جست‌وجوی استاد و پژوهشگر

Nikolaos Samarasمشاهده پروفایل

استاد

• Algorithm Analysis
• Computational Complexity
• Algorithmic Operations Research
• +۹ مورد دیگر

Nikolaos Samaras is a Full Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia in Thessaloniki, Greece. He has been serving as director of the Computational Methodologies & Operations Research (CMOR) Laboratory since April 2016. His academic career includes positions as Assistant Professor (2007-2012) and Lecturer (2003-2007) at the same institution, and earlier as an adjacent Lecturer at the Technological Institute of Western Macedonia (1998-2000). Dr. Samaras earned his Diploma in Applied Informatics from the University of Macedonia in 1996 and his Ph.D. in Applied Informatics from the same university in 2001. His educational background forms the foundation for his extensive research in computational optimization and operations research. Professor Samaras's research focuses on the interface between computer science and operations research, with particular expertise in linear and nonlinear optimization, network optimization, integer optimization, and scientific computing including HPC and GPU programming. His work has resulted in the development of new algorithmic families for optimization problems, efficient GPU implementations of the revised simplex algorithm, and novel algorithms and software for operations research. His research spans theoretical algorithm development, practical implementation, and real-world applications across various engineering and scientific domains. His extensive publication record includes over 35 journal papers in prestigious venues such as Computers and Operations Research, European Journal of Operational Research, and Journal of Artificial Intelligence Research, more than 85 conference papers, and four textbooks (two in English and two in Greek). His work has been recognized through citations and the Thomson ISI/ASIS&T Citation Analysis Research Grant in 2005. ACM Senior Member (2016) Thomson ISI/ASIS&T Citation Analysis Research Grant (2005) Editorial board member of Operations Research: An International Journal Reviewer for numerous top journals including Mathematical Programming Computation and European Journal of Operational Research Professor Samaras has supervised four current Ph.D. students working on hybrid simplex algorithms, large-scale optimization using Apache Hadoop, algorithmic procedures in matrix theory, and smoothed complexity analysis. He has successfully guided five Ph.D. students to completion, including Nikolaos Ploskas who won the 2014 HELORS Doctoral Dissertation Award. Additionally, he has supervised 45 master's theses and 84 bachelor's theses. His research group has secured funding from diverse sources including the European Union, Greek Secretariat of Research and Technology, and industry partners like Veltio Greece LTD. The Computational Methodologies & Operations Research (CMOR) Laboratory, which he directs, focuses on developing and implementing optimization algorithms with applications in transportation, energy systems, and business process design. The lab has produced notable software tools including Euclides and Visual LinProg, which have educational applications in linear programming.

Kibaek Kimمشاهده پروفایل

پژوهشگر

• Computational Optimization
• Stochastic Programming
• Integer Programming
• +۵ مورد دیگر

Kibaek Kim is a Computational Mathematician in the Laboratory for Applied Mathematics, Numerical Software, and Statistics at Argonne National Laboratory's Mathematics and Computer Science Division. He also serves as a Senior Scientist at-Large at the University of Chicago Consortium for Advanced Science and Engineering. His educational background includes a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2014) under the supervision of Dr. Sanjay Mehrotra. Dr. Kim's research focuses primarily on computational optimization, including stochastic programming and integer programming, with applications to complex systems. His recent work has centered on developing algorithms for solving stochastic mixed-integer programs using high-performance computing clusters. He is particularly known for his contributions to federated learning algorithms and software development, as well as modeling and numerical algorithms for large-scale optimization on high-performance computing systems and GPUs. His work finds applications in electric grid systems, healthcare, and key scientific domains of interest to the Department of Energy. Dr. Kim's publications demonstrate a strong focus on federated learning frameworks, with significant contributions to privacy-preserving techniques and distributed training algorithms. His work bridges theoretical optimization with practical implementation on high-performance computing infrastructure. DOE Early Career Research Program award (2019) IEEE Senior Member (2022) Multiple IMPACT Argonne Awards (2021) George L. Nemhauser Best Student Paper (2014) Throughout his career, Dr. Kim has mentored numerous postdoctoral researchers, predocs, and Ph.D. interns from top institutions including MIT, UIUC, Georgia Tech, and Northwestern University. He serves as associate editor for Mathematical Programming Computation and Naval Research Logistics, and as a board member for COIN-OR Foundation and IISE Energy Systems. Dr. Kim leads research on the APPFL (Advanced Privacy-Preserving Federated Learning) framework, which provides a comprehensive environment for implementing, testing, and validating privacy-preserving federated learning algorithms across distributed computing environments.

Amine Mhedhbiمشاهده پروفایل

استادیار

• Database Management
• Graph-Structured Databases
• Query Optimization
• +۴ مورد دیگر

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.

• Algorithm development
• Data analytics
• Time-series data analysis
• +۶ مورد دیگر

Emran Ali is a Graduate Researcher (Ph.D. candidate) and Part-Time Lecturer at Deakin University's School of Information Technology within the Faculty of Science, Engineering and Built Environment. He holds concurrent faculty appointments at Hajee Mohammad Danesh Science & Technology University (HSTU) in Bangladesh where he teaches computer science courses while on study leave. His academic journey includes a Master of Science (Research) in Information Technology from Deakin University (2022) and a Bachelor of Science in Computer Science and Engineering from HSTU. Doctor of Philosophy (Ph.D.) in Information Technology, Deakin University (2023–present) Doctor of Philosophy (Ph.D.) in Machine Learning, Coventry University (Cotutelle program, 2023–present) Master of Science (Research) in Information Technology, Deakin University (2020–2022) Bachelor of Science in Computer Science and Engineering, HSTU Bangladesh (2007–2012) Ali's research focuses on algorithm development and applied machine learning in health informatics, specializing in biosignal processing for neurological and sleep disorder detection. His work integrates time-series data analysis with explainable AI techniques to develop clinical decision support systems. Current projects include ML/DL modeling of sleep-stage transitions in aging populations and causal relationship analysis in sleep disorders using EEG data. Analysis of his 10 recent publications reveals strong concentration in biomedical ML applications (60%), particularly EEG-based neurological disorder detection and mental health diagnostics. Secondary focus areas include environmental monitoring systems (20%) and foundational computer science (20%). His work consistently employs ensemble methods and feature optimization techniques across diverse datasets, with increasing emphasis on real-world clinical applicability in recent publications. Deakin University Post-graduate Research Scholarship (DUPRS) through Cotutelle program with Coventry University National Fellowship from Bangladesh Ministry of Science and Technology (2020) Best Presentation Award at Deakin School of IT Conference (2021) AWS AI/ML Scholarships (2023, 2024) Next Generation Tech Booster Scholarship (2024) Ali provides research supervision at HSTU while serving as a Graduate Research Teaching Fellow at Deakin University for Machine Learning and Data Analytics units. His industry collaborations include projects with Monash University, Alfred Health, and AETMOS Australia focused on health informatics applications. Current funding includes AWS-sponsored nanodegrees and Deakin University research scholarships supporting his sleep disorder research. His technical work integrates cloud-based AI/ML platforms (AWS, Azure) with biosignal processing pipelines, utilizing collaborations across Australian healthcare institutions to validate clinical applications. Recent projects emphasize explainability in deep learning models for medical diagnostics, particularly in resource-constrained environments relevant to Bangladesh healthcare contexts.

• Empirical Software Engineering
• Requirements Engineering
• Applied Natural Language Processing
• +۷ مورد دیگر

Dr. Chetan Arora is a Senior Lecturer in Software Engineering at Deakin University's School of Information Technology, part of the Faculty of Science Engineering and Built Environment. He holds a PhD from the University of Luxembourg where he received the best PhD thesis award in the ICT domain. His research focuses on applied Artificial Intelligence in Software Engineering, with particular emphasis on Empirical Software Engineering, Requirements Engineering, and Applied Natural Language Processing. PhD in Computer Science from University of Luxembourg Masters in Software Engineering from Technische Universitat Kaiserslautern (Germany) Bachelors in Engineering (CS) from Thapar University (India) Arora's research interests center on the intersection of AI and Software Engineering, particularly how machine learning and natural language processing can enhance software development processes. His work explores requirements engineering, test automation, software trustworthiness, and human-centric software development. He investigates how large language models can be effectively deployed for tasks like test case generation, requirements analysis, and traceability. His recent publications reveal a strong focus on practical applications of AI in software engineering, with numerous studies examining the real-world implementation challenges and benefits. His publication record shows significant activity in top software engineering venues, with a notable emphasis on AI applications in software engineering processes. His recent work demonstrates expertise in retrieval-augmented generation systems, requirements-driven testing, and human-centric software development approaches. The publications collectively highlight his focus on bridging theoretical AI advancements with practical software engineering challenges. Best Ph.D. thesis award in the ICT domain at University of Luxembourg Arora actively supervises doctoral students working on cutting-edge topics including satellite communication systems, extended reality applications, and human-centered AI requirements engineering. His industry collaborations include work with Department of Defence on projects like Contextually Situated Anomaly Detection and Planning and Optimisation of Resources in Defence Satellite Communication Systems. He previously worked at SES Satellites on applied AI for IoT and Satcom, and as an FNR-PPP research fellow at the University of Luxembourg in software quality assurance. His laboratory work focuses on developing practical AI solutions for software engineering challenges, particularly in requirements engineering and test automation. Current projects involve multi-orbit satellite constellation optimization, dynamic radio resource management, and extended reality enabled human-centric requirements engineering.

Sunil Aryalمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۰ مورد دیگر

Sunil Aryal is an Associate Professor of Data Science at the School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Australia. He received his PhD and Master by Research degrees from Monash University Australia and has published over 70 papers in top-tier international venues in Artificial Intelligence, Machine Learning and Data Mining. Dr. Aryal's educational background includes: Graduate Certificate of Higher Education Learning and Teaching, Deakin University (2020) PhD in Computer Science, Monash University (2017) Master of Information Technology (Research), Monash University (2012) Master of Information Technology (Coursework), University of Southern Queensland (2008) Bachelor of Information Technology, Purbanchal University, Nepal (2005) His primary research interests focus on making Machine Learning and Data Mining algorithms robust and flexible to handle heterogeneous, noisy and uncertain data in real-world problems. His work spans across several specific areas including anomaly detection, clustering, kernel/similarity-based learning, ensemble methods, learning from limited data, reinforcement learning, natural language processing, and computer vision. Dr. Aryal is particularly interested in applying these techniques to solve challenges in Defence, National Intelligence, Engineering, Manufacturing, Healthcare and Education. Dr. Aryal co-leads the Machine Learning for Decision Support (MLDS) Research Group at Deakin University and has secured over AUD 4.5 million in external research funding. His research is supported by diverse organizations including US and Australia Defence Agencies, the Australian Office of National Intelligence, Worksafe Victoria, the Victorian State Department of Education and Training, the Technology Innovation Institute (TII) UAE, and Table Tennis Australia (TTA). His notable awards include multiple Deakin University research and teaching awards, the Australian Postgraduate Award for his PhD studies, and several student travel awards during his doctoral candidature. Dr. Aryal actively supervises numerous PhD and Master's students and has contributed significantly to teaching in various courses at Deakin University and previously at Federation University. He serves on several university committees and contributes to the research community as a reviewer, program committee member, and editor for various journals and conferences.

Chung Hwan Kimمشاهده پروفایل

استادیار

• Computer Systems Security
• Cyber-Physical Security
• Software Security and Reliability
• +۳ مورد دیگر

Chung Hwan Kim serves as an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas, where he directs the Software & Systems Security Laboratory (S³ Lab). His research focuses on critical security challenges in cyber-physical systems, embedded devices, and cloud infrastructure, with recognition including the NSF CAREER Award and UT Dallas New Faculty Research Symposium Grant. His expertise spans Computer Systems Security , Cyber-Physical Security , and Software Security and Reliability , emphasizing practical solutions for robotic vehicles, autonomous systems, and trusted execution environments. Current projects address signal injection attacks, resilience testing, and confidential computing through innovative fuzzing frameworks and hardware-assisted protections. Recent publications (2020-2026) reveal three dominant research thrusts: (1) Security for autonomous/robotic systems ( DriveFuzz , IMUFUZZER ), (2) Trusted execution in constrained environments ( Vessels , GEVisor ), and (3) Automated vulnerability discovery ( HFL , TZ-DATASHIELD ), consistently appearing in top venues like IEEE S&P and USENIX Security. Key honors include: NSF CAREER Award (premier early-career recognition) UT Dallas New Faculty Research Symposium Grant Top 10 finalist for CSAW Best Applied Research Paper Award (2018) As principal investigator of the S³ Lab, Kim mentors graduate researchers and secures competitive funding for projects spanning robotic vehicle security, embedded systems hardening, and confidential computing. His teaching portfolio includes Operating Systems, Information Security, and specialized courses on CPS/IoT security. The S³ Lab develops deployable security tools like TZ-DATASHIELD for embedded data protection and IMUFUZZER for resilience testing of aerial vehicles, collaborating with industry partners to translate research into real-world solutions for autonomous systems and critical infrastructure.

Jacky Wai Keungمشاهده پروفایل

دانشیار

• Software Engineering
• Empirical Studies
• Machine Learning
• +۷ مورد دیگر

Jacky Wai Keung is an Associate Professor in the Department of Computer Science at City University of Hong Kong with extensive industry connections across the Asia Pacific region. He leads the Artificial Intelligence and Software Engineering Research Group (AiSE) and serves as Chairman of IEEE Computer Society Hong Kong Chapter and Vice-President of Hong Kong STEM Education Alliance. Prof. Keung received his B.Sc.(Hons) in Computer Science from the University of Sydney and Ph.D. in Software Engineering from the University of New South Wales, Australia, before working as a Research Scientist at NICTA (now DATA61, CSIRO) in Sydney. His research spans software engineering, data science, AI, FinTech, machine learning, blockchain systems, and large language models for code generation and analysis. His recent work focuses on applying large language models to software engineering challenges, with publications examining code translation, anomaly detection, and autonomous driving system testing. The research shows a strong trend toward practical applications of AI in software development processes, particularly in FinTech and autonomous systems domains. Among his numerous accolades, Prof. Keung has been named in Stanford's top 2% most highly cited scientists for both 2022 and 2023, received the President's Teaching Excellence Award in 2020, and earned multiple IEEE best paper awards. His editorial service includes roles as Area Editor for Journal of Systems and Software since 2017 and Associate Editor for Information and Software Technology since 2020. Prof. Keung has successfully secured over HK$20 million in research funding through GRF, ITF, and TDG grants, including major projects like 'Smart Intelligent Process Automation for the Mortgage Lending Industry' (HK$2.62 million) and 'Software Data Analytics and Blockchain Technological Advancements' (HK$6 million). His industry collaborations have significantly enhanced student opportunities, with CS student starting salaries increasing by over 15% year-on-year for the past three years. He currently leads multiple research initiatives including RealisticCodeBench for evaluating LLMs in code generation and FedLAD for federated log anomaly detection, with several active projects focused on AI-enhanced InsurTech systems and deep probabilistic reasoning using deep learning.

• Large Language Models
• Generative AI
• Fuzzy Cognitive Maps
• +۷ مورد دیگر

Professor Yuan Miao is a distinguished academic at Victoria University (VU), serving as Professor in the College of Arts, Business, Law, Education & IT and Head of the Information Technology Program. With a PhD from Tsinghua University's Automation Department, his academic journey spans prestigious institutions including the University of Melbourne and Nanyang Technological University in Singapore before settling at VU where he has been Professor since January 2010, following his Associate Professorship from August 2004 to December 2009. Education: BSc, Shandong University, China MEng, Tsinghua University, China PhD, Tsinghua University, Automation Department, China Professor Miao's research centers on Large Language Models (LLMs) and Generative AI, where he has identified critical barriers in practical applications including limited memory length in systems like ChatGPT and Gemini, contradictory explanations, lack of local knowledge integration, and significant errors in text-data hybrid reasoning (up to 38%). His innovative solutions involve cognitive map graphs and rational intelligence models to create customized AI systems. His work spans diverse application areas including human knowledge modeling, multimodal interaction, healthcare analytics (particularly dementia detection), cybersecurity, and robotics powered by rational intelligence. Analysis of Professor Miao's recent publications reveals a strong focus on integrating LLMs with specialized knowledge domains across healthcare, cybersecurity, and social media analysis. His research consistently addresses practical limitations of current AI systems while developing novel frameworks for more reliable and context-aware applications. The interdisciplinary nature of his work is evident in publications spanning medical informatics, cybersecurity analytics, and educational technology. Scientific Recognition: Two articles in fuzzy cognitive map modeling ranked among top 10 most cited works since 2000 (Google Scholar 2000-2016) Development of adversarial dataset based on SQuAD 2.0 that reduced BERT and ELECTRA accuracy from ~90% to ORCID identifier 0000-0002-6712-3465 with 138 peer-reviewed publications Professor Miao actively supervises PhD and Master's students across diverse research topics including access control systems, healthcare analytics, cybersecurity, and social behavior analysis. His research has secured substantial funding from both industry giants (Microsoft, Amazon, Oracle, Google) and government bodies (Australia Research Council, Data61, Singapore's NRF), with recent projects including Digital Transformation for Construction Industry ($1.258 million), Western Health SharePoint Development ($68,000), and Big Data Analysis for Domestic Violence Research (US$100,000). His current grant portfolio demonstrates strong industry-academia collaboration addressing real-world challenges. Professor Miao leads research teams focused on rational intelligence systems that overcome current LLM limitations, with particular emphasis on creating practical AI solutions for healthcare, cybersecurity, and smart city applications. His work with Maribyrnong City Council on the Smart City at Footscray Park project ($850,000) exemplifies his commitment to applying advanced AI research to community-level challenges.

Sourav Duttaمشاهده پروفایل

پژوهشگر ارشد

• Model Order Reduction
• Computational Fluid Dynamics
• Scientific Machine Learning
• +۴ مورد دیگر

Sourav Dutta is a Research Fellow at the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin, where he works with Clint Dawson in the Computational Hydraulics Group. Prior to joining UT Austin in October 2022, he was an ORISE postdoctoral fellow at the Coastal & Hydraulics Laboratory of the U.S. Army Engineer Research and Development Center (ERDC) from September 2017 to August 2022. Dr. Dutta's research lies at the intersection of physics-based computational methods and data-driven machine learning techniques for environmental flow problems. His work focuses on developing efficient numerical approximations by combining physical principles with modern machine learning algorithms. His primary research areas include: Model Order Reduction Computational Fluid Dynamics Scientific Machine Learning Uncertainty Quantification Applied Mathematics His publication record reveals a strong trajectory in reduced order modeling for environmental hydrodynamics, particularly for advection-dominated problems that challenge traditional numerical methods. His research has evolved from foundational work on hybrid numerical methods for porous media flows to cutting-edge applications of deep learning and neural ODEs in computational hydraulics. Dr. Dutta has presented his research at major venues including SIAM conferences, Computational Methods in Water Resources meetings, and the HydroML Symposium. In September 2022, he co-organized a minisymposium on Machine Learning and Data-Driven Methods for Forward and Inverse Problems at the SIAM Mathematics of Data Science meeting. His educational background includes a PhD in Mathematics from Texas A&M University (2017), supervised by Dr. Prabir Daripa, and an Integrated BSc & MSc in Mathematics & Computing from the Indian Institute of Technology, Kharagpur (2010). At the Oden Institute, Dr. Dutta is currently working on compound flood simulation capabilities within Adaptive Hydraulics (AdH), reduced order modeling for coastal engineering applications, and physics-based operator learning frameworks for environmental flows. He has also developed pyNIROM, an open-source Python package for non-intrusive reduced order modeling of time-dependent problems.

Gregory Gayمشاهده پروفایل

دانشیار

• Software Testing
• Search-Based Software Engineering
• AI for Software Engineering
• +۴ مورد دیگر

Gregory Gay is an Associate Professor in the Interaction Design and Software Engineering division within the Department of Computer Science and Engineering at Chalmers University of Technology and the University of Gothenburg, Sweden. His academic profile spans numerous software engineering conferences where he has served as committee member, program chair, and active researcher since at least 2018. Dr. Gay's research focuses on the intersection of software engineering and artificial intelligence, with particular emphasis on: Software Testing and Analysis Search-Based Software Engineering AI for Software Engineering (AI4SE) AI Engineering Automation of development tasks Software Carbon Footprint and sustainability His recent publications demonstrate a strong trend toward applying AI and optimization techniques to software testing challenges, with increasing focus on sustainability aspects of software development. Many studies take an industrial perspective, examining real-world applications in automotive software systems. His work blends theoretical foundations with practical applications, making significant contributions to both academic research and industrial practice in software engineering. Dr. Gay has been actively involved in numerous top software engineering conferences including ASE, ICSE, ESEC/FSE, ISSTA, and ICST, serving on program committees and organizing tracks. His research methodology typically combines optimization, artificial intelligence, and machine learning to help developers deliver complex systems in a safe, secure, and efficient manner.

Tegawendé F. Bissyandéمشاهده پروفایل

استاد

• Program repair
• Software Security
• Code Search
• +۶ مورد دیگر

Prof. Tegawendé F. Bissyandé is a Chief Scientist in the Professor category at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. He holds the prestigious position of ERC Fellow and serves as Principal Investigator of the NATURAL project focused on Artificial Intelligence for Program Repair. His research spans software engineering, cybersecurity, and artificial intelligence, with particular emphasis on applying machine learning techniques to software development and security challenges. Dr. Bissyandé's research interests include: Software Debugging (especially bug localization and program repair) Software Security (especially malware detection and analysis) Code Search (both free-form and semantic code-to-code) Machine Learning and Natural Language Processing for software engineering Cyber-security applications in mobile and cloud environments His recent work demonstrates a strong focus on leveraging Large Language Models (LLMs) for various software engineering tasks. Analysis of his 15 most recent publications reveals several key trends: extensive application of LLMs to program repair and code generation; innovative approaches to Android security and malware detection; development of novel techniques for code search and understanding; and exploration of the intersection between natural language processing and software engineering. His research increasingly bridges theoretical software engineering with practical applications in mobile security and developer productivity tools, with a significant portion of his work focusing on Android ecosystem security and program repair technologies. Dr. Bissyandé has received numerous prestigious awards throughout his career: APSEC Best ERA Paper Award (2018) for 'LSRepair: Live Search of Fix Ingredients for Automated Program Repair' IPSJ SIG SE Excellent Research Award (2018) for 'FaCOY: a Code-to-Code Search Engine' FOSS Impact Paper Award (2018) for 'Characterizing Deprecated Android APIs' SANER Best ERA Paper Award (2016) for 'Parameter Values of Android APIs: A Preliminary Study on 100,000 Apps' ASE Best Paper Award (2012) for 'Diagnosys: automatic generation of a debugging interface to the Linux kernel' As an active member of the software engineering research community, Dr. Bissyandé serves on program committees for major conferences including ICSE, ASE, and ISSTA, and has been an Area Chair for ICSE 2024. His industry partnerships include significant collaborations with BGL BNP Paribas (since January 2019), Luxembourg Stock Exchange (since January 2018), and Paul Wurth (January 2015 to 2018), demonstrating the practical impact of his research. He leads the SerVAL lab at SnT, which focuses on software validation and analysis, with particular expertise in mobile security and program repair technologies, and actively mentors PhD candidates through FNR research grants.

Haipeng Caiمشاهده پروفایل

دانشیار

• Software Engineering
• Program Analysis
• Software Security
• +۵ مورد دیگر

Haipeng Cai serves as an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. His academic work spans software engineering, program analysis, and software security with particular emphasis on adaptive analysis techniques for mobile and distributed systems. His research interests center on adaptive/data-driven static and dynamic analysis for security applications targeting mobile apps, distributed systems, and multilingual software. Current work focuses on enhancing vulnerability detection, cross-language bug analysis, and automated security tooling through machine learning approaches. His lab produces tools like VinJ for vulnerability data generation and PolyFax for multilingual software characterization. Recent publications reveal strong trends in multilingual system security and AI-enhanced analysis , with 15+ papers since 2022 addressing cross-language vulnerabilities, Android security, and learning-based vulnerability detection. His work bridges theoretical program analysis with practical security applications in real-world software ecosystems. As an active academic contributor, he serves on program committees for major conferences including ASE, ICSE, and FSE, and will deliver a keynote at PROMISE 2025. His leadership includes journal-first paper chair roles and session chair positions at top software engineering venues. Dr. Cai maintains an active research presence through his personal website , GitHub repository ( github.com/chapering ), and academic social media profiles, with consistent contributions to the software engineering research community since 2018.

Huong Haمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Software Engineering
• Automated Machine Learning
• +۵ مورد دیگر

Dr. Huong Ha is a tenured Senior Lecturer in Computer Science and Program Manager of the Bachelor of Computer Science at the School of Computing Technologies, RMIT University, Australia. Previously, she worked at the Applied Artificial Intelligence Institute (A2I2), Deakin University, Australia. She received her PhD from the University of Newcastle, Australia in May 2017. Her educational background includes: PhD in Computer Science from the University of Newcastle, Australia (2017) Dr. Ha's research focuses on the intersection of Machine Learning and Software Engineering, specializing in Automated Machine Learning (particularly Bayesian Optimization), Trustworthy Machine Learning, and Data-driven Software Engineering. Her work addresses critical challenges in intelligent incident management of software systems and quality prediction and optimization for software systems. She has made significant contributions to root cause analysis for microservice systems using causal inference and Bayesian optimization techniques. Her publication record shows a strong trend in applying advanced machine learning techniques to software engineering problems, particularly in microservices architecture. Her recent work demonstrates expertise in developing benchmarking frameworks (RCAEval) and optimization methods (MOCA-HESP) that bridge theoretical advancements with practical applications in software systems. Dr. Ha has received numerous awards and recognitions: Top High-volume Course at RMIT (Semester 1, 2025) Outstanding Reviewer of KDD 2025 Research Track Special Commendation for 2024 RMIT STEM College Learning and Teaching Award ACM SIGSOFT Best Artifact Award at FSE 2024 Top Reviewer of NeurIPS 2022-2023 As Program Manager of the Bachelor of Computer Science, Dr. Ha plays a key role in curriculum development and academic leadership. She actively supervises PhD students including Thuan (Solving Mathematical Problems with LLMs) and Thanh (Fraud Detection for Blockchain Networks), and has secured multiple research grants including the OpenAI Researcher Access Grant (10,000 USD) and several RMIT RACE Merit Allocation Scheme grants. Her academic service includes serving on program committees for major conferences in both machine learning (NeurIPS, ICML) and software engineering (ASE, ICSE).

Rahmatullah Rocheمشاهده پروفایل

استادیار

• Computational Biology
• Applied Machine Learning
• Data Science
• +۱ مورد دیگر

Rahmatullah Roche serves as an Assistant Professor in the Department of Computer Science at Columbus State University's TSYS School of Computer Science, joining the tenure-track faculty in Fall 2024. His academic credentials include: Ph.D. in Computer Science and Applications from Virginia Tech (2024) M.S. in Computer Science and Software Engineering from Auburn University (2021) B.S. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (2016) Dr. Roche's research spans Computational Biology , Applied Machine Learning , Data Science , and Human-Computer Interaction , with primary focus on macromolecular predictive modeling for intra- and inter-molecular interactions using cutting-edge AI techniques. His work bridges computer science and biology to solve complex structural bioinformatics challenges. Analysis of his 14 publications (2020-2025) reveals a consistent trajectory in applying deep learning—particularly transformer networks, equivariant graph neural networks, and biological language models—to protein-RNA complex prediction, protein-nucleic acid binding, and protein-protein interaction site identification, published in high-impact journals including Cell Systems and Nucleic Acids Research. Key scientific recognitions include: Pratt Fellowship Award (2023-2024) Turner College Research Award (2025) Professional Development Award for Core Course redesign (2025) Core Course Design Institute Professional Development Award (2024) 3rd place Flash Talk award at VT GPSS Symposium (2024) Best Poster Award at ACM-BCB 2020 YSEA Finalist Travel Award at MCBIOS 2024 Dr. Roche actively recruits undergraduate and graduate students for his research lab while securing resources through NSF ACCESS grants. His teaching portfolio includes Computer Science I, Computer Organization, and Graphical User Interface Development, complemented by service as Discipline Coordinator for Web Development and reviewer for top bioinformatics journals. His research group collaborates with Virginia Tech on advancing biomolecular modeling through interdisciplinary AI applications.