Find Academic Supervisors

Meng WangView profile

Professor

• Machine Learning
• Deep Learning
• Signal Processing
• +5 more

Meng Wang is a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where she was promoted to Full Professor in June 2025. She received her B.S. and M.S. degrees (both with honors) in Electrical Engineering from Tsinghua University, China, in 2005 and 2007, respectively, and her Ph.D. in Electrical and Computer Engineering from Cornell University in 2012. After a postdoctoral position at Duke University, she joined RPI in December 2012 as an Assistant Professor, was promoted to Associate Professor with Tenure in 2019, and then to Full Professor in 2025. Her research spans machine learning and artificial intelligence, high-dimensional data analytics, power system monitoring, signal processing, and optimization methods. She has made fundamental contributions in sparse signal recovery and monitoring and control of smart grid using high frequency data from phase measurement unit (PMU). More recently, she has collaborated with IBM to produce theoretical guarantees of modern AI architectures such as graph neural networks and transformers used in large language models (LLMs). Wang's recent publications (2023-2025) reveal a strong focus on theoretical foundations of deep learning, particularly transformer architectures and graph neural networks. Her work bridges theoretical guarantees with practical applications in power systems, demonstrating how fundamental insights in machine learning can solve real-world energy challenges. She has increasingly focused on the intersection of AI and energy systems, developing methods for building-level load forecasting, energy disaggregation, and smart grid monitoring with behind-the-meter solar integration. AFOSR Young Investigator Program (YIP) Award (2019) Army Research Office (ARO) YIP Award (2017) James M. Tien '66 Early Career Award and Grant for Faculty (2022) School of Engineering Research Excellence Award (2018) IEEE Signal Processing Society Best Reviewer Award (2018) Professor Wang has mentored numerous Ph.D. students who have gone on to successful careers in academia and industry, including HongKang Li (now postdoc at University of Pennsylvania), Yi Ming (postdoc at University of Michigan), and Shuai Zhang (Assistant Professor at New Jersey Institute of Technology). Her research has been supported by multiple grants from the National Science Foundation, Air Force Office of Scientific Research, Army Research Office, and industry partners including IBM. She is actively involved with research centers including the Center for Future Energy Systems (CFES) and the Center for Materials, Devices, and Integrated Systems (CMDIS), where her group develops cutting-edge methods for power system monitoring and control. Her recent work has increasingly focused on the theoretical foundations of large language models and their applications to energy systems, positioning her at the forefront of AI for critical infrastructure.

• Keystroke Dynamics
• Machine Learning
• Data Mining
• +6 more

Ioannis Tsimperidis is a Researcher at the Department of Informatics, Democritus University of Thrace (DUTH). His career includes roles as a Secondary Education teacher (2006-2021) and industry executive (2003-2004). Since 2021, he has been affiliated with DUTH's DI.PAE unit, focusing on interdisciplinary research. Education: BSc (Electrical & Computer Engineering, Aristotle University of Thessaloniki, 1997); MSc/PhD (Democritus University of Thrace, 2002/2017). Research interests span keystroke dynamics for user profiling, machine learning applications in cybersecurity and education, and deep learning for material defect detection. His work bridges behavioral science and engineering, with notable contributions to age/education-level prediction via typing patterns and marble crack detection via RGB-thermal fusion. Publications reflect a dual focus: technical advancements in keystroke-based user identification and pedagogical innovations in STEM education. His 2023-2024 work highlights cross-disciplinary impact, applying AI to both industrial (marble quality control) and educational challenges (mobile tech barriers). No awards/grants explicitly mentioned. Advising record unavailable. Active in multiple educational structures including IN.EP., D.I.E.K., and K.E.K., demonstrating sustained engagement with diverse educational frameworks.

Abigail KoayView profile

Research Fellow

• Cyber Security
• Machine Learning
• Industrial Control System (ICS) Security
• +3 more

Dr. Abigail Koay is an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. Her research focuses on cybersecurity, machine learning applications in industrial systems, and healthcare technology. She has contributed to projects such as real-time cyber-attack detection using weakly supervised learning, supported by UQ Cyber Seed Funding (2021–2022). Her work spans multiple disciplines including: Cybersecurity for Industrial Control Systems (ICS) IoT network anomaly detection using fog-assisted frameworks Machine learning for medical imaging (e.g., glaucoma detection) AI-driven cybersecurity strategies for smart grids Recent publications highlight advancements in: Irregular time series analysis using GNNs Positive-unlabeled learning with random forests Domain generalization in retinal image analysis Dr. Koay has authored/co-authored 15+ peer-reviewed articles across journals like Frontiers of Computer Science , IEEE Access , and conferences including NeurIPS and ISGT Asia. She collaborates with industry partners on projects like Plan2Defend for smart grid security and SDGen for synthetic cybersecurity dataset generation. Her research integrates theoretical machine learning with practical cybersecurity challenges, emphasizing real-world applicability in critical infrastructure and healthcare systems.

Christian GagnéView profile

Professor

• Deep Learning
• Computer Vision
• Machine Learning
• +7 more

Christian Gagné is a Full Professor in the Department of Electrical and Computer Engineering at Laval University and Director of the Institute for Intelligence and Data (IID). He holds a prestigious Canada CIFAR AI Chair and is an Associate Academic Member at Mila – Quebec Artificial Intelligence Institute. His research spans multiple interdisciplinary domains with significant contributions to machine learning and its applications. Dr. Gagné completed his doctorate in electrical engineering at Université Laval in 2005, followed by a postdoctoral fellowship at INRIA Saclay (France) and the University of Lausanne (Switzerland) from 2005-2006. Prior to his academic appointment, he worked as a research associate in an industrial environment between 2006 and 2008. His research interests focus on deep learning and stochastic optimization, with particular emphasis on the robustness and generalization of deep neural networks, neurosymbolic approaches to enhance interpretability, and the development of multimodal foundational models. His work has practical applications across computer vision, super-resolution microscopy, healthcare, transportation, and energy sectors. Dr. Gagné has developed important methodologies in representation learning, meta-learning, transfer learning, and optimization approaches based on probabilistic models and evolutionary algorithms. Recent publications demonstrate a strong focus on improving the robustness of machine learning models, with significant contributions to data augmentation techniques for medical imaging, adversarial attacks on visual object trackers, and test time adaptation methods. His research group has produced influential work at the intersection of theoretical machine learning and practical applications, particularly in medical imaging and computer vision domains. Canada CIFAR AI Chair As an academic advisor, Dr. Gagné has supervised numerous doctoral and master's students including Frédéric Beaupré, Catherine Bouchard, Fatemeh Nourilenjan Nokabadi, Sabyasachi Sahoo, and Adam Tupper. His research is supported through various grants, including his Canada CIFAR AI Chair position and collaborations with multiple research centers. Dr. Gagné is actively involved with several research entities including the Vision and Digital Systems Laboratory (LVSN), the Center for Research in Robotics, Vision and Machine Intelligence (CeRVIM), the Center for Research in Massive Data (CRDM), as well as strategic groups REPARTI and UNIQUE of the FRQNT, the VITAM center of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technology (OBVIA).

Osmar ZaianeView profile

Professor

• Data Mining
• Machine Learning
• Social Network Analysis
• +14 more

Osmar Zaiane is a Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. With over 20 years of service at the university, he has established himself as a leading researcher in data mining and machine learning with applications across multiple domains. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (1999), a Master's in Computer Science from Laval University (1992), a Master's in Electronics from Institut National des Sciences et Techniques Nucléaires and Paris XI University (1989), and a Bachelor's in Computing Science from Institut Supérieur de Gestion, Université de Tunis (1988). Zaiane's research focuses on discovering patterns in large complex datasets with practical applications. His primary research interests include data mining, machine learning, social network analysis (particularly community mining and link prediction), and precision health applications. He has made significant contributions to associative classifiers, class imbalance learning, explainable AI, and educational data mining. His work spans multiple application domains including healthcare (particularly for Alzheimer's disease prediction, diabetic retinopathy grading, and lung cancer detection), social media analysis, and natural language processing. His publication record shows a strong focus on medical AI applications, with numerous recent papers on medical image segmentation, brain network analysis, and diagnostic systems. His work increasingly integrates large language models and transformer architectures with traditional machine learning approaches. Best Paper Award at IEEE/ACM Int. Conf. on Advances in Social Network Analysis and Mining (2023) Best Paper Award at International Symposium on Foundations and Applications of Big Data Analytics (2022) Best Paper Award at International AAAI Conference on Web and Social Media (2019) Best Paper Award at 29th International Conference on Database and Expert Systems Applications (DEXA) (2018) Zaiane has supervised over 80 graduate students during his career at the University of Alberta. His research has been supported by numerous grants, particularly in the areas of precision health and educational data mining. He maintains an active research lab focusing on applied machine learning with strong industry and healthcare partnerships. His current work explores the intersection of traditional machine learning techniques with emerging large language models and vision-language models for healthcare applications.

• Artificial Neural Networks
• Machine Learning
• Self-Organized Learning
• +7 more

Erzsébet Merényi is a Research Professor at Rice University with dual appointments in the Department of Statistics and the Department of Electrical and Computer Engineering. She is also affiliated with the Rice Space Institute as a Faculty Affiliate. Her academic career spans several decades with significant contributions to neural computation and hyperspectral image analysis. Dr. Merényi earned her M.Sc. in Mathematics and Ph.D. in Computational Science from Szeged (Attila József) University in Hungary. Her educational background provided the foundation for her interdisciplinary research that bridges mathematics, computer science, and planetary science. Her research focuses on neural computation, machine learning, and self-organized learning techniques applied to high-dimensional data analysis. She has developed methods for structure discovery in complex data, segmentation and classification, pattern recognition, parameter inference, variable selection, data fusion, and knowledge integration. Her work has particular emphasis on applying these techniques to multispectral and hyperspectral image analysis for planetary surface composition determination, environmental resource mapping, and medical image analysis. Analysis of her publication record reveals a consistent focus on self-organizing neural networks for spectral image analysis, with applications spanning planetary science (Mars, Moon, asteroids, and comets), Earth observation, and medical applications. Her work demonstrates a progression from theoretical neural network development to practical applications in remote sensing, with particular expertise in handling high-dimensional spectral data. NASA Applied Information Systems Research Program (AISRP) funding 2000-2004 NASA Applied Information Systems Research Program (AISRP) funding 2004-2009 NASA Applied Information Systems Research Program (AISRP) funding 1997-2000 Dr. Merényi has advised multiple graduate students including Kadim Tasdemir, Captain Michael Mendenhall, and Abha Jain. Her research has been supported by NASA grants through the Applied Information Systems Research Program, focusing on precision mining of spectral data for planetary resource identification. She has collaborated with researchers from institutions including the University of Arizona's Lunar and Planetary Laboratory, Paisley University in Scotland, and the University of Leipzig. Her work has involved developing software tools for neural network analysis of spectral data, with applications to Mars surface composition mapping, lunar geology studies using Clementine data, analysis of Halley's Comet images, and Earth resource mapping. She has created specialized neural network approaches for handling the high dimensionality of hyperspectral data while maintaining computational efficiency.

Sikha BaguiView profile

Professor

• Big Data Analytics
• Machine Learning
• Data Mining
• +7 more

Dr. Sikha Bagui is a Distinguished University Professor in the Department of Computer Science at the University of West Florida , within the Hal Marcus College of Science and Engineering . She previously served as Chair of the department and Founding Director of the Center for Cybersecurity. Her research spans Big Data Analytics, Machine Learning, Data Mining, and Database Design, with extensive publications and funded projects from NSF and NSA. Ed.D. in Curriculum & Instruction: Math & Stat / Science / Computer Science, University of West Florida M.B.A., University of Toledo B.S., Cuttington University (Liberia) Dr. Bagui's research centers on data-intensive computing , focusing on scalable algorithms for Big Data analytics, optimization in distributed environments (Hadoop, Spark, Hive), and applications in cybersecurity such as intrusion detection and phishing classification. She is particularly known for her work in data preprocessing, association rule mining, and improving classifier performance on imbalanced datasets. Her recent publications reveal a strong trend toward cybersecurity applications of machine learning , leveraging frameworks like MapReduce and Spark for scalable solutions. Topics include network traffic classification, load balancing in FP-Growth, and resampling techniques for intrusion detection. Her work bridges theoretical algorithm development with practical implementation in real-world Big Data systems. Distinguished University Professor Askew Fellow NSF CSForALL Grant ($300,000) NSA NCAE Grant ($375,511) Dr. Bagui has successfully led multiple federally funded research projects and mentored numerous students through research and academic programs. While specific advisees are not listed, her leadership in research groups and outreach initiatives like Women in Computing demonstrates strong mentorship. She has also authored influential textbooks used internationally. Her lab and research efforts are aligned with the UWF Smart Home Research , AI Research Group , and High Performance Computing Research , contributing to interdisciplinary innovation.

Markus MaucherView profile

Researcher

• Computational Biology
• Bioinformatics
• Machine Learning
• +4 more

Markus Maucher is a Subject Advisor in the Department of Computer Science at the University of Ulm . He has taught exercises for courses like Einführung in die Informatik I (EidI 1) and Einführung in die Informatik II (EidI 2) across multiple semesters from 2004 to 2022. His research focuses on Computational Biology , Boolean Networks , and Ant Colony Optimization , with applications in Machine Learning , Gene Expression Analysis , and Evolutionary Algorithms . His publications span journals such as Bioinformatics , IEEE/ACM Transactions on Computational Biology and Bioinformatics , and Computational Statistics , as well as conferences like the Genetic and Evolutionary Computation Conference . He holds a PhD in Computer Science, evidenced by his 2009 dissertation and subsequent book on non-perfect randomness in probabilistic algorithms . Maucher has collaborated with researchers including H.A. Kestler , U. Schöning , and C. Müssel . The keywords from his recent work include Computational Biology , Boolean Networks , and Algorithm Design . His sub-fields encompass Probabilistic Algorithms , Correlation Analysis , and Feature Selection . Contact details: markus.maucher@uni-ulm.de

Shahram RahimiView profile

Professor

• Artificial Intelligence
• Generative AI & LLMs
• Machine Learning
• +7 more

Shahram Rahimi is a Professor and Head of the Department of Computer Science at The University of Alabama, College of Engineering. He also serves as Chief Advisor to the Alabama Cyber Institute, demonstrating leadership in both academic administration and cybersecurity research. His career includes prior roles as the Gloria & Douglas Marchant Endowed Chair Professor and department head at Mississippi State University. Education: Ph.D., Computational Sciences, The University of Southern Mississippi, NASA John C. Stennis Space Center M.S., Computer Science, The University of Southern Mississippi M.S., Computer Engineering Technology, The University of Southern Mississippi M.S., Computer Architecture, Sharif University of Technology B.S., Computer Engineering, Shahid Beheshti University Dr. Rahimi's research is centered on Artificial Intelligence , with deep expertise in Generative AI & LLMs, Machine Learning, Neural Networks, and Quantum Computing . His work bridges theoretical innovation and real-world applications in healthcare, public safety, and policy. He has developed influential systems such as Foresight , a game theory-based decision support tool for foreign policy, and EmTime/Symphony , an AI algorithm optimizing emergency room staffing used in over 1,000 hospitals. His research integrates computational intelligence, multi-agent systems, and predictive analytics to solve complex societal challenges. The analysis of his recent publications reveals a consistent focus on hybrid AI models, uncertainty reasoning (e.g., Z-numbers), predictive modeling in healthcare and transportation, and the integration of game theory with machine learning. His work increasingly explores quantum computing and generative AI, reflecting cutting-edge trends in computer science. These publications span top journals such as IEEE Transactions on Cybernetics and Expert Systems and Applications. Scientific Recognition: EmTime/Symphony recognized as one of the top 10 AI healthcare technologies by HealthTech magazine (2018) Editor-in-Chief of two major AI journals Member of editorial boards for several AI and computing journals Advisor to federal task forces on predictive analytics for policy and defense Dr. Rahimi has been principal investigator on over $25 million in federally and industry-funded research projects, reflecting strong grant acquisition and project leadership. He has advised numerous students and mentored researchers in AI and computational intelligence. He has organized 15 international conferences on computational intelligence and multi-agent systems, fostering global academic collaboration. He leads key initiatives including the Alabama Center for the Advancement of AI and collaborates with multiple research centers such as the Center for Advanced Public Safety and the Center for Sustainable Infrastructure. His lab work integrates interdisciplinary teams focused on intelligent systems, cybersecurity, and AI for social good.

Marek KurzyńskiView profile

Professor

• Machine Learning
• Multi-criteria Optimization
• Evolutionary Algorithms
• +7 more

Marek Kurzyński is a Professor actively engaged in research and teaching, associated with multiple interdisciplinary research teams including the Machine Learning Team, Advanced Data Analysis Methods Team, and Metaheuristics Team. His work spans computational intelligence, optimization, and data-driven decision systems. His research focuses on developing novel optimization techniques such as Dark-Box Optimization and evolutionary methods for multi-criteria problems, with applications in network optimization and classifier training. These efforts reflect a strong integration of theoretical algorithm development and practical implementation in complex systems. The publication and project trends indicate a consistent focus on AI-based optimization, particularly in handling high-dimensional, multi-objective problems using gene-interaction modeling and application-aware architectures. He supervises diploma theses, contributing to academic training in advanced computing disciplines. While no formal awards are listed, his leadership in key research initiatives underscores significant scholarly impact. He is involved in collaborative technological innovation, including the development of a bionic prosthetic hand, demonstrating applied research in biomedical engineering contexts.

Krzysztof WalkowiakView profile

Professor

• Computer Network Optimization
• Machine Learning in Networks
• Artificial Intelligence
• +13 more

Krzysztof Walkowiak is a Professor at the Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology (PWr), where he also serves as the Dean of the Doctoral School. He leads the Computer Networks Team (ZSK) and is actively involved in multiple research projects, including Dark-Box Optimization and evolutionary methods for multi-criteria network design. A Senior Member of IEEE and IEEE ComSoc, he contributes to academic governance as a member of the Polish Academy of Sciences’ Committee on Electronics and Telecommunications. PhD in Computer Science (2000, with distinction) Habilitation in Computer Science (2008) Professor of Technical Sciences (2017) Dean of the Doctoral School, PWr (since 2020) Senior Member, IEEE and IEEE Communications Society Member, Committee on Electronics and Telecommunications, Polish Academy of Sciences His research spans computer network optimization, machine learning in networks, evolutionary algorithms, survivable optical networks, and intelligent computational techniques . He has pioneered the integration of AI methods in teleinformatics and leads initiatives in intent-based and cognitive networking. His work emphasizes multi-layer, application-aware network design and distributed processing systems. The 15 most recent publications reflect a strong trend in optimization under uncertainty, AI-driven networking, and metaheuristic algorithm development . There is a clear focus on survivability, scalability, and automation in modern network architectures, with increasing attention to edge intelligence via TinyML and cognitive systems. His recent work also addresses internationalization and pedagogical innovation in doctoral education. Fabio Neri Best Paper Award 2014 (Elsevier Journal of Optical Switching and Networking) Best Paper Award, DRCN2009 (Washington, USA) Best Paper Award, RNDM2015 (Munich, Germany) Medal of the National Education Commission (2011) Scientific Scholarship, Wrocław University of Science and Technology (2017) Star of Internationalization 2024 – Teaching Star Professor Walkowiak has supervised 10 completed PhD theses and currently mentors 5 doctoral students. He has led or managed 13 research projects funded by NCN (including 4 OPUS grants), EU, NAWA, and national agencies. His grant leadership includes the NAWA InterDocSchool project (2021–2023) and the Unite! Doctoral School curriculum development. He has reviewed over 300 journal submissions and served as session chair at 22 international conferences. He leads several research teams: Computer Networks Team (ZSK), Machine Learning Team, Advanced Data Analysis Methods Team, Metaheuristics Team, and Teaching Team . He has developed innovative educational programs such as the Research Skills course and Recent Research Trends , promoting international collaboration and doctoral training. He championed the transition to English-language instruction in the Doctoral School and promotes internationalization as a 'team sport' across institutional levels.

Michał WoźniakView profile

Professor

• machine learning
• pattern recognition
• classifier ensemble
• +12 more

Michał Woźniak is a Professor at the Department of Systems and Computer Networks, Wrocław University of Science and Technology. He serves as Head of the Department and leads the Machine Learning Research Team. His research spans machine learning, pattern recognition, data stream mining, and imbalanced data classification. He actively supervises MSc theses and leads multiple research projects including those on continual learning, classifier ensembles, and fake news detection. Research Interests: Machine learning, particularly inductive and continual learning Pattern recognition and classifier ensembles Data stream mining under concept drift Imbalanced data classification Fake news and disinformation detection Cybersecurity and medical decision support His recent publications (2024–2025) focus on continual learning under concept drift, deep learning for image fusion and forgery detection, ensemble methods for imbalanced data, and AI applications in network optimization. These works reflect strong trends in adaptive machine learning, robust classification, and real-world AI deployment. Scientific Awards: BEST PAPER AWARD FOR CLVISION CVPR WORKSHOP 2024 He supervises numerous students and collaborates extensively on interdisciplinary projects. He has been involved in projects such as LM LDS (2021–2024), MOO (2020–2025), IDStream (2018–2022), and others. He is also a project manager and active in academic service, including membership in the Committee on Informatics of the Polish Academy of Sciences. His research group maintains a strong presence in AI and machine learning applications. Laboratory and Teams: He leads the Machine Learning Team and is involved in multiple research groups including the Advanced Data Analysis Methods Team and Metaheuristics Team . His lab focuses on developing robust, adaptive AI models for real-world challenges.

Agata Kirjanów-BłażejView profile

Researcher

• Optimization
• Multi-criteria Optimization
• Evolutionary Algorithms
• +11 more

Dr. Eng. Agata Kirjanów-Błażej is an active researcher at Wrocław University of Science and Technology, affiliated with the Advanced Data Analysis Methods Team (ZZMAD) and involved in interdisciplinary research connecting computer science, optimization, and industrial engineering. Her research interests include: Conveyor transport in underground and open-pit mines Non-invasive diagnostics of conveyor belts Production and project management Multi-criteria and evolutionary optimization Machine learning and classifier training Application-aware network optimization Her recent work focuses on developing advanced optimization methods, including dark-box optimization and gene-linkage techniques for high-dimensional problems. She also contributes to industrial innovation, particularly in mining technology diagnostics. Dr. Kirjanów-Błażej has been recognized with multiple awards, including the Rector’s Award (2017, 2018), Lower Silesian Master of Technology, an innovation distinction in mining technology, and the prestigious Secundus Award in 2023 for early-career researchers under 40 without habilitation, based on high-impact publications. Lower Silesian Master of Technology First degree award "For outstanding achievements in the field of technology" Rector's Award for scientific achievements 2017 Rector's Award for scientific achievements 2018 Mining success of the year - Distinction in the innovation category for the "Integrated system for assessing the condition of DiagBelt conveyor belts" Secundus Award (5th edition) - 2023 She serves as a project manager in a research initiative funded by the National Centre for Research and Development (NCBR) under the LEADER program, focusing on innovative diagnostic devices for measuring conveyor belt thickness during operation. She supervises diploma theses and is actively involved in academic mentoring. Dr. Kirjanów-Błažej is a member of several research teams, including the Machine Learning Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team, reflecting her interdisciplinary approach to solving complex engineering and computational problems.

• Variable Coefficient Mathematics
• Schrödinger Equation Mathematics
• Nonlinear Schrödinger Equation
• +5 more

Cihangir Özemir serves as an Associate Professor (Docent) in the Department of Mathematics Engineering at Istanbul Technical University, Turkey. His research integrates advanced mathematical techniques with applications in physics and interdisciplinary sciences, maintaining active institutional affiliation through ongoing projects and publications. His scholarly work centers on Lie symmetry analysis of nonlinear partial differential equations, particularly focusing on wave systems including Davey-Stewartson, Rosenau, and Benney-Roskes/Zakharov-Rubenchik equations. He specializes in deriving exact solutions, classifying symmetry algebras, and investigating bifurcation phenomena in dynamical systems. This research bridges pure mathematics with practical applications in fluid dynamics, economic modeling, and epidemic theory. Analysis of his 2022-2025 publications reveals consistent methodological focus on symmetry-based solution techniques for nonlinear wave equations, with expanding interdisciplinary applications into economic cycles and epidemic dynamics. His work demonstrates progressive complexity in handling higher-dimensional systems and incorporating delay mechanisms in dynamical models. Özemir actively mentors graduate researchers with 7 supervised students documented and currently leads multiple research projects including "ROSENAU VE DAVEY-STEWARTSON SINIFI DALGA DENKLEMLERİNDE LIE SİMETRİLERİ" (2024-2026) and "Gecikmeli Keen Modelinde Çatallanma Analizi" (2023-2024), securing institutional funding for advanced mathematical investigations.

Jamie TwycrossView profile

Associate Professor

• computational and mathematical modelling
• machine learning and artificial intelligence
• data analytics and visualisation
• +11 more

Dr. Jamie Twycross is an Associate Professor in the School of Computer Science at the University of Nottingham. He leads the Intelligent Modelling and Analysis Group and serves as the Modelling Group lead in the Synthetic Biology Research Centre. His work bridges computer science with biological sciences, focusing on interdisciplinary applications of AI to solve complex biological problems. His research spans computational and mathematical modeling, machine learning, data analytics, and software engineering. Key areas include computational biology, synthetic biology, systems biology, systems medicine, sustainable chemistry, artificial life, biologically-inspired computing, artificial immune systems, computer security, and robotics. He has developed computational approaches to address real-world biological challenges, with particular emphasis on modeling complex biological systems. Dr. Twycross's publications reveal a strong trend toward interdisciplinary research that integrates computational methods with biological applications. His recent work demonstrates expertise in metabolic modeling, synthetic biology tools development, machine learning applications in bioinformatics, and sustainable chemistry software solutions. His research consistently addresses complex problems at the intersection of computer science and life sciences. Discipline External Examiner, Computer Science, Northeastern University London (2023-2027) Panel Member, UKRI Interdisciplinary Assessment College (2023-2025) Quality Assessor, Office for Students (OfS) (2023-2027) Member of Pool of Experts, Biotechnology and Biological Sciences Research Council (BBSRC) (2023-2026) Dr. Twycross has supervised an extensive number of students across multiple levels, including 4 postdocs, 14 PhD students, 1 M.Phil student, 21 M.Sc. dissertation students, and 21 B.Sc. dissertation students. His grant portfolio includes funding from national and international agencies, reflecting the significance and impact of his interdisciplinary research. He has also served on numerous grant review panels for UKRI, BBSRC, EPSRC, and other major funding organizations. As group lead of the Intelligent Modelling and Analysis Group and the Modelling Group in the Synthetic Biology Research Centre, Dr. Twycross directs research teams focused on developing computational approaches to address hard, real-world biological problems. His team's work includes developing software tools like libtissue for implementing artificial immune systems, as well as contributing to significant advancements in metabolic modeling and synthetic biology applications.