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Sumsun NaherView profile

Senior Lecturer

• Materials Processing
• Laser Engineering
• Metal Matrix Composites
• +3 more

Dr. Sumsun Naher is a Senior Lecturer in the Department of Engineering at City, University of London , where she has worked since 2013. Previously, she served as Lecturer and Research Development Officer at Dublin City University (2006–2013) and as Scientific Officer at Bangladesh Council of Scientific & Industrial Research (1998–2000). Her academic career includes a Post Graduate Diploma in Academic Practice from City, University of London. PhD , School of Mechanical & Manufacturing Engineering, Dublin City University MSc , Materials & Metallurgical Engineering, Bangladesh University of Engineering and Technology BSc , Materials & Metallurgical Engineering, Bangladesh University of Engineering and Technology Her research focuses on semi-solid processing , laser processing , simulation & modelling of materials technologies , and materials characterisation . Recent work explores cellulose nanofiber-based water filters for antibiotic removal and phase change materials in geothermal energy systems. Key article trends reveal expertise in: Laser Surface Modification of metals and composites Advanced Casting Methodologies and semi-solid metal forming Nanoparticle Reinforcement in metal matrix composites Thermal Modelling for energy systems Sustainable Material Solutions in water treatment and energy Computational Materials Science via finite element analysis Naher has received the DCU Invent Commercialisation Award (2011) and holds fellowships from IMechE , Institute of Materials, Minerals & Mining , and Advance Higher Education Authority . She actively reviews for funding bodies and examines PhD theses internationally. As an organiser of the ESAFORM Conference and co-organiser of its Additive Manufacturing symposium since 2017, she contributes to academic leadership. Her professional roles include Board of Directors for the European Association of Materials Forming and participation in EU COST Action projects (Thixoforming, Thixosteel, Nanostructured Materials).

Nikolaus KriegeskorteView profile

Professor

• Computational Neuroscience
• Artificial Neural Networks
• Visual Perception
• +3 more

Nikolaus Kriegeskorte is a Professor of Psychology and Neuroscience, and Director of Cognitive Imaging at the Mortimer B. Zuckerman Mind Brain Behavior Institute at Columbia University. He is affiliated with the Departments of Psychology, Neuroscience, and Electrical Engineering. Institution: Columbia University Academic Roles: Professor of Psychology and Neuroscience; Director of Cognitive Imaging Email: nk2765@columbia.edu Location: Jerome L. Greene Science Center, 3227 Broadway, L3-064 Research Focus: The lab explores the cognitive neuroscience of vision, modeling biological visual systems with artificial neural networks. Key areas include developing statistical inference and visualization techniques to bridge theory and experimental data, understanding representational geometry in neural systems, and optimizing deep learning frameworks for neuroscience. Recent Publications: Highlighted work spans neural network modeling of visual perception, representational similarity analysis, and the topology of brain representations. The lab's methods, such as the TorchLens Python package, enable transparent extraction and visualization of hidden layer activations in neural networks. Grants: Projects are supported by funding from the National Science Foundation (NSF) - Cognitive Neuroscience and the National Institutes of Health (NIH) - NIMH. Laboratory: The Visual Inference Lab (kriegeskortelab.zuckermaninstitute.columbia.edu) is located at Quad 3D, Zuckerman Institute, 3227 Broadway.

Jinjin HaView profile

Assistant Professor

• Machine Learning in Materials Science
• Plasticity
• Ductile Fracture
• +3 more

Jinjin Ha serves as an Assistant Professor in the Department of Mechanical Engineering at the University of New Hampshire, with her office located in Kingsbury Hall, Room W101a, Durham, NH. She teaches core mechanical engineering courses including Statics (ME 525), Materials Processing in Manufacturing (ME 742/842), Theory of Plasticity (ME 927), and Doctoral Research (ME 999), demonstrating active engagement in both undergraduate and graduate education. Her research program integrates computational mechanics with advanced manufacturing, focusing on: Machine learning applications for plasticity modeling and fracture prediction Deformation mechanics in incremental sheet forming processes Martensitic phase transformations in stainless steels Anisotropic material behavior and yield function development Ductile fracture characterization of titanium and aluminum alloys Analysis of her 2023-2024 publications reveals a decisive shift toward AI-driven mechanics, where neural networks solve complex constitutive modeling challenges in metal forming. This interdisciplinary approach bridges fundamental material science with industrial manufacturing optimization, particularly in toolpath design and phase transformation control. No scientific awards were documented in the provided profile information. While doctoral research supervision is indicated through ME 999 course listings, specific student names, grant funding details, laboratory facilities, or collaborative team structures were not disclosed in the available text.

Sebastian RisiView profile

Professor

• Neuroevolution
• Evolutionary Robotics
• Self-Organizing AI
• +9 more

Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .

Ioannis KatakisView profile

Professor

• Mining Social Data
• Mining Web Data
• Mining Urban Data
• +6 more

Professor Ioannis Katakis is a Faculty Member at the University of Nicosia, where he is affiliated with the School of Sciences and Engineering and the Department of Computer Science. He has held various academic positions across multiple institutions including Aristotle University of Thessaloniki, University of Cyprus, Cyprus University of Technology, Open University of Cyprus, Hellenic Open University, Athens University of Economics and Business, and National and Kapodistrian University of Athens. His educational background includes a PhD in Machine Learning for Automated Text Classification (2005-2009), a Master's in Information Systems (2005-2007), and a Bachelor's in Computer Science (2000-2004), all from Aristotle University of Thessaloniki. Professor Katakis specializes in several cutting-edge areas of computer science and data analysis. His primary research interests include Mining Social, Web and Urban Data , Sentiment Analysis and Opinion Mining , Data Streams , and Multi-label Learning . His work bridges theoretical machine learning approaches with practical applications in social media analysis, healthcare informatics, privacy protection, and smart city technologies. He has published extensively in top venues including CIKM, ECML/PKDD, IEEE TKDE, and ECAI. His recent publications demonstrate a clear trend toward applying machine learning techniques to real-world problems with societal impact. He has focused on areas such as GDPR compliance in smart devices, sentiment analysis in crowd-sourced content, healthcare applications including drug reaction classification and brain disease monitoring, and privacy protection in wearable technologies. His work often involves multi-modal data analysis and addresses challenges in data streams and multi-label classification. Professor Katakis has made significant contributions to his field, with his research cited over 4,200 times. He serves as an Editor for the journal Information Systems and has edited four special issues in journals such as DAMI and InfSys. He regularly contributes to the academic community by serving on program committees for major conferences including ECML/PKDD, WSDM, DEBS, and IJCAI, and by reviewing for prestigious journals like TPAMI, DMKD, TKDE, TKDD, JMLR, TWEB, and ML. He has been actively involved in European research projects, notably serving as Quality Assurance Coordinator and Senior Researcher for projects such as VAVEL (www.vavel-project.eu) and INSIGHT (www.insight-ict.eu). His grant activities demonstrate a strong focus on collaborative, interdisciplinary research with practical applications in urban data management, social media analysis, and healthcare informatics. He has organized three workshops at major conferences (ICML, ECML/PKDD, EDBT/ICDT) and has extensive experience translating research into practical applications through his involvement in European projects.

Daniel GroßeView profile

Professor

• Formal Verification
• Hardware Verification
• Electronic Design Automation
• +3 more

Prof. Daniel Große serves as Professor at the Institute for Complex Systems (ICS) within Johannes Kepler University Linz, Austria, while maintaining a dual affiliation with the German Research Center for Artificial Intelligence (DFKI) in Bremen. His research centers on Electronic Design Automation (EDA), specializing in verification, debugging, and synthesis of complex hardware/software systems through advanced virtual prototyping techniques. His primary research domains include Formal Verification , Hardware Verification , and Virtual Prototyping , with significant contributions to RISC-V architecture development and embedded systems validation. Current projects like PaSVer (automotive electronics), AUTOASSERT (analog-digital verification), and VerSys (RISC-V software platforms) demonstrate his focus on bridging simulation and formal methods for safety-critical applications. Recent publications reveal accelerating trends in metamorphic testing for embedded graphics libraries and RISC-V vector extensions, alongside innovations in waveform analysis tools like Surfer. His work increasingly integrates AI-assisted verification while maintaining rigorous formal methods foundations. Prof. Große actively supervises student research, having guided Lucas Klemmer's PhD thesis and nine Bachelor theses on topics ranging from RISC-V processor design to transaction visualization. His projects including SATiSFy (autonomous vehicle security) and CONVERS (complex system design automation) secure substantial research funding from academic and industry partners. He leads the Institute for Complex Systems' development of open-source tools such as RISC-V VP++ and contributes to international standards through program committee roles at DATE, ICCAD, and RISC-V Summit Europe.

Katarina DomijanView profile

Associate Professor

• Bayesian applications to data analysis
• Machine learning
• Statistical computing
• +3 more

Katarina Domijan is an Associate Professor in Statistics at the Department of Mathematics and Statistics, Maynooth University, Ireland. She holds a PhD in Statistics from Trinity College Dublin (2008) and has been affiliated with Maynooth University since 2008, transitioning from Lecturer/Assistant Professor to her current role in 2024. Her academic career includes editorial roles as Associate Editor for The R Journal (2021–present) and the Journal of Computational and Graphical Statistics (2015–2024). Research Interests focus on Bayesian methods for high-dimensional data, particularly in classification problems. She specializes in feature selection and model visualization, with applications spanning agricultural data analysis (e.g., hyperspectral imaging for lactose prediction), medical diagnostics (e.g., sepsis and cancer detection), and space physics (e.g., Saturn Kilometric Radiation classification). Her work bridges theoretical statistics with real-world challenges, including socio-economic studies and forensic science. Key Research Areas Bayesian statistical inference Machine learning for large feature spaces Statistical computing and model interpretability Data visualization and chemometrics Scientific Contributions include leading projects like VistaMilk Phase II (2024–2030, €152,300) and Measuring Carbon Sequestration (2024–2028, €174,788.90). Her 15 most recent publications highlight advancements in ensemble modeling, spatial statistics, and medical diagnostics. Scientific Awards Associate Editor, The R Journal (2021–present) Associate Editor, Journal of Computational and Graphical Statistics (2015–2024) Student Supervision includes PhD and MSc graduates such as Dr. Bruna Wundervald (2024) and Dr. Mark O’Connell (2017). She also collaborates with researchers across disciplines, including Dr. Nadim Akasheh in food hypersensitivity studies.

• Additive Manufacturing
• Laser Powder Bed Fusion
• Materials Science
• +2 more

Prof. Dr.-Ing. Johannes Henrich Schleifenbaum is a Professor and Chair of Digital Additive Production at RWTH Aachen University, where he leads research in the Profile area Production Engineering (ProdE). His work advances additive manufacturing (AM) through interdisciplinary approaches combining materials science, process engineering, and digital technologies. His research encompasses: Laser powder bed fusion (LPBF) process optimization and defect mitigation Development of novel alloys/composites for AM applications Sustainable manufacturing practices including material recycling Integration of AI/ML for accelerated material and process design Digital tools for automated design and distributed manufacturing Recent publications (2023-2025) demonstrate a strong focus on: Multi-material processing and microstructure control Machine learning-driven alloy development Standardization and scalability of AM processes Advanced simulations for meltpool dynamics and thermal behavior Applications in aerospace, construction, and biochemical engineering He leads the Chair of Digital Additive Production, collaborating with industry partners to translate research into industrial solutions for next-generation manufacturing.

Jun YeView profile

Research Professor

• Atomic Physics
• Molecular Physics
• Optical Physics
• +4 more

Jun Ye is a Chinese-American physicist affiliated with JILA, the National Institute of Standards and Technology (NIST), and the University of Colorado Boulder. He holds the academic rank of Research Professor and has made groundbreaking contributions to atomic, molecular, and optical (AMO) physics, particularly in precision measurement and quantum control of atomic and molecular systems. Education: BS in Physics (Shanghai Jiao Tong University, 1989), MS in Quantum Optics (University of New Mexico, 1991), PhD in Physics (University of Colorado Boulder, 1997) under Nobel laureate John L. Hall. His research spans ultracold atoms, ultracold molecules, and laser-based precision measurement, including the development of the 3D quantum gas clock with 2.5 × 10⁻¹⁹ frequency precision. He explores applications in gravitational field sensing, dark matter detection, quantum simulations, and fundamental physics tests. Recent work includes thorium nuclear clocks, topological optical lattice clocks, and spin dynamics in ultracold polar molecules. Jun Ye's publications (e.g., 2025 articles) focus on atomic clocks, quantum sensing, and precision metrology, with keywords like quantum physics, atomic physics, and optical engineering. His subfields include frequency combs, spin squeezing, superexchange interactions, and many-body quantum systems. Scientific Awards: Department of Commerce Gold Medal (multiple years), Breakthrough Prize in Fundamental Physics (2022), National Academy of Sciences (2011), Norman F. Ramsey Prize (2019), and numerous fellowships. Patents: Four U.S. patents for frequency combs and laser technology. He leads research at JILA, collaborating with institutions like Caltech and MIT, and has been featured in documentaries such as The Most Unknown (2018). His work bridges experimental physics, quantum information science, and applied photonics.

• Automated Transportation Systems
• Connected Vehicles
• Electric Vehicles
• +3 more

Dr. Xiaopeng Li is the Harvey D. Spangler Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, with an affiliation in the Department of Electrical and Computer Engineering. He leads the USDOT Rural Autonomous Vehicle Program and previously directed the National Institute for Congestion Reduction. He earned his B.S. in Civil Engineering from Tsinghua University (2006), M.S. in Civil Engineering (2007), M.S. in Applied Mathematics (2010), and Ph.D. in Civil Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on modeling and field experiments for connected, electric, and automated vehicles (CAVs), infrastructure systems analysis, and interdependent network modeling. He has pioneered physics-enhanced machine learning frameworks for vehicle control and developed simulation tools for CAV deployment. His 2025-2024 publications highlight advancements in Connected vehicle trajectory modeling Energy consumption optimization Edge computing for autonomous operations Residual learning control systems Equity analysis in AV deployment Communication technologies for V2X Awards include: TRB Best Paper Award (2025) NSF CAREER (2015) ASCE Fellow (2024) IEEE Senior Member (2022) Multiple institution-specific fellowships He has advised 15+ graduate students, secured $35M+ in grants from NSF, USDOT, and industry partners, and chairs the IEEE ITSS Emerging Transportation Technology Testing committee. His work addresses real-world AV implementation, safety validation, and sustainable transportation systems.

Eunice Eunhee JangView profile

Professor

• Language Assessment
• Educational Measurement
• Program Evaluation
• +8 more

Dr. Eunice Eunhee Jang is a Professor in the Department of Applied Psychology and Human Development at the Ontario Institute for Studies in Education (OISE), University of Toronto. Her research focuses on synergistic learner modeling, dynamic assessment systems, and the intersection of language testing with educational measurement. PhD with specializations in language testing, educational measurement, and program evaluation Develops interactive digital assessment interfaces for struggling readers Author of "Focus on Assessment" (2014) and co-author of OECD Reviews on Evaluation and Assessment in Education Research Interests Dr. Jang's work explores prismatic assessment analytics to understand learner potential and predict learning pathways. She integrates natural language processing and machine learning to create diagnostic feedback systems that support cognitive, metacognitive, and affective growth in technology-rich classrooms. Scientific Awards Jacqueline Ross TOEFL Dissertation Award Caroline Clapham IELTS Master’s Award Tatsuoka Measurement Award Professional Contributions She has served on major advisory boards including EQAO provincial assessments and TOEFL Committees of Examiners. Currently, she is an elected board member for the International Language Testing Association and contributes to the Broader Measures of Success Advisory Committee for People for Education.

Joshua J. SolanoView profile

Associate Professor

• Emergency Medicine
• Geriatric Emergency Care
• Medical Education
• +4 more

Joshua J. Solano, M.D., FAAEM is an Associate Professor of Emergency Medicine and Clerkship Director for the Emergency Medicine Residency program at the Florida Atlantic University Charles E. Schmidt College of Medicine. As Clerkship Director, he works alongside residents to promote best practices for emergency department patient care. Dr. Solano is board certified in emergency medicine and maintains active membership in the American College of Emergency Physicians and the American Academy of Emergency Medicine. Dr. Solano's educational background includes a BS in Biology and BA in History from Duke University, followed by his MD from the University of Florida. He completed his emergency medicine residency at Beth Israel Deaconess Medical Center in Boston and served on faculty at Harvard Medical School as an instructor and assistant program director before joining FAU. In his final year in Boston, he completed the Rabkin Medical Education Fellowship. Dr. Solano's research focuses on geriatric emergency medicine, particularly head trauma management in elderly patients, anticoagulation risks, and emergency department quality assurance. His recent publications demonstrate a strong emphasis on developing clinical decision rules for geriatric head trauma and understanding medication interactions in older emergency patients. He has also contributed significantly to medical education literature, developing innovative residency training curricula. His scholarly work shows a clear progression toward establishing evidence-based guidelines for geriatric emergency care, with numerous publications on intracranial hemorrhage risk assessment, anticoagulation management, and fall prevention strategies. Dr. Solano's research bridges clinical practice with educational innovation, making significant contributions to both patient care protocols and emergency medicine training. 2017: Attending Role Model Award, Beth Israel Deaconess Medical Center 2016-2017: Rabkin Medical Education Fellowship 2011: Alpha Omega Alpha Medical Honor Society 2011: Chapman Humanism Society 2010: Equal Access Clinic Service Award 2010: Lawrence M. Goodman Award for Medical Student Research 2010: Community Service Award As Clerkship Director, Dr. Solano oversees medical student education in emergency medicine and has implemented electronic evaluation systems that have increased the quality and quantity of student assessments. His clinical work includes staff positions at Bethesda East Hospital and Delray Medical Center. Dr. Solano's research program demonstrates strong institutional support through multiple publications and ongoing investigations into geriatric emergency care protocols.

• Computer Science
• Deep Learning
• Network Security
• +3 more

George Kesidis is a Professor in Computer Science and Engineering and Electrical Engineering at Penn State University. His research spans deep learning security, virtual reality optimization, and cloud computing. College of Engineering (Penn State University) Research Focus: Backdoor Attacks, DNN Robustness, Edge Caching Active in NSF and U.S. Navy-funded projects (2022-2026) His work addresses backdoor data poisoning , test-time evasion attacks , and DNN overfitting mitigation . He develops techniques like activation clipping, perturbation analysis, and statistical defense models. Recent projects include edge caching systems for VR and security-driven AI frameworks. Key article trends reveal expertise in adversarial deep learning, immersive media delivery, and cloud resource optimization. Current grants focus on multi-user VR, GPU scheduling, and serverless-cloud hybrid architectures. He collaborates extensively with researchers like David J. Miller and Xinyu Li, particularly on cloud-based adversarial defense mechanisms and VR streaming benchmarks.

• Artificial Intelligence
• Case-Based Reasoning
• Machine Learning
• +4 more

Prof. Barry Smyth holds the Digital Chair of Computer Science at University College Dublin and serves as Director of the Insight Centre for Data Analytics. A Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and Member of the Royal Irish Academy since 2011, he previously directed the Clarity Centre for Sensor Web Technologies (2008-2013) and led UCD's School of Computer Science and Informatics as Head of School. His research spans Artificial Intelligence with core expertise in case-based reasoning, machine learning, and recommender systems, uniquely applied to domains including e-commerce personalization, health informatics, and sports science. Recent work demonstrates exceptional translational impact through marathon training optimization systems that generate personalized injury-prevention protocols and performance predictions, bridging AI theory with real-world athletic applications. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary AI applications: 60% focus on sports science (particularly marathon running), 25% on privacy-enhanced recommender systems, and 15% on financial time-series analysis. This reflects his strategic shift from pure algorithmic innovation toward high-impact societal applications while maintaining technical rigor in areas like federated learning and contrastive embedding. Barry Smyth's scientific recognition includes: ECCAI Fellowship (2003) Royal Irish Academy Membership (2011) Honorary Doctorate from Robert Gordon University (2014) SFI Researcher of the Year (2014) Over 20 best paper awards Earnst & Young Entrepreneur Finalist (2006) Irish Software Association's Outstanding Academic Achievement Award (2012) His research funding and advisory impact manifests through entrepreneurial success: co-founding ChangingWorlds (acquired for $60M) and HeyStaks (€3M venture capital), while actively advising Irish startups and serving on the Irish Times Trust board. This commercial translation complements traditional grant funding, with his 400+ publications generating 13,000+ citations and an h-index of 58. Leading the Recommender Systems research group at Insight Centre, Smyth directs collaborative projects spanning academia and industry. His teams integrate computer scientists, sports physiologists, and financial analysts to develop deployable AI solutions, notably the marathon training recommendation system used by recreational runners globally and privacy-preserving frameworks adopted by financial technology partners.

Max PellertView profile

Assistant Professor

• Computational Social Science
• Digital Traces
• Affective Expression
• +9 more

Max Pellert is a computational social scientist and cognitive scientist with faculty appointments at multiple institutions. Since 2022, he has served as an Assistant Professor at the Chair for Data Science in the Economic and Social Sciences at the University of Mannheim . He previously held an interim Professor position at the University of Konstanz and an Assistant Researcher role at Sony Computer Science Laboratories Rome. His work bridges computational methods with social science theory. Education M.Sc., University of Vienna (2017) in Middle European interdisciplinary Master's program in Cognitive Science (with distinction) Ph.D., Medical University of Vienna (2022) in Medical Informatics, Biostatistics & Complex Systems Research interests center on Computational Social Science , Digital Traces , and Natural Language Processing for emotion and sentiment analysis. He develops Temporal Adapters for tracking longitudinal emotional patterns and FAULTANA pipeline for polarization studies. His AI Psychometrics framework assesses personality-like traits in large language models. Recent publications include: (1) 2025 ACL work on political bias in LLMs; (2) 2025 ICWSM study of temporal emotion analysis; (3) 2024 Perspectives on Psychological Science paper on LLM psychometrics; (4) 2024 PNAS Nexus polarization analysis; (5) 2023 Emotion cross-cultural study of pandemic emotions. Scientific Awards Habilitation candidate status at University of Mannheim Teaching includes IS 616: Large Scale Data Analysis , IS 809: Advanced Text Mining Lab , and IS 723: Data Science Seminar at master’s and PhD levels. His Barcelona Supercomputing Center role focuses on principal investigator duties for computational social science projects.