جست‌وجوی استاد و پژوهشگر

Alexandra Reichenbachمشاهده پروفایل

استاد پژوهشی

• Cognitive Neuroscience
• Neuroinformatics
• Artificial Intelligence in Mental Health
• +۶ مورد دیگر

Prof. Dr. rer. nat. Alexandra Reichenbach is a Research Professor for Neuroinformatics at Heilbronn University of Applied Sciences and an Associate Professor at Heidelberg University's Medical Faculty . As Director of the Center for Machine Learning (ZML) , she leads the "Motor Control & Artificial Intelligence in Mental Health" research group and chairs HHN's Ethics Board. Her research bridges cognitive neuroscience and artificial intelligence , focusing on: Human sensorimotor function and its neural basis AI-driven biomarker development for psychiatric disorders (depression, schizophrenia) EEG and fMRI-TMS multimodal neuroimaging Robot-assisted motor rehabilitation technologies Neuroethics of AI in clinical diagnostics Recent work includes GAN-based schizophrenia classification , EEG biomarker optimization , and Alzheimer's disease stratification models . She advocates for interdisciplinary AI education , co-developing HHN's Applied AI bachelor's program and planning its master's/doctoral expansion. Key collaborations include: Heidelberg Medical Faculty Human Robotics Group at Imperial College National Hospital for Neurology and Neurosurgery Medical Informatics Master Seminar (Heidelberg/HHN partnership) Her lab (ZML) integrates virtual AI experimentation environments for both research and public education, including interactive AI & Society installations.

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Xinchao Wang is an Assistant Professor and Presidential Young Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS), College of Design and Engineering. He leads the xML Lab, focusing on machine learning and artificial intelligence. Prior to joining NUS, he was an Assistant Professor at Stevens Institute of Technology in the Department of Computer Science and a Postdoctoral Researcher at the Beckman Institute, University of Illinois Urbana-Champaign, under the late Professor Thomas Huang. He earned his Ph.D. in Computer Vision from École polytechnique fédérale de Lausanne (EPFL) under Professor Pascal Fua and a B.Sc. in Computing from the Hong Kong Polytechnic University with first-class honors and a perfect GPA. His research spans machine learning, computer vision, and artificial intelligence, with a focus on diffusion models, vision-language models, 3D generation, and dataset distillation. His recent publications demonstrate strong trends in efficient generative modeling, including acceleration techniques for diffusion models, 3D scene reconstruction, and novel architectures for vision and language tasks. His work frequently appears in top-tier conferences such as CVPR, NeurIPS, ICCV, ECCV, and ICLR. AI's 10 to Watch, IEEE (2025) Young Research Award, NUS (2025) Young Research Award, NUS CDE (2024) Best Paper Award, IEEE VCIP 2022 Outstanding Early Career Award, NUS CDE (2023) College Teaching Excellence Award, NUS CDE (2023) He actively advises Ph.D. students, many of whom have received prestigious awards including the Google Fellowship and ByteDance Scholarship. He has served as an Associate Editor for IEEE Transactions on Image Processing since 2023. His lab is actively recruiting Ph.D. students, postdocs, visiting scholars, and research assistants in machine learning and AI. He teaches core courses at NUS including Introduction to Machine Learning, Deep Learning, and Pattern Recognition.

• Artificial Chemical Intelligence
• Chemical Engineering
• Artificial Intelligence in Chemistry
• +۵ مورد دیگر

Junwu Chen is a Doctoral Assistant and PhD student at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB), the Institute of Chemical Sciences and Engineering (ISIC), and the Laboratory of Artificial Chemical Intelligence (LIAC). He is pursuing his doctoral studies in the Doctoral Program in Chemistry and Chemical Engineering (EDCH) under the EDOC doctoral school. The Laboratory of Artificial Chemical Intelligence focuses on integrating artificial intelligence with chemical research for autonomous discovery and intelligent molecular design. Junwu Chen's work lies at the intersection of machine learning and chemistry, aiming to develop data-driven methodologies for accelerating chemical innovation. His research interests include artificial chemical intelligence, computational chemistry, machine learning for molecular design, and smart laboratory automation. The lab emphasizes AI-driven solutions for complex chemical problems, suggesting a strong interdisciplinary approach combining computer science and chemical engineering. The publications in this domain typically span artificial intelligence, chemistry, and materials science, with subfields such as neural network modeling of reaction pathways, generative models for novel compounds, and reinforcement learning in chemical synthesis planning. However, no specific publications are listed in the current profile. There are no scientific awards mentioned in the current profile. As a Doctoral Assistant, Junwu Chen contributes to ongoing research projects within LIAC. While no formal advisees are listed, he likely collaborates with other researchers and students in the lab. There is no mention of independent grants, though he may be supported through institutional or supervisory funding. He is part of the Laboratory of Artificial Chemical Intelligence (LIAC) at EPFL, a research group dedicated to pioneering intelligent systems for chemical discovery. The lab combines AI, robotics, and chemistry to enable self-driving laboratories and accelerate scientific innovation.

Andreas G. Andreouمشاهده پروفایل

استاد

• Computing Machinery
• Sensory Information Processing
• Theoretical Neuroscience
• +۶ مورد دیگر

Andreas G. Andreou is a Professor at Johns Hopkins University with primary appointments in the Department of Electrical and Computer Engineering, and secondary appointments in the Department of Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the Johns Hopkins University Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab alongside Philippe Pouliquen. His research focuses on brain-inspired microsystems for sensory information processing, neuromorphic computing, and theoretical neuroscience. Education: Born in Nicosia, Cyprus; resides in Baltimore, Maryland. Research Interests: Computing Machinery, Sensory Information Processing, Theoretical Neuroscience, Pattern Analysis, Machine Intelligence, Microsystems Technologies, and Integrated Circuits. Awards: IEEE Fellow, 3rd Best Paper Award at IEEE BioCAS 2018, and recognition for the award-winning Stethovest wearable acoustic sensing array. His lab explores energy-efficient computing beyond Moore's Law, integrating neuroscience principles into microsystem design. Recent projects include quantum-inspired neuromorphic optimizers, AI-generated chips using ChatGPT4, and applications in cardiac acoustics and wearable technology. Collaborations span institutions like NSF, JHU-APL, and the Telluride Neuromorphic AI workshop. His work is supported by grants from DARPA, NSF, NIH, ONR, AFRL, and JHU-APL. He also holds honorary professorships at the University of Cyprus and Universidad Nacional del Sur.

• Robotics
• Artificial Intelligence
• Computer Vision
• +۶ مورد دیگر

Francisco Gómez Donoso is an Assistant Professor at the University of Alicante , affiliated with the Department of Computer Science and Artificial Intelligence and the Institute of Computer Research (IU Informática) . He serves as Deputy Director of the Institute of Computer Research since September 2024. His research focuses on Robotics, Artificial Intelligence, and Human-Robot Interaction , with applications in assistive technologies, emotion recognition, and augmented reality. PhD in Computer Science (Doctoral School, 2020) Degree in Computer Engineering (University of Alicante, 2015) Research interests include: Development of multimodal robotic assistants for emotion recognition Photorealistic augmented reality landscapes and holographic classroom integration Deformable object manipulation in robotics Assistive technologies for visually impaired individuals and elderly care Computer vision and optical flow architectures for activity recognition AI-driven recycling solutions using multispectral imaging Key projects (public and transfer): Coordinated 10 transfer projects (2022–2025) with companies like CADEL Recycling Lab and CYPE Soft Collaborated on national research projects funded by the Ministry of Science and Valencian Government Teaching includes courses on Perception Systems, Artificial Vision, Robotics, and Discrete Mathematics at multiple engineering programs since 2013.

Apan Dastiderمشاهده پروفایل

استادیار

• Human-Robot Interaction
• Generative AI for Robotic Control
• Knowledge Distillation
• +۴ مورد دیگر

Dr. Apan Dastider is a tenure-track Assistant Professor in the Division of Engineering Technology at Wayne State University’s James and Patricia Anderson College of Engineering. He leads research in Robotics and AI, focusing on adaptive robotic systems, human-robot interaction, and generative AI for intelligent control. Previously, he earned his Ph.D. and M.S. in Electrical Engineering from the University of Central Florida, where he worked as a Graduate Research Assistant in the Autonomous Robotic Computing Lab. Education: Ph.D. in Electrical Engineering, University of Central Florida, 2025 M.S. in Electrical Engineering, University of Central Florida, 2023 B.S. in Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, 2017 Research Interests: Dr. Dastider’s research lies at the intersection of robotics, AI, and control theory. His work emphasizes: Knowledge distillation across heterogeneous domains Human-robot interaction and motion synthesis Generative AI for adaptive robotic control Non-parametric reinforcement learning Low-dimensional manifold learning for motion planning Publications Overview: His recent publications demonstrate a strong focus on real-time robotic planning, diffusion models for motion generation, and safe human-robot collaboration. Notable works include APEX (dual-arm manipulation), RETRO (reactive trajectory optimization), and unified frameworks for interception and obstacle avoidance using diffusion variational autoencoders. Awards & Recognition: UCF ECE Outstanding Graduate Researcher Award Reviewer for IEEE ICRA and IROS conferences Teaching & Mentorship: Dr. Dastider is committed to developing the next generation of AI and robotics professionals. He teaches courses like MIT5700 - Industrial Robots Modeling and Simulation at Wayne State University and actively mentors students in cutting-edge research projects.

• Formal Verification
• Hardware Design
• Quantum Computing
• +۵ مورد دیگر

Professor Rolf Drechsler is affiliated with the Department of Mathematics and Computer Science at the University of Bremen, where he maintains an active research profile in formal verification, hardware design, and quantum computing. His office is located in the Multi-purpose high-rise building (MZH) 4330, and he can be reached at drechsler@uni-bremen.de or drechsler@informatik.uni-bremen.de. Dr. Drechsler's research focuses on formal verification techniques, particularly polynomial formal verification methods, binary decision diagrams (BDDs), in-memory computing architectures, and quantum circuit verification. His work bridges theoretical computer science with practical hardware implementation challenges. Notably, he has recently explored the integration of large language models (LLMs) with hardware verification and design automation, representing an emerging interdisciplinary research direction. An analysis of his 2024-2025 publications reveals a strong emphasis on verification methodologies for emerging computing paradigms. His research spans quantum computing verification (qSAT, quantum circuit debugging), in-memory computing (MAGIC-based architectures, memristive crossbars), and traditional hardware verification enhanced by AI techniques. The publications show a pattern of addressing verification challenges in novel computing architectures while maintaining theoretical rigor in formal methods. Professor Drechsler has made significant contributions to Binary Decision Diagram optimization, formal verification of arithmetic circuits, and hardware security. His work on polynomial formal verification represents a distinctive research thread that has evolved over recent years, addressing verification challenges for sequential circuits, approximate adders, and multi-valued logic circuits.

Hans-Peter deRuiterمشاهده پروفایل

استاد

• Ethics of Health Technology
• Global Health and Citizenship
• Mental Health
• +۳ مورد دیگر

Hans-Peter deRuiter serves as Professor and Director of the Taylor Nursing Institute for Family and Society at Minnesota State University, Mankato's School of Nursing since 2009, with concurrent affiliate appointments at the University of Minnesota Center for Bioethics (2015–present), University of Applied Sciences St Pölten, Austria (2018–present), and Halmstad University, Sweden (2017–present). His leadership spans nursing education, bioethics research, and global health initiatives through the Taylor Nursing Institute. His educational journey includes: Post-Doctoral Fellowship, University of Toronto (2012-2013) PhD in Nursing (Minor: Bioethics), University of Minnesota (2008) MS in Nursing, Winona State University (2004) BS in Nursing (Minor: Psychology), University of the State of New York (1996) Diploma in Mental Health Nursing, St Willibrordus Psychiatric Center, Netherlands (1989) Diploma in General Nursing, Free University School of Nursing, Amsterdam (1987) Dr. deRuiter's research examines ethical dimensions of health technology through institutional ethnography, focusing on electronic health records, patient safety systems, and global health citizenship. His work critically analyzes how digital tools reshape nursing practice, patient autonomy, and healthcare equity across US and European contexts. He investigates mental health service delivery through linguistic frameworks while addressing social determinants of health in international settings. Analysis of his 15 most recent publications reveals consistent focus on technology ethics (60%), patient safety systems (25%), and global health citizenship (15%). His work increasingly incorporates comparative policy analysis between US and European healthcare systems, with growing emphasis on future-oriented ethics frameworks for emerging health technologies since 2016. His scientific recognition includes: Minnesota Nurses Association Educator of the Year (2018) Minnesota Nurses Association Researcher of the Year (2012) Minnesota State University Global Citizen Award (2011) University of Minnesota Graduate Assistant of the Year (2006) University of the State of New York Silver Anniversary Alumnus Award (2001) As Director of the Taylor Nursing Institute, he mentors nursing students through global health programs while leading international collaborations with Swedish and Austrian institutions. His research receives support through university appointments and professional associations rather than external grants, focusing on practical ethics implementation within clinical settings. Current projects examine AI ethics in nursing through the Swedish welfare policy lens. He leads the Taylor Nursing Institute for Family and Society as an interdisciplinary hub connecting nursing practice with bioethics, social work, and public health. His international teams at Halmstad University investigate technology-mediated care delivery while Austrian collaborators develop educational frameworks for ethical technology adoption in healthcare systems.

Eric Fosler-Lussierمشاهده پروفایل

استاد

• Speech Processing
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Eric Fosler-Lussier is the John I. Makhoul Professor and Acting Department Chair of Computer Science and Engineering at The Ohio State University. He holds courtesy appointments in Linguistics and Biomedical Informatics, and serves as Assistant Dean for Faculty Lifecycle in the College of Engineering. His research focuses on speech processing, natural language understanding, and clinical NLP applications. PhD in Computer Science, University of California, Berkeley (1999) B.A.S. in Computer and Cognitive Science, University of Pennsylvania (1993) B.A. in Linguistics, University of Pennsylvania (1993) His research spans core technologies for speech recognition and NLP, with applications in medical training systems and clinical record analysis. Recent work includes neural diarization techniques, conversational AI for healthcare, and cross-domain text-to-SQL frameworks. Recent publications highlight innovations in end-to-end speech systems, knowledge transfer between textual and speech models, and robust error prediction frameworks. His lab has produced 15+ publications since 2022, with a focus on educational and medical applications. NSF CAREER award recipient IEEE Signal Processing Best Paper Award IMIA Yearbook Best Paper (twice) IBM Faculty Award IEEE and ISCA Fellow He advises numerous PhD students and has mentored graduates now working at Amazon, Google, IBM, and universities worldwide. His Speech and Language Technologies Laboratory develops systems for speech enhancement, virtual patients, and knowledge transfer.

Daniel Sanchez Morilloمشاهده پروفایل

استاد

• Biomedical Engineering
• Artificial Intelligence
• Signal Processing
• +۳ مورد دیگر

Daniel Sanchez Morillo serves as Full Professor at the University of Cádiz within the School of Engineering and Department of Automation, Electronics, Architecture and Computer Networks Engineering. He leads the Bioengineering, Automation, and Robotics (TIC212) research group at UCA and co-directs the Biomedical Engineering and Telemedicine (IBT) group at the Institute for Research and Innovation in Biomedical Sciences (INiBICA). Education PhD in Telecommunications Engineering, University of Cádiz (2008). Thesis: "Processing and transmission of biomedical signals for the diagnosis of sleep disorders and diseases" supervised by Dr. Juan Luis Rojas Ojeda Research Focus Dr. Morillo's work centers on biomedical engineering and artificial intelligence applications in healthcare, with particular expertise in signal processing for physiological monitoring and image analysis for medical diagnostics. His research spans: Development of AI-driven diagnostic tools for respiratory diseases Robotics and haptic systems for rehabilitation Environmental monitoring solutions for chronic conditions Biomedical signal processing for sleep disorder analysis Publication Trends Recent work (2024-2025) demonstrates a strategic shift toward deep learning applications in occupational lung disease screening and sensor-integrated rehabilitation devices . Key trends include the fusion of virtual reality with physiological monitoring, AI-enhanced medical imaging for silicosis detection, and innovative approaches to gait analysis using wearable technology. Research Leadership As leader of the TIC212 group and co-director of the IBT group, Dr. Morillo oversees interdisciplinary projects bridging engineering and clinical medicine. His teams develop cutting-edge solutions including smart orthoses with haptic feedback, automated cough detection systems, and virtual reality rehabilitation environments, fostering collaboration between engineers, clinicians, and industry partners.

Charles E. Hughesمشاهده پروفایل

استاد

• Virtual Environments
• Mixed Reality
• Augmented Reality
• +۱۷ مورد دیگر

Charles E. Hughes is a Pegasus Professor at the University of Central Florida, holding appointments in the Department of Electrical Engineering & Computer Science, the School of Visual Arts and Design, and contributing to the School of Modeling, Simulation & Training. He serves as Director of the School of Modeling, Simulation & Training and co-directs the Synthetic Reality Laboratory (SREAL) and the Center for Research in Education Simulation Technology (CREST). He is also affiliated with the Institute for Simulation & Training, Computational Imaging Laboratory, and Graphics Laboratory. Education: Ph.D. in Computer Science, Penn State University, 1970 M.S. in Computer Science, Penn State University, 1968 B.A. in Mathematics, Northeastern University, 1966 His research focuses on virtual environments and their applications in learning, particularly in teacher preparation, physician training, interpersonal skills, and de-escalation techniques. He integrates mixed reality, digital puppetry, and avatar-based systems to create immersive educational experiences. His work emphasizes embodied cognition, gesture analysis, and simulation-driven skill development. The most recent publications highlight trends in virtual embodiment, affective computing, educational simulation, and assistive technologies. Key themes include body ownership in VR, gesture-based interaction, curiosity detection, and social skill development for individuals with autism. His interdisciplinary approach bridges computer science, education, and healthcare. Scientific Awards: Best Paper Award, ACM SUI 2018 Best Paper Award, IEEE SeGAH 2016 Grand Challenge People’s Choice Award, ACM ICMI 2015 Dr. Hughes has advised over 26 Ph.D. students and secured over $20 million in research funding as PI or co-PI. His collaborative projects span institutions and disciplines, involving teams from education, computer science, psychology, and medicine. He leads major initiatives in learning sciences and simulation-based training, fostering innovation in pedagogy and technology. He is a key figure in the Synthetic Reality Laboratory and the Learning Sciences Faculty Cluster, driving research in immersive technologies and their real-world applications. His labs focus on developing next-generation simulation tools for training, rehabilitation, and education.

Mayur Naikمشاهده پروفایل

استاد

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +۲ مورد دیگر

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research specializes in programming languages and artificial intelligence, with current focus areas including neurosymbolic programming, trustworthy AI for healthcare, and AI-enhanced development tools. He leads the development of the Scallop neurosymbolic programming language and maintains active collaborations in bioinformatics and clinical research. Education: Ph.D. in Computer Science, Stanford University (2008) M.S. in Computer Science, Purdue University (2003) B.S. from BITS Pilani (1999) Research Focus: His work bridges symbolic AI with neural approaches through neurosymbolic programming, enabling interpretable and domain-aware solutions. Current projects include Dolphin (scalable neurosymbolic learning), IRIS (LLM-assisted static analysis), and applications in computer vision and healthcare. Research emphasizes both theoretical foundations and practical system implementations. Publication Trends (Recent Focus): Recent work demonstrates strong emphasis on neurosymbolic learning frameworks (55%), AI-enabled programming tools (30%), and healthcare applications (15%). Key technical threads include LLM integration with program analysis, GPU acceleration for symbolic reasoning, and weakly-supervised learning paradigms. Awards & Honors: Test of Time Award, Foundations of SW Engineering (2023) Distinguished Paper Awards (PLDI 2019, SIGSOFT 2015, FSE 2015, SIGPLAN 2014) NSF CAREER Award (2013) Lockheed-Martin Teaching Excellence Award (2015) Outstanding Junior Faculty Award, Georgia Tech (2016) Academic Leadership: Advises 8 PhD students with research spanning neurosymbolic systems, program analysis, and AI security. Teaches core courses CIS 5470 (Software Analysis) and CIS 5500 (Database Systems). Research supported by NSF and industry partnerships. Labs & Teams: Leads the neurosymbolic programming research group at UPenn, collaborating with Intel Labs, Google AI, and healthcare institutions. Group develops open-source tools including Scallop compiler and LLM-assisted analysis frameworks.

Adam Cainمشاهده پروفایل

مدرس

• Digital Arts
• Games Art
• Generative Art
• +۵ مورد دیگر

Adam Cain is a Lecturer at School of Digital Arts (SODA) , Manchester Metropolitan University , specializing in Interdisciplinary Digital Arts and Games Art . With 17+ years of experience in arts education across KS2 to postgraduate levels, he has held significant roles including Programme Leader for Games Art at Nottingham Trent University and currently leads modules on Concept Art, Games for Good, and transdisciplinary collaboration. Current resident artist at Centre for Art, Science and Sustainability Collaborator in Independents Biennial 2025 and Archipelago Arts projects His practice-based research explores intergenerational memory , synthetic media , and generative processes to create experimental audio-visual works. Recent projects like Time Pops Like Chewing Gum and PATIENCE XI examine themes of singularity, transhumanism, and the intersection of nature, technology, and human identity. Scientific Awards & Recognition : Nominated for Aesthetica Art Prize 2025 Shortlisted for multiple teaching awards Adam's work integrates generative AI into transdisciplinary teaching through initiatives like the Future Spaces pilot program, fostering student agency while critically examining ethical implications of emerging technologies.

Kristóf Marussyمشاهده پروفایل

استادیار

• Model-Driven Engineering
• Dependability Analysis
• Logic Solvers
• +۴ مورد دیگر

Kristóf Marussy is an Assistant Professor at the Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics. His work bridges model-driven engineering , dependability analysis , and logic solvers to address challenges in critical cyber-physical systems (railway, automotive, aerospace). He leads research in formal verification , automated reasoning , and graph generation , integrating AI with logic-based techniques for system reliability. His recent publications focus on stochastic analysis , abstraction algorithms , and distributed ledger architectures , often leveraging his tool Refinery for constraint-based model generation. Notable awards include the 2024 University Researcher Scholarship Programme (EKÖP) and the 2023 Josef Heim Award . He has served on program committees for OOPSLA, ECOOP, and MODELS. Education: PhD in Computer Science, Budapest University of Technology and Economics (2018–2023) MSc and BSc in Computer Science, Budapest University of Technology and Economics Professional Highlights: 2025–: Assistant Professor at BME, leading research in formal verification and graph-based logic reasoning 2020–2023: Assistant Research Fellow in Hungarian national projects (railway/automotive verification) 2018–2019: Assistant Research Fellow at MTA-BME Lendület Cyber-Physical Systems Research Group Scientific Contributions: Developed Refinery , an open-source graph solver for model generation Contributed to European Space Agency's VAMPIR project for positioning systems Created formal verification methods for critical subsystems in railway/automotive industries Awards: 2024 EKÖP Postdoctoral Scholarship Multiple ÚNKP Excellence Program grants (2016–2023) 2023 Josef Heim Award for innovation Conferences & Journals: Published 15+ papers in top venues (ICSE, ASE, MODELS, IEEETSE, etc.) Active in program committees for OOPSLA, ECOOP, and LLM4MDE Teaching: Course Coordinator for Critical Systems Laboratory since 2024 TA for 9 years, supervising 7 BSc and 4 MSc theses Advising Scientific Students' Association (5 Gold, 5 Silver awards)

Emir Demirovićمشاهده پروفایل

استادیار

• Constraint Programming
• Combinatorial Optimisation
• Explainable AI
• +۴ مورد دیگر

Dr. Emir Demirović is an Assistant Professor in the Department of Computer Science at Delft University of Technology (TU Delft), The Netherlands. He leads the Constraint Solving ("ConSol") research group and co-directs the Explainable AI in Transportation Lab ("XAIT") as part of Delft AI Labs. Prior roles: Postdoc at University of Melbourne (2017-2020), PhD at Vienna University of Technology (2017) Collaborations: Civil Engineering, QuTech, and industry partners Funding sources: Dutch national funding agency, TU Delft, VoestAlpine Research Focus: Constraint programming and combinatorial optimisation Explainable AI methods for decision-making systems Integration of optimisation with machine learning Robust/resilient optimisation for industrial applications Optimal decision trees with dynamic programming Quantum computing scheduling techniques Scientific Contributions: Pioneered "Pseudo-Boolean Reasoning" for algorithm certification Developed "Blossom" algorithm for optimal decision trees Advances in "Predict+Optimise" frameworks Created Pumpkin constraint programming solver Bridge between SAT/CP and Machine Learning Awards: First Place, MaxSAT Evaluation 2018+ First Place, ROADEF/EURO 2012 Adoption of methods in Google OR-Tools Collaborations: Research visits to EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien Participated in Dagstuhl seminars, Lorentz workshops, and Simons-Berkeley programme