

ORCID

 

Education:

  • Ph.D. in Software Engineering and Intelligent Systems
    • University of Alberta, Canada
    • Thesis: Enhancing Microwave Sensors with Advanced Machine-Learning Techniques
  • M.Sc. in Electromagnetics and Microwaves
    • Iran University of Science and Technology, Iran
  • B.Sc. in Electrical Engineering
    • Iran University of Science and Technology, Iran

Current Position:

  • Postdoctoral Fellow
    • Polytechnique Montreal, Canada
    • Department of Electrical Engineering
    • Specializing in Microwave Sensors with a focus on Machine Learning

Previous Positions:

  • Research Associate
    • Energy Digitization Lab, University of Alberta
    • Projects: Energy harvesting using metasurfaces, anomaly detection in smart grids, glucose sensing with predictive features
  • RF/Antenna Consultant
    • Polaris RF Antenna Solutions Inc., Canada
    • Developed wireless power transfer systems for electric vehicles

Teaching Experience:

  • Humber College, Canada
    • Courses: Digital fundamentals, networking technologies, sensors
    • Integrated theoretical knowledge with practical applications

Research Focus Areas:

  • Microwave Sensors
  • Machine Learning
  • Active Circuit Design
  • Radar and Remote Sensing

Academic Achievements:

  • Published numerous papers in Biosensors and Bioelectronics, IEEE Transactions, Sensors, etc.
  • Recognized with several awards and accolades for innovation and research excellence

This background underscores Nazli Kazemi’s extensive academic journey and professional contributions in advancing microwave sensor technology through innovative research and practical applications.

 

Publication top Notes:

 

Distribution Grid Fault Classification and Localization using Convolutional Neural Networks

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

AI-Assisted Ultra-High-Sensitivity/Resolution Active-Coupled CSRR-Based Sensor with Embedded Selectivity

In-human testing of a non-invasive continuous low-energy microwave glucose sensor with advanced machine learning capabilities

Resolution enhancement of microwave sensors using super-resolution generative adversarial network