Maksym Lazirko | Computer Science | Innovation Catalyst Award

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Prof. Dr. Maksym Lazirko | Computer Science | Innovation Catalyst Award

Professor of Audit Analytics | Rutgers University | United States

Prof. Dr. Maksym Orest Lazirko is a dedicated scholar, educator, and technologist specializing in Accounting Information Systems and emerging technologies including Quantum Computing, Blockchain, and ESG analytics. As an ABD Doctoral Candidate and Adjunct Professor at Rutgers University, Newark, he integrates academic rigor with innovation in digital accounting and intelligent systems. He holds a Bachelor of Science in Management of Information Systems with a minor in Geological Science from Rutgers University, where his interdisciplinary curiosity led him toward research on quantum-enhanced financial reporting, blockchain validation, and sustainable business analytics. Maksym’s teaching and research experience spans managerial and financial accounting, data warehousing, and systems design, complemented by roles as research assistant, server administrator, and mentor. His work bridges technology and transparency, emphasizing quantum models, blockchain assurance, and ESG integration for improved auditability. Recognized for his academic contributions, teaching excellence, and community engagement, Maksym exemplifies the new generation of scholars driving interdisciplinary innovation across accounting, technology, and sustainability.

Profile : Scopus | Google Scholar 

Featured Publications 

Lazirko, M., Appelbaum, D., & Vasarhelyi, M. (2025). Proof of reserves: A double-helix framework. The British Accounting Review. DOI: 10.1016/j.bar.2025.101730 — Cited by 5 articles.

Lazirko, M. (2025). The quantum dynamics of cost accounting: Investigating WIP via the time-independent Schrödinger equation. Journal of Decision Science and Optimization, 1(1), 35–54. DOI: 10.55578/jdso.2506.002 — Cited by 3 articles.

Lazirko, M., & Gates, A. (2025). Unveiling nature’s fury: Natech disasters for business – A continuous monitoring and reporting framework. Journal of Risk Research. DOI: 10.1080/13669877.2025.2466534 — Cited by 2 articles.

Dr. Hadi Amirpour | Computer Science | Best Researcher Award

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Dr. Hadi Amirpour | Computer Science | Best Researcher Award

Dr. Hadi Amirpour , University of Klagenfurt , Austria.

Dr. Hadi Amirpour 🎓 is a dynamic researcher in Computer Science, currently based in Austria 🇦🇹. He earned his Ph.D. from the University of Klagenfurt in 2022 🧠. With a solid foundation in both Biomedical 🧬 and Electrical Engineering ⚡, Hadi blends interdisciplinary expertise to drive innovation in technology and healthcare. His academic journey spans prestigious institutions in Iran 🇮🇷 and Europe, showcasing a global perspective 🌍. Passionate about AI, systems design, and signal processing 🤖📡, Hadi is also active in international collaborations and knowledge sharing via Skype and other platforms 💬🌐.

Professional Profile

Orcid
Scopus
Google Scholar

Education & Experience

  • 🎓 Ph.D. in Computer Science – University of Klagenfurt, Austria (2022)

  • 🎓 M.Sc. in Electrical Engineering – K.N. Toosi University of Technology, Iran (2014)

  • 🎓 B.Sc. in Biomedical Engineering – Azad University, Iran (2016)

  • 🎓 B.Sc. in Electrical Engineering – Amirkabir University of Technology, Iran (2009)

  • 🧑‍💻 Researcher and Developer – Involved in interdisciplinary research across Europe and Asia

  • 🌐 International Academic Collaborator – Actively participating in global research networks

Summary Suitability

Dr. Hadi Amirpour is an exceptional candidate for the Best Researcher Award, recognized for his groundbreaking work at the intersection of computer science, multimedia systems, and biomedical signal processing. With a proven track record of high-impact research, pioneering patents, and leadership in global academic forums, Dr. Amirpour stands out as a thought leader who consistently advances the frontiers of multimedia communication and immersive media technologies.

Professional Development

Dr. Hadi Amirpour continually pursues professional development through cutting-edge research 🔍, academic collaborations 🤝, and participation in scientific communities 🧑‍🔬. He regularly attends international conferences 🌍, contributes to peer-reviewed journals 📚, and engages in mentorship programs to support young researchers 🎓. His multi-disciplinary background in electrical, biomedical, and computer science empowers him to navigate complex research challenges 🧠⚙️. Hadi maintains active communication through professional platforms like Skype 💻 and stays updated on emerging technologies, including AI and data-driven solutions 🤖📈. His commitment to lifelong learning and global innovation sets him apart as a proactive academic leader 🚀.

Research Focus 

Dr. Hadi Amirpour’s research focus lies at the intersection of Computer Science, Biomedical Engineering, and Electrical Systems 🧠⚡🧬. His primary interests include artificial intelligence 🤖, signal processing 🎛️, embedded systems ⚙️, and data analysis for healthcare applications 🏥📊. By integrating engineering techniques with computational intelligence, he contributes to the advancement of smart medical technologies 💉📱 and real-time system optimization 🌐🕒. Hadi’s cross-disciplinary expertise allows him to approach research problems with a holistic view 🔬, fostering innovation in both academic and industrial settings 🏢💡. He aims to make impactful contributions to technology that improves human health and well-being 🌍❤️.

Awards & Honors

  • 🧪 Organizer & Contributor – ACM Multimedia Workshops
    • Multimedia Computing for Health and Medicine (MCHM), 2025 🩺📹
    • Interactive eXtended Reality (IXR), 2022 & 2023 🕶️🌐

  • 🎓 Organizer – MobiSys SMS Workshop
    • Students in MobiSys (SMS), 2021 👨‍💻📱

  • 📘 Tutorial Presenter at Prestigious Conferences
    • ACM Multimedia 2025 – Perceptual Visual Quality Assessment 🧠📺
    • IEEE ICME 2025 – Video Coding in HTTP Adaptive Streaming 🎞️🌍
    • IEEE ICME 2023 – HAS, Video Codecs & Encoding Optimization ⚙️🔄
    • IEEE VCIP 2023 – Video Encoding for Adaptive Streaming 📡🎬

Publication Top Notes

  • 📊 VCA: Video Complexity Analyzer
    Authors: V.V. Menon, C. Feldmann, H. Amirpour, M. Ghanbari, C. Timmerer
    Venue: Proceedings of the 13th ACM Multimedia Systems Conference, pp. 259–264
    Citations: 67
    Year: 2022
    🔍 A comprehensive tool for analyzing video complexity to optimize streaming workflows.

  • 🌐 A Tutorial on Immersive Video Delivery: From Omnidirectional Video to Holography
    Authors: J. Van Der Hooft, H. Amirpour, M.T. Vega, Y. Sanchez, R. Schatz, T. Schierl, et al.
    Venue: IEEE Communications Surveys & Tutorials, Vol. 25(2), pp. 1336–1375
    Citations: 58
    Year: 2023
    🧠 An authoritative tutorial exploring advanced immersive media delivery technologies.

  • 📽️ PSTR: Per-Title Encoding using Spatio-Temporal Resolutions
    Authors: H. Amirpour, C. Timmerer, M. Ghanbari
    Venue: IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6
    Citations: 49
    Year: 2021
    🧩 Proposes a novel per-title encoding method that leverages spatio-temporal optimization.

  • 📉 INTENSE: In-depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in HTTP Adaptive Streaming
    Authors: B. Taraghi, M. Nguyen, H. Amirpour, C. Timmerer
    Venue: IEEE Access, Vol. 9, pp. 118087–118098
    Citations: 44
    Year: 2021
    ⚙️ Explores the user experience impact of buffering and bitrate changes in streaming.

  • 🎬 OPTE: Online Per-Title Encoding for Live Video Streaming
    Authors: V.V. Menon, H. Amirpour, M. Ghanbari, C. Timmerer
    Venue: ICASSP 2022 – IEEE International Conference on Acoustics, Speech and Signal Processing
    Citations: 39
    Year: 2022
    🕒 Introduces a real-time approach to optimize live streaming quality through encoding.

  • 🤖 DeepStream: Video Streaming Enhancements using Compressed Deep Neural Networks
    Authors: H. Amirpour, M. Ghanbari, C. Timmerer
    Venue: IEEE Transactions on Circuits and Systems for Video Technology
    Citations: 38
    Year: 2022
    🧠 Applies deep learning for enhanced video streaming performance and compression.

  • 📦 Multi-Codec Ultra High Definition 8K MPEG-DASH Dataset
    Authors: B. Taraghi, H. Amirpour, C. Timmerer
    Venue: Proceedings of the 13th ACM Multimedia Systems Conference, pp. 216–220
    Citations: 32
    Year: 2022
    🎞️ Presents a rich dataset to support benchmarking and research in 8K video streaming.

Conclusion

Dr. Hadi Amirpour’s sustained research excellence, technical innovations, and global academic contributions make him highly deserving of the Best Researcher Award. His work not only demonstrates scholarly depth and innovation but also shows clear translational impact on the future of multimedia technologies and intelligent streaming systems. His unique combination of interdisciplinary expertise, prolific output, and international leadership defines the essence of a world-class researcher.

Licheng Liu | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Licheng Liu | Computer Science | Best Researcher Award 

Associated professor, at Hunan University, China.

Dr. Licheng Liu (刘立成) is an Associate Professor at the School of Electrical and Information Engineering, Hunan University, China. He earned his Ph.D. from the University of Macau under the mentorship of Prof. C.L. Philip Chen, a Fellow of the IEEE and a Member of the European Academy of Sciences. Dr. Liu is recognized as a Yue Lu Scholar and serves as a Ph.D. advisor. He is a Senior Member of IEEE and has received the Hunan Provincial Outstanding Young Scientist Fund. His research interests encompass deep learning, broad learning systems, and sparse manifold learning. He has authored nearly 50 papers in top-tier journals and conferences, including IEEE Transactions on Cybernetics, Neural Networks and Learning Systems, and Circuits and Systems for Video Technology. His work has garnered over 1,265 citations and an h-index of 17.

Professional Profile

Scopus

🎓 Education 

Dr. Liu’s academic journey began with a Bachelor’s degree in Mathematics and Physics from China University of Geosciences (Wuhan) in 2010. He then pursued a Master’s degree in Mathematics at Hunan University, graduating in 2012. His doctoral studies were completed at the University of Macau in 2016, where he worked under the supervision of Prof. C.L. Philip Chen. Throughout his education, Dr. Liu focused on areas such as sparse representation, image processing, and machine learning, laying a strong foundation for his subsequent research endeavors.

💼 Experience

Dr. Liu commenced his professional career as an Assistant Professor at Hunan University’s School of Electrical and Information Engineering in 2016. By 2019, he was promoted to Associate Professor and was honored as a Yue Lu Scholar. In his academic role, Dr. Liu has supervised numerous graduate students and has been actively involved in various research projects, particularly those funded by the National Natural Science Foundation of China. His research contributions have significantly advanced the fields of image restoration, face hallucination, and noise reduction in visual data.

🔬 Research Interests 

Dr. Liu’s research interests are centered on deep learning, broad learning systems, and sparse manifold learning. He is particularly focused on developing novel algorithms and models to enhance image restoration, low-light object detection, and low-quality image recognition. His work aims to address challenges in visual data processing, such as noise reduction and image enhancement, by leveraging advanced machine learning techniques. Dr. Liu’s innovative approaches have led to the development of robust models capable of improving the quality and accuracy of visual data interpretation in various applications.

🏆 Awards 

Dr. Liu has received several prestigious awards throughout his career. In 2016, he was honored with the Macao SAR Graduate Student Science and Technology Research Award by the Macao Science and Technology Development Fund. In 2018, he was recognized as a Yue Lu Scholar by Hunan University. His excellence in teaching was acknowledged in 2021 when he received the First-Class Teaching Achievement Award from Hunan University. The following year, he was awarded the Special Prize for Higher Education Teaching Achievement by the Hunan Provincial Department of Education. In 2023, Dr. Liu received the National Teaching Achievement Award (Second Class), and in 2024, he was named an Outstanding Master’s Thesis Advisor in Hunan Province. Additionally, he was honored with the Third Prize in Natural Science by the Chinese Association of Automation in 2024.

📚Top Noted  Publications 

Dr. Liu has authored nearly 50 research papers, with 25 published in IEEE/ACM journals. Notable publications include:

1. Weighted Joint Sparse Representation for Removing Mixed Noise in Image (2017)

  • Journal: IEEE Transactions on Cybernetics, 47(3), 600–611.

  • Summary: This paper introduces a method for removing mixed noise in images using a weighted joint sparse representation. The approach aims to effectively address challenges posed by mixed noise types in image processing.

2. Robust Face Hallucination via Locality-Constrained Bi-Layer Representation (2018)

  • Journal: IEEE Transactions on Cybernetics, 48(4), 1189–1201.

  • Summary: The authors propose a robust face hallucination method that utilizes a locality-constrained bi-layer representation. This technique enhances face image resolution while maintaining robustness against noise and outliers. europepmc.org

3. Mixed Noise Removal via Robust Constrained Sparse Representation (2018)

  • Journal: IEEE Transactions on Circuits and Systems for Video Technology, 28(9), 2177–2189.

  • Summary: This paper presents a robust constrained sparse representation method for removing mixed noise from images. The approach adapts to different noise types and effectively restores image quality. figshare.com

4. Discriminative Face Hallucination via Locality-Constrained and Category Embedding Representation (2021)

  • Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 7314–7325.

  • Summary: The authors introduce a discriminative face hallucination method that combines locality-constrained representation with category embedding. This approach improves the quality of face image super-resolution by considering category-specific information.

5. Modal-Regression-Based Broad Learning System for Robust Regression and Classification (2023)

  • Journal: IEEE Transactions on Neural Networks and Learning Systems, 35(9), 12344–12357.

  • Summary: This paper proposes a modal-regression-based broad learning system to enhance robustness in regression and classification tasks. The method addresses challenges posed by noisy and outlier-prone data, improving model performance. pubmed.ncbi.nlm.nih.gov

Conclusion

Dr. Licheng Liu demonstrates exceptional strength as a mid-career researcher with an outstanding publication record, robust funding history, and recognized academic leadership in AI and image processing. His ability to balance theoretical innovation with practical application is evident in his funded projects and impactful publications.

Uday Garg | Artificial Intelligence | Best Researcher Award

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Mr. Uday Garg | Artificial Intelligence | Best Researcher Award 

Mr. Uday Garg, at Chandigarh University, India.

Uday Garg is a passionate and dynamic Computer Science Engineer with a proven record in leadership and technology. A former Head Boy and current Class Representative, he balances academic rigor with hands-on experience in development and event management. Proficient in programming languages such as Python and C++, Uday has contributed to both web and Android development, delivering projects in collaboration with startups like Zonson Infotech. His involvement in IEEE at Chandigarh University, both as Treasurer and Executive Member, underscores his dedication to community engagement and tech growth. With a minor in AI from IIT Ropar and multiple NPTEL certifications, Uday exemplifies a commitment to continuous learning. His creative interests in graphic design and love for classical music add depth to his well-rounded personality. A tech innovator with a vision, Uday aspires to bridge technological solutions with real-world challenges.

Professional Profile

ORCID

Education 🎓

Uday Garg’s educational journey showcases both academic versatility and technical depth. Currently pursuing a Minor in Artificial Intelligence (2024–25) from IIT Ropar, he earlier completed his Bachelor of Engineering in Computer Science from Chandigarh University (2020–2024), securing a CGPA of 7.9. His foundation in school academics was laid at Chapra Central School, achieving 9.6 CGPA in Class X and 60.5% in Class XII (CBSE). Uday has also earned industry-recognized certifications, including Software Testing from NPTEL IIIT Bangalore, Data Mining from NPTEL IIT Kharagpur, and an A-grade in Space Technology Awareness Training by ISRO. This academic trajectory reflects a persistent curiosity and a commitment to expanding both theoretical and applied knowledge. With a strong command of programming, AI, and emerging technologies, Uday’s academic record complements his innovative drive and career ambitions.

Experience 💼 

Uday Garg’s professional experience bridges academia and industry with impactful contributions. In 2024, he interned as a Digital Marketing Intern at Sharkify Technology Pvt Ltd, gaining valuable exposure to SEO, analytics, and digital strategies. His practical expertise also extends to software development; between September 2022 and February 2023, he worked as a Freelance Developer at Zonson Infotech Private Limited, where he handled real-time development projects, especially Android and UI/UX tasks. Uday’s collaborative development of a Dormitory Management App using Flutter and Firebase highlights his technical execution skills. He also designed and deployed a Semester Syllabus Portal independently using HTML, CSS, and JavaScript within a tight 20-day timeline. From RPA-based invoice generators to full-stack food blogging platforms, Uday’s hands-on experience reflects adaptability and competence in solving diverse technical challenges. His leadership roles in IEEE also speak to his management skills and collaborative spirit.

Research Interest 🔬

Uday Garg’s research interests lie at the intersection of Artificial Intelligence, Cybersecurity, and Human-Centric Computing. He is particularly fascinated by the vulnerabilities within facial recognition systems and how advanced algorithms can enhance both security and privacy. His recent work titled “An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems” presented at AIDE-2023, underscores his commitment to identifying real-world issues in biometric technologies and proposing viable solutions. With a minor in AI from IIT Ropar and foundational training in data mining, Uday is also keen on exploring Machine Learning-based automation, natural language processing, and ethical AI. His practical orientation is balanced by philosophical curiosity, often delving into ethics, privacy, and the societal implications of AI. Uday aspires to contribute to research that not only pushes technical boundaries but also remains grounded in human values, making technology inclusive, accountable, and forward-looking.

Awards 🏅 

Uday Garg’s academic and extracurricular achievements reflect a balanced profile of intellect and leadership. He was elected Treasurer and Executive Member of the IEEE Chandigarh University Student Branch (2021–2022), a role where he managed event planning, financial oversight, and community engagement. One of his most notable accolades was presenting his research paper at the International Conference on Artificial Intelligence and Data Engineering (AIDE-2023). His paper, focused on cybersecurity in facial recognition systems, was accepted for publication in the American Institute of Physics (AIP)—a significant milestone for a budding researcher. Uday’s appointment as Head Boy in school and his ongoing service as Class Representative in university further reinforce his leadership credentials. These honors and roles speak volumes about his credibility, dedication, and the trust placed in him by peers and mentors alike. With a strong track record of excellence, Uday is poised to make impactful contributions to the tech world.

Top Noted Publication 📚

Uday Garg’s publication record began with a compelling research contribution to the AI and cybersecurity landscape. He authored and presented the paper titled “An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems” at the International Conference on Artificial Intelligence and Data Engineering (AIDE-2023). This paper was published by the American Institute of Physics (AIP) in 2023 and focuses on identifying systemic flaws in facial recognition systems and proposing mitigation techniques through advanced algorithms and encryption.

The paper titled “An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems” by Uday Garg and Arti Rani was presented at a conference and published in the AIP Conference Proceedings on March 1, 2025. It is part of the series with the ISSN: 0094-243X.

Citation Information

  • Title: An Overview of Vulnerabilities and Mitigation Strategies in Facial Recognition Systems

  • Authors: Uday Garg, Arti Rani

  • Publication Date: March 1, 2025

  • Journal: AIP Conference Proceedings

  • ISSN: 0094-243X

  • DOI: 10.1063/5.0262020

Accessing the Paper

You can access the full paper through the DOI link provided above. If you encounter any access restrictions, you might consider checking if your institution provides access or contacting the authors directly for a copy.

Conclusion

Uday Garg shows promising research potential, especially in the intersection of AI, cybersecurity, and application development. His technical breadth, leadership experience, and a published research paper mark him as a strong early-career researcher. However, to fully align with the standards typically expected for a Best Researcher Award, continued focus on depth of research, academic performance, and publication output is advised.

Dharmapuri Siri | Computer Science | Best Researcher Award

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Dr. Dharmapuri Siri | Computer Science | Best Researcher Award 

Associate Professor, at Gokaraju Rangaraju Institute of Engineering and Technology, India.

Dr. D. Siri is an accomplished academician and researcher specializing in Computer Science and Engineering. Currently serving as an Associate Professor at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, she has over 14 years of teaching experience. Her academic journey includes a B.Tech in Information Technology and an M.Tech in Computer Science and Engineering from JNTU, Hyderabad. She earned her Ph.D. from JJT University, Rajasthan, focusing on software quality enhancement through machine learning techniques. Dr. Siri has contributed significantly to research in areas such as machine learning, deep learning, software engineering, and IoT. Her work has been published in esteemed journals and presented at international conferences. Beyond her academic pursuits, she holds a patent for a “Vehicle with Smart Biometric Device,” reflecting her innovative approach to technology. Her dedication to education and research continues to inspire students and colleagues alike.PMC+1ScienceDirect+1ScienceDirect

Professional Profile

Scopus

ORCID

Google Scholar

🎓 Education

Dr. D. Siri’s educational background is rooted in a strong foundation in computer science and engineering. She completed her B.Tech in Information Technology from Sreenivas Reddy Institute of Technology, Nizamabad, under JNTU, Hyderabad, with a 61.36% score. Pursuing further specialization, she obtained an M.Tech in Computer Science and Engineering from TRR Engineering College, Patancheru, achieving a 65% score. Her academic excellence culminated in a Ph.D. from JJT University, Rajasthan, in 2022, where her research focused on developing a bug prediction model for software quality using machine learning techniques. This comprehensive educational journey equipped Dr. Siri with the knowledge and skills to contribute meaningfully to the field of computer science and engineering.

💼 Experience

Dr. D. Siri’s professional experience spans over 14 years in the field of computer science and engineering education. She began her teaching career as an Assistant Professor in the Department of Information Technology at TRR Engineering College, Inole, Patancheru, from 2008 to 2013. Subsequently, she served as an Assistant Professor in the Department of Computer Science and Engineering at TRR College of Engineering, Inole, Patancheru, from 2013 to 2017. Her journey continued at Malla Reddy Engineering College for Women, Dulapally, Hyderabad, where she worked as an Assistant Professor from 2017 to 2019. Currently, Dr. Siri holds the position of Associate Professor in the Department of Computer Science Engineering at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, since 2024. Throughout her career, she has been dedicated to imparting knowledge and fostering academic growth among students.

🔬 Research Interests

Dr. D. Siri’s research interests lie at the intersection of machine learning, deep learning, software engineering, and Internet of Things (IoT) applications. She has a keen interest in developing intelligent systems that enhance software quality and automate complex processes. Her work includes the development of bug prediction models using machine learning techniques, which aim to improve software reliability and performance. Additionally, Dr. Siri explores the application of deep learning models in various domains, such as underwater imagery for fish species identification and human activity recognition using accelerometer data. Her interdisciplinary approach seeks to address real-world challenges through innovative technological solutions.

🏆 Awards

Dr. D. Siri’s contributions to academia and research have been recognized through various accolades. Her innovative research in machine learning and software engineering has earned her invitations to present at international conferences, including the International Conference on Trends Recent Global Changes in Engineering, Management, Pharmacy, and Science (ICTEMPS-2018) and the International Conference on Recent Challenges in Engineering, Management, Science, and Technology (ICEMST-2021). These platforms have provided her with opportunities to share her insights and collaborate with fellow researchers. Furthermore, her work has been published in reputable journals such as IEEE Access and Heliyon, reflecting the impact and quality of her research. Dr. Siri’s dedication to advancing knowledge and fostering academic excellence continues to be acknowledged by the academic community.

📚 Top Noted Publications

Dr. D. Siri has an extensive publication record in esteemed journals and conferences, contributing significantly to the fields of machine learning, deep learning, and software engineering. Notable among her journal publications are:

1. Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278901

  • Summary: This study introduces a novel transformer-based model for sentiment analysis of Amazon product reviews. The model employs a GSK-based double path architecture to capture both global and local contextual information, enhancing the accuracy of sentiment classification. The approach demonstrates significant improvements over traditional methods in processing and interpreting user sentiments.

2. Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278902

  • Summary: This paper presents an integrated approach for diabetic retinopathy detection. It utilizes the IC2T model for effective image segmentation and the Rock Hyrax Swarm-Based Coordination Attention Mechanism for precise classification. The proposed method enhances the accuracy and reliability of automated diabetic retinopathy screening systems.

3. Enhanced Deep Learning Models for Automatic Fish Species Identification in Underwater Imagery

  • Published in: Heliyon, August 2024

  • DOI: 10.1016/j.heliyon.2024.e35217

  • Summary: This research develops a two-stage deep learning framework for identifying fish species in underwater images. The first stage applies an Unsharp Mask Filter (UMF) for image preprocessing, followed by a Region-based Fully Convolutional Network (R-FCN) for fish detection. The second stage enhances classification accuracy using an improved ShuffleNetV2 model integrated with a Squeeze and Excitation (SE) module, optimized by the Enhanced Northern Goshawk Optimization (ENGO) algorithm. The models achieve high performance metrics, including 99.94% accuracy.ScienceDirect+2PubMed+2PMC+2PubMed+2PMC+2ScienceDirect+2

4. Segment-Based Unsupervised Deep Learning for Human Activity Recognition Using Accelerometer Data and SBOA-Based Channel Attention Networks

  • Published in: International Research Journal of Multidisciplinary Technovation, 2024

  • DOI: 10.54392/irjmt2461

  • Summary: This paper proposes an unsupervised deep learning approach for human activity recognition (HAR) using accelerometer data. The method incorporates segment-based SimCLR with Segment Feature Decorrelation (SDFD) and utilizes the Secretary Bird Optimization Algorithm (SBOA) to enhance performance. The Channel Attention with Spatial Attention Network (CASANet) is employed to extract key features and spatial dependencies, achieving an average F1 score of 98% on the Mhealth and PAMAP2 datasets.Asian Research Association

Conclusion

Dr. D. Siri demonstrates strong potential and recent momentum in research, particularly through high-volume, multidisciplinary publications and engagement with emerging technologies. Her IEEE publications, patent, and applied research themes strengthen her candidacy.

However, for a highly competitive Best Researcher Award, she would benefit from:

More indexed journal publications,

Evidence of citations/impact,

Greater leadership in research initiatives (e.g., funded projects or Ph.D. guidance).

Ernesto Diaz | Computer Science | Best Researcher Award

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Mr  Ernesto  Diaz |  Computer Science |  Best Researcher Award

Assistant Specialist at University of California, San Francisco – Radiology & Biomedical Imaging, United States

Ernesto Diaz is a highly skilled Data Scientist at the University of California, San Francisco (UCSF), specializing in Hyperpolarized Carbon-13 Metabolic Imaging within the Department of Radiology and Biomedical Imaging. He earned his Bachelor of Science in Computer Science from San Francisco State University in 2022, consistently achieving Dean’s List honors.

Profile:

🎓 Education:

San Francisco State University, San Francisco, CA

  • Bachelor of Science in Computer Science, 2022
  • Dean’s List: 2020-2022

💻 Technical Skills:

Languages & Environments: Python, R-Code, C++, MATLAB, HTML, CSS, Shell Scripting, CUDA, UNIX
Packages: Pandas, Matplotlib, NumPy, PyDicom, TensorFlow, PyTorch, Tkinter, Conda, CMake

🔬 Research Experience:

Data Scientist
University of California San Francisco, Department of Radiology and Biomedical Imaging
June 2022 – Present

  • Developed data processing methods for Hyperpolarized Carbon-13 Metabolic Imaging.
  • Lead developer of a Python application for HP 13C DICOM Images metadata integration.
  • Co-developed a deep learning image segmentation tool achieving 80% accuracy in prostate cancer segmentation.
  • Contributed to UCSF’s open-source MR Spectroscopy tool (SIVIC).

Undergraduate Researcher
UCSF, Department of Radiation Oncology
August 2021 – June 2022

  • Automated 3D spine metastases radiation planning.
  • Designed a Python/Tkinter GUI for treatment planning, boosting efficiency by 50%.

NIH Funded SF BUILD Scholar
San Francisco State University
June 2021 – June 2022

  • Selected for leadership potential and dedication to underserved communities.
  • Focused on diversity in NIH-funded research.

🏆 Awards & Scholarships:

  • NIH Diversity Supplement Award: 2022-2024
  • NIH-SF BUILD Scholar: 2021-2022
  • Dean’s List: San Francisco State University, 2020-2022

📅 Committees & Leadership:

  • UCSF PROPEL Scholar: Promoting equity in scientific research (2022-2024)
  • Metro Near-Peer Mentoring Program: Mentored first-year college students (2020-2022)
  • Google Developer Student Club: Engaged in peer-to-peer learning and community solutions (2019)

🎯 Certifications:

  • CITI Training Certificate: Social/Behavioral Research, 2022
  • Near-Peer Mentor Training: Metro Student First, 2020

Publication Top Notes:

  • The genomic landscape of hypodiploid acute lymphoblastic leukemia
    L. Holmfeldt, L. Wei, E. Diaz-Flores, M. Walsh, J. Zhang, L. Ding, …
    Nature Genetics, 45(3), 242-252, 2013. (Citations: 841)

  • Single-cell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates
    N. Kotecha, N.J. Flores, J.M. Irish, E.F. Simonds, D.S. Sakai, S. Archambeault, …
    Cancer Cell, 14(4), 335-343, 2008. (Citations: 290)

  • Ex vivo drug response profiling detects recurrent sensitivity patterns in drug-resistant acute lymphoblastic leukemia
    V. Frismantas, M.P. Dobay, A. Rinaldi, J. Tchinda, S.H. Dunn, J. Kunz, …
    Blood, The Journal of the American Society of Hematology, 129(11), e26-e37, 2017. (Citations: 240)

  • p53 loss promotes acute myeloid leukemia by enabling aberrant self-renewal
    Z. Zhao, J. Zuber, E. Diaz-Flores, L. Lintault, S.C. Kogan, K. Shannon, …
    Genes & Development, 24(13), 1389-1402, 2010. (Citations: 211)

  • Response and resistance to MEK inhibition in leukaemias initiated by hyperactive Ras
    J.O. Lauchle, D. Kim, D.T. Le, K. Akagi, M. Crone, K. Krisman, K. Warner, …
    Nature, 461(7262), 411-414, 2009. (Citations: 156)

  • K-RasG12D expression induces hyperproliferation and aberrant signaling in primary hematopoietic stem/progenitor cells
    M.E.M. Van Meter, E. Díaz-Flores, J.A. Archard, E. Passegué, J.M. Irish, …
    Blood, 109(9), 3945-3952, 2007. (Citations: 138)

  • Phase II study of the oral MEK inhibitor selumetinib in advanced acute myelogenous leukemia: a University of Chicago phase II consortium trial
    N. Jain, E. Curran, N.M. Iyengar, E. Diaz-Flores, R. Kunnavakkam, …
    Clinical Cancer Research, 20(2), 490-498, 2014. (Citations: 133)

  • Bcl-2 is a therapeutic target for hypodiploid B-lineage acute lymphoblastic leukemia
    E. Diaz-Flores, E.Q. Comeaux, K.L. Kim, E. Melnik, K. Beckman, K.L. Davis, …
    Cancer Research, 79(9), 2339-2351, 2019. (Citations: 80)

  • Diacylglycerol kinase inhibition prevents IL-2-induced G1 to S transition through a phosphatidylinositol-3 kinase-independent mechanism
    I. Flores, D.R. Jones, A. Ciprés, E. Díaz-Flores, M.A. Sanjuan, I. Mérida
    The Journal of Immunology, 163(2), 708-714, 1999. (Citations: 71)

  • Regulation of diacylglycerol kinase α by phosphoinositide 3-kinase lipid products
    A. Ciprés, S. Carrasco, E. Merino, E. Díaz, U.M. Krishna, J.R. Falck, …
    Journal of Biological Chemistry, 278(37), 35629-35635, 2003. (Citations: 69)

  • Targeting oncogenic ras
    E. Diaz-Flores, K. Shannon
    Genes & Development, 21(16), 1989-1992, 2007. (Citations: 65)

  • Cooperative loss of RAS feedback regulation drives myeloid leukemogenesis
    Z. Zhao, C.C. Chen, C.D. Rillahan, R. Shen, T. Kitzing, M.E. McNerney, …
    Nature Genetics, 47(5), 539-543, 2015. (Citations: 48)

  • Abnormal hematopoiesis in Gab2 mutant mice
    Y. Zhang, E. Diaz-Flores, G. Li, Z. Wang, Z. Kang, E. Haviernikova, S. Rowe, …
    Blood, 110(1), 116-124, 2007. (Citations: 48)

  • β2-chimaerin provides a diacylglycerol-dependent mechanism for regulation of adhesion and chemotaxis of T cells
    M. Siliceo, D. García-Bernal, S. Carrasco, E. Díaz-Flores, F.C. Leskow, …
    Journal of Cell Science, 119(1), 141-152, 2006. (Citations: 48)

  • Membrane translocation of protein kinase Cθ during T lymphocyte activation requires phospholipase C-γ-generated diacylglycerol
    E. Díaz-Flores, M. Siliceo, C. Martínez-A, I. Mérida
    Journal of Biological Chemistry, 278(31), 29208-29215, 2003. (Citations: 48)

  • Rapid screening of COVID‐19 patients using white blood cell scattergrams, a study on 381 patients
    J. Osman, J. Lambert, M. Templé, F. Devaux, R. Favre, C. Flaujac, D. Bridoux, …
    British Journal of Haematology, 190(5), 718-722, 2020. (Citations: 34)

  • NRAS G12V oncogene facilitates self-renewal in a murine model of acute myelogenous leukemia
    Z. Sachs, R.S. LaRue, H.T. Nguyen, K. Sachs, K.E. Noble, N.A. Mohd Hassan, …
    Blood, 124(22), 3274-3283, 2014. (Citations: 30)

  • PLC-γ and PI3K link cytokines to ERK activation in hematopoietic cells with normal and oncogenic Kras
    E. Diaz-Flores, H. Goldschmidt, P. Depeille, V. Ng, J. Akutagawa, K. Krisman, …
    Science Signaling, 6(304), ra105-ra105, 2013. (Citations: 21)

  • Evolution of artificial intelligence-powered technologies in biomedical research and healthcare
    E. Diaz-Flores, T. Meyer, A. Giorkallos
    Smart Biolabs of the Future, 23-60, 2022. (Citations: 18)

  • Stat5 is critical for the development and maintenance of myeloproliferative neoplasm initiated by Nf1 deficiency
    Z. Sachs, R.A. Been, K.J. DeCoursin, H.T. Nguyen, N.A.M. Hassan, …
    Haematologica, 101(10), 1190, 2016. (Citations: 18)