جست‌وجوی استاد و پژوهشگر

Megan Valentineمشاهده پروفایل

استاد

• Biophysics
• Mechanical Engineering
• Materials Science
• +۵ مورد دیگر

Megan Valentine is a Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. She leads an interdisciplinary research group focused on biological and bioinspired materials, investigating how forces are generated and transmitted in living systems to design responsive synthetic materials. Her work bridges engineering, physics, chemistry, and biology. Education: PhD in Physics from Harvard University, MS in Physics from the University of Pennsylvania, and BS in Physics from Lehigh University. Affiliations include the California NanoSystems Institute (CNSI), Materials Research Laboratory (MRL), Neuroscience Research Institute, and the Center for Stem Cell Biology and Engineering. Research interests span soft material mechanics, bioengineering, and systems biology, with applications in marine-inspired materials, mechanobiology, and soft robotics. Her lab employs advanced experimental techniques to study biophysical and biochemical mechanisms in living systems and translate them into engineered materials capable of self-healing, movement, and environmental responsiveness. Notable awards include the NSF Early CAREER Award, Fulbright Scholarship, and election as Fellow of the American Physical Society and American Institute for Medical and Biological Engineering. Her contributions emphasize creativity, collaboration, and diversity, with a focus on addressing societal challenges through interdisciplinary innovation.

Robert MacCurdyمشاهده پروفایل

استادیار

• Automated robot design
• Additive manufacturing
• Soft robotics
• +۶ مورد دیگر

Robert MacCurdy is an Assistant Professor at the Department of Mechanical Engineering, University of Colorado Boulder . He leads the Matter Assembly Computation Lab (MACLab) focused on automating robot design and fabrication. His research bridges computational design and advanced manufacturing to create "robots that walk out of the printer." The lab develops tools like OpenVCAD , an open-source volumetric multi-material geometry compiler.

• Electrical Power Networks
• Predictive Maintenance
• Renewable Energy Integration
• +۷ مورد دیگر

Lina Bertling Tjernberg is a Professor at the Department of Electrical Engineering, KTH Royal Institute of Technology, and Deputy Head of the School of Electrical Engineering and Computer Science (EECS) with responsibility for research conditions and impact. She served as Director of KTH's Energy Platform during 2018-2024 and holds memberships in IVA (Swedish Royal Academy of Engineering Sciences) and the IEEE Power & Energy Society. Research Focus: Applying mathematics (statistics, optimization, life cycle assessment) to enhance reliability and predictive maintenance in electric power systems, with emphasis on future electricity grids integrating microgrids, battery storage, HVDC, nuclear/pumped/hydro/wind/solar power, hydrogen, and electrified transport. Collaborations: Engaged with Comillas Pontifical University (Madrid), Addis Ababa University, Norwegian University of Science and Technology (NTNU), and IEA Wind. Key Research Trends: Recent articles highlight advancements in microgrid control (2025), SMR nuclear energy integration (2025), AI-driven asset management (2024), hydrogen sector coupling (2024), and renewable forecasting techniques (2024). Awards: 2021 Power Woman of the Year 2022 Energy Power List (Sweden’s top 20 energy influencers) Leadership Roles: Swedish Electromobility Center (SEC) board Chair of Swedish Electrical Standards (SEK Svensk Elstandard) Member, IEEE PES ISGT Europe steering committee

• Medical Microwave Imaging
• Microwave Engineering
• Millimeter-wave Engineering
• +۳ مورد دیگر

Professor Amin Abbosh is a faculty member at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on Medical Microwave Imaging and Millimeter-wave Engineering, with contributions to advanced imaging systems, antenna design, and communication technologies. He leads projects in electromagnetic medical sensing, including portable brain scanners and wearable diagnostic systems. His work integrates applied electromagnetics with AI-driven algorithms, addressing challenges in stroke detection, liver health monitoring, and deep vein thrombosis diagnosis. With over 16 patents and collaborations across biomedical and engineering domains, his research bridges clinical needs with cutting-edge electromagnetic techniques. Key projects include the development of low-cost healthcare monitoring systems and reconfigurable antennas for satellite communications. Research interests span medical imaging systems, antenna array design, and signal processing for healthcare applications. His team innovates in areas like phased arrays, dielectric property analysis, and non-invasive diagnostics. Recent advancements include synthetic microwave focusing techniques and self-supervised deep learning models for clutter removal in imaging. Publications highlight contributions in IEEE journals and conferences, emphasizing clinical applications and device prototyping. Collaborations with institutions like the University of Queensland’s medical faculty and industry partners ensure practical implementation of his research.

Conor Ryanمشاهده پروفایل

استاد

• Genetic Programming
• Genetic Algorithms
• Grammatical Evolution
• +۷ مورد دیگر

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.

Amy E. Fraleyمشاهده پروفایل

استادیار

• Natural Products Biosynthesis
• Biotechnology
• Biochemistry
• +۶ مورد دیگر

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.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +۵ مورد دیگر

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Tom Leinsterمشاهده پروفایل

استاد

• Category Theory
• Metric Geometry
• Algebra
• +۳ مورد دیگر

Tom Leinster is a mathematician at the University of Edinburgh, specializing in category theory, metric geometry, and their applications to areas such as algebra, topology, and mathematical biology. His research focuses on the concept of magnitude, a measure for metric spaces and enriched categories, as well as entropy and diversity. He has authored influential books including *Basic Category Theory* and *Entropy and Diversity: The Axiomatic Approach*. Leinster's work bridges foundational mathematics with interdisciplinary applications, emphasizing the interplay between abstract structures and concrete problems. His research interests span category theory, metric geometry, algebraic topology, and mathematical biology. Key contributions include foundational work on magnitude and its connections to geometric measure theory, entropy characterization, and categorical frameworks for diversity measurement. Leinster also engages in mathematical education and ethics, advocating for responsible research practices. Notable publications include recent advancements in magnitude homology of Euclidean sets, extremal magnitude in metric spaces, and entropy modulo primes. His work often highlights interdisciplinary applications, such as biodiversity quantification and information-theoretic foundations.

Max Alekseyevمشاهده پروفایل

دانشیار

• Computational Graph Theory
• Enumerative Combinatorics
• Computational/Algorithmic Biology
• +۱ مورد دیگر

Max Alekseyev is an Associate Professor in the Mathematics Department and Computational Biology Institute. His research spans computational graph theory, enumerative combinatorics, computational/algorithmic biology, and comparative genomics. He focuses on interdisciplinary problems, blending mathematics with biological applications, particularly in genome assembly and analysis. His work includes advancements in genome scaffolding algorithms, combinatorial sequence analysis, and mathematical biology. Notable contributions involve genome assembly tools like CAMSA and studies on ancestral genome reconstruction. He also explores theoretical topics such as Bernoulli series generalizations and modular data classification. His research trends highlight a blend of pure mathematics (e.g., number theory, graph theory) and applied computational methods, addressing challenges in genomics and evolutionary biology. He secured an NSF Student Travel Grant in 2018 for computational molecular biology.

Jonathan Külzمشاهده پروفایل

پژوهشگر

• Modular Robotics
• Robotics
• Reinforcement Learning
• +۳ مورد دیگر

Jonathan Külz is a Researcher at the Technical University of Munich (TUM) , affiliated with the Department of Informatics 6 - Chair for Cyber Physical Systems under Prof. Matthias Althoff. His research focuses on Modular Robotics , Reinforcement Learning , Cyber-Physical Systems , and Control Systems . His work includes algorithmic synthesis of modular robot compositions, model-based manipulator co-design, and unifying benchmarks for robotics. He has supervised multiple Master’s theses Bachelor’s theses Practical courses on topics like Robot Workspace Representation and Dynamic Model Identification . Notable supervised projects include Autonomous Navigation of Reachbot and Task-Based Modular Robot Configuration Synthesis . His recent publications span Robotics , Benchmarking , and Computational Social Science . Key trends include Computationally efficient assessment of robot capabilities Deep reinforcement learning for robotics Analysis of political discourse polarization Jonathan emphasizes structured thesis supervision, requiring exposés, shared folders, and protocol-driven meetings. He advocates for LaTeX in scientific writing and tools like NotebookLM and Zettlr for research documentation.

Kunihiko Kanekoمشاهده پروفایل

استاد

• Theoretical Biophysics
• Complex Systems
• Chaos
• +۶ مورد دیگر

Kunihiko Kaneko is a Professor at the Niels Bohr Institute, University of Copenhagen, with a distinguished career in theoretical biophysics and complex systems. He received his PhD and MSc in Physics from the University of Tokyo, and has held leadership roles at the Universal Biology Institute and Center for Complex Systems Biology. PhD Physics, 1984 - University of Tokyo MSc Physics, 1981 - University of Tokyo His research spans five primary areas: Universal Biology, Evolutionary Constraints, Ecosystem Dynamics, Neural Cognition, and Universal Anthropology. He has published extensively on multi-level consistency principles, dimensional reduction in biological systems, and reciprocity between robustness and plasticity across scales. Recent publications show strong focus on microbial ecosystems (2025), evolutionary game theory (2025), neural modular architectures (2024), and dimensional reduction in cellular systems (2024). His work bridges physics and biology through dynamical systems theory applied to diverse phenomena from protocells to human societies.

Sebastijan Dumancicمشاهده پروفایل

استادیار

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Travis Desellمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• High Performance Computing
• +۴ مورد دیگر

Travis Desell is a Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT), part of the B. Thomas Golisano College of Computing and Information Sciences. His research focuses on data science and machine learning applied to large-scale datasets using high-performance and distributed computing. He specializes in neuro-evolution, combining evolutionary algorithms with neural networks, particularly through his EXACT and EXAMM algorithms. He leads the D2S2 Lab and has developed the SALSA programming language based on the actor model. Currently funded projects include the National General Aviation Flight Information Database (NGAFID) and an NSF award exploring contextual bandits for decision-making in cyber-physical systems. His work emphasizes practical scientific applications, including stock forecasting, power plant data prediction, and explainable time series models. Education details are not explicitly provided, but his roles and publications indicate advanced academic credentials. Research interests span neuro-evolutionary techniques, recurrent neural networks, and distributed computing frameworks. Key projects include EXAMM for time series forecasting and NGAFID for flight safety analysis. Collaborations involve students and teams at RIT and beyond, with a focus on advancing AI-driven solutions in dynamic environments. Lab affiliations include the D2S2 Lab, where he mentors students and conducts cutting-edge research. Current opportunities exist for PhD students with backgrounds in software engineering and expertise in areas like NLP, web development, and distributed systems.

Stéphane Doncieuxمشاهده پروفایل

استاد

• Cognitive Robotics
• Developmental Robotics
• Evolutionary Robotics
• +۱۶ مورد دیگر

Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.

Miler T. Leeمشاهده پروفایل

دانشیار

• Gene Regulation
• Developmental Biology
• Computational Genomics
• +۹ مورد دیگر

Miler T. Lee is an Associate Professor at the University of Pittsburgh , focusing on gene regulation during early embryonic development through high-throughput experimental and computational genomics. He earned his Ph.D. in Genomics and Computational Biology in 2009 from the University of Pennsylvania under Dr. Junhyong Kim, followed by postdoctoral work with Dr. Antonio Giraldez at Yale University. Joining the university in 2016, his research spans maternal-to-zygotic transition (MZT), RNA stability, pluripotency networks, and evolutionary developmental biology, utilizing model organisms like zebrafish, Xenopus, and Hydractinia symbiolongicarpus. Key Research Themes: Maternally inherited RNA dynamics during embryogenesis Mechanisms of RNA degradation and transcriptome remodeling Evolution of pluripotency networks in hybrid species Role of zinc signaling in fertilization barriers Computational tools for RNA regulation and sensing Scientific Awards: Pan-American Society for Evolutionary Developmental Biology Junior Faculty Award (2024) Outstanding New Investigator – International Xenopus Board (2023) Basil O'Connor Scholar – March of Dimes (2017-2019) Recent publications highlight his work on enhancer classification, RNA degradation mechanisms, and cross-species MZT comparisons. His lab develops innovative methods like RESA for regulatory sequence analysis and studies evolutionary divergence in RNA localization patterns. While the articles span computational and experimental approaches, they consistently address RNA's role in cellular identity, developmental timing, and evolutionary adaptation. Applications include understanding pluripotency, designing RNA biosensors, and elucidating fertilization barriers. Prospective Ph.D. students are encouraged to contact him for opportunities in gene regulation, development, evo-devo, and computational genomics.