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Stephen LichtView profile

Associate Professor

• Marine Robotics
• Biologically Inspired Propulsion
• Nonlinear Attitude Control
• +5 more

Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.

Hanumant SinghView profile

Professor

• Field Robotics
• Machine Learning
• SLAM (Simultaneous Localization and Mapping)
• +5 more

Professor Hanumant Singh leads the Electrical and Computer Engineering department at Northeastern University, with a joint appointment in Mechanical and Industrial Engineering , and serves as Program Director for the Master of Science in Robotics. He earned his Ph.D. from MIT/WHOI Joint Program in 1995 and has conducted over 60 expeditions globally, focusing on marine geology, polar studies, and coral reef ecology. His research emphasizes field robotics , including SLAM, underwater manipulation, and imaging in extreme environments. He developed the Seabed AUV and Jetyak ASV , widely used in scientific research. His labs include the Field Robotics Lab and the Institute for Experiential Robotics . Research Interests: Machine Learning for Fisheries SLAM with dynamic objects Underwater imaging and manipulation Autonomous surface and aerial systems Polar and marine robotics Awards: ICRA Best Student Paper Award, IEEE Oceanic Engineering Society Distinguished Faculty Award (2025), Lifetime Achievement Award (2022), and IEEE Fellow status. His work has been featured in Nature Geoscience , Polar Biology , and media outlets like WGBH. Students & Collaborations: Advises students like Srinidhi Pattala (MS Robotics) and Dennis Giaya (PhD Computer Engineering). Collaborates with institutions on projects such as Antarctic sea ice thickness estimation and deep-sea submersible missions.

• Reconfigurable Computing
• Field-Programmable Gate Arrays (FPGA)
• High-Level Synthesis
• +3 more

Paolo Ienne is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), where he leads the Processor Architecture Laboratory (LAP) within the School of Computer and Communication Sciences. His research focuses on advancing reconfigurable computing systems through innovative FPGA architectures and high-level synthesis methodologies. His primary research domains include reconfigurable computing, FPGA architecture design, dynamically scheduled dataflow circuits, and hardware acceleration techniques. Recent work emphasizes memory system optimization for FPGAs, formal verification of circuit transformations, and rapid C-to-hardware compilation flows. He has pioneered approaches for handling thousands of outstanding memory misses in FPGA accelerators and developed novel techniques for switch-block exploration without explicit pattern enumeration. Analysis of his 2023-2025 publications reveals a strong trend toward practical FPGA deployment challenges, with increasing focus on HBM integration, virtual memory systems for PCIe-attached devices, and formally verified circuit transformations. His work consistently targets real-world bottlenecks in high-level synthesis toolchains while maintaining theoretical rigor in dataflow architecture design. Professor Ienne's laboratory receives support from the Swiss National Science Foundation and industry partners including Huawei, enabling cutting-edge research in FPGA-based acceleration. His collaborative network spans major semiconductor companies and academic institutions worldwide, with frequent co-authorship on conference proceedings and journal publications in IEEE and ACM venues.

Stephen GravesView profile

Professor

• Operations Research
• Supply Chain Management
• Manufacturing Systems
• +3 more

Stephen Graves is the Abraham J. Siegel Professor of Management and a Professor of Operations Management at MIT Sloan School of Management, with a joint appointment in Mechanical Engineering. He holds a PhD in Operations Research from the University of Rochester. His research focuses on operations research models applied to manufacturing systems, supply chains, and service operations, including strategic inventory positioning and order fulfillment optimization. Graves has served as MIT Sloan’s Deputy Dean and Chair of MIT’s Faculty Council (2001-2003). He is a Fellow of INFORMS, recipient of the Kimball Medal, and member of the National Academy of Engineering. Education: PhD in Operations Research, University of Rochester MBA and AB in Mathematics/Social Sciences, Dartmouth College Research Interests: Graves develops analytical frameworks for supply chain optimization, inventory management, and production planning. His recent work addresses post-pandemic supply chain resilience, digital twins for production systems, and closed-loop supply chains. Current projects explore operational challenges in e-commerce fulfillment and global supply networks. Awards: INFORMS Kimball Medal (2023) Elected to National Academy of Engineering INFORMS Prize for Operations Research President of INFORMS (2020) Advising & Grants: Supervisor to 25+ PhD students across manufacturing, supply chain analytics, and healthcare operations. Active in industry collaborations with companies like Intel, Polaroid, and Monsanto. Leads MIT initiatives including the Leaders for Global Operations program. Labs/Teams: Core member of MIT’s Institute for Data, Systems, and Society (IDSS). Co-director of the MIT Operations Research Center. Involved in interdisciplinary projects combining data science with operations management.

Mirjana StojilovicView profile

Researcher

• Field-programmable technology
• Electronic design automation (EDA)
• Hardware security
• +5 more

Mirjana Stojilovic is a Researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC), specifically within the Institute of Computer Engineering (IINFCOM) and the Parallel Systems Architecture Laboratory (PARSA). She also serves as a Lecturer in the SSC - Teaching department at EPFL. Her office is located at INJ 235, Station 14, 1015 Lausanne, Switzerland. Mirjana Stojilović received her Dipl. Ing. and Ph.D. degrees from the School of Electrical Engineering, University of Belgrade, in 2006 and 2013, respectively. Her academic journey includes collaborating with the Processor Architecture Laboratory at EPFL as a Guest Researcher from 2010 to 2013, working at the University of Applied Sciences Western Switzerland as a senior researcher from 2013 to 2016, and joining the Parallel Systems Architecture Lab at EPFL in October 2016. Dr. Stojilovic's research spans field-programmable technology, electronic design automation (EDA), and electrical-level attacks and countermeasures for reconfigurable hardware. Her work bridges the gap between hardware design and security, with a particular focus on vulnerability analysis and protection mechanisms for FPGA-based systems in cloud environments. She has made significant contributions to understanding side-channel attacks, fault injection techniques, and secure multi-tenancy solutions for shared hardware resources. Her extensive publication record reveals a clear research trajectory from traditional FPGA design and EDA topics toward increasingly security-focused investigations. Recent work demonstrates deep expertise in power analysis attacks, hardware trojans, and countermeasures for cloud-based FPGA systems, reflecting the growing importance of hardware security in distributed computing environments. Scientific Awards and Recognitions: Best Paper Award at 2016 International Symposium on Electromagnetic Compatibility (EMC Europe 2016) Young Scientist Award at 33rd International Conference on Lightning Protection (ICLP2016) Young Author Best Paper Award at the 20th Telecommunication Forum in Belgrade (TELFOR 2012) EPFL School of Computer and Communication Sciences (IC) Teaching Award (2015) Nominated for Best Paper Award at the International Conference on Field-Programmable Technology (FPT) (2020) Dr. Stojilovic has advised numerous PhD students including Coulon Louis, Pirayadi Rouzbeh, and Shrivastava Shashwat, along with past EPFL PhD students Glamocanin Ognjen and Mahmoud Dina. She has supervised dozens of semester and diploma projects focusing on hardware security, FPGAs, design automation, side-channel attacks, and cloud computing. Her service to the academic community includes serving on program committees for FPGA, FCCM, FPL, and DATE conferences, reviewing for multiple IEEE and ACM journals, and acting as associate editor for IEEE ESL and ACM TRETS. As a member of the Parallel Systems Architecture Laboratory at EPFL, Dr. Stojilovic leads research projects investigating security aspects of reconfigurable hardware systems. Her team works at the intersection of computer architecture, electronic design automation, and hardware security, with particular emphasis on vulnerabilities and protections for shared FPGA resources in cloud environments.

Mariana ResenerView profile

Assistant Professor

• Power Systems Optimization
• Distributed Energy Resources
• Renewable Energy Integration
• +5 more

Mariana Resener is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). She holds a Ph.D. in Electrical Engineering (2016) from the Federal University of Rio Grande do Sul, Brazil, alongside M.Sc. (2011) and B.Sc. (2008) degrees in the same field. Her research focuses on optimizing power systems, particularly in distributed energy resources, energy storage, and volt/var control. She teaches courses like Power Electronics and Power Systems Analysis & Design. Her work emphasizes sustainable development in grid planning and energy infrastructure. As a Senior Member of IEEE and an Associate Editor for the Energy Systems Journal (Springer), she contributes to advancing smart grid technologies and renewable integration. Her research spans metaheuristic optimization, stochastic modeling, and grid resilience strategies for distributed systems. Recent projects include hybrid renewable energy systems for substations, EV charging station optimization, and fault analysis in unbalanced grids. She collaborates with industry on practical solutions for grid modernization and reliability enhancement.

Elisa B SchweigerView profile

Senior Lecturer

• Retailing
• Marketing Analytics
• AI in Retail
• +5 more

Elisa B Schweiger is a Senior Lecturer (equivalent to Associate Professor) in Marketing at King’s Business School, King’s College London. She specializes in retailing, AI applications in marketing, multi-sensory marketing, and pricing strategies. Her research employs behavioral experiments, field studies, and text mining. Education: PhD in Consumer Psychology from the University of Bath. She also holds a background in psychology and marketing. Research Interests: Elisa’s work focuses on retail innovation, AI-driven consumer interactions, luxury branding, and the psychological underpinnings of consumer decisions. She explores how technology (e.g., voice assistants, mixed reality) impacts customer journeys and brand relationships. Her research bridges theory and practice, addressing topics like AI ethics, retail atmospherics, and consumer risk perceptions in energy technologies. Publications: Her work appears in top journals such as the Journal of the Academy of Marketing Science , Journal of Retailing , and Journal of Business Research . Recent topics include voice assistant evaluations, AI in lateral markets, and digital retailing trends. Awards: Recipient of the Dean’s Award for Early Career Researcher of the Year (2022), Journal of Retailing Davidson Best Paper Award Runner-up, and MMA Teaching Excellence Award. Teaching & Engagement: Teaches Marketing Analytics, Retailing, and research methods in graduate and executive programs. She serves on the editorial boards of Journal of Retailing and Journal of Business Research , and reviews for top marketing journals. Labs/Teams: Active in King’s Business School’s marketing research teams, focusing on innovation in retail and AI applications.

Roland A. FischerView profile

Professor

• Catalysis
• Energy Conversion
• Gas Storage and Separation
• +5 more

Prof. Roland A. Fischer is a Full Professor at the Chair of Inorganic and Metal-Organic Chemistry at Technical University of Munich (TUM). Previously, he held a Full Professorship at Ruhr University Bochum (1997–2015). His research focuses on multifunctional metal-organic frameworks (MOFs), clusters, and composites for energy conversion, catalysis, gas storage, and environmental applications. He leads the Catalysis Research Center and has pioneered advancements in MOF-based catalytic systems and stimuli-responsive materials. Education: 1981–1986: Diplom in Chemistry (TUM) 1989: PhD, Dr. rer. nat. (TUM) 1995: Habilitation (TUM) Research Interests: His work integrates molecular and extended catalytic systems, including: - Design of MOFs for photocatalytic fuel production - Nanoparticle encapsulation in robust frameworks - Redox-switchable materials and photochromic systems - Cluster chemistry and superatom complexes - Applications in energy storage, environmental remediation, and biomedical technologies. Major Achievements: Over 680+ publications, h-index 101 (Scopus 2025) Coordinator of EU projects (SURMOF, ENHANCE, DEFNET) Recipient of Heinz-Maier-Leibnitz Award (1993) and Alfried Krupp Award (1996) Editorial roles: Angewandte Chemie , Chemical Vapour Deposition Grants & Teams: He has secured major grants including DFG Priority Programs (CVD-Materials, COORNETs) and led interdisciplinary teams in EU initiatives. His lab collaborates globally, including visiting professorships at Kyoto University and IIT Bombay. Labs & Facilities: His research uses advanced facilities like the Catalysis Research Center and contributes to platforms such as the Munich Catalysis Alliance. Key tools include atomic layer deposition, in situ characterization, and MOF-based device fabrication.

Roger J.E. JaspersView profile

Professor

• Energetic particles in fusion plasmas
• Alternative fusion concepts
• Plasma physics
• +4 more

Roger J.E. Jaspers is an Associate Professor at Eindhoven University of Technology (TU/e) and a part-time Professor at Ghent University in Belgium, affiliated with the Applied Physics and Science Education school and specializing in the Science and Technology of Nuclear Fusion. His research focuses on spectroscopic diagnostics of ion processes in fusion plasmas, particularly energetic alpha particles in fusion-born reactions. Collaborations include international fusion experiments like W7-X (Germany), JET (UK), and KSTAR (South Korea). He leads the scientific R&D for the ITER CXRS instrumentation system and has authored over 90 peer-reviewed papers. His work spans topics such as: Relativistic electrons Plasma energy transport Magneto-hydrodynamics (MHD) Fusion reactor instrumentation He contributes to educational initiatives like the TU/e Fusion Master program, FUSENET, and the Erasmus Mundus Programme FUSION-DC.

• Microbiome
• Host-Microbe Interactions
• Environmental Microbiology
• +5 more

Prof. Dr. Michael Schloter serves as Director of the Research Unit for Comparative Microbiome Analysis at Helmholtz Munich since 2011 and holds a Professorship in Microbiology at the Technical University of Munich since 2010. He concurrently acts as Principal Investigator at both the German Center for Lung Diseases (DZL) and the Central for Food and Nutrition (ZIEL) at TU Munich, driving interdisciplinary research at the intersection of environmental and human health through the "Planetary Health" framework. His educational foundation was built through diploma studies at Ludwig Maximilian University Munich and doctoral research at the University of Bayreuth, followed by formative work in Brazil and the United States where he pioneered bioinocula development for stress-resilient agriculture. At Helmholtz Munich (formerly GSF), he evolved from group leader in soil microbial ecology (2001) to Research Unit Director, expanding his focus from agricultural systems to human microbiome-health connections. Dr. Schloter's research program centers on microbiome-host crosstalk across ecological scales, investigating how environmental microbiota interact with human and plant microbiomes to influence health outcomes. His work integrates ecological theory with microbiome analysis to decode host-microbe co-evolution patterns, with applications spanning sustainable agriculture, allergy prevention, and infection control. Key thematic pillars include holobiont theory, probiotic development, and microbiome-mediated disease prevention through environmental quality improvement. Analysis of his 2025 publications reveals a strategic research portfolio bridging fundamental and applied microbiome science. Agricultural studies dominate (maize, potato, apple systems), examining microbial inoculants and soil management, while human health investigations explore preterm birth complications and Antarctic mammal microbiomes. Computational approaches for antimicrobial resistance assessment and ecosystem-scale projects like JenaTron demonstrate methodological breadth, all converging on translating microbiome functionality into real-world sustainability solutions. Major recognitions include: Election to the Bavarian Academy of Science (2021) Consistent placement among the top 1% of highly cited global researchers (2019-2021) Heinrich Baur Research Award (2011) for agricultural microbiology contributions As Research Unit Director, Schloter leads large-scale collaborative initiatives including the DZL and ZIEL consortia, securing major funding for microbiome standardization projects and international research networks. His leadership extends to developing analytical frameworks for cross-ecosystem microbiome comparisons and establishing protocols for translating microbial ecology principles into clinical and agricultural applications. The Comparative Microbiome Analysis unit operates as a nexus for multi-host microbiome research, employing comparative genomics, field-scale agricultural trials, and clinical cohort studies to investigate microbiome dynamics across environmental, plant, and human systems. Current infrastructure includes advanced sequencing facilities and experimental platforms for plant-microbe and host-microbe interaction studies under controlled and natural conditions.

Roummel F. MarciaView profile

Professor

• Optimization
• Signal Processing
• Data Science
• +5 more

Roummel F. Marcia is a Professor and current Chair of the Department of Applied Mathematics at the University of California, Merced, within the School of Natural Sciences. He received his Ph.D. from UC San Diego under Professor Philip Gill and previously held postdoctoral positions at the San Diego Supercomputer Center and University of Wisconsin-Madison, as well as a research scientist position in electrical engineering at Duke University. His research spans multiple areas in optimization and its applications, with a focus on signal processing, data science, machine learning, linear algebra, and mathematical biology. Dr. Marcia's work has significant interdisciplinary impact, particularly in biomedical imaging, computational biology, and quantum computing applications. His research methodology often combines theoretical optimization approaches with practical applications in data-intensive fields. Dr. Marcia's recent publications demonstrate a strong trend toward integrating optimization theory with deep learning architectures, particularly in applications requiring sparse data handling, biomedical imaging, and quantum computing. His work shows increasing focus on developing novel optimization algorithms specifically designed for machine learning contexts, including quasi-Newton methods adapted for deep learning and specialized techniques for handling non-convex optimization problems. School of Natural Sciences Faculty Award for 'Developing or Improving Academic Programs and Tracks' (2021-22) Leadership roles in SIAM Activity Group on Applied Mathematics Education Recognition as a Math Alliance Mentor for supporting underrepresented students Dr. Marcia has successfully mentored numerous doctoral students to completion, with graduates moving to positions at Meta, Johns Hopkins University Applied Physics Laboratory, Lawrence Livermore National Laboratory, and other prestigious institutions. His research has been consistently funded by major agencies including NSF (with grants IIS 1741490, DMS 1840265, DMS 2229495, CCF 2343610), DARPA, and ARPA-E. As the current graduate chair of the Applied Math Graduate Program, he plays a key role in shaping the next generation of mathematical scientists. His work with the SMaRT (Scientific Mathematics Research and Training) team demonstrates his commitment to collaborative, interdisciplinary research.

• Negotiation
• Behavioral Decision Making
• Ethics in Business
• +7 more

Max H. Bazerman is the Jesse Isidor Straus Professor of Business Administration at Harvard Business School, specializing in negotiation, decision making, and behavioral ethics. His academic career spans several decades during which he has become a leading authority on how cognitive biases and ethical considerations influence organizational decision processes. Professor Bazerman's primary research interests include negotiation strategies, ethical decision making, judgment heuristics, and bounded awareness. His work explores how individuals and organizations can recognize and overcome cognitive limitations to make more ethical and effective decisions. He has pioneered research on blind spots in ethical judgment, the psychology of complicity, and decision architectures that promote better choices. His publications reveal a consistent focus on practical applications of behavioral science to real-world business challenges. Recent work examines resource allocation during crises, ethical leadership frameworks, and the role of experimentation in improving organizational decision making. The research shows increasing emphasis on systemic ethical failures and how individuals contribute to complicity in unethical behavior. Honorary doctorate from the University of London Life Achievement Award from the Aspen Institute's Business and Society Program Distinguished Educator Award from the Academy of Management Academy of Management Career Award for Scholarly Contributions to Management Lifetime Achievement Award from the Organizational Behavior Division of the Academy of Management Ethisphere's 100 Most Influential in Business Ethics Daily Kos' Heroes recognition for whistleblowing on the Bush Administration's corruption of the RICO Tobacco trial Bazerman has advised numerous organizations including Abbott, Aetna, AIG, Alcoa, Allstate, Amgen, and many others across 30 countries. His work bridges academic research and practical application, with significant influence on how businesses approach ethical decision making and negotiation strategy. He has trained doctoral students who now hold positions at leading business schools worldwide, including Harvard, Wharton, Kellogg, and Stanford.

Riccardo RaheliView profile

Professor

• Telecommunications
• Digital Signal Processing
• Wireless Communications
• +5 more

Riccardo Raheli is a Full Professor at the University of Parma , Department of Engineering and Architecture, with a career spanning over three decades in Information and Communication Technologies (ICT). He has served as Chair of the Councils for Telecommunications and Communication Engineering programs, and as representative of the University of Parma in CNIT and its Members' Assembly. Education: Laurea in Electronic Engineering (University of Pisa, 1983), M.Sc. in Electrical and Computer Engineering (University of Massachusetts, 1986), Postgraduate Diploma (Scuola Superiore Sant'Anna, 1987) Key Roles: President of Degree Councils (2002-2018), CNIT Committee Member (2000-2005), Editorial Board member for IEEE Transactions, Springer and MDPI journals His research bridges telecommunications , digital signal processing , and healthcare applications , producing extensive international publications and industrial patents. He has co-authored monographs including Detection Algorithms for Wireless Communications (Wiley, 2004) and LDPC Coded Modulations (Springer, 2009). Recent article trends show interdisciplinary work in automotive stress monitoring (IoT/Matlab-based systems), video processing for healthcare (neonatal seizures, respiratory monitoring), and acoustic field control (microphone virtualization, personal sound zones). His work spans machine learning applications in automotive systems, stochastic acoustic modeling , and power-line communications . Scientific Leadership : Co-Chair for IEEE conferences (ICC 2010, GLOBECOM 2011, ISPLC 2020) Editorial roles in 7+ international journals Grants & Collaborations : Led industrial patents in communications systems Coordinated CNIT Technical Reports series (2025) He teaches Wireless Communications and Digital Signals Laboratory , emphasizing Matlab/Simulink proficiency. His laboratory sessions focus on practical implementation of signal processing algorithms, requiring full software installation on personal devices.

Scott P. BarnicleView profile

Associate Professor

• Sport Psychology
• Exercise Psychology
• Performance Enhancement
• +3 more

Scott P. Barnicle is an Associate Professor of Practice in Sport Psychology at the University of Tennessee, Knoxville, within the College of Education, Health, and Human Sciences. He holds a PhD in Sport and Exercise Psychology from the University of Idaho, an MA in Counseling Psychology from Boston College, and a BA in Psychology from Clemson University. His professional experience spans roles at West Virginia University (Teaching Professor and Assistant Professor) and the United States Army’s Comprehensive Soldier & Family Fitness Program as a Performance Enhancement Consultant. He has also served as a volunteer assistant coach in collegiate golf programs, including the University of Idaho, Nebraska, and South Carolina. His research focuses on applied sport psychology, emphasizing mental skills training, athlete enjoyment, and performance enhancement across diverse sports like soccer, golf, and powerlifting. He explores topics such as autonomy in training, resilience (hardiness), and the intersection of sport psychology with business and military contexts. Courses taught include Exercise Psychology, Professional Practice in Sport Psychology, and field placements in applied settings. His work bridges theoretical frameworks (e.g., Self-Determination Theory) with practical interventions to improve athlete well-being and performance. Barnicle’s publications span journals in sport psychology, education, and kinesiology. His recent work includes integrating collaborative online learning in kinesiology education and studying hardiness in baseball athletes. He has contributed to textbooks on career pathways in sport psychology and case studies in applied settings. His research consistently emphasizes the psychological factors that optimize athlete performance and enjoyment across competitive and recreational domains.

• Machine Learning
• Bayesian Inference
• Probabilistic Machine Learning
• +17 more

Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.