Jinjin Ha serves as an Assistant Professor in the Department of Mechanical Engineering at the University of New Hampshire, with her office located in Kingsbury Hall, Room W101a, Durham, NH. She teaches core mechanical engineering courses including Statics (ME 525), Materials Processing in Manufacturing (ME 742/842), Theory of Plasticity (ME 927), and Doctoral Research (ME 999), demonstrating active engagement in both undergraduate and graduate education. Her research program integrates computational mechanics with advanced manufacturing, focusing on: Machine learning applications for plasticity modeling and fracture prediction Deformation mechanics in incremental sheet forming processes Martensitic phase transformations in stainless steels Anisotropic material behavior and yield function development Ductile fracture characterization of titanium and aluminum alloys Analysis of her 2023-2024 publications reveals a decisive shift toward AI-driven mechanics, where neural networks solve complex constitutive modeling challenges in metal forming. This interdisciplinary approach bridges fundamental material science with industrial manufacturing optimization, particularly in toolpath design and phase transformation control. No scientific awards were documented in the provided profile information. While doctoral research supervision is indicated through ME 999 course listings, specific student names, grant funding details, laboratory facilities, or collaborative team structures were not disclosed in the available text.
Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.
Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement
Professor Hu Qing serves as a Professor at the School of Environmental Science and Engineering, Southern University of Science and Technology (SUSTech), and Director of the Engineering Innovation Center (Beijing) at SUSTech. With over 30 years of research and professional experience in environmental science and engineering, she has established herself as a leading expert in soil and groundwater remediation, environmental policy, and sustainable development. Education: PhD in Soil Environmental Pollution and Hydrology, Imperial College London (1993-1998) MSc in Water Treatment and Environmental Chemistry, Chinese Academy of Sciences (1987-1990) BSc in Environmental Engineering, Tsinghua University (1982-1987) Professor Hu's research focuses on critical environmental challenges including soil and groundwater remediation, contaminated site management, environmental policy development, and sustainable urban development. Her work integrates technical innovation with policy implementation, particularly in the areas of green and sustainable remediation methodologies for contaminated sites. She has pioneered approaches that combine big data applications with traditional environmental engineering practices, contributing significantly to China's environmental protection framework and international collaboration efforts in ecological sustainability. Analysis of Professor Hu's recent publications reveals a strong emphasis on practical environmental solutions with policy implications. Her work spans soil and groundwater remediation technologies, environmental policy development, and sustainable approaches to contaminated site management. A notable trend is the integration of international best practices with China-specific environmental challenges, particularly in the areas of brownfield redevelopment and soil pollution control. Her research demonstrates a consistent focus on translating scientific findings into practical applications that inform national environmental policies and standards. Scientific Awards: State Council Special Allowance Experts (2013) Second Prize of National Science and Technology Progress Award (2017) First Prize of Environmental Protection Science and Technology Award (2021) Science and Technology Progress Award of Ministry of Education (2012) IBM Global Distinguished Scholar Award (2016) Professor Hu has led and participated in more than ten national and provincial research projects, including the National 863 Program and the National Water Pollution Control and Treatment Science and Technology Major Project. She has managed dozens of site investigation and remediation projects across multiple industries including chemicals, printing and dyeing, petroleum, and electroplating. Her international collaboration experience is extensive, having served as the Chinese leading expert in the Sino-UK Soil & Groundwater Remediation Science & Technology Cooperation Program and as project leader for multiple World Bank and Asian Development Bank projects. As Director of the Engineering Innovation Center (Beijing), Professor Hu leads a multidisciplinary team focused on developing innovative solutions for environmental challenges. The center serves as a bridge between academic research and practical implementation, working closely with government agencies including the National Development and Reform Commission, Ministry of Science and Technology, and Ministry of Environmental Protection to shape national environmental policies and standards.
Kevin Chenchuan Chang is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the FORWARD Data Lab and the Data and Information Systems Laboratories. His research focuses on bridging structured and unstructured data through natural language processing, data mining, machine learning, and information retrieval, with applications in web search, social media analytics, and knowledge acquisition. He co-founded Cazoodle and developed GrantForward.com, a funding discovery platform used by leading institutions globally. Education: Ph.D. in Electrical Engineering from Stanford University (2001), B.S. from National Taiwan University. Professional roles include service on program committees for SIGMOD, VLDB, KDD, and NeurIPS, as well as editorial roles for PVLDB, TKDE, and the Encyclopedia of Database Systems. His awards include the ICDE 10-Year Test of Time Award (2022), NSF CAREER Award (2002), and multiple UIUC teaching excellence recognitions. He teaches courses such as CS 411 (Database Systems), CS 598 KCC (Understanding LLMs), and CS 511 (Advanced Data Management). Research contributions span graph algorithms (e.g., Geom-GCN, SimRank), social network analysis (ROSE), and NLP (DEER, Open Relation Modeling). The FORWARD Lab emphasizes real-world impact through systems like GrantForward and tools for analyzing large-scale data.
Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.
May Tajima is an Assistant Professor in the Department of Management & Organizational Studies at Western University. She holds a Ph.D. in Management Sciences from the University of Waterloo (1998). Her research focuses on RFID technology applications in supply chains, operations management, and supply chain agility. She teaches courses including Operations Management (as course coordinator) and Statistics at Western's DAN Management program. Teaching Experience: Previously taught at University of Waterloo and Wilfrid Laurier University in areas like Production Management, Operations Research, and Engineering Economics. Industry background includes supply chain analysis at Chesapeake Decision Sciences. Key Research Themes: RFID adoption challenges across industries, technology standardization in pharmaceutical contexts, and strategic supply chain adaptability. Authored five major publications between 2005-2012 and contributed to an engineering economics textbook (2005). Awards: Recognized on University Student Council Teaching Honour Roll (2016) Office: SSC 4415 | Phone: 519-661-2111 x87619
Saeed Mehraban is an Assistant Professor of Computer Science at Tufts University's School of Engineering and an Assistant Professor in the Department of Physics & Astronomy within the School of Arts and Sciences. He joined Tufts University in June 2022 as an Assistant Professor after serving as a Visiting Assistant Professor from June 2021 to May 2022. Prior to his position at Tufts, he was an IQIM Postdoctoral Scholar at the California Institute of Technology and a research fellow at the Simons Institute for the Theory of Computing during spring 2020. Doctor of Philosophy in Electrical Engineering and Computer Science from MIT (2019) Master of Science from MIT (2015) B.Sc. in Physics from Sharif University of Technology, Iran (2013) B.Sc. in Electrical Engineering from Sharif University of Technology, Iran (2013) Saeed Mehraban's research focuses on quantum computation and information, exploring the profound connections between computer science and physics. His work particularly addresses quantum computational complexity and continuous variable systems. A significant portion of his recent research concerns delineating the boundary between classical and quantum computing in noisy intermediate-scale quantum devices. His research bridges theoretical computer science with quantum physics, examining fundamental questions about what quantum computers can and cannot efficiently solve, with particular emphasis on mathematical foundations and computational complexity aspects of quantum information processing. Mehraban's publication record demonstrates a strong focus on quantum computing theory, with particular emphasis on quantum complexity, quantum algorithms, and the mathematical foundations of quantum information. His recent work (2021-2023) has explored topics like unitary t-designs, holomorphic representations of quantum computations, and quantum-inspired identities. Earlier publications (2015-2020) examined computational complexity in quantum theories, approximation algorithms for matrix problems, and connections between classical algorithms and quantum many-body systems. His research consistently sits at the intersection of theoretical computer science and quantum physics, addressing fundamental questions about computational advantages of quantum systems. Gold Medalist, National Physics Olympiad (2007) Bronze Medalist, National Astronomy Olympiad (2005) Identified as Exceptional Talent by the Iranian Educational System (2004) Mehraban teaches dissertation research courses at Tufts University, indicating his involvement in mentoring graduate students. His teaching activities include specialized courses in quantum information science, quantum computer science, and quantum complexity theory. His professional activities show invitations to speak at prestigious institutions including Microsoft Research Station Q, Mila Institute in Quebec, and the Simons Institute, suggesting recognition of his research contributions. His postdoctoral work at Caltech's Institute for Quantum Information and Matter (IQIM) demonstrates his connection to leading quantum research groups. While specific lab affiliations at Tufts aren't explicitly detailed in the provided information, Mehraban's teaching of specialized quantum courses and his research profile suggest he likely contributes to quantum computing research initiatives at Tufts University. His background at Caltech's IQIM and involvement with the Simons Institute's Quantum Wave in Computing Program indicate strong connections to the broader quantum information science community.
Antti Poso is a Professor of Drug Design at the University of Eastern Finland (Kuopio), affiliated with the School of Pharmacy under the Faculty of Health Sciences. His research focuses on computer-aided molecular design, particularly targeting anti-cancer drugs and anti-microbials. Key projects include the EDCMET project (2019–2024) and the GeneCellNano Flagship (2020–2028). He leads the Molecular Modeling and Drug Design Research Group, specializing in QSAR analysis, kinase inhibition profiling, and systems-level drug response modeling. Recent work includes studies on SARS-CoV-2 inhibitors, endocrine disruptors, and bacterial pathogenesis. His findings bridge chemical structure with biological outcomes, leveraging computational tools like CCA and molecular dynamics simulations. Collaborations span medicinal chemistry, pharmacology, and systems biology, contributing to both academic and applied drug discovery efforts. Education: Not explicitly stated in texts; assumed to hold advanced degrees in pharmacy or chemistry. Research Themes: Drug design, molecular modeling, QSAR, computational biology, and anti-infective agents. Key Contributions: Over 150+ publications, including influential works on chemoinformatics-driven drug response analysis and structure-based inhibitor design. Publications highlight advancements in kinase inhibitors, anti-microbial strategies, and viral hijacking mechanisms. His work emphasizes translating computational insights into therapeutic solutions for cancer, infectious diseases, and metabolic disorders.
Martyn Hann is a Lecturer in Coastal Engineering and Associate Head of School (PG Education) at the School of Engineering, Computing and Mathematics, University of Plymouth. He leads the COAST laboratory and is a member of the COAST Engineering Research group. His roles include Academic Lead for the Postgraduate Taught programs within the school. Martyn specializes in coastal engineering, offshore renewable energy systems, and sustainable coastal management. His teaching focuses on MSc Coastal Engineering and BEng/MEng Civil and Coastal Engineering programs, where he leads modules such as COUE509 (Coastal Engineering), COUE508 (Advanced Coastal Engineering Modelling), and COUE300 (Coastal Engineering Analysis and Design). He also contributes to offshore renewable energy modules. His research aligns with UN Sustainable Development Goals, particularly in environmental sustainability and infrastructure resilience. No specific scientific awards or grants are listed, but his involvement in the COAST laboratory indicates active engagement in applied coastal research.
João F. Mano is a Full Professor at the Department of Chemistry, University of Aveiro, and Director of the Doctoral Program on Biotechnology. He leads the COMPASS Research Group and serves as Vice-Director at CICECO - Aveiro Institute of Materials. His academic appointments include Invited Professor at University of Lorraine (France), Visiting Professor at KAIST (South Korea), and Adjunct Professor at Ajou University (South Korea). Education: PhD in Chemistry (1996, Technical University of Lisbon); D.Sc. in Tissue Engineering, Regenerative Medicine and Stem Cells (2012, University of Minho) Research Interests focus on Biomaterials for Regenerative Medicine , integrating Nanotechnology , Microtechnology , and Biofabrication . His group develops Bioinspired Materials using polymer chemistry, Decellularized Extracellular Matrix , and 3D Bioprinting to engineer Cell Microenvironments for therapeutic applications. Recent Publications highlight advancements in Human-Derived Hydrogels , Photopolymerizable Scaffolds , Magneto-Responsive Biomaterials , and Programmable Bioinks . Trends show emphasis on Organ-on-a-Chip integration, Smart Living Materials , and Green Bioprinting methodologies. Scientific Awards include: European Research Council Advanced Grants (2015, 2020) Fellow at IUPAC, European Academy of Sciences, and American Institute of Medical and Biological Engineering ERC Proof of Concept Grants Doctor Honoris Causa from University of Lorraine and Utrecht UNESCO Chair on Biomaterials George Winter Award (European Society for Biomaterials) Supervisions & Collaborations encompass 74+ MSc, 26+ PhD students, and 40+ postdocs. He co-founded METATISSUE and CELLULARIS Biomodels , and serves as Editor-in-Chief of Materials Today Bio .
Forest Agostinelli is an Assistant Professor in the Department of Computer Science and Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina, where he is also affiliated with the AI Institute. His research focuses on designing AI algorithms for pathfinding problems, integrating deep learning, reinforcement learning, heuristic search, and formal logic. He holds a Ph.D. in Computer Science from the University of California, Irvine, an M.S. from the University of Michigan, and a B.S. in Electrical and Computer Engineering from The Ohio State University. Research Overview : Agostinelli’s work emphasizes solving pathfinding problems in domains like robotics, theorem proving, and molecular optimization. His group develops explainable AI methods to enable collaboration between humans and machines. Key projects include DeepCubeA (solving the Rubik’s Cube via deep reinforcement learning) and neural activation function research. Funding & Awards : He has secured grants from NSF, NASA EPSCoR, and South Carolina’s ASPIRE and MADE programs. Notable awards include the NSF Graduate Research Fellowship and the Graduate Education for Minority Students Fellowship. Teaching : He teaches courses in Artificial Intelligence (CSCE 580) and Deep Reinforcement Learning and Search (CSCE 790), mentoring over 15 students at undergraduate and graduate levels. Labs & Collaborations : Active in AI-driven education and interdisciplinary projects, his lab contributes to tools like ALLURE for children’s learning and Bioinformatics platforms like CircadiOmics.
Associate Professor Nikolas Fokialakis is affiliated with the Department of Pharmacy at the National and Kapodistrian University of Athens, specifically within the Section of Pharmacognosy and Chemistry of Natural Products. His academic journey includes a PhD in Pharmacognosy, postdoctoral research at the USDA, and advanced degrees in Pharmacognosy and Pharmacy. His research focuses on natural products from plants, microorganisms, and marine organisms, with emphasis on anti-aging compounds, cosmeceuticals, and bioactive molecule discovery. Notable awards include the Arthur Neish Award (2010) and the ASP Award (2006). He holds editorial roles in international societies and has contributed to over 150 publications. His work integrates biotechnology for sustainable production of bioactive agents and explores microbial biodiversity for drug leads. Teaching responsibilities include courses in Pharmacognosy, Biotechnology, and Pharmaceutical Analysis. Education Highlights: PhD in Pharmacognosy (University of Athens, 2004) Postdoc at USDA Natural Products Lab Postgraduate Diploma in Pharmacognosy (2000) Pharmacy Degree (University of Athens, 1998) Research Interests: Isolation of bioactive small molecules from terrestrial and marine sources Anti-aging mechanisms targeting proteostasis Cosmeceutical development using microbial metabolites Phytochemical analysis of Mediterranean plants Publications Trends: Recent work emphasizes marine microbiota for anti-aging agents, pyrrolizidine alkaloid profiling, and sustainable bioengineering of microbes for industrial metabolites. Over 150 indexed articles demonstrate cross-disciplinary impact in natural products research. Awards: 2011-2019: Board Member, Society for Medicinal Plant Research ANR Research Committee (2015-2018) OECD Scholarship (2005-2006) Advising & Grants: Active in mentoring graduate students in natural products and biotechnology projects. Leads EU-funded initiatives on microbial diversity exploitation. Collaborates internationally on marine invertebrate chemistry and cosmeceutical development. Labs/Teams: Coordinates the Pharmacognosy and Natural Products Chemistry lab at University of Athens, focusing on metabolomics and bioactive compound discovery pipelines.
Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.
Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.
