جست‌وجوی استاد و پژوهشگر

Charlotte Kloftمشاهده پروفایل

استاد

• Pharmacometrics
• Computational Disease Modeling
• Therapeutic Drug Monitoring
• +۶ مورد دیگر

Professor Charlotte Kloft is Head of the Department of Clinical Pharmacy & Biochemistry at the Institute of Pharmacy, Free University of Berlin since 2011, and spokesperson for the interdisciplinary Graduate Research Training Program PharMetrX 'Pharmacometrics and Computational Disease Modelling' since 2008. She previously served as Professor and Head of the Department of Clinical Pharmacy at Martin-Luther-University Halle-Wittenberg (2005-2011) and as Scientific Assistant/Senior Assistant at Freie Universität Berlin (1999-2005). Her academic credentials include a habilitation in Clinical Pharmacy from Freie Universität Berlin (2003) and a Dr. rer. nat. (summa cum laude) from the same institution (1997). She earned her license as a pharmacist in 1992 after completing pharmacy studies at Johannes Gutenberg-University, Mainz (1987-1991). Professor Kloft's research focuses on pharmacometrics, computational disease modeling, and therapeutic drug monitoring across multiple therapeutic areas. Her work bridges pharmaceutical sciences with clinical practice, with particular emphasis on personalized dosing strategies for oncology, antimicrobial resistance, and inflammatory bowel diseases. She has pioneered research in optimizing drug dosing through pharmacokinetic/pharmacodynamic modeling, with applications in both antibiotic and cancer therapies. Her recent publications demonstrate a strong evolution toward integrating advanced computational approaches with clinical pharmacology, including machine learning applications in pharmacometrics, personalized dosing strategies for biologics during pregnancy, and developing nationwide infrastructure for therapeutic drug monitoring in cancer therapy through the ON-TARGET study. Academic Center of Excellence of Pharsight (now Certara), USA (since 2000) Habilitationsreisestipendium of Dr. August and Dr. Anni Lesmüller-Stiftung (2002) Ernst-Reuter-Preis of Ernst-Reuter-Gesellschaft (1998) Joachim-Tiburtius-Preis of Berlin Senate (1998) Young Investigator Award of EORTC-PAMM (1997) Professor Kloft has successfully supervised numerous doctoral students whose research spans diverse areas including CAR-T cell therapy, antimicrobial resistance, inflammatory bowel disease treatment optimization, and pharmacokinetic modeling of novel therapeutic agents. Her research group has secured significant funding for projects including GlobalResist, ON-TARGET, ABIMMUNE, COMBINATORIALS, and TAIN. She leads a vibrant research ecosystem at the Free University of Berlin with strong collaborations across Europe and internationally, maintaining state-of-the-art laboratory facilities including Biosafety Level 2 certified labs for infectious disease research.

Lyudmila Mihaylovaمشاهده پروفایل

استاد

• Signal Processing
• Bayesian Methods
• Monte Carlo Methods
• +۹ مورد دیگر

Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.

Keenan Albeeمشاهده پروفایل

استادیار

• Robotic Motion Planning
• Autonomous Systems
• Optimal Control
• +۵ مورد دیگر

Keenan Albee is a Robotics Technologist at NASA’s Jet Propulsion Laboratory and an incoming Assistant Professor at the University of Southern California (starting Fall 2025). His research focuses on autonomous robotics for extreme environments including lunar missions, microgravity, and underwater operations. Education: Ph.D. in Aeronautics and Astronautics (Autonomous Systems), MIT (2022) S.M. in Aeronautics and Astronautics, MIT (2019) B.S. in Mechanical Engineering, Columbia University (2017) Albee’s work integrates optimal control , reinforcement learning , and motion planning to develop autonomy for mobile robotic systems operating under uncertainty. His expertise spans space robotics , microgravity systems , and underwater robotics , with a focus on environment-aware algorithm design. Recent research includes parametric information-aware motion planning (RATTLE algorithm), distributed multi-agent exploration, and robust control for uncooperative targets. His publications highlight on-orbit validation of autonomy algorithms via NASA’s Astrobee platform and upcoming lunar missions. Scientific Awards: NASA Space Technology Research Fellowship (2022) Albee actively develops open-source autonomy frameworks and will establish the Laboratory for Autonomous Systems in Exploration and Robotics (LASER) at USC. His work bridges theoretical control methods with real-world deployment, including first-of-its-kind achievements in space robotics.

Marco Morales Aguirreمشاهده پروفایل

عضو هیئت علمی

• Motion Planning
• Multi-Robot Systems
• Artificial Intelligence
• +۵ مورد دیگر

Marco Morales Aguirre is a Teaching Associate Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign and an Associate Professor at Instituto Tecnológico Autónomo de México (ITAM). He directs research at the Parasol Laboratory and has held significant leadership roles including founding member and former president of the Mexican Federation of Robotics (FMR). His academic journey spans both US and Mexican institutions, reflecting his international impact in the robotics community. Dr. Morales received his educational foundation from prestigious institutions: a Ph.D. in Computer Science from Texas A&M University, an M.S. in Electrical Engineering, and a B.S. in Computer Engineering from Universidad Nacional Autónoma de México (UNAM). His academic path has included positions as Visiting Professor at Texas A&M University and Lecturer at UNAM and the System of Technological Universities in México. His research focuses on motion planning algorithms for robotics, with particular expertise in multi-robot systems where he's pioneered frameworks like Adaptive Robot Coordination (ARC). His work bridges theoretical algorithm development with practical applications in industrial settings, computational biology, and extended reality interfaces. He has made significant contributions to topological guidance methods that improve planning efficiency in complex environments with narrow passages. Analysis of his recent publications reveals a strong trajectory toward more complex multi-robot coordination problems, with increasing emphasis on integrating task and motion planning. His research group has developed innovative approaches that scale to larger robot teams while maintaining computational efficiency, particularly in congested environments where traditional methods struggle. Member of the National System of Researchers of Mexico (level II) Founding member and former president of the Mexican Federation of Robotics (FMR) Member of the Mexican Academy of Computing Editor of multiple Algorithmic Foundations of Robotics (WAFR) proceedings Dr. Morales actively mentors a diverse group of graduate students who frequently appear as co-authors on his publications. His Parasol Laboratory conducts research funded through various academic and industrial collaborations, including significant projects with manufacturing partners exploring collaborative assembly systems. The laboratory has developed several notable frameworks including ARC, K-ARC, and HAS-RRT that have advanced the state of the art in multi-robot motion planning.

• Operations Research
• Combinatorial Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.

Istvan David McMasterمشاهده پروفایل

استادیار

• Digital Twins
• Model-Driven Engineering
• Sustainability
• +۶ مورد دیگر

Istvan David is an Assistant Professor in the Department of Computing and Software at McMaster University , with research expertise spanning Digital Twins , Model-Driven Engineering , and Sustainability . His work bridges theoretical and applied domains, focusing on smart ecosystems , collaborative modeling , and AI-driven simulation . Key contributions include frameworks for digital twin evolution and interoperability in sustainable systems. Education : BSc, MSc, and PhD in Computer Engineering and Computer Science from Budapest University of Technology and Economics, and University of Antwerp. Research Areas : Digital Twins, Model-Driven Engineering, Reinforcement Learning, Smart Ecosystems, Sustainability, Collaborative Modeling, Cyber-Biophysical Systems, and Software Architecture. Recent Article Trends emphasize AI integration with digital twins, collaborative modeling in industrial contexts, and sustainable systems engineering . His work often combines machine learning with formal modeling to address challenges in technical sustainability and smart agriculture .

Thad Starnerمشاهده پروفایل

استاد

• Wearable Computing
• Human-Computer Interaction (HCI)
• Augmented Reality
• +۷ مورد دیگر

Thad Starner is a Professor in the College of Computing at Georgia Institute of Technology and Technical Lead/Manager on Google's Glass. He directs the Contextual Computing Group (CCG), co-founded the Animal Computer Interaction Lab, and contributes to Georgia Tech's Ubicomp Group and Brainlab. A wearable computing pioneer since 1993, he has over 500 publications and 80 issued U.S. patents. Coined 'augmented reality' in 1990 Developed CopyCat for ASL learning in deaf children Invented Passive Haptic Learning for skill acquisition His research spans wearable interfaces for Deaf-hearing communication, dolphin interaction systems (CHAT), dog-handler communication (FIDO), and brain-computer interfaces for ALS patients. Current projects focus on optical aging simulation, XR input methods, and animal behavior telemetry. Recent publications (2023-2025) explore AR display ergonomics, AI-augmented reasoning, sign language recognition, and animal-computer interaction. His work has been featured in 60 Minutes, BBC, National Geographic, and Time Magazine. CHI Academy (2017) Lemelson-MIT Prize finalist White House Champions of Change finalist He advises graduate students in wearable systems and teaches AI and prototyping courses. His lab developed the Perceptive Workbench for gesture tracking and created early Eigenfaces research for face recognition.

• Wireless Communications
• 5G/6G Networks
• Mobile Communications
• +۶ مورد دیگر

Marina Petrova is a Professor at RWTH Aachen University, holding positions in both the Teaching and Research Area of Mobile Communications and Computing and the Chair and Institute for Networked Systems. She is also a member of the Steering Committee for the Mobility & Transport Engineering (MTE) profile area at the university. Her office is located at Kackertstraße 9, 52072 Aachen, Germany. Professor Petrova's research focuses on cutting-edge wireless communication technologies, with particular emphasis on next-generation mobile networks. Her work spans multiple dimensions of wireless systems including: 5G and 6G network architectures and protocols Cell-Free Massive MIMO systems Millimeter-wave communications Resource allocation and scheduling in wireless networks Wi-Fi sensing and coexistence analysis Integration of distributed learning services in wireless networks Beamforming and beam management techniques Ultra-Reliable Low-Latency Communications (URLLC) Her recent publications demonstrate a strong trend toward the integration of artificial intelligence and machine learning techniques in wireless network design and optimization. She has been particularly active in exploring the convergence of communication and sensing functionalities (ISAC - Integrated Sensing and Communication), which is considered a key enabler for future 6G networks. Professor Petrova's research also addresses practical implementation challenges in next-generation wireless systems, with several publications focusing on ns-3 implementations and experimental validations. Professor Petrova has received recognition for her contributions to the field through numerous publications in top-tier venues, though specific awards are not mentioned in the available information. Her work shows strong industry relevance with applications in smart industries, autonomous systems, and future communication networks.

Colin Drummondمشاهده پروفایل

استاد

• Simulation
• Healthcare Informatics
• Wearable Technology
• +۳ مورد دیگر

Colin Drummond, PhD, MBA is a Professor in the Department of Biomedical Engineering at both the Case School of Engineering and School of Medicine , where he also serves as Assistant Department Chair. His research focuses on simulation and applied informatics to enhance healthcare decision-making with translational applications in wearable technology and conversational agents for isolated patients. Doctorate in Biomedical Engineering (PhD) Master of Business Administration (MBA) Research interests span healthcare informatics , wearable analytics , and clinical decision support systems . Recent work explores: Physiological monitoring through wearable sensors Heart rate accuracy in consumer devices like Apple Watch Conversational agents for polypharmacy management Performance assessment in athletes and post-surgical rehabilitation

• Artificial Intelligence
• Cognitive Science
• Human-Agent Interaction
• +۵ مورد دیگر

Prof. Dr.-Ing. Stefan Kopp is a faculty member at Bielefeld University's Faculty of Engineering and serves as Research Group Leader of the Cognitive Systems and Social Interaction Group . He also holds administrative roles as Vice Dean and Deputy CITEC Coordinator . His work focuses on Artificial Intelligence , Cognitive Systems , and Socio-Technical World research areas. Research Group Leader: Cognitive Systems and Social Interaction Group Vice Dean: Faculty of Engineering Deputy Coordinator: Center for Cognitive Interaction Technology (CITEC) Project Manager: TRR 318 "Constructing Explainability" subprojects His research explores human-agent interaction , multimodal conversational agents , and social AI through projects like 39-Inf-11 Human-Machine Interaction and 39-M-Inf-VKI Virtual Humans and Conversational Agents . Publications address topics including adaptive explanation generation , gesture synthesis , and social cognition in dynamic environments. Current research topics span cooperative AI , explainable decision-making , and sensorimotor grounding in artificial systems.

• Computer Vision
• Image Analysis
• Machine Learning
• +۵ مورد دیگر

Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.

Yun-Nung Chenمشاهده پروفایل

استاد

• Natural Language Processing (NLP)
• Dialogue Systems
• Machine Learning
• +۳ مورد دیگر

Professor Yun-Nung Chen works at the Department of Computer Science and Information Engineering , National Taiwan University , focusing on Natural Language Processing and Dialogue Systems . With a Ph.D. from Carnegie Mellon University , their research bridges Machine Learning and Language Understanding in conversational AI. Education Ph.D. in Language Technologies, Carnegie Mellon University (2015) M.S. in Computer Science, National Taiwan University (2011) B.S. in Computer Science, National Taiwan University (2009) Research Trends Recent work emphasizes Retrieval-Augmented Generation , Knowledge Editing in LLMs , and Temporal Modeling for dialogue systems. Key themes include cross-modal understanding , semantics-driven dialogue , and robust language modeling across domains. Scientific Recognition Best Student Paper, IEEE ASRU 2013 Best Student Paper, IEEE SLT 2010 Distinguished Master Thesis, ACLCLP 2011 Best Paper Finalist, ISCA INTERSPEECH 2012 Current projects involve StreamBench for continuous agent improvement and Taiwan LLM for culturally aligned language models.

Xujie Siمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۵ مورد دیگر

Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.

Andreas Malikopoulosمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Transportation Systems
• Control Theory
• +۴ مورد دیگر

Andreas Malikopoulos is a Professor at Cornell University's School of Civil & Environmental Engineering and Director of the Information and Decision Science Lab (IDS Lab). Previously, he held roles as the Terri Connor Kelly and John Kelly Career Development Professor at the University of Delaware (UD) and founding Director of UD's Sociotechnical Systems Center. He also served as the Alvin M. Weinberg Fellow at Oak Ridge National Laboratory (ORNL), Deputy Director of ORNL's Urban Dynamics Institute, and Senior Researcher at General Motors R&D. His research focuses on cyber-physical systems (CPS), stochastic control, and learning-driven approaches for optimizing energy efficiency and sustainable mobility in smart cities and transportation systems. Education: PhD (Mechanical Engineering, University of Michigan, 2008), M.S. (Mechanical Engineering, University of Michigan, 2004), Diploma (National Technical University of Athens, 2000). Research Interests: Analysis and control of CPS, stochastic scheduling, game theory, and mechanism design applied to emerging mobility systems (e.g., autonomous vehicles, electric vehicles). He emphasizes integrating learning and control for socially optimal solutions in transportation networks. Awards: IEEE ITS Young Researcher Award (2019), UD’s Outstanding Junior Faculty Award (2020), Alvin M. Weinberg Fellowship (2010), and recognition as a NAS Kavli Frontiers of Science Scholar (2012). He is an IEEE Senior Member, ASME Fellow, and serves on editorial boards of leading journals. Teaching: Focuses on optimal decision-making, control theory, and emerging mobility systems. Courses include stochastic optimal control and game theory at Cornell. Labs: Leads the IDS Lab, which develops scalable frameworks for CPS and smart city applications. Current projects include coordinated routing for mixed-traffic systems and AI-driven recommendations for autonomous vehicles.

Zohreh Shamsمشاهده پروفایل

پژوهشگر

• ML Interpretability
• Explainable AI
• Knowledge Discovery
• +۵ مورد دیگر

Zohreh Shams is a Visiting Fellow at the Computer Laboratory, University of Cambridge, and Chief Scientific Officer at Leap Labs. Previously, she served as a Senior Research Associate at the University of Cambridge and held roles at Babylon Health as a Senior ML Scientist. Her research focuses on ML interpretability, explainable AI, knowledge discovery, and automated reasoning with applications in healthcare and safety-critical systems. Dr. Shams completed her PhD in Artificial Intelligence at the University of Bath, specializing in explanatory decision-making in multi-agent systems using Argumentation Theory. Her work bridges cognitive science and AI, collaborating with institutions like the University of Brighton on projects such as Accessible Reasoning with Diagrams , which explores explainable ontology reasoning systems. Her research interests include generative modeling, concept-based representations, and the integration of domain knowledge into AI systems. Notable contributions include developing frameworks like CGXplain for neural network explanations and REM for healthcare data analysis. Her publications span venues such as ECCV, AAAI, and TMLR. Shams has contributed to interdisciplinary projects, including the Integrated Cancer Medicine initiative, and maintains affiliations with Wolfson College as a former Junior Research Fellow. Her work emphasizes ethical AI practices, clinician collaboration, and the societal impact of explainable AI systems.