I am a passionate researcher currently doing Ph.D. at Georgia State University, Atlanta, Georgia. My interests lies in the areas of Deep Learning, Machine Learning, Bioinformatics, Data Mining, Artificial Intelligence, Algorithms, and Combinatorial Optimization.
I am open to collaboration with motivated researchers. Do reach out if interested!!
- Education
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Georgia State University - Atlanta, GA, USA
(Jan 2021 - Present)Major: Ph.D., Computer science
Advisor: Murray Patterson -
Lahore University of Management Science - Lahore PAKISTAN
(Aug 2016 - July 2018)Major: MS, Computer science
Thesis:Predicting Attributes of Nodes using Network Structure
Link: [PDF]
Advisor: Imdadullah Khan -
University of Engineering and Technology - Peshawar PAKISTAN
(Oct 2012 - July 2016)Major: BS, Computer science
Final Year Project: Cache Replacement Algorithm
Link: [PDF]
Advisor: Iftikhar Ahmad - Research Experience
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Georgia State University - Atlanta, GA, USA
(Jan 2021 - Present)Role: Graduate Research Assistant
Advisor: Murray Patterson -
Boston College - Boston, MA, USA
(Aug 2022 - Dec 2022)Role: Visiting Research Scientist, Computer science, Bioinformatics
Advisor: Jose Bento -
Robert Bosch - Sunnyvale, CA, USA
(May 2022 - Aug 2022)Role: Knowledge Engineering Intern
Advisor: Kim Hyeongsik -
IBM Research - Yorktown Heights, NY, USA
(Sept 2021 - Present)Role: Research Collaboration
Advisor: Pin-Yu Chen -
University of Newcastle - NSW, Australia
(March 2020 - Dec. 2021)Role: Research Collaboration
Advisor: Pablo Moscato - Teaching
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Georgia State University - Atlanta, GA, USA
Lecturer (CSC 4850/6850 Introduction To Machine Learning) - Academic Talks
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Grand Front, Osaka, Japan
Title: BioSequence2Vec: Efficient Embedding Generation For Biological SequencesSlides: URL
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Gold Coast Convention and Exhibition Centre Queensland, Australia
Title: PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host ClassificationSlides: URL
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Gold Coast Convention and Exhibition Centre Queensland, Australia
Title: Empowering Pandemic Response with Federated Learning for Protein Sequence Data AnalysisSlides: URL
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Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Title: Virus2Vec: Viral Sequence Classification Using Machine LearningSlides: URL
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Boston College, Newton, MA, USA
Title: Efficient Sequence Embedding For SARS-CoV-2 Variants ClassificationSlides: URL
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Osaka International Convention Center, Osaka, Japan
Title: Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike SequencesSlides: URL
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March, 2024: Won the Graduate Research Award for the year 2023 at Georgia State University
[Link]
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March, 2024: Our paper (Elliptic Geometry-Based Kernel Matrix for Improved Biological Sequence Classification) got accepted for publication at IEEE World Congress on Computational Intelligence (IEEE WCCI 2024)
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March, 2024: Our paper (SsAG: Summarization and Sparsification of Attributed Graphs) got accepted for publication at ACM Transactions on Knowledge Discovery from Data (TKDD)
[PDF]
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January, 2024: 2 papers got accepted for publication at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
- A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis
- Weighted Chaos Game Representation For Molecular Sequence Classification
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January, 2024: Our paper (PseAAC2Vec protein encoding for TCR protein sequence classification) got accepted for publication at Elsevier Computers in Biology and Medicine (CIBM) (Impact Factor 7.7)
[PDF]
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November, 2023: Our paper (Gaussian Beltrami-Klein Model for Protein Sequence Classification: A Hyperbolic Approach) got accepted for publication as Findings at Machine Learning for Health (ML4H) Symposium
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October, 2023: Our paper (Circular Arc Length-Based Kernel Matrix For Protein Sequence Classification) got accepted for publication at IEEE Big Data Conference (Acceptance Rate 17%)
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September, 2023: 3 papers got accepted for publication at International Conference on Information Management and Big Data (SimBig)
- Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information
[PDF]
- Expanding Chemical Representation with k-mers and Fragment-based Fingerprints for Molecular Fingerprinting
- Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning
- Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information
[PDF]
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August, 2023: 6 papers got accepted for publication at International Symposium on Bioinformatics Research and Applications (ISBRA)
- Sequence-Based Nanobody-Antigen Binding Prediction
- Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences
- PDB2Vec: Using 3D Structural Information For Improved Protein Analysis
- Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection
[PDF]
- Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification
- Efficient Sequence Embedding For SARS-CoV-2 Variants Classification
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July, 2023: Our paper (T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification) got accepted for publication at International Conference on Neural Information Processing (ICONIP)
[PDF]
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July, 2023: Our paper (Spike2CGR: An Efficient Method For Spike Sequence Classification Using Chaos Game Representation) got accepted for publication at ML Journal Track at ECML-PKDD (Impact Factor: 7.5)
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July, 2023: Our paper (Short-Term Load Forecasting Using AMI Data) got accepted for publication at IEEE Internet of Things Journal (Impact Factor: 10.6)
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April, 2023: Our paper (Virus2Vec: Viral Sequence Classification Using Machine Learning) got accepted for publication at Conference on Health, Inference, and Learning (CHIL) (Oral Presentation)
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April, 2023: Our paper (When Biology has Chemistry: Solubility And Drug Subcategory Prediction using SMILES Strings) got accepted for publication at ICLR (Tiny Paper Track)
[PDF]
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April, 2023: 3 papers accepted for publication at International Joint Conference on Neural Networks (IJCNN)
- PCD2Vec: A Poisson Correction Distance Based Approach for Viral Host Classification [Slides]
- Empowering Pandemic Response with Federated Learning for Protein Sequence Data Analysis [Slides]
- A New Direction in Membranolytic Anticancer Peptides Classification: Combining Spaced k-Mers with Chaos Game Representation [Slides]
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April, 2023: Our paper (Computing Graph Descriptors on Edge Streams) got accepted for publication at ACM Transactions on Knowledge Discovery from Data (TKDD)
[PDF]
- March, 2023: Our paper (Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification) got accepted for publication at Nature Scientific Reports Journal (Impact Factor: 4.9) [PDF]
- Feb, 2023: Our paper (BioSequence2Vec: Efficient Embedding Generation For Biological Sequences) got accepted for publication at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (Acceptance Rate 17%) [PDF]
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Oct, 2022: Four papers got accepted for publication at IEEE Big Data Conference (Acceptance Rate 18.6%)
- Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences [PDF]
- Impact of missing data imputation on the fairness and accuracy of graph node classifiers [PDF]
- Efficient Data Analytics on Augmented Similarity Triplets [PDF] [Slides]
- Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model [PDF]
- June, 2022: Our paper (PSSM2Vec: A Compact Alignment-Free Embedding Approach for Coronavirus Spike Sequence Classification) got accepted for publication at International Conference on Neural Information Processing (ICONIP) [PDF]
- June-2022: One paper got accepted for publication at Asian Conference on Machine Learning (ACML)
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Oct, 2021: Our paper "Spike2vec: An efficient and scalable embedding approach for covid-19 spike sequences" got accepted for publication at IEEE Big Data Conference (Acceptance Rate 19.7%)
[PDF]
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Oct, 2021: Our paper "Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index (TMQI-3)" got the Best Paper Award at International Conference on Intelligent Vision and Computing (ICIVC)
[PDF]
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Sept, 2021: Our paper "A k-mer based approach for sars-cov-2 variant identification" got accepted for publication at International Symposium on Bioinformatics Research and Applications (ISBRA)
[PDF]
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Sept, 2021: Our paper "Effective and scalable clustering of SARS-CoV-2 sequences" got accepted at for publication at International Conference on Big Data Research (ICBDR)
[PDF]
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July, 2021: Our paper "Simpler and faster development of tumor phylogeny pipelines" got published at Journal of Computational Biology (JCB)
[PDF]
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Jan, 2021: Started Ph.D. at Georgia State University
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Dec, 2020: Our paper "Predicting Attributes of Nodes using Network Structure" got accepted at Transactions on Intelligent Systems and Technology (TIST)
[PDF]
[Slides]
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May, 2020: Our paper "Combinatorial trace method for network immunization" got accepted at Information Sciences (Acceptance rate: 22%)
[PDF]
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Feb, 2020: Our paper "Detecting DDoS Attack on SDN Due to Vulnerabilities in OpenFlow" got accepted at International Conference on Advances in the Emerging Computing Technologies (AECT)
[PDF]
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Feb, 2020: Our paper "Fair Allocation Based Soft Load Shedding" got accepted at Intelligent Systems Conference (IntelliSys)
[PDF]
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Feb, 2020: Our paper "Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal" got accepted at Intelligent Systems Conference (IntelliSys)
[PDF]
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June, 2019: Our paper "Short Term Load Forecasting using Smart Meter Data" got accepted at International Conference on Future Energy Systems
[PDF]