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Mehmet BaysanView profile

Associate Professor

• Bioinformatics
• Computational Biology
• Cancer Genomics
• +8 more

Assoc. Prof. Dr. Mehmet Baysan is a faculty member in the Department of Computer Engineering at Istanbul Technical University (ITU), where he leads BaysanLab, focusing on computational genomics and bioinformatics. He holds a Ph.D. in Computer Science from The University of Texas at Dallas and completed postdoctoral training at the National Institutes of Health (NIH), specifically the National Cancer Institute. His academic journey includes research scientist roles at NYU Cancer Institute and Weill Cornell Medical College before returning to Turkey. B.S., Computer Engineering, Bilkent University (1999–2003) M.S. and Ph.D., Computer Science, The University of Texas at Dallas (2003–2008) Postdoctoral Fellow, National Cancer Institute, NIH (2010–2013) Dr. Baysan's research lies at the intersection of computer science and genomics, with a strong emphasis on cancer genomics , bioinformatics pipeline development , and machine learning applications in biological data. His work involves integrating multi-omics datasets to improve cancer diagnosis and treatment strategies, particularly in glioblastoma and colorectal cancer. He has developed tools like COSAP for comparative sequencing analysis and VCF Observer for variant file inspection. His recent publications (2023–2024) demonstrate sustained activity in somatic exome sequencing , genetic variant interpretation using tree-based models , and evaluation of NGS pipelines across different tumor microenvironments. These works appear in high-impact journals such as BMC Bioinformatics and Stresses , highlighting his contributions to methodological rigor and reproducibility in computational genomics. Dr. Baysan has led multiple research projects funded by TTO, SRP, and TUSEB, including initiatives on spinal muscular atrophy diagnostics and cancer sequencing algorithm comparisons. While specific awards are not listed, his h-index of 11 and 393 citations reflect significant scholarly impact. He mentors students through supervised research and advises on bioinformatics projects. His lab, BaysanLab, is actively involved in developing efficient and accurate sequencing analysis workflows, contributing to precision medicine. Collaborations span institutions like Istanbul University and involve interdisciplinary teams working on cancer and rare disease genomics.

• Veterinary Pathology
• Virology
• Ocular Pathology
• +4 more

Kelsey Brakel is an Assistant Professor at the Michigan State University Veterinary Diagnostic Lab, where she specializes in veterinary pathology. She earned her DVM from MSU in 2016 and later completed a Ph.D. in Virology at The Ohio State University in 2021, alongside an anatomic pathology residency. Her work spans necropsy analysis, zoonotic disease prevention, and forensic veterinary support for legal cases involving animal abuse. Her research focuses on diagnosing animal diseases through organ and tissue analysis, with particular attention to conservation efforts for endangered species like rhinoceroses. She integrates environmental and clinical data to identify pathogens, cancers, or congenital defects, often referencing human pathology literature. The lab processes over 300,000 specimens annually, ranging from small samples to entire large animals. Brakel emphasizes the social impact of veterinary pathology, noting its role in uncovering human abuse linked to animal neglect. Her career path reflects interdisciplinary training, including undergraduate research at Hillsdale College’s G.H. Gordon Biological Station, a summer internship at the Henry Doorly Zoo, and a fellowship at the Comparative Ocular Pathology Laboratory of Wisconsin.

Florian SchuberthView profile

Associate Professor

• Structural Equation Modeling
• Partial Least Squares
• Composite-based Methods
• +7 more

Florian Schuberth is an Associate Professor at the Chair of Product–Market Relations within the Faculty of Engineering Technology at the University of Twente. His research focuses on composite-based structural equation modeling (SEM), particularly Partial Least Squares (PLS) methods, with applications across Information Systems, Social Sciences, and Computer Science. He actively contributes to quantitative research methodology and open science. Bachelor's and Master's in Business Administration and Economics, University of Würzburg PhD in Econometrics, summa cum laude, University of Würzburg (2017) His research interests center on advancing composite-based SEM, including model specification, fit assessment, higher-order constructs, and robust estimation techniques. He emphasizes methodological rigor and reproducibility in social science research. His recent work, reflected in publications and software development, demonstrates a strong trend toward improving the transparency, validity, and accessibility of composite modeling techniques, particularly through R-based tools. He has made significant contributions to model evaluation, measurement invariance, and endogeneity correction in PLS-SEM. Florian Schuberth is a passionate educator, coordinating and teaching courses on quantitative research methods. He is also deeply committed to open science, serving as coordinator of the Open Science Community Twente. Notable contributions include the development and maintenance of the cSEM R package, which enables researchers to perform composite-based SEM in R, promoting open, reproducible research practices. Maintainer of the R package cSEM Coordinator, Open Science Community Twente (since 2022) Active contributor to methodological software and open science infrastructure

Pengcheng LiuView profile

Senior Lecturer

• Robotics
• Bio-inspired Robotics
• Soft Robotics
• +14 more

Pengcheng Liu is an Associate Professor (Senior Lecturer) in Robotics and Applied Control at the Department of Computer Science, University of York, UK. He has previously held academic positions at Cardiff Metropolitan University, University of Lincoln, Bournemouth University, and visiting roles in China. He is a member of IEEE and IFAC, and serves on several IEEE Technical Committees related to robotics and automation. PhD in Robotics and Control MSc in Control Theory and Control Engineering BEng in Measurement Control and Instrumentation His research focuses on the convergence of robotics and biology, particularly in bio-inspired design, soft robotics, human-robot collaboration, rehabilitation, and embodied AI. He explores how biological systems can inform adaptive, resilient, and energy-efficient robotic systems. His work spans modeling animal/human motion, sensory-motor learning, morphological design, and control optimization. The 15 most recent publications reflect a strong interdisciplinary trend combining robotics, control theory, machine learning, and biomedical applications. Key themes include soft robotics, bio-inspired actuators, motion planning, rehabilitation systems, and wireless communication for robotic systems. The research integrates simulation, hardware prototyping, and intelligent control, often using ROS and Gazebo. Global Peer Review Awards — Top 1% in Cross-Field (2019) Global Peer Review Awards — Top 1% in Engineering (2019) Outstanding Associate Editor of 2020, IEEE Access Outstanding Contribution Awards, Elsevier (2017) Dr Liu has extensive experience in securing and managing research grants from EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, and NSFC. He serves as a regular reviewer for around 30 journals and 8 flagship conferences in robotics, AI, and control. He is an Associate Editor for IEEE Access and Academic Editor for PeerJ Computer Science, and has contributed to special issues on computational learning for robotics. He leads research in bio-inspired robotics and advises PhD students in areas such as lifelong learning machine intelligence, soft robotics, motion planning, and cyber-physical systems. He is actively recruiting motivated PhD candidates with backgrounds in engineering, computer science, or applied mathematics.

Enrico RiccardiView profile

Associate Professor

• Molecular Dynamics
• Machine Learning
• Rare Event Simulation
• +7 more

Enrico Riccardi is an Associate Professor in Computational Engineering at the Department of Energy Resources, Faculty of Science and Technology, University of Stavanger (UIS), Norway. His work bridges computational chemistry, machine learning, and multi-scale modeling, with applications in energy, environmental science, and biophysics. Research Interests: His core expertise lies in molecular dynamics , rare event simulation methods (e.g., reaction kinetics and adsorption), and multi-scale modeling from molecular to continuum levels. He is a key developer of path sampling methodologies and software such as PyRETIS and PyVisA , enabling the study of slow and rare processes in complex systems. His research spans interfacial phenomena in emulsions, membrane permeation, atmospheric chemistry, and data-driven discovery of reaction pathways using machine learning. Recent Publication Trends: Over the past decade, Riccardi has consistently published in high-impact journals such as Journal of Chemical Physics , Physical Chemistry Chemical Physics , and Nature Machine Intelligence . His recent work (2023–2025) shows an expanded scope into educational technology , environmental science , and open-source tool development (e.g., GeoSight), reflecting a growing interdisciplinary impact. The publications reveal a strong focus on algorithmic innovation in simulation methods and their application across chemistry, biology, and engineering. Scientific Contributions: Lead and co-developer of PyRETIS, a widely used open-source library for rare event simulations. Contributor to immuneML, a machine learning ecosystem for immune repertoire analysis published in Nature Machine Intelligence . Active in promoting open science, data sharing, and academic integrity through public commentary and educational initiatives. Advising and Grants: While no formal students are listed in the provided text, Riccardi has mentored or collaborated with numerous early-career researchers and PhD candidates, particularly within the van Erp group. He has contributed to multiple collaborative research projects, likely funded by Norwegian and European research councils, though specific grants are not mentioned. His outreach on postdoctoral challenges suggests engagement with academic policy and mentorship. Labs and Teams: Riccardi is part of a vibrant computational research group at UIS, closely collaborating with Prof. Titus Sebastiaan van Erp and colleagues in the Department of Energy Resources. His work is embedded in a team focused on advanced simulation techniques, with strong ties to international networks in computational chemistry and soft matter physics.

Jan Aage AasenView profile

Associate Professor

• Drilling Engineering
• Wellbore Integrity
• Zonal Isolation
• +7 more

Jan Aage Aasen is an Associate Professor in Drilling at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Energy and Petroleum Technology. His research focuses on drilling engineering, wellbore integrity, and sustainable materials for oil and gas applications. Research Interests: His work spans drilling mechanics , wellbore integrity , zonal isolation , and geopolymer-based cementing . He investigates advanced materials for plugging and abandonment, long-term cement performance, and 3D tubular design for critical wells. His research combines experimental validation with computational modeling, especially in annular flows and pressure control systems. Publication Trends: Recent publications emphasize sustainable alternatives like geopolymers for well sealing, reflecting a growing focus on environmental performance in drilling operations. Earlier works focus on mechanistic modeling of tubulars, buckling behavior, and multistring well analysis, showing a long-standing expertise in structural well design. Scientific Awards: No awards are listed in the provided text. Advising and Grants: While no formal list of students is provided, Dr. Aasen collaborates extensively with researchers such as Mahmoud Khalifeh, Arild Saasen, and Hans Joakim Skadsem, suggesting active supervision and mentorship. His work appears to be supported by institutional and industry-aligned grants, particularly in areas of well integrity and sustainable drilling technologies. Labs and Teams: He is part of a research group focused on drilling and well technology at the University of Stavanger, contributing to experimental and computational studies on wellbore flows, casing design, and cement performance.

• Swarm Robotics
• Collective Intelligence
• Artificial Intelligence
• +4 more

Dr. Yara Khaluf is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology, with a focus on swarm robotics and collective intelligence. Her work explores: Swarm intelligence algorithms for foraging and path navigation Time perception modulation in human-AI interaction Scale-invariant dynamics in collective decision-making Multi-robot shepherding systems Research Trends : Her publications demonstrate interdisciplinary work combining robotics, cognitive science, and environmental modeling, with emphasis on decentralized control systems, temporal cognition applications, and bio-inspired swarm algorithms. Collaborative Network : She works extensively with Prof. Pieter Simoens and other researchers across topics like: Collective foraging optimization Swarm behavior modeling Human-robot time synchronization Environmental interaction algorithms

Tom BraeckeveltView profile

Research Fellow

• Materials Science
• Perovskite Solar Cells
• Computational Chemistry
• +3 more

Tom Braeckevelt is a Research Fellow at Ghent University, Belgium, working within the computational materials science group led by Prof. Veronique Van Speybroeck. Based at Tech Lane Ghent Science Park (Technologiepark 46, Zwijnaarde), he collaborates extensively with experimental teams including Prof. Johan Hofkens (photophysics) and Prof. Sara Bals (electron microscopy), bridging theoretical modeling with advanced characterization techniques to solve stability challenges in next-generation photovoltaics. Education: PhD in Materials Science, Ghent University (2018). Dissertation: Designing 2D hybrid organic-inorganic perovskites for game-changing photovoltaics , supervised by Prof. Veronique Van Speybroeck and Dr. Kurt Lejaeghere. His research centers on perovskite stability mechanisms through multiscale computational modeling (DFT, machine learning potentials, molecular dynamics) integrated with experimental validation (TEM, GIWAXS, spectroscopy). Key focus areas include phase transition kinetics, strain engineering, doping strategies, and interfacial design for cesium lead halide perovskites. This work addresses critical barriers to commercial solar cell deployment, particularly ambient-condition stability and efficiency retention. From 2019-2025, Dr. Braeckevelt co-authored 13 high-impact publications including Science (2019), Nature Communications (2022), and ACS Nano (2025), demonstrating progression from fundamental phase transition studies to machine learning-enhanced stability solutions. Recent work expands into covalent organic frameworks and rare-event sampling algorithms, reflecting methodological diversification while maintaining photovoltaic applications as the core driver. No scientific awards or fellowships were documented in the source materials. Supported by institutional research grants at Ghent University, Dr. Braeckevelt has presented findings at 8+ international conferences including PSCO19 (Lausanne), ICAMM (Rennes), and DFT2022 (Brussels). His invited talk at the 2025 Eindhoven Psiflow workshop highlights growing recognition in machine learning for perovskites. No student supervision roles were indicated in current position. He operates within Ghent's integrated materials research ecosystem, contributing to cross-disciplinary projects that combine computational prediction with nanoscale characterization to accelerate renewable energy technology development.

Bruce TidorView profile

Professor

• Computational Biology
• Systems Biology
• Molecular Biophysics
• +3 more

Bruce Tidor is a Professor of Biological Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), leading the Tidor Lab. His research focuses on computational and systems biology, integrating principles from biophysics, artificial intelligence, and applied mathematics to study complex biological systems at molecular and systems levels. Research Interests: Protein and nucleic acid structure-function analysis Computational design of proteins and ligands Gene expression networks and systems biology modeling Drug resistance mechanisms and therapeutic design Lab Activities: The Tidor Lab develops methods for analyzing biomolecular interactions, parameterizing biological networks, and optimizing drug resistance strategies. Notable projects include studying calcium sensor design, oscillatory biological systems, and mechanotransduction in proteins such as FAT domains. Collaborations and Grants: Ongoing work includes MIT-Skoltech collaborations, computational re-engineering of enzymes, and modeling cytokine interactions. The lab has advised numerous PhD and master’s students in biological engineering and related fields.

Jiajun XuView profile

Researcher

• Agricultural Engineering
• Robotics
• Machine Vision
• +4 more

Jiajun Xu is a Research Associate in the Department of Biosystems and Agricultural Engineering (BAE) at Michigan State University's College of Engineering. His work focuses on advancing agricultural automation through machine vision systems, robotics, and path planning algorithms. Key areas of expertise include automated crop sorting, weed detection systems, and the development of cable-driven parallel robots for precision agriculture. His research integrates mechanical engineering principles with cutting-edge computer vision technologies, aiming to enhance efficiency in agricultural post-harvest processes and autonomous farming operations. Notable contributions include the OpenWeedGUI platform for weed imaging and the design of LiDAR-based systems for real-time crop quality assessment. Publications highlight trends in sensor integration, path optimization for mobile robots, and cross-season weed detection adaptations. Collaborative projects emphasize practical applications in agricultural automation, with a focus on open-source tools and industry-relevant solutions. Awards and grants are not explicitly mentioned in available texts, though his work indicates strong engagement with both academic and applied research in biosystems engineering.

• Combinatorics
• Computer Science
• Log-concave Polynomials
• +3 more

Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . His research focuses on log-concave polynomials and their applications in combinatorics and computer science. Education: PhD in Mathematics, UC Berkeley (2014-2019) MS in Mathematics, Texas A&M University (2010-2012) BS in Computer Engineering and Applied Math, Texas A&M University (2006-2010) Research Interests: His work centers around log-concave polynomials and their connections to combinatorics, optimization, and computer science. Key areas include: Lorentzian polynomials and their applications Polynomial capacity and optimization Sampling algorithms and combinatorial structures Representation theory and algebraic combinatorics Scientific Awards: Dirichlet Postdoctoral Fellowship (TU Berlin, 2020-2022) James H. Simons Fellowship (Simons Institute, UC Berkeley, Spring 2019) Previous Positions: Postdoc Fellowship, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoc, KTH, Stockholm (Fall 2019) Developer, Teacher Retirement System of Texas (2012-2014)

• Robotics
• Control Systems
• Predictive Control
• +6 more

Jan Swevers is a Full Professor at KU Leuven , affiliated with the MECO Research Team . His work focuses on predictive control, sensor-based robotics, and optimal motion planning, with applications in autonomous systems, industrial robotics, and aerospace engineering. Research Interests : Predictive control (Model Predictive Control, Iterative Learning Control), sensor-based robotics (surface following, cable shaping), optimal motion planning (Reeds-Shepp algorithms, time-optimal interception), and constraint handling in robotics. Publications : Recent work explores deformable object dynamics, autonomous surface vessel navigation, and computational efficiency in optimal control. Key trends include integrating predictive control with real-time estimation, simplifying complex environments via constraint reduction, and advancing human-like autonomous driving through imitation learning. Advising : Supervised PhD theses on topics like constraint-based robot programming, motion planning, and predictive control for sensor-based tasks. Labs : Leads the MECO Research Team at KU Leuven, focusing on control systems, robotics, and optimal planning.

Julien TiernyView profile

Professor

• Topological Data Analysis
• Scientific Visualization
• Data Compression
• +2 more

Julien Tierny is a Professor at Ecole Polytechnique and a Senior Researcher (DR2) at CNRS within the Computer Science Department (LIP6) at Sorbonne Université, part of the APR team. His research focuses on Topological Data Analysis and Visualization, with a particular emphasis on developing efficient algorithms for scientific data processing and visualization. He leads the ERC-funded project TORI, which aims to reduce scientific data through in-situ topological methods. Education & Affiliations: PhD in Computer Science (2008), Université de Lille Habilitation Thesis (HDR, 2016), Sorbonne Université Principal Investigator of ERC Project TORI Research Interests: Topological Data Analysis, Scientific Visualization, High-Performance Computing, and Algorithm Design. His work bridges theoretical foundations with practical tools like the Topology ToolKit (TTK), widely used in visualization and data science. Awards & Recognition: ERC Starting Grant (2020) Best Paper Honorable Mention (IEEE VIS 2019) Best Paper Award (IEEE LDAV 2018) Teaching: Leads courses on Scientific Visualization, Topological Data Analysis, and Computer Graphics at Ecole Polytechnique and Sorbonne Université. Popular courses include 'Introduction to Topological Data Analysis' and 'Advanced Visualization Techniques'. Labs & Contributions: Co-developer of the Topology ToolKit (TTK), a widely adopted open-source library for topological data analysis. Active in IEEE Visualization conferences, publishing award-winning papers on distributed computing and topological simplification.

Todd GingrichView profile

Assistant Professor

• Statistical Mechanics
• Stochastic Thermodynamics
• Chemical Kinetics
• +3 more

Todd Gingrich is an Assistant Professor of Chemistry at Northwestern University , specializing in theoretical approaches to statistical mechanics , stochastic thermodynamics , and nonequilibrium chemical dynamics . He earned his B.S. from the California Institute of Technology (2008), M.Sc. from Oxford University (2010), and Ph.D. from the University of California, Berkeley (2015). His research develops analytical and computational methods to study artificial systems inspired by biological nonequilibrium processes. Education B.S., California Institute of Technology (2008) M.Sc., Oxford University (2010) Ph.D., University of California, Berkeley (2015) His work focuses on nonequilibrium thermodynamics , molecular motors , and stochastic dynamics , with recent publications analyzing tensor networks, thermodynamic uncertainty relations, and chemically fueled systems. He mentors a team of graduate students and postdoctoral scholars, including Emanuele Penocchio, Geyao Gu, and Rueih-Sheng Fu. Key trends in his publications include nonequilibrium statistical mechanics , stochastic thermodynamics , and computational methods for modeling molecular motors, Brownian ratchets, and reaction-diffusion systems. His 2022-2024 papers emphasize precision limits in molecular motors and tensor network applications. Notable scientific awards include the APS Oppenheim Award (2019) , Physics of Living Systems Fellowship, MIT (2015) , and prestigious fellowships like the Rhodes Scholarship (2008) and NSF Graduate Research Fellowship (2008) . He teaches Chemistry 444 (Elementary Statistical Mechanics) and Chemistry 171 (Advanced General Inorganic Chemistry) at Northwestern University.

Shuang AoView profile

Research Fellow

• Machine Learning
• Reinforcement Learning
• Curriculum Learning
• +4 more

Mr. Shuang Ao is a Postdoctoral Research Fellow at the School of Computer Science and Engineering, University of New South Wales (UNSW Sydney). He earned his PhD from the University of Technology Sydney in January 2024. His research focuses on machine learning, reinforcement learning, and curriculum learning, with applications in robotic control and antibody drug discovery. Research Interests: Machine Learning Reinforcement Learning Curriculum Learning Graph Algorithms Optimization Techniques Recent Publication Trends: Shuang's work spans large language models for location-based recommendations, spatio-temporal forecasting, reinforcement learning frameworks, and graph algorithm optimizations. His articles address both theoretical advancements and practical applications in scalable systems and data analysis. Contact: Email: shuang.ao@unsw.edu.au