Conor Ryan is a Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a Science Foundation Ireland-funded Investigator since 2002 and a member of multiple research centres including Lero – the Irish Software Research Centre and the Limerick Digital Cancer Research Centre. His research focuses on Genetic Programming, Grammatical Evolution, and their applications in domains like healthcare analytics, digital circuit design, and financial modeling. He has authored over 250 publications, with recent work emphasizing automated feature selection in medical diagnostics, neural architecture search, and blockchain ecosystems. Teaching includes courses on Foundations of Computer Science and Computer Games Programming. Research interests span evolutionary computation, machine learning, and interdisciplinary applications. Collaborations involve global institutions, reflecting his work's impact across computer science, engineering, and healthcare. His research has addressed challenges in breast cancer diagnosis via genetic algorithms, cryptocurrency volatility prediction using random forests, and automated generation of digital circuits. Ongoing projects explore interpretability in AI, energy-efficient computing, and sustainable transport systems through predictive analytics. Professional memberships include roles in the Centre for Research Training in Foundations of Data Science and the Data-Driven Computer Engineering Research Centre, underscoring his commitment to interdisciplinary innovation.
Xiang Ji is an Assistant Professor in the Department of Mathematics at Tulane University, affiliated with the School of Science & Engineering. His research focuses on statistical phylogenetics, computational biology, and bioinformatics, particularly in viral evolution and genomic epidemiology. He collaborates with Dr. Wu-Min Deng on cancer biology research from a bioinformatics perspective. Education: Ph.D., 2017: Bioinformatics and Statistics (Co-Major), North Carolina State University M.S., 2013: Material Science and Engineering, North Carolina State University B.S., 2011: Economics (Double Major) and Physics, Peking University Research Interests: Dr. Ji develops statistical models and computational tools for phylogenetic analysis, including scalable algorithms for large-scale genomic data. His work spans viral evolution, zoonotic disease surveillance, and parallel computing libraries for Bayesian inference. He emphasizes practical implementations such as Torchtree and TreeFlow . Articles Trends: Recent publications emphasize viral evolution dynamics (e.g., SARS-CoV-2, avian influenza), genomic surveillance strategies, and computational methods for phylogenetic inference. His work often bridges statistical theory with real-world applications in public health and epidemiology. Advising & Grants: While specific grant details are not listed, his active research program indicates involvement in funding initiatives related to computational biology and viral evolution. He teaches advanced courses in data analysis, linear models, and probability theory. Labs & Teams: Collaborates with Tulane’s Cancer Biology group and maintains partnerships with institutions globally, focusing on genomic epidemiology and phylogenetic software development.
Douglas H. Werner is the John L. and Genevieve H. McCain Chair Professor in the Department of Electrical Engineering at Pennsylvania State University's College of Engineering. He directs the Computational Electromagnetics and Antennas Research Lab (CEARL) and holds a faculty position at the Materials Research Institute (MRI). His education includes B.S., M.S., and Ph.D. degrees in Electrical Engineering, along with an M.A. in Mathematics, all from Pennsylvania State University. Werner's research encompasses computational electromagnetics, antenna systems, metamaterials, and AI-driven electromagnetic design. Current work focuses on developing deep learning techniques for rapid simulation and inverse-design in electromagnetics/optics. Key areas include: Advanced computational methods (FDTD, FEM, MoM) Next-generation antenna systems (wearable, reconfigurable, RFID) Metamaterial physics and transformation optics Evolutionary optimization algorithms Awards and honors include: IEEE Antennas and Propagation Society Educator Award (2019) DoD Technical Achievement Award (2018) R.W.P. King Paper Award (2006) Fellowships in 5 professional societies 14 additional research/teaching awards He leads CEARL research group, holds 20 patents, and has supervised numerous graduate students. His publication record includes 900+ papers and 6 books.
Dalibor Radovanović is a researcher at Singidunum University , affiliated with the Faculty of Business Informatics . His work spans cybersecurity, blockchain technologies, and their applications in business and IoT systems. Education: Doctoral Dissertation (2016, Singidunum University) Master's & Basic Studies (Faculty of Business Informatics) Secondary Education: ETŠ Nikola Tesla Research Interests include: Security frameworks for IoT and blockchain integration Smart card and wireless network vulnerabilities E-governance and corporate IT audit methodologies Machine learning applications in cybersecurity Environmental performance optimization in agribusiness Publication Trends reveal a focus on blockchain (2022), cybersecurity (2009-2022), and IT governance (2010-2017). His work bridges theoretical analysis with practical implementations in Serbia's digital economy. Collaborations with scholars like Marko Šarac and Saša Adamović highlight interdisciplinary approaches to securing financial systems, educational institutions, and industrial IoT applications.
Dr. Rasmus Ibsen-Jensen is a Lecturer in Computer Science at the University of Liverpool. Previously, he held a Postdoctoral position at IST Austria under Krishnendu Chatterjee and completed his PhD under Peter Bro Miltersen. Research Focus: Algorithmic game theory, strategy complexity in two-player zero-sum games, control flow graph algorithms, edit distance for automata, and theoretical biology applications. Teaching: Module Coordinator for second-year courses in database development (COMP207), C++ programming (COMP282), and industrial placement (COMP299). His work bridges computational game theory and formal verification, with recent publications exploring memory constraints in partial-information games, algebraic path properties in concurrent systems, and evolutionary spatial dynamics. While no scientific awards are explicitly mentioned in the provided text, his contributions to algorithmic complexity and interdisciplinary research (e.g., theoretical biology) highlight his academic impact.
Professor Julie Harris is a faculty member at the University of St Andrews, holding the position of Professor of Psychology within the School of Psychology and Neuroscience since 2005. She previously held academic posts at Newcastle University and the University of Edinburgh, and conducted postdoctoral research at the Smith-Kettlewell Eye Research Institute in California. Her research focuses on human visual processing, including vision and camouflage, depth perception, binocular stereopsis, and eye movements in natural environments. She earned a BSc in Physics from Imperial College London and a DPhil from Oxford University. Research interests include exploring how visual systems process environmental cues for depth and motion, with applications in camouflage design and stereo display technologies. Her lab employs psychophysical, behavioral, and computational methods. Notable projects address countershading camouflage effectiveness, motion-in-depth perception, and visual-motor interactions in driving scenarios. Selected grants include funding from BBSRC, Leverhulme Trust, and Medical Research Scotland. Collaborations span evolutionary biology, neuroscience, and applied vision science. She supervises PhD students Federico De Filippi and Rebecca Maguire. Awards include the Davida Teller Award Nomination and Fellowship in the Society of Biology. Labs/Teams: Julie Harris Lab (St Andrews), St Andrews Vision Labs, and the Institute of Behavioural and Neural Sciences. Current projects investigate warning signal design, visual perception in virtual environments, and the impact of visual processing on driving ability.
Amy E. Fraley is an Assistant Professor at ETH Zürich, Department of Chemistry and Applied Biosciences, and leads the Medicinal Chemistry Research Group. Her cross-disciplinary work bridges natural products biosynthesis, pharmaceutical sciences, and environmental sustainability through enzymatic chemistry and biotechnology. BSc in Chemistry, Millersville University of Pennsylvania (2014) PhD in Medicinal Chemistry, University of Michigan College of Pharmacy (2019) Postdoctoral work at ETH Zürich Institute of Microbiology under Prof. Jörn Piel Her research focuses on harnessing biosynthetic enzymes (e.g., halogenases, monooxygenases, Diels-Alderases) to create sustainable bioactive metabolites. She explores natural product biosynthesis in fungi and marine bacteria, targeting disease mechanisms and green chemistry applications. Recent work includes metagenomic studies of Lake Chilika microbial mats and polyketide synthase engineering. Notable scientific awards include the 2024 JSP Fellow at Bürgenstock Conference, 2018 Rackham Predoctoral Fellowship, and multiple University of Michigan honors. Her group secured funding from the ETH4D Doctoral Mentorship Grant and Messerli Foundation . Collaborations span structural biology, synthetic chemistry, and microbiology, with key contributions to flavoenzyme characterization and bioactive compound libraries . The lab emphasizes interdisciplinary training and innovation in biocatalytic tools for organic synthesis.
Prof. Dr. Oliver Krüger is a behavioral ecologist and evolutionary biologist at Bielefeld University 's Faculty of Biology , where he leads the Department of Animal Behaviour since 2013. His research spans avian and marine mammal systems, focusing on life history strategies, parasite-host interactions, and environmental adaptation. Education: Biology studies at Bielefeld University (1994-1996) MSc in Oxford (1996-1997) PhD at Bielefeld University with Fritz Trillmich and Jan Lindström (1998-2000) Research Themes: Behavioral ecology, evolutionary biology, and population dynamics across tropical and temperate ecosystems. Key projects include NC³ (Niche Choice/Construction) and studies on Galápagos sea lions, common buzzards, and pinniped species. Scientific Leadership: Spokesperson, SFB TRR 212 "NC³" (2018-2025) Advisory Board member: German Ornithologists Union, IUCN SSC pinniped group, German Primate Centre Peer review roles: Humboldt Foundation, DFG, HFSP, NSF Awards: Leopoldina Prize (2001) Niko Tinbergen Award (2008) DFG Heisenberg Professorship (2010-2015)
Dr. John A. Copland III is a Professor of Cancer Biology and Biochemistry & Molecular Biology at Mayo Clinic in Jacksonville, Florida. He leads the Cancer Biology and Translational Research Laboratory, focusing on molecular mechanisms of carcinogenesis, tumor progression, and development of targeted cancer therapies. Education: PhD in Physiology & Endocrinology (Medical College of Georgia), MS in Endocrinology (Medical College of Georgia), BS in Chemistry (Columbus College), with postdoctoral training at University of Texas Medical Branch. Research interests center on: Identifying tumor suppressor genes (e.g., RhoB, TBR3, GATA3) and oncogenes (e.g., FOXO3a, SCD1, NPTX2). Developing patient-derived xenografts and live cell models for personalized medicine. Designing SCD1 inhibitors via in silico modeling for clinical trials. Recent publications highlight his work on SCD1 inhibition in leukemia and thyroid cancer ImmunoPET imaging of thyroid tumors CRISPR-identified drug synergies in cholangiocarcinoma Patient-specific combination therapies using xenograft models
Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
Jiao Licheng is a Distinguished Professor and Doctoral Supervisor at Xidian University, leading the School of Artificial Intelligence and the Department of Computer Science and Technology. He holds prominent roles such as Director of the Key Laboratory of Intelligent Perception and Image Understanding (Ministry of Education) and the International Joint Research Center for Intelligent Perception and Computing. His research focuses on Artificial Intelligence, Deep Learning, Evolutionary Computation, and Remote Sensing, with significant contributions to image understanding and brain-inspired computing. Education: B.E. (1982) from Shanghai Jiao Tong University, M.E. (1984) and Ph.D. (1990) from Xi'an Jiaotong University. Postdoctoral research at Xidian University (1990–1992). Research Interests include AI, Machine Learning, Image Processing, and Big Data Analysis. His work bridges theoretical advancements and practical applications, such as medical imaging, SAR image analysis, and autonomous systems. Recent articles emphasize innovations in remote sensing, deep learning architectures, and evolutionary algorithms. Awards include IEEE Fellow, IET Fellow, and the Wu Wenjun Artificial Intelligence Outstanding Contribution Award. Labs/Teams: Key Lab of Intelligent Perception, International Joint Research Center, and leadership in national innovation bases. Active in academic societies, including editorial roles in IEEE Transactions on Cybernetics and Geoscience and Remote Sensing.
Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.
Westley Weimer is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, College of Engineering. He teaches advanced courses such as EECS 590 (Advanced Programming Languages) and EECS 481 (Software Engineering), and has previously taught at the University of Virginia. His research integrates software engineering, programming languages, and cognitive science, focusing on automated program repair, program analysis, and the neuroscience of code comprehension. University: University of Michigan School: College of Engineering Department: Department of Electrical Engineering and Computer Science Academic Rank: Professor His research interests include automated program repair (e.g., GenProg), software quality, cognitive modeling of programming, neuroimaging studies of code review, and the application of medical imaging to software engineering. He explores deep questions at the intersection of consciousness, time, and computation, advocating for interdisciplinary approaches to understanding the mind through programming behavior. The most recent publications reflect a trend toward empirical and cognitive studies in software engineering, combining automated repair with human factors, neuroimaging (fMRI, TMS), and real-world software challenges. Themes include bias in code review, programming under cognitive influences, and the neurological basis of code comprehension. His work increasingly bridges computer science with psychology, neuroscience, and social science. Scientific awards include multiple Distinguished Paper Awards at ICSE, FSE, and ESEC/FSE, Best Paper and Runner-up awards, and several 10-Year Most Influential Paper Awards from ASE, GECCO, POPL, and ASPLOS, recognizing the lasting impact of his contributions to automated software repair and program analysis. He has advised numerous PhD and Master’s students, many of whom have gone on to faculty positions or industry research roles. He contributes to academic service through organizing diversity and inclusion initiatives, maintaining graduate career resources, and promoting ethical and inclusive practices in computing. He leads a vibrant research group focused on improving software quality through both technical and human-centered innovations, with ongoing projects in automated repair, cognitive modeling, and secure systems.
Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .
