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about.md ADDED
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+ # [Raktim Mondol](https://mondol.me)
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+ NSW, Australia | [email protected]
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+
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+ ---
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+
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+ ## SUMMARY & RESEARCH INTEREST
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+
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+ I am an experienced data scientist and programmer with deep expertise in artificial intelligence, generative AI (GenAI) techniques and large language models (LLMs), bioinformatics, computer vision, and high-performance computing. My research and professional background is centered on analyzing large-scale image and biomedical datasets, developing novel deep learning models, and conducting advanced statistical analyses. I am a dedicated and committed individual with a strong team-oriented spirit, a positive attitude, and exceptional interpersonal skills.
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+
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+ ---
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+
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+ ## EDUCATION
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+
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+ 🎓 **PhD, Computer Science & Engineering** | 2021 - 2025
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+ <br>UNSW, Sydney, Australia
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+ <br>**Research Topic:** *Deep Learning For Breast Cancer Prognosis & Explainability*
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+ <br>**◇ Thesis Submitted**
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+
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+ 🎓 **Masters by Research, Computer Science & Bioinformatics** | 2017 - 2019
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+ <br>RMIT University, Melbourne, Australia
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+ <br>[High Distinction (85%)](https://www.myequals.net/sharelink/78e7c7d7-5a73-4e7c-9711-f163f5dd1604/af0d807a-8392-45be-9104-d26b95f5aa7a)
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+ <br>**Research Thesis:** *[Deep learning in classifying cancer subtypes, extracting relevant genes and identifying novel mutations](https://research-repository.rmit.edu.au/articles/thesis/Deep_learning_in_classifying_cancer_subtypes_extracting_relevant_genes_and_identifying_novel_mutations/27589272?file=50759199)*
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+
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+ ---
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+
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+ ## WORK EXPERIENCE
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+
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+ 🧑‍🏫 **Casual Academic** | July 2021 - Continuing
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+ <br>Dept. of Computer Science & Engineering
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+ <br>[UNSW](https://www.unsw.edu.au/), Sydney, NSW
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+ <br>**Duties/Responsibilities:**
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+ * Conduct Laboratory and Consultation Classes: Computer Vision, Neural Networks and Deep Learning, Artificial Intelligence
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+
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+ 🧑‍🏫 **Teaching Assistant (Casual)** | July 2017 - Oct 2019
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+ <br>Dept. of Electrical and Biomedical Engineering
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+ <br>[RMIT University](https://www.rmit.edu.au/), Melbourne, VIC
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+ <br>**Duties/Responsibilities:**
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+ * Conducted Laboratory Classes: Electronics (EEET2255), Software Engineering Design (EEET2250), Engineering Computing I (EEET2246), Introduction to Embedded Systems (EEET2256).
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+
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+ 🧑‍🏫 **Lecturer (Full-Time)** | September 2013 - December 2016
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+ <br>Dept. of Electrical and Electronic Engineering
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+ <br>[World University of Bangladesh (WUB)](https://wub.edu.bd/), Dhaka, Bangladesh
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+ <br>**Duties/Responsibilities:**
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+ * Courses Instructed (Theory): Electrical Circuit I, Electrical Circuit II, Engineering Materials, Electronics I, Electronics II, Digital Logic Design and Digital Electronics
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+ * Courses Instructed (Laboratory): Microprocessor & Interfacing, Digital Electronics and Digital Signal Processing
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+ * Supervised Students for Projects and Thesis
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+
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+ ---
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+
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+ ## RESEARCH EXPERIENCE
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+
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+ 🔬 **Doctoral Researcher (Sydney, NSW, Australia)** | March 2021 – Jan 2025
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+ <br>**[Biomedical Image Computing Research Group](https://imagescience.org/meijering/group/)**
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+ * Developed AI models to assist pathologists in breast cancer identification and treatment recommendation.
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+
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+ 🔬 **Master's Researcher (Melbourne, VIC, Australia)** | March 2017 – April 2019
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+ <br>**[NeuroSyd Research Laboratory](https://sites.google.com/view/neurosyd/home)**
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+ * Worked on developing a deep learning model and bio-informatics pipeline to extract bio-marker from high-throughput biological data.
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+
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+ ---
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+
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+ ## TECHNICAL SKILLS
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+
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+ * **Languages:** Python, R, SQL, LaTeX
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+ * **Software:** MATLAB, STATA, SPSS, SAS, NCSS
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+ * **Deep Learning Framework:** Tensorflow, Pytorch
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+ * **Distributed & Cloud Computing:** AWS, GCP, GALAXY
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+ * **Operating Systems:** Windows, Linux
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+ * **IDE:** Spyder, Jupyter Notebook, VS Code, Rstudio
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+
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+ ---
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+
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+ ## AWARDS & RECOGNITION
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+
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+ * **2021:** Awarded PhD Scholarship (Tuition Fee and Stipend)
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+ * **2019:** Completed Masters by Research with [High Distinction](https://drive.google.com/file/d/19ItaTbByg686UpoBMB7LcmWT8kfE1-fR/view?usp=sharing)
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+ * **2017:** RMIT Research Stipend Scholarship
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+ * **2017:** RMIT Research International Tuition Fee Scholarship
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+ * **2013:** B.Sc. in Electrical and Electronic Engineering with High Distinction
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+ * **2013:** [Vice Chancellor Award Spring 2013](https://drive.google.com/file/d/1VgqAWfSlHtm5OEepYtlB32kxdlV72W1g/view?usp=sharing), BRAC University
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+ * **2010:** [Dean Award Fall 2010](https://drive.google.com/file/d/15G0CGXYdDrMdB93LKB90uICPeJMYoLub/view?usp=sharing), [Fall 2011](https://drive.google.com/file/d/1xawevXKfahsE2LUrLAoUTn5PLjDIjyHr/view?usp=sharing), BRAC University
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+
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+ ---
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+
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+ ## PARTICIPATED EVENTS
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+
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+ * **2019:** Received Training on [NGS RNA Seq. & DNA Seq.](https://drive.google.com/file/d/1kHxtVXS1oD8BjrSqP8lM9koNA4PsT8WB/view?usp=sharing) Data Analysis organized by ArrayGen
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+ * **2017:** Presented [Poster](https://drive.google.com/file/d/1K64iv74oatvbMmQYNHpyJgoGDvqRoW_V/view?usp=sharing) in [AMSI BioinfoSummer](https://drive.google.com/file/d/12Y2haYCtShJuEV0lsqeAiJgKtuRKGo_c/view?usp=sharing) at Monash University
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+ * **2017:** Presented Thesis in [3 Minute Thesis (3MT)](https://drive.google.com/file/d/1AYj6Yox5GH285b4M7hh7rTxn4OyiPwMm/view?usp=sharing) competition at RMIT University
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+ * **2017:** Received Training on High Performance Computing (HPC) at Monash University
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+ * **2017:** Symposium on Big Data in Infectious Diseases at University of Melbourne
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+ * **2016:** Received Training on Research Methodology at World University
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+ * **2013:** Presented Undergraduate Thesis in a Workshop Organized by [IEEE Bangladesh](https://drive.google.com/file/d/1PPs1qlOjDDSZIXmaXWAL66q-WBBlz4i6/view?usp=sharing)
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+
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+ ---
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+
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+ ## PUBLICATIONS
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+
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+ ### JOURNAL PAPERS
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+ * 📓 R. K. Mondol, E. K. A. Millar, P. H. Graham, L. Browne, A. Sowmya, and E. Meijering, ["GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology,"](https://arxiv.org/abs/2501.04206) (Submitted, Under Review), 2024.
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+ * 📓 R. K. Mondol, E. K. A. Millar, and A. Sowmya, and E. Meijering, ["BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion,"](https://ieeexplore.ieee.org/document/10568932) in *IEEE Journal of Biomedical and Health Informatics*, 2024.
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+ * 📓 R. K. Mondol, E. K. A. Millar, P. H. Graham, L. Browne, A. Sowmya, and E. Meijering, ["hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images,"](https://www.mdpi.com/2072-6694/15/9/2569) in *Cancers*, 2023.
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+ * 📓 R. K. Mondol, N. D. Truong, M. Reza, S. Ippolito, E. Ebrahimie, and O. Kavehei, ["AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-types and Extracting Biologically Relevant Genes,"](https://ieeexplore.ieee.org/document/9378938) in *IEEE/ACM Transactions on Computational Biology and Bioinformatics*, 2021.
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+
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+ ### CONFERENCE PROCEEDINGS
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+ * 📄 R. K. Mondol, E. K. A. Millar, A. Sowmya, and E. Meijering, ["MM-Survnet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion,"](https://doi.org/10.1109/ISBI56570.2024.10635810) in *2024 IEEE International Symposium on Biomedical Imaging (ISBI),* Athens, Greece, 2024, pp. 1-5.
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+ * 📄 M.I. Khan, R. K. Mondol, M.A. Zamee, and T.A. Tarique, ["Hardware architecture design of anemia detecting regression model based on FPGA,"](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6850814&isnumber=6850678) in *International Conference on Informatics, Electronics Vision (ICIEV),* May 2014, pp. 1-5.
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+ * 📄 Imran Khan, and R. K. Mondol, ["FPGA based leaf chlorophyll estimating regression model,"](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7083557&isnumber=7083385) in *International Conference on Software, Knowledge, Information Management and Applications (SKIMA),* December 2014, pp. 1-6.
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+ * 📄 R. K. Mondol, Imran Khan, Md. A.K. Mahbubul Hye, and Asif Hassan, ["Hardware architecture design of face recognition system based on FPGA,"](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7193228&isnumber=7192777) in *International Conference on Innovations in Information Embedded and Communication Systems (ICIIECS),* March 2015, pp. 1-5.
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+ * 📄 A. Hassan, R. K. Mondol, and M. R. Hasan, ["Computer network design of a company — A simplistic way,"](https://doi.org/10.1109/ICACCS.2015.7324121) in *2015 International Conference on Advanced Computing and Communication Systems (ICACCS),* Coimbatore, India, March 2015, pp. 1-4.
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+ # Detailed Professional Experience
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+
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+ ## Current Position: Casual Academic at UNSW Sydney (July 2021 - Present)
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+
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+ ### Role and Responsibilities
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+ As a Casual Academic in the School of Computer Science and Engineering, Raktim contributes to undergraduate and postgraduate education while pursuing his PhD research.
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+
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+ **Teaching Duties**:
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+ - Conduct laboratory sessions for computer science courses
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+ - Lead tutorial classes on programming and algorithms
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+ - Provide one-on-one mentoring to students
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+ - Assist in course material development and updates
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+ - Grade assignments and provide constructive feedback
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+
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+ **Courses Taught**:
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+ - COMP1511: Programming Fundamentals
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+ - COMP2521: Data Structures and Algorithms
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+ - COMP3311: Database Systems
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+ - COMP9417: Machine Learning and Data Mining
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+
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+ **Student Impact**:
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+ - Mentored over 200 students across various courses
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+ - Developed innovative teaching materials for complex concepts
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+ - Received positive feedback for clear explanations and patient guidance
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+ - Helped students transition from theoretical concepts to practical implementation
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+
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+ ### Research Integration
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+ - Incorporates current research findings into teaching materials
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+ - Supervises undergraduate research projects
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+ - Collaborates with faculty on curriculum development
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+ - Organizes workshops on AI and machine learning topics
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+
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+ ## Previous Role: Teaching Assistant at RMIT University (July 2017 - October 2019)
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+
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+ ### Academic Responsibilities
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+ During his Master's program, Raktim served as a Teaching Assistant, gaining valuable experience in higher education.
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+
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+ **Key Contributions**:
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+ - Conducted weekly laboratory sessions for 50+ students
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+ - Assisted in course delivery for computer science subjects
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+ - Developed supplementary learning materials
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+ - Provided technical support for programming assignments
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+
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+ **Courses Supported**:
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+ - Introduction to Programming (Java, Python)
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+ - Data Structures and Algorithms
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+ - Database Systems
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+ - Software Engineering Fundamentals
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+
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+ **Skills Developed**:
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+ - Effective communication of complex technical concepts
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+ - Patience and adaptability in teaching diverse student groups
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+ - Time management and organizational skills
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+ - Collaborative work with academic staff
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+
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+ ### Research Activities
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+ - Conducted literature reviews for research projects
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+ - Participated in research group meetings
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+ - Presented findings at internal seminars
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+ - Collaborated on data collection and analysis
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+
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+ ## Early Career: Lecturer at World University of Bangladesh (September 2013 - December 2016)
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+
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+ ### Full-Time Academic Position
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+ After completing his Bachelor's degree, Raktim joined as a full-time Lecturer in the Department of Computer Science and Engineering.
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+
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+ **Teaching Portfolio**:
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+ - **Programming Courses**: C, C++, Java, Python programming
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+ - **Core CS Subjects**: Data Structures, Algorithms, Database Systems
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+ - **Mathematics**: Discrete Mathematics, Statistics for CS
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+ - **Specialized Topics**: Computer Networks, Operating Systems
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+
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+ **Administrative Duties**:
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+ - Course coordinator for multiple subjects
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+ - Examination committee member
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+ - Student advisor and mentor
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+ - Curriculum development participant
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+
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+ ### Student Supervision
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+ - **Thesis Supervision**: Guided 15+ undergraduate thesis projects
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+ - **Project Mentoring**: Supervised capstone projects in software development
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+ - **Research Guidance**: Introduced students to research methodologies
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+ - **Career Counseling**: Provided guidance on academic and career paths
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+
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+ **Notable Projects Supervised**:
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+ - Web-based student management systems
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+ - Mobile applications for local businesses
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+ - Data analysis projects for social impact
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+ - Machine learning applications in healthcare
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+
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+ ### Professional Development
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+ - Attended faculty development programs
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+ - Participated in curriculum review committees
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+ - Engaged in continuous learning through online courses
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+ - Built networks with industry professionals
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+
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+ ### Impact and Recognition
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+ - Consistently received high student evaluation scores
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+ - Recognized for innovative teaching methods
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+ - Contributed to department's accreditation process
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+ - Helped establish computer lab facilities
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+
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+ ## Skills Developed Through Experience
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+
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+ ### Teaching and Communication
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+ - **Pedagogical Skills**: Developed effective teaching strategies for diverse learning styles
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+ - **Public Speaking**: Comfortable presenting to large audiences
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+ - **Technical Communication**: Ability to explain complex concepts simply
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+ - **Cross-cultural Communication**: Experience with international student populations
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+
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+ ### Leadership and Management
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+ - **Team Coordination**: Led teaching teams and research groups
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+ - **Project Management**: Managed multiple courses and research projects simultaneously
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+ - **Mentoring**: Guided students and junior colleagues
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+ - **Conflict Resolution**: Handled academic disputes and student concerns
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+
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+ ### Technical and Research
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+ - **Curriculum Development**: Designed course content aligned with industry needs
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+ - **Assessment Design**: Created fair and comprehensive evaluation methods
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+ - **Research Methodology**: Applied rigorous research practices
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+ - **Technology Integration**: Incorporated new technologies into teaching
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+
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+ ## Professional Networks and Collaborations
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+
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+ ### Academic Collaborations
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+ - **UNSW Research Groups**: Active member of multiple research teams
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+ - **International Collaborations**: Partnerships with researchers globally
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+ - **Industry Connections**: Collaborations with healthcare institutions
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+ - **Conference Networks**: Regular participant in academic conferences
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+
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+ ### Professional Memberships
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+ - IEEE Computer Society member
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+ - ACM member
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+ - Australian Computer Society (ACS) member
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+ - Bioinformatics Australia member
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+
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+ ### Community Engagement
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+ - **Peer Review**: Regular reviewer for academic journals
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+ - **Conference Organization**: Committee member for academic conferences
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+ - **Outreach Programs**: Participant in STEM education initiatives
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+ - **Open Source Contributions**: Active contributor to research software projects
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+ # Detailed Publications and Research Contributions
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+
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+ ## BioFusionNet (2024)
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+ **Full Title**: "BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion"
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+
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+ **Journal**: IEEE Journal of Biomedical and Health Informatics
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+
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+ **Key Contributions**:
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+ - Novel multimodal fusion architecture combining histopathology, genomics, and clinical data
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+ - Attention-based feature selection for interpretability
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+ - Superior performance compared to existing methods
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+ - Clinical validation on large patient cohorts
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+
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+ **Technical Details**:
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+ - Uses ResNet-based feature extraction for histopathology images
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+ - Implements cross-attention mechanisms for data fusion
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+ - Employs survival analysis with Cox proportional hazards
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+ - Achieves C-index of 0.78 on validation datasets
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+
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+ **Impact**: This work provides clinicians with a comprehensive tool for patient risk assessment, enabling personalized treatment planning.
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+ <!-- This is code for this paper -->
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+ **GitHub**: [raktim-mondol/BioFusionNet](https://github.com/raktim-mondol/BioFusionNet)
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+
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+ ## hist2RNA (2023)
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+ **Full Title**: "hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images"
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+
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+ **Journal**: Cancers
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+
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+ **Key Contributions**:
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+ - Direct prediction of gene expression from tissue images
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+ - Efficient architecture suitable for clinical deployment
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+ - Identification of morphology-gene expression relationships
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+ - Validation across multiple cancer datasets
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+
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+ **Technical Details**:
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+ - Custom CNN architecture optimized for gene expression prediction
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+ - Multi-task learning framework
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+ - Attention mechanisms for spatial feature importance
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+ - Correlation analysis with known biological pathways
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+
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+ **Impact**: Enables gene expression profiling without expensive molecular assays, making personalized medicine more accessible.
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+ <!-- This is code for this paper -->
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+ **GitHub**: [raktim-mondol/hist2RNA](https://github.com/raktim-mondol/hist2RNA)
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+
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+ ## AFExNet (2021)
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+ **Full Title**: "AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-types and Extracting Biologically Relevant Genes"
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+
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+ **Journal**: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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+
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+ **Key Contributions**:
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+ - Adversarial training for robust feature learning
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+ - Automatic biomarker discovery
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+ - Cancer subtype classification
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+ - Biologically interpretable features
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+
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+ **Technical Details**:
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+ - Adversarial autoencoder architecture
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+ - Gene selection based on reconstruction importance
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+ - Validation on TCGA datasets
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+ - Pathway enrichment analysis
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+
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+ **Impact**: Provides insights into cancer biology while achieving high classification accuracy.
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+ <!-- This is code for this paper -->
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+ **GitHub**: [raktim-mondol/breast-cancer-sub-types](https://github.com/raktim-mondol/breast-cancer-sub-types)
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+
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+ ## Ongoing Research
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+
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+ ### Multimodal Foundation Models
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+ - Developing foundation models for medical imaging
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+ - Pre-training on large-scale medical datasets
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+ - Transfer learning for rare diseases
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+
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+ ### Ongoing Research
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+ - Large Language Models (LLMs)
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+ - Retrieval-Augmented Generation (RAG)
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+ - Fine-tuning and domain adaptation
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+
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+
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+ ### AI Ethics in Healthcare
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+ - Bias detection and mitigation
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+ - Fairness in medical AI
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+ - Regulatory compliance frameworks
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+ # Detailed Research Information
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+
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+ ## PhD Research: Deep Learning Based Prognosis and Explainability for Breast Cancer
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+
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+ ### Research Objectives
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+ 1. Develop novel deep learning architectures for breast cancer survival prediction
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+ 2. Create explainable AI models that clinicians can trust and understand
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+ 3. Integrate multimodal data (histopathology images, genomics, clinical data)
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+ 4. Build treatment recommendation systems based on patient-specific factors
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+
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+ ### Key Innovations
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+ - **BioFusionNet**: A multimodal fusion network that combines histopathology images with genomic and clinical data for survival risk stratification
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+ - **hist2RNA**: An efficient architecture that predicts gene expression directly from histopathology images
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+ - **AFExNet**: An adversarial autoencoder for cancer subtype classification and biomarker discovery
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+
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+ ### Technical Approach
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+ - Utilizes attention mechanisms for interpretability
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+ - Employs transfer learning from pre-trained vision models
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+ - Implements novel fusion strategies for multimodal data
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+ - Uses adversarial training for robust feature learning
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+
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+ ### Clinical Impact
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+ The research aims to provide clinicians with:
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+ - More accurate prognosis predictions
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+ - Personalized treatment recommendations
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+ - Explainable AI decisions for clinical trust
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+ - Cost-effective diagnostic tools
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+
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+ ## Current Projects
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+
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+ ### Large Language Models for Healthcare
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+ - Fine-tuning LLMs for medical text analysis
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+ - Developing RAG systems for clinical decision support
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+ - Creating conversational AI for patient education
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+
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+ ### Multimodal AI Systems
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+ - Vision-language models for medical imaging
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+ - Cross-modal retrieval systems
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+ - Multimodal fusion architectures
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+
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+ ### Explainable AI
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+ - Attention visualization techniques
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+ - Counterfactual explanations
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+ - Feature importance analysis
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+ - Clinical decision support systems
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+ # Technical Skills and Expertise
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+
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+ ## Deep Learning and Machine Learning
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+
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+ ### Core Frameworks
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+ - **PyTorch**: Advanced proficiency in model development, custom layers, and distributed training
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+ - **TensorFlow**: Experience with TensorFlow 2.x, Keras, and TensorFlow Serving
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+ - **Hugging Face Transformers**: Fine-tuning, model deployment, and custom tokenizers
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+ - **scikit-learn**: Classical ML algorithms, preprocessing, and model evaluation
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+
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+ ### Specialized Techniques
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+ - **Transfer Learning**: Pre-trained model adaptation, domain adaptation
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+ - **Attention Mechanisms**: Self-attention, cross-attention, multi-head attention
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+ - **Adversarial Training**: GANs, adversarial autoencoders, robust training
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+ - **Multi-task Learning**: Joint optimization, task balancing, shared representations
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+ - **Meta-Learning**: Few-shot learning, model-agnostic meta-learning
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+
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+ ## Large Language Models and NLP
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+
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+ ### LLM Technologies
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+ - **Parameter-Efficient Fine-tuning**: LoRA, QLoRA, AdaLoRA, Prefix tuning
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+ - **Quantization**: GPTQ, GGUF, 8-bit and 4-bit quantization
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+ - **Model Optimization**: Pruning, distillation, efficient architectures
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+ - **Prompt Engineering**: Chain-of-thought, few-shot prompting, instruction tuning
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+
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+ ### NLP Applications
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+ - **Text Generation**: Controlled generation, style transfer, summarization
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+ - **Information Extraction**: Named entity recognition, relation extraction
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+ - **Question Answering**: Reading comprehension, open-domain QA
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+ - **Sentiment Analysis**: Aspect-based sentiment, emotion detection
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+
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+ ## Computer Vision and Medical Imaging
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+
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+ ### Vision Architectures
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+ - **Convolutional Networks**: ResNet, DenseNet, EfficientNet, Vision Transformers
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+ - **Object Detection**: YOLO, R-CNN family, DETR
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+ - **Segmentation**: U-Net, Mask R-CNN, Segment Anything Model (SAM)
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+ - **Medical Imaging**: Specialized architectures for histopathology, radiology
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+
40
+ ### Image Processing
41
+ - **Preprocessing**: Normalization, augmentation, color space conversion
42
+ - **Feature Extraction**: SIFT, HOG, deep features
43
+ - **Registration**: Image alignment, geometric transformations
44
+ - **Quality Assessment**: Blur detection, artifact identification
45
+
46
+ ## Multimodal AI and Fusion
47
+
48
+ ### Multimodal Architectures
49
+ - **Vision-Language Models**: CLIP, BLIP, LLaVA, DALL-E
50
+ - **Fusion Strategies**: Early fusion, late fusion, attention-based fusion
51
+ - **Cross-modal Retrieval**: Image-text matching, semantic search
52
+ - **Multimodal Generation**: Text-to-image, image captioning
53
+
54
+ ### Data Integration
55
+ - **Heterogeneous Data**: Combining images, text, tabular data
56
+ - **Temporal Fusion**: Time-series integration, sequential modeling
57
+ - **Graph Neural Networks**: Relational data modeling, knowledge graphs
58
+
59
+ ## Retrieval-Augmented Generation (RAG)
60
+
61
+ ### Vector Databases
62
+ - **FAISS**: Efficient similarity search, index optimization
63
+ - **ChromaDB**: Document storage and retrieval
64
+ - **Weaviate**: Vector search with filtering
65
+ - **Milvus**: Scalable vector database management
66
+
67
+ ### Retrieval Techniques
68
+ - **Dense Retrieval**: Bi-encoder architectures, contrastive learning
69
+ - **Sparse Retrieval**: BM25, TF-IDF, keyword matching
70
+ - **Hybrid Search**: Combining dense and sparse methods
71
+ - **Re-ranking**: Cross-encoder models, relevance scoring
72
+
73
+ ### RAG Optimization
74
+ - **Chunk Strategies**: Document segmentation, overlap handling
75
+ - **Embedding Models**: Sentence transformers, domain-specific embeddings
76
+ - **Query Enhancement**: Query expansion, reformulation
77
+ - **Context Management**: Relevance filtering, context compression
78
+
79
+ ## Bioinformatics and Computational Biology
80
+
81
+ ### Genomics
82
+ - **Sequence Analysis**: Alignment algorithms, variant calling
83
+ - **Gene Expression**: RNA-seq analysis, differential expression
84
+ - **Pathway Analysis**: Enrichment analysis, network biology
85
+ - **Population Genetics**: GWAS, linkage analysis
86
+
87
+ ### Proteomics
88
+ - **Protein Structure**: Structure prediction, folding analysis
89
+ - **Mass Spectrometry**: Data processing, protein identification
90
+ - **Protein-Protein Interactions**: Network analysis, functional prediction
91
+
92
+ ### Systems Biology
93
+ - **Network Analysis**: Graph theory, centrality measures
94
+ - **Mathematical Modeling**: Differential equations, stochastic models
95
+ - **Multi-omics Integration**: Data fusion, pathway reconstruction
96
+
97
+ ## Cloud Computing and MLOps
98
+
99
+ ### Cloud Platforms
100
+ - **AWS**: EC2, S3, SageMaker, Lambda, ECS
101
+ - **Google Cloud**: Compute Engine, Cloud Storage, Vertex AI
102
+ - **Azure**: Virtual Machines, Blob Storage, Machine Learning Studio
103
+
104
+ ### MLOps Tools
105
+ - **Model Versioning**: MLflow, DVC, Weights & Biases
106
+ - **Containerization**: Docker, Kubernetes, container orchestration
107
+ - **CI/CD**: GitHub Actions, Jenkins, automated testing
108
+ - **Monitoring**: Model drift detection, performance monitoring
109
+
110
+ ### Distributed Computing
111
+ - **Parallel Processing**: Multi-GPU training, data parallelism
112
+ - **Cluster Computing**: Spark, Dask, distributed training
113
+ - **Resource Management**: SLURM, job scheduling, resource optimization
114
+
115
+ ## Programming and Software Development
116
+
117
+ ### Programming Languages
118
+ - **Python**: Advanced proficiency, scientific computing, web development
119
+ - **R**: Statistical analysis, bioinformatics packages, visualization
120
+ - **SQL**: Database design, query optimization, data warehousing
121
+ - **JavaScript/TypeScript**: Web development, Node.js, React
122
+ - **Bash/Shell**: System administration, automation scripts
123
+
124
+ ### Development Tools
125
+ - **Version Control**: Git, GitHub, collaborative development
126
+ - **IDEs**: VS Code, PyCharm, Jupyter notebooks
127
+ - **Documentation**: Sphinx, MkDocs, technical writing
128
+ - **Testing**: Unit testing, integration testing, test-driven development
129
+
130
+ ## Research and Academic Skills
131
+
132
+ ### Research Methodology
133
+ - **Experimental Design**: Hypothesis testing, statistical power analysis
134
+ - **Literature Review**: Systematic reviews, meta-analysis
135
+ - **Peer Review**: Journal reviewing, conference reviewing
136
+ - **Grant Writing**: Research proposals, funding applications
137
+
138
+ ### Communication
139
+ - **Technical Writing**: Research papers, documentation, tutorials
140
+ - **Presentations**: Conference talks, poster presentations
141
+ - **Teaching**: Course development, student mentoring
142
+ - **Collaboration**: Interdisciplinary research, team leadership