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David Bernal NeiraView profile

Assistant Professor

• Quantum Computing
• Optimization Algorithms
• Chemical Engineering
• +3 more

David Bernal Neira is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University, joined in August 2023. His research focuses on optimization algorithms, quantum computing, and computational methods applied to chemical and energy systems. He holds a PhD in Chemical Engineering from Carnegie Mellon University and degrees from Universidad de Los Andes, Colombia. Education: PhD in Chemical Engineering, Carnegie Mellon University (2017–2021) M.Sc. in Chemical Engineering, Universidad de Los Andes (2014–2016) B.A.Sc. in Chemical Engineering, Universidad de Los Andes (2010–2014) B.A.Sc. in Physics, Universidad de Los Andes (2011–2018) Research Interests: His work bridges classical and quantum optimization, with applications in process systems, energy, and chemical engineering. Key areas include mathematical modeling, quantum annealing, and federated learning. He develops algorithms and software tools, such as GDP and QUBO frameworks, and explores quantum computing for chemistry and combinatorial problems. Publications: Over 30 peer-reviewed articles since 2020, focusing on quantum optimization, federated learning, and algorithm design. Recent work emphasizes benchmarking quantum hardware and hybrid quantum-classical methods. Awards: Fellow, National Academies (2025, 2023) Best Talk Award (2022) Outstanding Teaching Assistant (2019) Advising & Grants: Supervises graduate students in quantum computing and optimization. Collaborates with NASA, USRA, and industry on quantum projects. Formerly an Associate Scientist at NASA QuAIL and Adjunct Professor at Carnegie Mellon. Labs & Teams: Leads the SECQUOIA Research Group at Purdue, focusing on systems engineering via quantum and classical optimization. Active in federally funded initiatives and industry partnerships.

• Quantum Physics
• Quantum Computing
• Quantum Control
• +3 more

Michael Biercuk is a Professor and Director of the Quantum Control Laboratory at the University of Sydney. He holds a dual role as founder and CEO of Q-CTRL, a quantum technology company. His academic work focuses on quantum control, quantum firmware, and trapped ion systems, with applications in quantum computing, quantum metrology, and quantum simulation. Biercuk earned his undergraduate degree from the University of Pennsylvania and his Master's and PhD from Harvard University. He has held research fellowships at NIST Boulder and advised agencies like DARPA. Education: BA (University of Pennsylvania), MSc/PhD (Harvard University) Research interests include developing quantum control techniques to suppress errors in qubits, engineering quantum firmware for scalable systems, and exploring trapped ion-based quantum sensors. His lab combines theory and experiment, leveraging ultra-high-vacuum systems and precision lasers to study quantum coherence. Awards include the 2021 Australian Financial Review 'Most Innovative Companies' recognition, 2015 Eureka Prize for Outstanding Early Career Researcher, and multiple innovation accolades. His work bridges academia and industry, with collaborations spanning Tsinghua University, MIT, and NIST. Key Projects: Quantum Control & Firmware, Quantum Simulation of Many-Body Systems, Quantum Metrology with Ions

• Computational Materials Science
• Defect Properties
• Mechanical Behavior of Materials
• +3 more

Dallas Trinkle is the Ivan Racheff Professor and Associate Head of the Department of Materials Science and Engineering at the University of Illinois, Urbana-Champaign. He holds a Ph.D. in Physics from Ohio State University (2003) and joined UIUC's faculty in 2006 after postdoctoral work at the Air Force Research Laboratory. Affiliations: Willett Faculty Scholar, NCSA Faculty Fellow, Center for Advanced Study Associate. Research Focus: Computational materials science, including defect properties (dislocations, point defects), mechanical behavior at atomic scales, and diffusion mechanisms using density-functional theory and machine learning. Key Achievements: NSF CAREER Award (2009), TMS Brimacombe Medal (2019), and over 150 peer-reviewed publications. His research group develops atomistic methods to study material defects and their impact on mechanical/thermal properties. Projects include magnesium alloy strengthening, hydrogen diffusion in palladium, and oxygen diffusion in titanium. They also create open-source computational tools like the magnesium solute database. Teaching: Courses on atomic-scale simulations, plasticity, and computational materials engineering (MSE 485, MSE 584, CSE 498 DM). Current Group:** Includes 7 Ph.D. students and a research scientist. Former advisees hold roles at institutions like MIT, Lawrence Berkeley Lab, and industry leaders such as MathWorks and General Motors.

José N. OnuchicView profile

Professor

• Biological Physics
• Theoretical Chemistry
• Protein Folding
• +3 more

José N. Onuchic is the Harry C. and Olga K. Wiess Chair of Physics and Professor of Chemistry and BioSciences at Rice University. His research focuses on integrating theoretical and computational approaches to understand biological systems, including protein folding, cancer metabolism, and chromatin architecture. He leads the Center for Theoretical Biological Physics and has pioneered concepts like protein folding funnels and energy landscape theory. His work spans scales from molecular interactions to cellular systems, with applications in cancer biology, viral mechanisms, and epigenetics. Education: B.S. in Electrical Engineering and Physics (1980, 1981), Universidade de São Paulo M.S. in Applied Physics (1982), Universidade de São Paulo Ph.D. in Chemistry (1987), California Institute of Technology Research Interests: Protein folding dynamics and energy landscapes Chromatin structure and transcription regulation Cancer cell metabolism and metastasis Quantum effects in biological systems Machine learning in biomolecular modeling Key Contributions: Developed the folding funnel theory to explain protein stability Investigated magnesium ion effects on RNA conformational switches Advanced computational models of interphase chromosome organization Explored SARS-CoV-2 spike protein mechanics for antiviral strategies Labs & Affiliations: Directs the Center for Theoretical Biological Physics at Rice University, fostering interdisciplinary research in theoretical biological physics.

Rajarshi RoyView profile

Professor

• Nonlinear Dynamics
• Optical Systems
• Chaos Theory
• +3 more

Rajarshi Roy is a Professor at the Institute for Physical Science and Technology (IPST) at the University of Maryland. His research focuses on nonlinear dynamics, chaos theory, and their applications in optical systems. He explores phenomena such as synchronization patterns, machine learning-driven network analysis, and quantum-optical systems. His experimental work includes studies on optoelectronic oscillators, delay-coupled systems, and photonic random number generation. His research interests span nonlinear dynamics , chaos theory , and optical systems . Key areas include synchronization of coupled oscillators, chimera states, and machine learning applications in network inference. He also investigates noise effects in photonics and quantum technologies, such as entanglement quality estimation in fiber systems. His recent work emphasizes combining machine learning with nonlinear dynamics to analyze complex systems. For instance, his studies on delayed dynamical systems and neuromorphic computing showcase innovations in network inference and reservoir computing. Experimental validations using optoelectronic setups highlight his interdisciplinary approach. Roy’s articles explore cutting-edge topics like entropy harvesting in photon-counting systems, topological control of synchronization, and suppression of optical scattering via chaos. His contributions bridge fundamental nonlinear science with technological applications in photonics and information systems.

Wei AiView profile

Assistant Professor

• Data Science for Social Good
• Machine Learning
• Causal Inference
• +4 more

Wei Ai is an Assistant Professor at the University of Maryland, affiliated with the College of Information (INFO) and the Institute for Advanced Computer Studies (UMIACS). His research focuses on data science for social good (DSSG), integrating machine learning, causal inference, and experimental design to address societal challenges in education, virtual collaboration, and quantum computing. He leads the Center for Educational Data Science and Innovation (EDSI) and has secured grants from the NSF, Gates Foundation, and Walton Family Foundation for projects like M-Powering Teachers and classroom quality assessment tools. Education: PhD in Information from the University of Michigan (advised by Qiaozhu Mei). Previous academic roles include teaching at the University of Michigan and Peking University in courses like Data Mining and Information Retrieval. Research Interests: Machine Learning and Causal Inference, AI for Education, Virtual Teams and Social Identity, Large Language Models for Social Applications. He has published in venues such as PNAS, Management Science, ACL, and the Web Conference. Grants: Major awards include a NSF grant on middle-grade math instruction analysis (with Min Sun) and a Gates Foundation grant for classroom dataset development (with Jing Liu). Labs/Teams: CLIP Lab member, collaborating on interdisciplinary projects with UMIACS and the Joint Quantum Institute. Prospective students: Open to mentoring PhD students through INFO and Computer Science programs. Actively supervises current students in education technology and quantum computing domains.

Niels AageView profile

Associate Professor

• Topology Optimization
• Acoustics and Vibroacoustics
• Biomechanics
• +5 more

Niels Aage is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on topology optimization, biomechanics, and multiphysics modeling, with applications in acoustic devices, biomedical implants, and microelectromechanical systems (MEMS). He holds roles such as Vice President of the International Society for Structural and Multidisciplinary Optimization (2023–2027). Education and Professional Background: Conducted a 5-month research visit at the University of Colorado, Boulder (USA) in 2010. Specializes in giga-scale numerical modeling, finite element methods, and topology optimization algorithms. Research Interests: Develops novel methods for topology optimization of fluidic, thermal, and acoustic systems. Explores applications in patient-specific spinal implants, metamaterials with vibroacoustic bandgaps, and nonlinear dynamic substructuring. His work integrates machine learning and reduced-order modeling for efficient simulation. Publications: Over 100 peer-reviewed articles, including recent contributions on connectivity promotion in topology optimization (2025), vibroacoustic metamaterial design (2025), and anatomically conforming spinal fusion cages (2024). Research emphasizes high-resolution modeling and bridging computational design with additive manufacturing. Scientific Awards: ISSMO Haftka Young Investigator Award (2021), Equinor Prize 2020, and Hyperion Innovation Excellence Award (2017). Recognized for contributions to structural optimization and computational mechanics. Advising and Grants: Supervises multiple PhD projects, including work on vibroacoustic shape optimization, quantum-opto-mechanical systems, and smart hearing aid modeling. Engages in collaborative projects funded by industry and academia. Labs/Teams: Collaborates with DTU’s Solid Mechanics group and industry partners on projects involving topology optimization, multiphysics simulation, and biomedical engineering. Active in international conferences and serves on editorial boards.

Christopher A. SuttonView profile

Assistant Professor

• Computational Materials Discovery
• Machine Learning in Materials Science
• Electronic Structure Calculations
• +3 more

Christopher A. Sutton is an Assistant Professor in the Department of Chemistry and Biochemistry at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on computational materials discovery, integrating machine learning and first-principles methods to design and understand functional materials for energy applications. Education : B.S., University of Central Arkansas, 2004–2008 Ph.D., Georgia Institute of Technology, 2009–2014 Research Interests : Sutton’s work emphasizes machine learning-driven materials design, electronic structure calculations, and high-throughput screening for energy storage, optoelectronics, and catalytic systems. His lab explores domains such as perovskites, battery materials, and defect engineering. Grants & Funding : DOE/HFTO (Co-PI): $1,000,000 NSF EPSCoR RII Track 1: $20,000,000 DOD/DEPSCOR: $600,000 Awards : Alexander von Humboldt Postdoctoral Fellowship (2016–2018) 67th Lindau Nobel Laureate Meeting Attendee (Chemistry) Recipient of multiple fellowships and scholarships Labs & Teams : The Sutton Lab at USC specializes in machine learning for quantum mechanical property prediction and computational materials discovery. Collaborations include experimentalists and AI experts to bridge theory and application.

Yannick SalaminView profile

Assistant Professor

• Quantum Optics
• Nonlinear Optics
• Photonics
• +3 more

Dr. Yannick Salamin is an Assistant Professor at CREOL, The College of Optics and Photonics at the University of Central Florida (UCF). His research focuses on quantum properties of light and nonlinear optical systems, particularly exploring quantum states of light, macroscopic quantum phenomena, and applications in quantum technology. His work utilizes optical parametric oscillators (OPOs) to generate and characterize quantum states, aiming to advance computing and metrology. Dr. Salamin holds a B.S. in Electrical Engineering from the University of Applied Sciences of Western Switzerland (2010), an M.S. from Zhejiang University (2014), and a Dr.Sc. from ETH Zurich (2019). His postdoctoral research at MIT under Prof. Marin Soljačić furthered his expertise in quantum nonlinear photonics. His research group develops systems to harness quantum vacuum noise for probabilistic computing and machine learning. Key areas include controlling nonlinear driven-dissipative systems, biasing quantum vacuums, and creating macroscopic probability distributions. Recent work includes generating large Fock states and squeezed states via nonlinear bound states in the continuum. Research Themes: Quantum vacuum engineering, nonlinear photonics, stochastic computing, and quantum metrology. Applications: Quantum computing, advanced sensors, and probabilistic machine learning. Dr. Salamin has received prestigious awards including the 2025 Ralph E. Powe Award, ABB Research Prize (2021), and the ETH Medal (2020). His lab includes three Ph.D. advisees and multiple undergraduate researchers. He collaborates widely, with publications in Nature Photonics , Science , and Proceedings of the National Academy of Sciences . Current projects emphasize scalable quantum systems and novel photonic devices.

Fahad AhmadView profile

Lecturer

• Machine Learning
• Cybersecurity
• Medical Informatics
• +6 more

Fahad Ahmad is a Lecturer in the School of Computing within the Faculty of Technology at the University of Portsmouth. He holds affiliations with the Portsmouth AI and Data Science Centre, Centre for Cybercrime and Economic Crime, and Portsmouth Centre for Advanced Materials and Manufacturing. His research focuses on machine learning applications in healthcare, cybersecurity, and quantum computing. He supervises PhD students in topics like quantum machine learning for securing IoT medical devices. Key research areas include: Medical imaging diagnostics using deep learning (e.g., echocardiograms, X-rays) Cybersecurity for financial systems and SDN-NFV networks Quantum key distribution for post-quantum security AI-driven health management systems Recent work emphasizes: Human activity recognition through machine learning Cancer subtype classification using RNA expression data Emotional empathy modeling in intelligent agents His articles span healthcare technology, cybersecurity frameworks, and hybrid AI architectures. He actively contributes to international conferences and journals, with over 60 peer-reviewed publications. Research collaborations include institutions in Pakistan and the UK.

Corrado LoglisciView profile

Assistant Professor

• Temporal Data Mining
• Temporal Learning
• Textual Data Mining
• +13 more

Corrado Loglisci is an Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research focuses on Temporal Data Mining , Machine Learning , and Quantum Computing , with applications in bioinformatics, medical informatics, and cybersecurity. He earned his Ph.D. in Computer Science with a thesis on temporal projection in longitudinal data. Research Highlights : Temporal Learning, Textual Data Mining, Quantum-Classical Hybrid Systems Collaborations : IRSTEA Research Institute (France), Aristotle University of Thessaloniki (Greece) His publications address dynamic network analysis , emotion detection in social media , and quantum-enhanced classification . He contributes to program committees and journal editorial work, including a special issue on Mining Complex Patterns in the Journal of Intelligent Information Systems . Notable contributions include the jKarma framework for change detection and studies on concept drift robustness in intrusion detection systems. His work spans European/National research projects, leveraging machine learning for tasks like mobile crowd sensing trustworthiness prediction (2020) and investor behavior analysis (2023-2025).

• Nanotechnology
• 2D Materials (van der Waals materials)
• Quantum Phenomena
• +5 more

Eui-Hyeok Yang is a tenured Professor in the Department of Mechanical Engineering at Stevens Institute of Technology, part of the Charles V. Schaefer School of Engineering and Science. He holds leadership roles, including Editor-in-Chief of Synthesis, Modeling and Characterization of Two-Dimensional Materials, and has held visiting professorships at Columbia University and Kyunghee University. Education: PhD (1996) and earlier degrees in Electrical/Control Engineering from Ajou University, followed by postdocs at Caltech's JPL (MEMS) and the University of Tokyo (MEMS via JSPS). Research focuses on 2D van der Waals materials, nanotechnology, and quantum phenomena, with emphasis on next-gen semiconductors and energy systems. Key honors include Fellowships from ASME, National Academy of Inventors, and IEEE Sensors Council. Awards span technical innovations at NASA JPL (e.g., piezoelectric microvalves, deformable mirrors) and academic recognition for 2D materials research. Over $11M in grants include Army-funded projects on quantum sensing and graphene-based devices. Professional service includes editorial roles for IEEE Sensors, Scientific Reports, and ASME journals, plus leadership in MEMS Division and IEEE Nanotechnology Council. Active in patents (18+), including innovations in smart polymers, 2D heterostructures, and graphene printing. Research interests span nanomaterials synthesis, quantum devices, flexible electronics, and environmental applications like oil cleanup. Courses taught include nanotechnology fundamentals, MEMS, and advanced materials engineering.

René PetersView profile

Professor

• Cooperative asymmetric catalysis
• Organometallic chemistry
• Stereoselective synthesis
• +3 more

René Peters is Professor and Head of the Examination Committee for Chemistry programs at the University of Stuttgart's Institute of Organic Chemistry. His research focuses on developing innovative cooperative asymmetric catalysis methods inspired by enzymatic processes, aiming to solve long-standing synthetic challenges with high efficiency and stereocontrol. Peters' group specializes in designing bifunctional and polyfunctional catalysts that activate both nucleophiles and electrophiles simultaneously through Lewis acid/base cooperativity. This biomimetic approach enables enantioselective transformations under mild conditions, including 1,4-additions, allylations, cycloadditions, and fluorinations. Recent work explores zwitterionic catalysts, palladacycle architectures, and gold nanoparticle systems for challenging bond formations. His publications demonstrate progressive refinement of cooperative catalyst systems, with recent advances in stereoretentive allylations, enantiodivergent syntheses, and computational-experimental integration for reaction optimization. The group maintains strong emphasis on practical methodologies with potential industrial scalability. Laboratory facilities support advanced synthesis, catalyst characterization (X-ray crystallography, NMR), and computational modeling. Peters leads collaborative projects on catalytic C-C bond formation and stereoselective fluorination. As Head of Examination Committee, he oversees chemistry curriculum development and assessment. Key innovations include planar chiral palladacycles for efficient enantioselective arylations and novel gold nanocatalysts for Stille couplings. Future work expands cooperative catalysis to underrepresented transformations and develops machine learning approaches for catalyst optimization.

Ali DehghantanhaView profile

Professor

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +7 more

Dr. Ali Dehghantanha is a Professor and Canada Research Chair in Cybersecurity and Threat Intelligence (Tier 2) at the University of Guelph's School of Computer Science. He founded the Cyber Science Lab, the Master of Cybersecurity and Threat Intelligence (MCTI) program, and the Canada Cyber Foundry. His expertise spans cybersecurity, AI, and critical infrastructure protection, with a focus on federated learning, threat intelligence, and enterprise risk management. Research Interests: Cybersecurity & AI Integration Federated Learning Security IoT and Industrial Cyber-Physical Systems Adversarial Machine Learning Cyber Threat Intelligence Automation His research emphasizes practical applications, such as securing agri-food systems, blockchain-enabled frameworks, and autonomous cybersecurity solutions. Notable achievements include the IEEE Outstanding Leadership Award (2021) and leadership in national cybersecurity initiatives. Recent Contributions: Recent work explores federated learning for privacy-preserving threat detection in IoT, AI-driven malware hunting, and autonomous systems for intrusion detection. His publications highlight interdisciplinary approaches to cybersecurity challenges in critical sectors like agriculture and transportation. Awards: Canada Research Chair (2020–present), IEEE Outstanding Leadership Award (2021). Labs & Initiatives: Cyber Science Lab: Pioneering research in AI-driven cybersecurity Canada Cyber Foundry: Bridging academic and industry cybersecurity innovation MCTI Program: Training next-generation cybersecurity professionals

Tara Maria BolandView profile

Research Fellow

• Computational Materials Science
• Materials Discovery
• Database Integration
• +3 more

Tara Maria Boland is a Postdoc in the Department of Physics at the Technical University of Denmark, specializing in Computational Atomic-scale Materials Design. She maintains an active research profile with significant contributions to materials science infrastructure. Her research spans critical areas of modern computational science: Computational Materials Science and Atomic-scale Modeling Materials Discovery and Design Database Integration and Data Exchange Systems API Development for Scientific Applications 2D Materials and Heterostructures Machine Learning Applications in Materials Science Boland's publication record reveals a dual focus on fundamental materials research and practical computational tools. Her work on the OPTIMADE API has established new standards for materials data exchange, while her contributions to GPAW provide essential infrastructure for electronic structure calculations. The research demonstrates strong international collaboration across 10+ institutions with notable media coverage. As part of DTU Physics' Computational Atomic-scale Materials Design group, Boland collaborates extensively on projects advancing computational methodologies for materials science, with particular emphasis on creating accessible research infrastructure for the global community.