Özüm Asirim is a Researcher at the Technical University of Munich (TUM) under the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek. Her work focuses on computational photonics , quantum optics , and nonlinear optical phenomena , particularly in micro-resonators and semiconductor devices. Education: Ph.D. in Electrical Engineering from Middle East Technical University (Ankara, Turkey). Research spans optical parametric amplification , Fourier domain mode-locked lasers , self-phase modulation , and machine learning applications in photonics . Her studies include optimizing gain factors, enhancing harmonic generation, and modeling supercontinuum sources via carrier injection. Recent publications (2019–2023) highlight interdisciplinary approaches, merging photonics with computational finance and nonlinear dynamics . She contributes to EU Project QOMBS and teaches courses like Python for Engineering Data Analysis and Quantum Engineering and Machine Learning seminars. Collaborations include Prof. Christian Jirauschek (TUM), Prof. Mustafa Kuzuoğlu (Middle East Technical University), and teams in computational photonics and quantum optics. Her work impacts semiconductor physics , laser technology , and adaptive optical systems .
Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.
Henry D. Pfister is the Addy Family Professor of Electrical and Computer Engineering at Duke University, with a secondary appointment in Mathematics. He holds affiliations with the Pratt School of Engineering and the Duke Quantum Center. His research focuses on information theory, error-correcting codes, quantum computing, and machine learning applications in communications. Pfister earned his Ph.D. from UC San Diego and has held prior roles at Texas A&M University, École Polytechnique Fédérale de Lausanne, and Qualcomm. Education: Ph.D. in Electrical Engineering, UC San Diego (2003); M.S. degrees in Public Policy and Environmental Management from Duke University; J.D. and additional degrees from UNC Chapel Hill. Research interests include Reed-Muller codes, quantum error correction, neural decoders for DNA storage, and capacity-achieving coding schemes. Recent work highlights include proving Reed-Muller codes achieve capacity on binary-erasure channels and developing quantum-enhanced classical communication protocols. Publications span topics like polar codes for quantum channels, belief-propagation algorithms, and neural network-based decoding. Notable grants include NSF funding for DNA storage coding and quantum simulation projects. Pfister has advised over 20 graduate students and is a recipient of the STOC Best Paper Award and NSF CAREER Award.
Dr. Sajedul Talukder is an Assistant Professor in the Department of Computer Science at The University of Texas at El Paso (UTEP), directing the SUPREME Lab. He holds a Ph.D. in Computer Science from Florida International University (2019) and has held prior faculty positions at Southern Illinois University (2021-2024) and Pennsylvania Western University (2019-2021). Education: Ph.D. in Computer Science, Florida International University (2019) M.S. in Computer Science, Florida International University (2018) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2014) Research Interests: Focuses on cybersecurity, privacy-enhanced machine learning, and AI-driven solutions for social good. Key areas include: Security and privacy in online systems Abuse detection in social networks Quantum security and distributed systems Federated learning for healthcare and industrial IoT His work emphasizes practical applications like AI for nuclear plant cybersecurity and mitigating sockpuppet attacks. Recent Article Trends: Recent publications highlight advancements in federated learning frameworks (e.g., SAFARI, FLASH), context-aware emotion detection (CAMERA), and AI-driven nuclear facility security (ContextGPT, AML-TIN). These contributions address privacy, scalability, and real-time threat monitoring. Awards & Grants: $500K NRC grant (2024) for AI-driven nuclear plant cybersecurity NSF CISE CRII Award ($157K) for sockpuppet defense IMEC/NIST grant ($99K) for industrial IoT security Best Paper Awards (ICEEICT 2014, ACM SAC 2022) Advising & Labs: Mentored over 40 students (K-12 to Ph.D.), including 2 recent M.S. graduates. Leads SUPREME Lab and affiliated with UTEP AI Institute and NSF IDEAS Center. Active in program committees for ASONAM, ICWSM, and CHI.
Xiaoqing Pan is a Professor and Henry Samueli Endowed Chair in Engineering at the University of California, Irvine, with dual appointments in the Department of Materials Science and Engineering and the Department of Physics and Astronomy. He serves as Director of the Irvine Materials Research Institute (IMRI) and the Center for Complex and Active Materials (NSF MRSEC). A renowned electron microscopy expert, Pan has developed advanced transmission electron microscopy (TEM) techniques for atomic-scale material characterization. Ph.D., Universität des Saarlandes, Germany (1991) His research focuses on atomic-scale structure-property relationships in oxide heterostructures, ferroelectrics, nanocatalysts, and 2D functional materials. Pan leads development of novel 4D-STEM and momentum-resolved vibrational electron microscopy methods to study single-atom catalysts and complex oxides. With over 400 high-impact publications in Nature , Science , and Nature Materials , his work has been recognized by major fellowships and awards from the American Ceramic Society, American Physical Society, and National Science Foundation. Pan's recent work includes: Atomic-scale analysis of grain boundary phonon anisotropy Advances in FeSe/SrTiO 3 interface electron-phonon coupling Plastic waste upcycling through carbon intermediate interception Control of metal-support interactions in photocatalysts Strain engineering in high-entropy oxide films His laboratory at UCI represents the forefront of materials characterization technology development.
Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.
Dr. Guillem Müller Rigat is a Postdoctoral Researcher at the Institute of Photonic Sciences (ICFO), working in the Quantum Optics Theory research group. He holds a PhD in Photonics from the Universitat Politècnica de Catalunya (Spain). His research focuses on quantum information theory and quantum optics, with a particular emphasis on entanglement, Bell inequalities, and many-body quantum systems. He explores topics such as quantum resource certification, symmetry in quantum states, and applications of machine learning in quantum tomography. Müller Rigat’s work bridges fundamental quantum theory and experimental feasibility, addressing challenges in quantum metrology, nonlocality, and chaos. His recent studies include developing methods to infer quantum correlations from observable data and enhancing protocols for entanglement detection in complex systems. He contributes to advancing theoretical frameworks for certifying quantum systems with minimal experimental resources. He is affiliated with ICFO’s Quantum Optics Theory group, where he collaborates on projects involving Bell inequalities, spin-nematic squeezing, and quantum Fisher information. Despite his postdoctoral focus, he actively publishes in high-impact journals, with a strong emphasis on interdisciplinary approaches combining quantum foundations and applied quantum technologies.
Kevin P. O'Brien is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE). He leads the Quantum Coherent Electronics (QCE) group, focusing on advancing superconducting quantum computing, microwave quantum optics, and quantum metamaterials. His research explores nonlinear and quantum-mechanical light-matter interactions using superconducting circuits, aiming to improve quantum technologies like qubits and amplifiers. Education: B.S. in Physics from Purdue University, Ph.D. in Physics from UC Berkeley, and postdoctoral research at UC Berkeley developing superconducting quantum processors. His group collaborates with MIT Lincoln Laboratory and institutions nationwide. Research Interests: Quantum computing hardware, superconducting circuits, parametric amplifiers, qubit measurement systems, and metamaterials for quantum applications. His work emphasizes scalable architecture design, noise reduction, and novel device concepts. Key projects include directional qubit readout resonators, Floquet-mode amplifiers, and quarton couplers for ultrafast readout. The group actively engages in training graduate students and postdocs, emphasizing open collaboration and problem-solving in quantum technologies. Advising & Grants: Supervises a dynamic team of graduate students and postdocs. Students like Bright Ye and Kaidong Peng have contributed to award-winning projects. The group receives support through fellowships (e.g., Jin Au Kong, NSF GRFP) and industry partnerships. Labs/Teams: Quantum Coherent Electronics Group at MIT, collaborating on quantum device fabrication, theoretical modeling, and experimental validation of quantum systems.
Hatice Altug is a Full Professor at EPFL's Institute of Bioengineering within the School of Engineering, where she leads the Bionanophotonic Systems Laboratory. Her research integrates nanophotonics, plasmonics, and microfluidics to develop advanced biosensors for real-time molecular diagnostics. She holds dual roles in EPFL's doctoral programs and academic committees. Education: PhD in Applied Physics, Stanford University (2000-2007) B.S. in Physics, Bilkent University (1996-2000) Her research centers on creating label-free, high-sensitivity optical biosensors using nanophotonic technologies. Key innovations include dielectric metasurfaces for mid-infrared spectroscopy, AI-enhanced detection platforms, and portable nanoplasmonic imagers for point-of-care diagnostics. Her work bridges fundamental light-matter interactions with clinical applications like sepsis monitoring and cancer biomarker detection. Her publications emphasize nanophotonic biosensor design, metasurface applications, and single-cell analysis. Recent trends show increased focus on AI integration, vibrational spectroscopy, and wafer-scale manufacturing for clinical translation. Awards & Honors: Optical Society Fellow (2020) Presidential Early Career Award (PECASE, 2011) ERC Consolidator Grant (2016) IEEE Photonics Society Young Investigator Award (2011) She mentors numerous PhD students and leads interdisciplinary teams developing optofluidic platforms. Her laboratory pioneers nanoplasmonic microarrays and collaborates globally on projects like neurodegenerative disease biomarker detection. She co-directs EPFL's doctoral program in photonics and champions women in STEM through executive roles in diversity initiatives.
Amy C. Foster is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Whiting School of Engineering. She leads the Integrated Photonics Laboratory, focusing on nanoscale design of silicon-based photonic devices for optical communication systems and security applications. Her work emphasizes CMOS-compatible fabrication techniques for integrated photonic devices with applications in sensing, imaging, and high-speed processing. Education: BS (Electrical Engineering, University at Buffalo, 2003); MS & PhD (Electrical and Computer Engineering, Cornell University, 2007 & 2009) Postdoctoral Research: Cornell University (2009–2010) Professional Roles: Associate Editor of Optics Express (OSA), Chair of OSA Frontiers in Optics Committee, IEEE Photonics Conference Committee Member Her research interests center on silicon photonics, nonlinear optics, and photonic physical unclonable functions (PUFs). Key areas include developing secure authentication systems using chaotic microcavities, optimizing high-index materials like NbTiOx for visible light photonics, and advancing integrated photonic interconnects for multi-layer systems. Recent work explores machine learning-resistant PUFs and parametric nonlinear effects in sputtered metal oxides. Foster's publications highlight advancements in optical frequency combs, autofluorescence analysis of waveguides, and GHz-rate optical parametric amplifiers. Her lab’s innovations address challenges in quantum photonics, secure communications, and ultra-low-power signal processing. Awards: 2016 Johns Hopkins Catalyst Award, 2012 DARPA Young Faculty Award Grants: IARPA, NSF, APL, DARPA Her lab develops cutting-edge photonic devices for applications in space communications, neural stimulation, and security. Current projects aim to enhance multi-layer photonic integration and leverage nonlinear effects for novel signal processing architectures.
Vladimir Bulović is a Professor of Electrical Engineering and Computer Science at MIT, holding the Fariborz Maseeh Chair in Emerging Technology. He serves as Founding Director of MIT.nano, a 20,000 m² nanofabrication and prototyping facility. His research focuses on nanoscale materials, renewable energy, and optoelectronics, with emphasis on scalable solar technologies and printed electronics. Education: B.S.E. and Ph.D. in Electrical Engineering from Princeton University. Research Interests: Development of thin-film photovoltaics (perovskites, organic PVs), energy-efficient optoelectronics, and advanced manufacturing techniques. His work bridges nanotechnology with real-world applications, such as transparent solar cells and flexible electronics. Key innovations include vapor transport deposition (VTD) for perovskite solar cells and scalable printed electronics. Publications: Over 250 articles (45,000+ citations) focus on perovskite materials, semiconductor fabrication, and optoelectronic device optimization. Recent trends emphasize machine learning-driven materials design and stability enhancement strategies for photovoltaics. Awards: MacVicar Fellowship (2018), Top 1% Highly Cited Researcher (2018) Advising & Grants: Co-founded Ubiquitous Energy, Kateeva, and QD Vision. Led projects on grid-edge solar solutions and MIT-Eni Solar Frontiers Center. Served as Associate Dean for Innovation and Director of MIT’s Innovation Initiative (2013–2018). Labs/Teams: Directs the Organic and Nanostructured Electronics Lab and oversees MIT.nano’s interdisciplinary research programs.
Dr. rer. nat. Thomas Hermann is a faculty member at Bielefeld University's Faculty of Engineering, leading the Ambient Intelligence Group and coordinating the Computer Science program. He specializes in sonification, auditory data science, and smart environments. Head of Ambient Intelligence Working Group Computer Science Program Coordinator Member of multiple academic advisory boards His research focuses on interactive sonification for biomedical applications, quantum systems, and smart environments. Key projects include ECG sonification for cardiac diagnosis, real-time auditory feedback in swimming, and sonic interfaces for AR cooperation. Recent publications span 2025 with Python-based sonification tools ( pya AGen ), quantum system sonification, and ST-elevation myocardial infarction monitoring. He contributes to open-access supplementary materials and interdisciplinary workshops. As a researcher , Hermann develops practical sonification frameworks like Panson for facial behavior analysis, CardioScope for portable ECG monitoring, and Base Cube One for smart environments. His work bridges academic research with industry applications.
Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. He was previously an Assistant Professor (Lecturer) at the same institution from 2020 to 2024 and a Departmental Lecturer at the University of Oxford until August 2020. He is also a Junior Research Fellow at University College, Oxford. His academic journey began with a Ph.D. from EPFL in 2018, where he was awarded the Google Ph.D. Fellowship in structured data analysis and a Ph.D. thesis distinction. His research centers on the design and implementation of data-analytics systems, drawing upon techniques from databases, programming languages, compilers, and machine learning. He develops high-performance systems such as SDQL.py, StructTensor, and VecHT, focusing on the compilation of data science workloads and optimization of tensor operations. His work bridges the gap between high-level abstractions and efficient execution, particularly in sparse and probabilistic computing domains. The recent publications highlight a strong trend in compiler-driven optimizations for data-intensive applications, including automatic differentiation, loop fusion, probabilistic programming, and domain-specific language (DSL) restaging. His research integrates machine learning for systems decisions and emphasizes reproducibility and performance. He has published consistently in top venues like PLDI, OOPSLA, SIGMOD, and CGO, reflecting sustained impact in programming languages and database systems. Dahl-Nygaard Junior Prize, 2025 Google Research Scholar Award, 2025 Most Influential Paper Award, GPCE 2024 Best Paper Award, GPCE 2017 Most Reproducible Paper Award, SIGMOD 2017 Google Ph.D. Fellowship, 2017 Amir Shaikhha has advised PhD students including Hesam Shahrokhi and has been nominated for Best Supervisor of the Year at the University of Edinburgh. He leads research projects that have received recognition and support through awards and grants, including the Google Research Scholar Award. He actively serves the community through program committees (e.g., GPCE, DBPL, DRAGSTERS), editorial roles, and peer review for premier journals. His leadership in organizing workshops and conferences underscores his role as a central figure in the programming languages and databases research communities. He leads a research group focused on compiler and database systems, with recent open-source releases such as StructTensor and VecHT. His team collaborates with researchers from institutions like MIT, EPFL, and TU Berlin, and he co-chairs workshops like Sparse@PLDI and DRAGSTERS. His lab emphasizes innovation in how data-intensive programs are compiled and executed efficiently across modern hardware.
Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.
Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .
