جست‌وجوی استاد و پژوهشگر

Ross Kingمشاهده پروفایل

استاد

• Automation of Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Ross King is a faculty member at the University of Cambridge, affiliated with the Department of Chemical Engineering and Biotechnology. His research focuses on the automation of scientific discovery, machine learning applications in biology and chemistry, and DNA computing. Developed the first autonomous 'Robot Scientist' systems (Adam, Eve, Genesis) capable of hypothesis generation, experimental design, and execution using AI Pioneer in DNA computing, demonstrating the first physical Nondeterministic Universal Turing Machine (NUTM) 35+ years of expertise in machine learning, particularly relational learning for complex biological/chemical data Organizer of the international 'Nobel Turing Grand Challenge' for AI scientists His work in computational biology spans eukaryotic cell modeling, cancer signaling pathways, and AI-driven drug discovery for neglected tropical diseases like malaria and Chagas disease. The Genesis system aims to automate 10,000 simultaneous closed-loop experiments using micro-chemostats to model cellular complexity. The DNA computing research demonstrates exponential theoretical advantages over classical and quantum computing architectures for NP-complete problems, utilizing Thue string rewriting systems and polymerase chain reaction techniques. This work has significant implications for computer science, physics, and practical computing resource utilization. King's machine learning contributions include active learning strategies for compound selection in drug design and meta-learning approaches to optimize ML applications in bioinformatics and chemoinformatics.

Joergen Kornfeldمشاهده پروفایل

پژوهشگر

• Connectomics
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Joergen Kornfeld is a researcher at the University of Cambridge, affiliated with the MRC Laboratory of Molecular Biology (LMB) in the Connectomics of Learned Behaviour group. His work focuses on understanding how learned behaviors are encoded in neural circuits through connectomic analysis. Institution: University of Cambridge Role: Connectomics Researcher Research Interests: • Connectomics and synaptic connectivity mapping • High-throughput 3D electron microscopy • Deep learning applications in neural network analysis • Behavioral memory storage mechanisms • Comparative neuroanatomy of learned behaviors • Computational modeling of neural circuits. Recent publications highlight his expertise in developing deep learning tools (e.g., DeepFocus, SyConn2) for connectomic reconstruction, with applications in zebra finch song learning and larval zebrafish neural circuits. His work bridges advanced imaging techniques, computational methods, and behavioral neuroscience. Techniques: High-throughput 3D electron microscopy, flood-filling networks Model Systems: Zebra finch, larval zebrafish

• AI for Software Engineering (AI4SE)
• Machine Learning
• Data Mining
• +۵ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he directs the Information Systems and Technology Cluster and the Center for Research on Intelligent Software Engineering. An ACM Fellow, IEEE Fellow, and ASE Fellow, his research focuses on AI for Software Engineering (AI4SE), leveraging machine learning, data mining, and NLP to enhance software analytics and automation. Research Highlights: AI4SE, code LLMs, human-AI synergy in software engineering, software reliability, and empirical studies of practitioner pain points Awards: IEEE TCSE Distinguished Service Award, university-wide Teaching Excellence Award, Outstanding Graduate Supervisor Award, 2 Test-of-Time Awards, and 11 ACM SIGSOFT/IEEE TCSE Distinguished Paper Awards Leadership: General Chair of ASE'16 and MSR'22, PC Co-Chair for ASE'20, FSE'24, and ICSE'25, ACM SIGSOFT Executive Committee member His work has received over 20 awards, 37,000 citations, and an H-index of 100. As an educator, he has mentored trainees who became faculty and R&D experts globally.

• Corporate Finance
• Corporate Social Responsibility
• Happiness Economics
• +۳ مورد دیگر

Leonardo Becchetti is a Full Professor at the University of Rome Tor Vergata's Department of Economics and Finance, teaching Microeconomics and Topics in Applied Economics for the 2025-2026 academic year. Based in room P2 S55, he maintains an active research profile with email contact becchetti@economia.uniroma2.it. His research centers on Corporate Finance, Corporate Social Responsibility, and Happiness Economics, extending to Sustainable Development and Ecological Transition. He investigates how economic structures influence well-being through relational goods, generativity, and sustainability frameworks, employing game-theoretic and empirical methodologies to analyze climate action, AI impacts, and cooperative behavior. Analysis of his 2024-2025 publications reveals intensifying focus on ecological transition mechanisms, including renewable energy communities, circular economy governance, and sustainable investment models. His work increasingly integrates climate economics with behavioral insights, examining conditional cooperation in environmental actions and AI's disruption of labor markets. No scientific awards or student advising information is publicly documented. He participates in departmental research initiatives including DEF Seminars and ROBO Seminars, contributing to the university's research centers on economic and social impact.

Louis Collinsمشاهده پروفایل

استاد

• Neuroimaging
• Medical Image Processing
• Magnetic Resonance Imaging (MRI)
• +۷ مورد دیگر

Louis Collins is a Professor in the Department of Biomedical Engineering and Department of Neurology and Neurosurgery at McGill University, with associate membership at the Center for Intelligent Machines. His work focuses on advanced medical imaging techniques for neurological applications. Key Expertise: Non-linear image registration, model-based segmentation, neuroimaging, MRI analysis Applications: Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, schizophrenia Methodology: Development of computer vision algorithms for image-guided neurosurgery (IGNS), automated atlasing, and biomarker quantification Collins' research combines computational neuroanatomy with clinical translation, particularly in: Quantifying brain atrophy and anatomical variability across populations Optimizing MRI templates for improved diagnostic accuracy Developing tools like SEEGAtlas for surgical electrode classification Exploring neurophysiological fingerprints of neurodegenerative diseases His lab (NIST-Lab) actively pursues CIHR-funded projects on ultrasound-based image-guided neurosurgery and machine learning applications in clinical trials.

Craig Jonesمشاهده پروفایل

استادیار

• Medical Image Processing
• Artificial Intelligence
• Neural Networks
• +۲ مورد دیگر

Craig Jones is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering. He is affiliated with the Malone Center for Engineering in Healthcare and contributes to the Precision Medicine Analytics Platform's Imaging and Data Science Subcommittees. BSc in Computer Science and Mathematics from Simon Fraser University MSc in Medical Biophysics from the University of Western Ontario PhD in Physics from the University of British Columbia His research focuses on applying artificial intelligence and neural networks to medical image processing, particularly for MRI, CT, optical coherence tomography (OCT), and ultrasound datasets. Key areas include 2D/3D image processing, anomaly detection, segmentation, and uncertainty quantification, with clinical applications in neurosurgery, ophthalmology, and oncology. Projects span robotic imaging, neuroendoscopic guidance, and cancer boundary detection. Recent publications highlight advancements in vision-language models for 3D medical imaging, automated segmentation of venous malformations, and AI-guided neurosurgical tools. Articles emphasize multimodal data fusion, self-supervised learning, and federated learning for rare cancer analytics. He received a $310,000 Department of Defense grant in 2022 to develop AI-guided treatments for venous malformations. His work bridges clinical imaging domains and computer vision as a member of the Radiology AI Lab (RAIL), a collaborative effort across Johns Hopkins Hospital, the Whiting School of Engineering, and the Applied Physics Laboratory.

Steve Zdancewicمشاهده پروفایل

استاد

• Programming Languages
• Type Theory
• Logic
• +۷ مورد دیگر

Steve Zdancewic is the Schlein Family President's Distinguished Professor and Associate Chair in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He is a leading researcher in programming languages, formal methods, and computer security with over two decades of impactful contributions to the field. His research interests span programming languages, type theory, logic, computer security, quantum programming, and formal verification. Zdancewic has made significant contributions to information-flow security, memory safety, program synthesis, and the verification of low-level systems. His work often bridges theoretical foundations with practical applications, particularly through the development of verified systems using Coq and other proof assistants. Analysis of his recent publications reveals a strong focus on formal verification techniques, particularly using Interaction Trees and the Coq proof assistant. His research trajectory shows consistent evolution from foundational work on information-flow security toward increasingly sophisticated verification of complex systems including LLVM, quantum computing, and distributed systems. His work demonstrates a commitment to building practically useful verification tools while maintaining rigorous theoretical foundations. Distinguished Paper Award for Semantics for Noninterference with Interaction Trees (ECOOP 2023) Schlein Family President's Distinguished Professor (2021) Distinguished Paper Award for Interaction Trees (POPL 2020) Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching (2018) IEEE MICRO top picks (2013) Alfred P. Sloan Fellow (2009-2010) NSF CAREER award (2004) Zdancewic has advised numerous PhD students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, including the NSF Expedition on the Science of Deep Specification. He is actively involved in multiple major research projects including Vellvm (verified LLVM), DeepSpec, and quantum programming verification. Zdancewic also co-organizes Penn's PL Club programming languages research group with Benjamin Pierce and Stephanie Weirich.

• Data Ethics
• Machine Ethics
• Artificial Intelligence
• +۷ مورد دیگر

Sylvie Delacroix is the Inaugural Jeff Price Chair in Digital Law and Director of the Centre for Data Futures at King's College London. She is also a Visiting Professor at Tohoku University, focusing on bridging theory and practice in ethics and public policy initiatives. Her research spans Data & machine ethics Ethical agency and habit Social sustainability of data ecosystems Participatory infrastructure for AI systems Uncertainty communication in LLMs Legal theory and digital law Recent publications address LLM interfaces as transitional spaces for democratic revival and sustainable data rivers in generative AI. Her work has attracted significant grant funding from the Patrick J. McGovern Foundation, Wellcome Trust, and Omidyar Network. Scientific awards include the Leverhulme Prize Peter Birks 2d Prize for Outstanding Legal Scholarship Montgomery Fellowship at Dartmouth College She supervises PhD research on large language models and uncertainty communication, as well as habit in ethical lives. Her policy contributions include advising the Law Society of England and Wales on algorithmic justice systems.

• Media Pedagogy
• Media Didactics
• Adaptive Learning Systems
• +۳ مورد دیگر

Luisa Wellert is a Research Associate at the Innovative Educational Technologies Department within the Tübingen Center for Digital Education (TüCeDE) at the University of Tübingen since September 2023. She is also a PhD student at TüCeDE since October 2022. Education: Master of Arts in Intermedia and General Educational Science (2020-2022) from University of Cologne Bachelor of Arts in Media Studies and Educational Science (2016-2019) from University of Tübingen Research Focus: Her work centers on media pedagogy , media didactics , and adaptive learning systems , with particular emphasis on AI integration in educational contexts. Recent projects examine automated qualitative coding of AI tutoring dialogues using large language models and effectiveness of self-developed adaptive systems in schools. Publications: Recent work analyzes assessment methodologies in adaptive learning systems, compares performance-based and cognitive load-based evaluation approaches, and explores implementation challenges of DIY adaptive technologies. She actively contributes to open-source AI tutoring initiatives through the OSATI project. Presentations: Luisa presents her research at major conferences including EARLI, LEAD Research Meeting, and GEBF Conference, focusing on practical implementations of AI-based educational tools and their cognitive impacts. Professional Activity: Prior to her current role, she served as Project Manager for Educational Research at TüCeDE (2022-2023) and worked as a Research Assistant at mmb Institut GmbH (2021-2022) and mecodia GmbH (2018-2020). She also has experience in media education and empirical media research.

Michael J. Cafarellaمشاهده پروفایل

دانشیار

• Databases
• Information Extraction
• Data Integration
• +۶ مورد دیگر

Michael J. Cafarella is an Associate Professor in the Computer Science and Engineering department at the University of Michigan . His research focuses on databases, information extraction, data integration, and data mining, with applications in economics, social media analysis, and combating human trafficking. He leads the Software Systems Lab and Michigan Database Group . Scientific Awards NSF CAREER award Sloan Research Fellowship (2016) 2018 VLDB Ten-Year Best Paper award Research Impact : Cafarella co-founded the Hadoop open-source project and Lattice Data (acquired by Apple). His work on DeepDive and DARPA MEMEX was featured on 60 Minutes and in Scientific American . Funding from The Census Bureau, DARPA, Google, NSF, Yahoo!, General Electric, and Dow.

Cornelius Puschmannمشاهده پروفایل

استاد

• Computational Communication Research
• Digital Media Usage
• Hate Speech
• +۵ مورد دیگر

Cornelius Puschmann is a Professor of Communication and Media Studies at the University of Bremen's ZeMKI, leading the Digital Communication and Information Diversity (DCID) Lab. He has held affiliations with institutions including Zeppelin University, the Alexander von Humboldt Institute for Internet and Society, and the Leibniz Institute for Media Research / Hans Bredow Institute. His research focuses on computational communication, digital media usage, hate speech, and algorithmic impacts on digital communication. Current projects: Informed by Influencers? (INDI), Political Polarization and Individualized Online Information Environments (POLTRACK) Member of Deutsche Gesellschaft für Publizistik- und Kommunikationswissenschaft (DGPuK), European Communication Research and Education Association (ECREA), and International Communication Association (ICA) His recent publications explore topics such as alternative news consumption, political polarization, and communicative AI. He has also contributed to open-source methodologies like the RPC-Lex dictionary for analyzing right-wing populist discourse. Notable affiliations include visiting scholar roles at the Oxford Internet Institute, Berkman Klein Center for Internet and Society, and University of Amsterdam's Department of Media Studies.

Michael Skinniderمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Metabolomics
• +۴ مورد دیگر

Michael Skinnider serves as Assistant Professor at Princeton University's Lewis-Sigler Institute for Integrative Genomics and Assistant Member of the Ludwig Princeton Branch. His research develops AI-driven computational methods to identify unknown small molecules in mass spectrometry data, with applications in cancer biology and forensic drug detection. His educational background includes: BArtsSc from McMaster University (2015) PhD from University of British Columbia (2021) MD from University of British Columbia (2023) Skinnider's work centers on illuminating the "metabolomic dark matter" —unidentified chemical entities in mass spectrometry data. His lab pioneers machine learning approaches for metabolite identification, focusing on connections between unknown metabolites, cancer risk, and the microbiome. Recent innovations include chemical language models that transform mass spectrometry outputs into chemical structures, with applications spanning cancer diagnostics to forensic analysis of designer drugs. His research bridges computational biology, chemistry, and clinical medicine through low-data learning techniques. Publication trends reveal three dominant themes: (1) AI-driven metabolite identification (25% of recent work), (2) single-cell/spatial data analysis (40%), and (3) molecular interaction networks (35%). His 2024 Nature Machine Intelligence paper demonstrated that invalid SMILES strings enhance chemical language models , overturning previous assumptions. Articles consistently apply computational methods to biological discovery, with growing emphasis on cancer metabolism and translational applications. Major recognitions include: Forbes 30 Under 30 (2022) International Birnstiel Award (2022) Dan David Prize Borealis AI Fellowship NIH Award C&EN's Talented Twelve (2023) Young Explorer Award Grand Prize Skinnider leads the Skinnider Research Lab at Princeton's Carl Icahn Laboratory, which collaborates with forensic laboratories and Ludwig cancer researchers. The lab specializes in transforming mass spectrometry data into biological insights through innovative algorithms. During his undergraduate studies, he co-founded Adapsyn Bioscience to translate natural product discovery research into commercial applications. Current projects include developing metabolome-wide identification tools and exploring diet-derived metabolites that modulate cancer progression.

Jonathan Gratchمشاهده پروفایل

استاد پژوهشی

• Affective Computing
• Cognitive Modeling
• Human-Computer Interaction
• +۶ مورد دیگر

Jonathan Gratch is a Research Professor at the University of Southern California (USC) with joint appointments in Computer Science , Psychology , and Media Arts and Practice . He serves as Director for Virtual Human Research at USC's Institute for Creative Technologies and leads the USC Affective Computing Group . His work focuses on computational models of human cognitive and social processes, particularly emotions, and their applications to virtual environments. Education: Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (1995) Gratch's research spans Affective Computing , Cognitive Modeling , and Human-Computer Interaction , with a focus on creating emotionally intelligent virtual agents. His projects include the development of computational emotion models (e.g., EMA) and virtual human systems for training, healthcare, and social interaction studies. Key applications include the Mission Rehearsal Exercise and Stability and Support Operations prototypes. Recent publications (2023) highlight his exploration of LLM-based emotion modeling , social influence dynamics in human-agent interactions, and ethical implications of AI assistants . Earlier work (2014-2015) established foundational frameworks for automated negotiation and rapport-building virtual agents . His methodological contributions include parasocial consensus sampling and appraisal theory-based emotion architectures. Scientific honors include Fellowships from AAAI, AAAC, and the Cognitive Science Society, the SIGART Autonomous Agents Award , and Senior Membership in IEEE . As founding Editor-in-Chief of IEEE Transactions on Affective Computing , he has significantly shaped the field's academic discourse. Gratch's virtual humans leverage immersive audio-visual environments (8'x30' screens, 10.2-channel audio) to study socio-emotional dynamics. His team's work demonstrates that anthropomorphism in machines can enhance interaction but may produce unintended consequences, informing design principles for effective human-machine collaboration.

• Software Metrics
• Model Driven Software Development
• Empirical Software Engineering
• +۵ مورد دیگر

Miroslaw Staron is a Professor of Interaction Design and Software Engineering at Chalmers University of Technology. He maintains a unique 50/50 work arrangement, spending half his time on field research at Ericsson while holding his academic position. His research bridges academic theory with industrial practice through collaborations with major companies including Volvo Car Corporation and Volvo Information Technology. His research spans several key areas in software engineering: Software metrics and measurement systems in industry Model driven software development and empirical studies Defect prediction in software projects Requirements engineering in model-based development Applications of AI and machine learning in software engineering Automotive software development and security Staron's recent work demonstrates a strategic shift toward integrating AI technologies into software engineering processes, with particular focus on automotive applications. His publications from 2024-2025 reveal expertise in generative AI applications for code review automation, testing methodologies, and requirements engineering, showing how these technologies can transform traditional software development practices while addressing domain-specific challenges in automotive systems. Current research projects include: Kvantdatorer för framtidens mobilitetslösningar (2025-2027) Automatiserad och designoptimerad programvarukonstruktion/kodgenerering (2025-2029) Förvandla fordonsarkitektur med hjälp från AI (2021-2023) Arkitektonisk design och verifiering/validering av system med maskininlärning komponenter (2020-2024) With 78 publications documented in Chalmers' research database, Staron has established himself as a significant contributor to evidence-based software engineering research with strong industrial relevance.

Demetriadis Stavrosمشاهده پروفایل

استاد

• Learning Technologies
• Educational Technology
• Conversational Agents in Education
• +۷ مورد دیگر

Full Professor at the School of Informatics, Aristotle University of Thessaloniki (AUTh), Greece. Previously served as Associate Professor (2015-2020), Assistant Professor (2008-2015), and Lecturer (2002-2008) at the same institution. Also worked as an Informatics Teacher in Greek Secondary Education from 1989-2002. His research focuses on Learning Technologies with emphasis on Conversational Agents in Education, Learning Analytics, Computer-Supported Collaborative Learning, Computational Thinking, Teaching Machine Learning at School, and Massive Open Online Courses (MOOCs). His recent work heavily investigates the application of AI, particularly conversational agents and large language models, in educational contexts. His publications show a strong trend toward AI applications in education, particularly focusing on conversational agents, learning analytics, and automated grading systems. The research spans multiple educational contexts from K-12 to higher education, with particular attention to student self-regulation, collaborative learning, and ethical considerations in AI implementation. 3 Best paper awards at international conferences Interview by Educational Technology Magazine (2013) Cubes Coding project - Winners of Open Education Challenge 2014 Cubes Coding project - Winners of NUMA Competition 2014 Has supervised 5 completed PhD theses, 4 ongoing PhD theses, over 60 Master's theses, and over 120 undergraduate theses. Led the colMOOC project (2018-2020), a €999,000 EU-funded project on integrating conversational agents and Learning Analytics in MOOCs. Also coordinated the T4E project (2020-2022) on Teachers' Fast-paced Distance Training on Tele-education and a MOOC in Greek on Introduction to Programming with Python. Previously served as Director of the Software and Interactive Technologies Laboratory (SWITCH Lab) until 2020, Member of AUTH Educational Policy Committee until 2020, and Chair of the Scientific Supervisory Board of the 2nd Experimental Junior High School in Thessaloniki until 2020.