Franz Franchetti is the Kavčić-Moura Professor of Electrical & Computer Engineering at Carnegie Mellon University. He serves as Associate Dean for Research and Director of the Engineering Research Accelerator at CMU. Education: Ph.D. in Computational Mathematics (Vienna University of Technology, 2003) M.Sc. in Technical Mathematics (Vienna University of Technology, 2000) His research interests focus on automatic performance tuning and program generation for emerging parallel computing platforms , including multicore CPUs , GPUs , and 3DIC chip design . He leads the SPIRAL effort to automate highly optimized software libraries and explores domain-specific compiler transformations in HPC applications for smart grids and material sciences . Recent work extends SPIRAL to quantum computing . The scientific awards Franchetti has received include the Gordon Bell Prize (2006) , HPC Challenge Class II Award (2010) , and the CIT Dean's Early Career Fellowship (2013) . He and his students have won multiple Best Paper Awards at HPEC, DAC, and ISPA ACM TODAES Best Paper (2014) Student Research Competition wins (PACT 2024, CGO 2023) Franchetti has advised students like Richard Veras and Thom Popovici . He has secured significant grants from agencies such as DARPA, DOE, NSF, and industry partners (Intel, NVIDIA, Mercury). He co-founded SpiralGen, Inc. and holds leadership roles in organizations like ASciNA Western Pennsylvania and as Honorary Consul of Austria in Pittsburgh.
Timo Minssen is Professor of Law at the University of Copenhagen (UCPH) and the Founding Director of UCPH's Center for Advanced Studies in Bioscience Innovation Law (CeBIL). He also holds affiliations as an LML Research Affiliate at the University of Cambridge and an Inter-CeBIL Research Affiliate at Harvard Law School's Petrie-Flom Centre. With extensive expertise in Intellectual Property, Competition, and Regulatory Law, Minssen focuses on the legal aspects of emerging health and life science technologies, including genome editing, big data, artificial intelligence, and quantum technology. His educational background includes a German law degree (Staatsexamen) from Georg-August-University in Göttingen, and Swedish biotech & IPR related LL.M., LL.Lic., and LL.D. degrees from Lund University and Uppsala University. His PhD thesis on the patentability of biopharmaceutical technology in the US & Europe received the prestigious Swedish King Oscar award. 2024: TUM Global Visiting Professor, Technical University of Munich (Germany) 2016: Visiting Research Fellow, University of Cambridge (UK) 2014: Visiting Research Fellow, University of Oxford (UK) 2013-14: Visiting Scholar, Harvard Law School (US) 2012: LL.D. - Doctor of Laws (Swedish "juris doktor"), EU/US patent law, Lund University, Sweden Minssen's research spans AI & Big Data in Health & Life Sciences, Sustainable and responsible innovation & tech transfer, Pharmaceutical-, Life Science- & Biotech Law, Comparative European & US Patent Law, Intellectual Property Law & Open Innovation, and EU Competition- & US Antitrust Law. His work addresses legal issues throughout the lifecycle of health and life science products and processes, from R&D regulation to technology transfer and commercialization. His extensive publication record includes 7 books and over 200 articles and book chapters published in leading journals such as Science, Nature Biotechnology, JAMA, and Harvard Business Review. His research has been featured in The Economist, Financial Times, and other major media outlets. Minssen's recent work shows a strong focus on AI regulation, quantum technology law, and data governance in health contexts, reflecting the evolving landscape of technology and law. Scientific Awards and Recognition King Oscar award for best Jur. Dr. thesis (2014) Jorcks Fonds Forsknings Pris (Jorck's Foundation Research Prize) (2017) Awapatent Research Prize (2009) Max Planck Research Scholarship (2005) Visiting Scholar appointments at Harvard Law School, University of Oxford, and University of Cambridge Recipient of a Novo Nordisk Foundation Grant for a "Collaborative Research Program in Biomedical Innovation Law" (2018) As an advisor, Minssen serves international organizations including the WHO, WIPO, and EU Commission. He has supervised numerous PhD students in areas including pharmaceutical law, biotechnology patents, and antimicrobial resistance. His current research projects include the Novo Nordisk Foundation's International Collaborative Bioscience Innovation & Law (Inter-CeBIL) Programme (50 million DKK), CLASSICA: EU Horizon Project on AI-assisted surgery, and AI@Care: Law and Ethics and Algorithmic Bias in Healthcare. Minssen leads the Center for Advanced Studies in Bioscience Innovation Law (CeBIL), which serves as a hub for interdisciplinary research on the intersection of law, technology, and innovation in the health and life sciences. The center collaborates with institutions worldwide to address pressing legal challenges in emerging technologies.
Stanislaw Jarecki is an Associate Professor of Computer Science at the Donald Bren School of Information and Computer Sciences (ICS) at the University of California, Irvine (UCI). He joined UCI in 2003 after earning his Ph.D. in Computer Science from MIT in 2001 under Prof. Shafi Goldwasser. His research focuses on applied and distributed cryptography, with significant contributions to threshold cryptography, secure computation, and password-authenticated key exchange (PAKE) protocols such as OPAQUE, which secures 2 billion WhatsApp users. He also worked at Intertrust’s StarLab and Stanford’s applied cryptography group under Prof. Dan Boneh. Education: Ph.D., Massachusetts Institute of Technology, 2001. Research Interests: Distributed cryptography, secure multi-party computation, privacy-preserving protocols, threshold security, blockchain applications, and efficient cryptographic primitives. His work emphasizes practical solutions for real-world systems, including protocols resilient to server compromises and scalable encryption methods. Awards: 2023 IACR Fellow for contributions to distributed cryptography and efficient secure computation. Advising and Grants: Jarecki’s research has been supported by grants including NSF SaTC programs. He advises students on cryptographic protocol design and security mechanisms. His work on OPAQUE and CHIC protocols exemplifies his focus on bridging theoretical cryptography with practical implementation. Labs/Teams: Leads a research group focused on applied cryptography and security within ICS at UCI, collaborating on projects like secure computation, privacy-preserving data processing, and cryptographic protocol development.
Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.
Yi Ding is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, where they lead the STYLE (Sustainable computing Systems and LEarning) Lab. Dr. Ding joined Purdue in August 2023 after completing a postdoctoral fellowship at MIT CSAIL as an NSF Computing Innovation Fellow, mentored by Michael Carbin. During their postdoc, they also held a visiting position at Meta Infra Data Center to improve server maintenance efficiency in hyperscale datacenters. They received their Ph.D. in Computer Science from the University of Chicago, advised by Henry Hoffmann. Dr. Ding's research focuses on computer systems, computer architecture, and AI/ML, with strong emphasis on applications in sustainability and healthcare. Their work spans sustainable computing, including energy efficiency in datacenters and LLM serving, as well as healthcare applications such as mental health prediction and EEG analysis. Their recent publications demonstrate a strong trend toward addressing environmental impacts of computing, particularly in datacenters and AI systems, while also exploring innovative healthcare applications. Their research bridges systems, sustainability, and health domains, creating novel solutions for pressing societal challenges. Dr. Ding has received notable recognition including the Seed Funding for High-Impact Review Papers (2024), the Meta Research Award (2021), and was selected as a Computing Innovation Fellow by CRA/CCC (2020). Seed Funding for High-Impact Review Papers (2024) with Inez Hua 1st Place in Research Talk in CoE at Fall 2024 Undergrad Research Expo (awarded to Gavin Fortwendel) 2020 Computing Innovation Fellow by CRA/CCC Meta Research Award on Statistics for Improving Insights, Models, and Decisions (2021) Dr. Ding is actively recruiting self-motivated Ph.D. students interested in AI/ML systems research. They have secured funding for undergraduate research projects through DUIRI, focusing on sustainable AI computing and energy use in training autonomous vehicles. Their lab collaborates with various institutions including MIT, Meta, and interdisciplinary partners at Purdue. The STYLE Lab under Dr. Ding's leadership is actively engaged in multiple research initiatives addressing sustainable computing and healthcare applications, with strong industry connections and funding support from both internal university sources and external partners.
Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.
Mingsheng Ying is a Distinguished Professor and Research Director of the Centre for Quantum Computation and Intelligent Systems (QCIS) at the Faculty of Engineering and Information Technology, University of Technology Sydney, Australia. He also holds the position of Cheung Kong Professor at the State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China. Professor Ying graduated from the Department of Mathematics, Fuzhou Teachers College, Jiangxi, China, in 1981. His primary research interests span quantum computation (particularly quantum programming and model-checking quantum systems), programming theory and formal methods, and the foundations of artificial intelligence (focusing on logic and uncertainty). As an author of the books "Foundations of Quantum Programming" (Elsevier - Morgan Kaufmann 2016) and "Topology in Process Calculus: Approximate Correctness and Infinite Evolution of Concurrent Programs" (Springer-Verlag, 2001), he has published over 100 papers in top international journals and conferences. His recent research publications demonstrate a strong focus on quantum programming languages, verification techniques for quantum systems, and the theoretical foundations of quantum computation. The trend in his work shows increasing emphasis on formal verification methods for quantum systems, particularly model-checking techniques for quantum Markov chains and quantum processes. His research bridges theoretical computer science with quantum information theory, creating frameworks for reliable quantum software development. Editorial Board, Artificial Intelligence, Elsevier, Amsterdam Editorial Board, Fuzzy Sets and Systems, Elsevier, Amsterdam Vice President, International Fuzzy Systems Association (elected in 2005) Program Chair, IFSA 2005, World Congress of International Fuzzy Systems Association Chairman, Chinese Association of Fuzzy Systems and Mathematics Professor Ying has secured significant research funding including multiple Australian Research Council Discovery Projects such as "Model-checking quantum Markov chains: towards verification techniques for quantum cryptographic systems" (2013-2015) and "Process algebra approach to distributed quantum computation and secure quantum communication" (2011-2013). His work has also received funding from the National Natural Science Foundation of China and Tsinghua University. At QCIS, he leads research in quantum software theory and methodology, with applications in quantum cryptography and secure communication.
Elette Boyle is an Associate Professor at Reichman University (IDC Herzliya) and a Senior Scientist at NTT Research . She holds a Ph.D. in Mathematics from MIT (advised by Shafi Goldwasser and Yael Tauman Kalai) and an undergraduate degree from Caltech . Education Ph.D. in Mathematics, MIT B.S. in Mathematics, Caltech Her research focuses on cryptographic solutions for secure data processing , particularly in secure multi-party computation , function/homomorphic secret sharing , and distributed point functions . Recent work explores topology-hiding communication , memory checking complexity , and sublinear-communication MPC . Key trends in her publications include: Advancements in Function Secret Sharing for branching programs and sparse vectors. Efficient Secure Multi-Party Computation protocols with preprocessing. Information-theoretic and computational Topology-Hiding Broadcast schemes. Optimized Oblivious Transfer with constant computational overhead. Scientific Awards European Research Council (ERC) Award Israeli Science Foundation (ISF) Grant United States Air Force Office of Scientific Research (AFOSR) Grant Google Research Scholar Award International Association for Cryptologic Research (IACR) Recognition As Director of the Foundations & Applications of Cryptography (FACT) Research Center , she leads collaborative work with institutions like Technion Israel , Cornell University , and NTT Research . Her students include Pierre Meyer (Ph.D.) , Matan Hamilis (Ph.D.) , and D'or Banon (MSc.) .
Henry Corrigan-Gibbs is an Assistant Professor in MIT's Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads research in computer security, cryptography, and privacy-preserving systems. His work focuses on practical cryptographic systems that empower users while maintaining strong security guarantees. Notable contributions include the Tiptoe private search engine, Prio for privacy-preserving data aggregation, and Larch for secure authentication. His research has influenced industry standards at Apple, Google, and Mozilla, and has been recognized with awards such as the Best Young Researcher Paper at Eurocrypt and the Caspar Bowden Award. Education: PhD in Computer Science (Stanford University, advised by Dan Boneh), Postdoc at EPFL (hosted by Bryan Ford). B.S. in Computer Science from Yale University. Research Interests: Private Information Retrieval, Secure Authentication, Cryptographic Systems, Privacy-Preserving Analytics, and Hardware Security. His lab collaborates with PDOS and CSS research groups at MIT and co-hosts the MIT Security Seminar series. Grants and Funding: Supported by industry and government agencies (details in paper acknowledgments). Teaching roles include co-instructor for Applied Cryptography (6.5610) and Foundations of Computer Security (6.1600). Key Projects: Prio (used in iOS/Android), Tiptoe (private web search), Larch (backdoor-resistant authentication), and Whisper/Poplar systems for private data aggregation. His team includes postdocs, PhD students, and undergrad researchers working on cutting-edge cryptographic protocols.
Julia Kempe is a Silver Professor of Computer Science, Mathematics, and Data Science at New York University (NYU), holding joint appointments at the Courant Institute and the Center for Data Science (CDS). She serves as Director of the CDS and is on research leave at the CSD, ENS, Paris (2023–24). Her expertise spans interdisciplinary research in quantum computing, machine learning, and data science. She holds PhDs in Mathematics (UC Berkeley, 2001) and Computer Science (École Nationale Supérieure des Télécommunications, Paris, 2001), alongside advanced degrees in theoretical physics and mathematics from prestigious institutions in France and Austria. Research Interests: Data Science, Machine Learning (theoretical foundations and applications to physics), and past contributions to quantum computing. She focuses on robustness in machine learning models, adversarial examples, and interdisciplinary applications of physics-informed AI. Awards and Honors: Knight of the National Order of Merit (France, 2010), Femme en Or de la Recherche (France, 2010), ERC Starting Grant (2007, top-ranked in Europe), and numerous academic fellowships. She is a member of Academia Europaea (2018) and a Fellow of the Asia-Pacific Artificial Intelligence Association (2022). Grants and Leadership: Principal investigator of NSF NRT grants for CDS PhD programs, co-PI on NASA TCAN grants, and leader in NYU’s Senior Leadership Team. She designed NYU’s Data Science undergraduate programs and expanded interdisciplinary collaborations in machine learning and quantum computing. Labs and Teams: Directs the CDS, collaborates with the Courant Institute, and leads research initiatives in Paris. Her work bridges theoretical computer science, physics, and applied data science, emphasizing interdisciplinary innovation.
Sergii Strelchuk is an Associate Professor of Computer Science at the University of Oxford, specializing in quantum computing and its applications. His research sits at the intersection of quantum information theory, computer science, and bioinformatics, with a focus on developing quantum algorithms for practical problems in genomics and beyond. Professor Strelchuk's primary research interests include quantum algorithms and their applications (particularly in bioinformatics), classical simulation methods for quantum computation, quantum complexity theory, and quantum learning theory. His work bridges theoretical quantum computing with practical applications, especially in the emerging field of quantum genomics and pangenomics, with significant implications for understanding human and pathogen genomes. His recent publications demonstrate a strong focus on applying quantum computing techniques to genomic data analysis, developing efficient fermion-qubit mappings for quantum simulation, and exploring fundamental aspects of quantum complexity theory. His research shows a clear trajectory toward making quantum computing practically applicable to biological data analysis and advancing our theoretical understanding of quantum computational models. Among his notable scientific achievements are: Royal Society University Research Fellow Leverhulme Early Career Fellow John and Delia Agar Research Fellow Professor Strelchuk leads several significant research projects including the Wellcome Leap "Human and Pathogen Quantum Pangenomics" project (2023-2026), which recently entered Phase 3 in April 2025, the EPSRC "Structure and symmetry in quantum verification" grant (2023-2025), and the "Quantum Algorithms for Quantum Field Theory" project (2022-2025). His research has attracted substantial funding for quantum computing applications in genomics. His work has received significant attention in both academic and popular science media, including coverage in Quanta Magazine and collaborations with institutions like the Sanger Institute to tackle complex genomic challenges using quantum computing approaches, with recent publicity about his leadership in the final phase of the Wellcome Leap-funded quantum pangenomics project.
Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.
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.
Immanuel Trummer is a Professor of Computer Science at Cornell University, specializing in database systems, query optimization, and applications of large language models (LLMs) and quantum computing. He leads research projects such as DB-BERT, UDO, and SkinnerDB, focusing on automated database tuning, adaptive query processing, and leveraging LLMs for code synthesis and system optimization. His research interests span quantum computing for database optimization, cost-efficient LLM utilization, and voice-based data exploration. Key contributions include developing systems like CEDAR for claim verification, CodexDB for LLM-driven code generation, and ThalamusDB for multimodal data querying. Trummer has received prestigious awards, including the NSF CAREER Award (2023-2028) and the Best Demonstration Award at BDA 2020. His work has been funded by NSF, Google, Huawei, and others, supporting projects like quantum-index selection and misinformation detection. He advises graduate students in database systems and teaches advanced courses such as CS 6320 (Advanced Database Systems) and CS 7390 (Seminar in Database Systems). His research lab hosts open-source tools like JoinGym and maintains extensive collaborations in industry and academia.
Jonas Bylander is a Professor at Chalmers University of Technology in the Department of Microtechnology and Nanoscience, specifically within the Quantum Technology division. He leads a research group focused on developing quantum computers using superconducting circuits.
