Florian Kerschbaum is a Professor and NSERC/RBC Industrial Research Chair in Data Security at the Cheriton School of Computer Science, University of Waterloo. His research focuses on data security and privacy, applied cryptography, and confidentiality in data science. Research interests span data collection/preparation management, secure multi-party computation, homomorphic encryption, differential privacy, and machine learning robustness/privacy. His work develops cryptographic solutions for practical data management challenges in distributed systems.
Thibault Mayor is a Professor in the Department of Biochemistry and Molecular Biology and the Michael Smith Laboratories at the University of British Columbia (Vancouver). His research focuses on understanding how cells manage misfolded proteins, with implications for neurodegenerative diseases like Parkinson's and Alzheimer's. He holds academic affiliations with the Centre for High-Throughput Biology (CHiBi) and has been recognized with awards including the UBC Killam Teaching Award (2020). Education: BSc, University of Geneva, Switzerland (1997) PhD, University of Geneva & Max Planck Institute of Biochemistry, Germany (2001) Postdoctoral Fellow, California Institute of Technology (2002) Research Interests: Mayor's lab investigates protein homeostasis, ubiquitin-proteasome system dynamics, and the molecular mechanisms underlying protein aggregation in aging and disease. Projects include proteomic approaches to identify aggregation-prone proteins and develop microbial cell factories for protein production. Grants & Awards: CIHR Project Grant ($730K, 2018) Michael Smith Foundation Career Award (2012) UBC Killam Teaching Award (2020) Labs & Collaborations: The Mayor Lab is part of the Michael Smith Laboratories and collaborates with computational biologists like Jörg Gsponer. They maintain active partnerships in proteomics and systems biology, contributing to initiatives like the BC Proteomics Network.
Stefano Tessaro is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He holds the Paul G. Allen Career Development Professorship. His research focuses on cryptography, theoretical computer science, and computer security, emphasizing practical applications of cryptographic techniques. He co-leads the cryptography group at UW with Andrea Coladangelo and Huijia Lin, and is part of the theory group. Education: PhD in Computer Science from ETH Zurich (2010) Postdoctoral researcher at MIT CSAIL (2010-2014) and UC San Diego (2014-2019) Joined UW in 2019 as faculty Research Interests: His work spans theoretical cryptography, privacy-preserving systems, cryptographic protocols, and security foundations. He explores practical applications of cryptography, including secure protocols, post-quantum cryptography, and efficient cryptographic constructions. Articles Trends: Recent publications emphasize threshold signatures, memory-tight security proofs, lattice-based cryptography, and privacy-preserving systems. Key themes include adaptive security, formal verification, and efficiency improvements in cryptographic protocols. Awards: NSF CAREER Award Sloan Research Fellowship Hellman Fellowship Research awards from Cisco, JP Morgan, and Microsoft Grants & Advising: His research has been supported by NSF grants and industry partnerships. He advises students in cryptographic theory and applications, though no specific student names are listed in the provided text. Labs & Teams: Co-leads the UW cryptography group, collaborating on projects like LERNA (secure aggregation) and Twinkle (threshold signatures). Engages with interdisciplinary teams in theoretical computer science and security.
Shasha Chong is an Assistant Professor of Chemistry at the California Institute of Technology and a Ronald and JoAnne Willens Scholar. She earned her B.S. from the University of Science & Technology of China (2008) and Ph.D. from Harvard University (2014). Her research bridges chemistry, physics, and biology to investigate the molecular mechanisms of cellular processes, focusing on intrinsically disordered regions (IDRs) in transcription proteins. Research Focus: IDRs in transcriptional regulation, cancer biology, liquid-liquid phase separation, and single-molecule imaging techniques. Grants & Awards: CCE Innovation Award (2024), ALSF Innovation Grant, Mallinckrodt Research Grant, Margaret E. Early Medical Research Trust Grant. Collaborations: Caltech-City of Hope Biomedical Research Initiative Grant (2025). Teaching: Co-instructor for courses like Biochemistry Laboratory (Ch 11) and Advanced Topics in Biochemistry (BMB/Bi/Ch 174). Labs & Teams: Leads the Chong Laboratory at Caltech, focusing on interdisciplinary approaches combining single-molecule imaging, genome editing, and bioinformatics.
Dr. Mohamed Ibrahem is an Assistant Professor at the School of Computer and Cyber Sciences, Augusta University, USA. His research focuses on Cyber-Security, IoT Privacy, and Machine Learning applications in Smart Grids and Cyber-Physical Systems. He received a Ph.D. in Electrical and Computer Engineering from Tennessee Tech University in 2021 and has been recognized with prestigious awards including the IEEE Senior Member (2024) and the Eminence Award for Best Ph.D. Paper (2021). Education: Ph.D., Electrical and Computer Engineering, Tennessee Tech University (2021) Research Interests: Security and Privacy in IoT, Applied Cryptography, Privacy-preserving Machine Learning, Secure Federated Learning, Smart Grid Cyber-Security, and Traffic Analysis Attacks. Publications: His work emphasizes privacy-preserving techniques in smart grids, federated learning for anomaly detection, and robust countermeasures against cyber-attacks. Recent trends include leveraging deep learning for real-time fraud detection in AMI systems and developing efficient decentralized learning frameworks. Awards: IEEE Senior Member (2024) Top 2% Scientists List (Stanford/Elsevier, 2024) Eminence Award for Best Ph.D. Paper (Tennessee Tech University, 2021) Advising & Grants: Supervises graduate research in Dissertation and Master's Thesis projects. Active in securing grants for IoT and Cyber-Security initiatives. Collaborates with industry partners on edge computing and secure AMI networks.
Emma Brunskill is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Education. She holds a PhD in Computer Science from MIT (2009). Her research focuses on reinforcement learning, educational technology, and healthcare applications, aiming to develop AI systems that support human learning and decision-making. Notable projects include AI tutoring systems, policy evaluation methods, and behavior change interventions using large language models. Her work bridges theory and practice, addressing challenges in off-policy evaluation, fairness-aware decision making, and scalable educational tools. Brunskill has contributed to foundational research in reinforcement learning algorithms and their applications in real-world scenarios such as healthcare, education, and human-AI collaboration. She also leads initiatives to improve equity and efficiency in educational technologies through data-driven approaches. Brunskill's research has been supported by grants such as the NSF RI: Small grant for data-efficient reinforcement learning. She actively explores the ethical implications of AI systems, particularly in healthcare and education settings. Her recent work emphasizes leveraging large language models (LLMs) for personalized feedback and simulated training environments, as seen in studies like GPTCoach and LLM-based counselor upskilling.
Dr. Rajesh Bera is a Research Fellow at ICFO's Functional Optoelectronic Nanomaterials group specializing in quantum-confined nanostructures. His research examines ultrafast carrier dynamics, excitonic properties, and optoelectronic applications of nanomaterials including quantum dots, nanoplatelets, and hybrid nanostructures. Current investigations focus on intraband transitions in doped nanocrystals, orientation-dependent excitonic behavior in 2D materials, and charge transfer mechanisms in heterostructure devices. Work bridges fundamental photophysics with applications in photodetection, sensing, and energy conversion. Recent publications demonstrate expertise in time-resolved spectroscopy of quantum materials, nanomaterial synthesis via colloidal chemistry, and rational design of optoelectronic devices. Continually develops novel characterization methods to probe ultrafast processes at nanoscale interfaces.
Antony Franklin is an Associate Professor and Head of the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IITH), India. He leads the Networked Wireless Systems (NeWS) Lab and is actively involved in 5G research, wireless networks, and cybersecurity. He earned his Ph.D. from IIT Madras in 2010. Education: Ph.D. in Computer Science and Engineering, IIT Madras, 2010 Research Interests: His research spans wireless networks , 5G systems , mobile edge computing , low-latency transport protocols , and cybersecurity . He focuses on next-generation mobile systems, especially 5G, aiming to meet the demands of ultra-low latency, high data rates, and quality of experience for services like IoT, autonomous driving, and VR. He has been involved in developing testbeds and prototypes to validate real-world performance of 5G technologies, including network slicing, LTE-WiFi integration, and edge computing. Scientific Awards: Best Academic Demo Award, IEEE COMSNETS 2018 Second Best Paper Award, IEEE ANTS 2017 Grants & Projects: Network Slice Life-cycle Management for 5G Mobile Networks – SPARC, MHRD (2019–2021) DNS/IPv6 for IoT Security – NASSCOM (2018–2019) CCRAN: Energy Efficiency in Cloud RAN – Intel India (2018–2021) End-to-End 5G Test-Bed – DoT, GoI (2018–2021) M2SMART Smart Cities – SATREPS (2018–2023) Ultra-Reliable Low Latency Protocols – STINT, Sweden (2017–2018) Low Latency 5G Protocols – SERB ECR (2016–2019) Lab & Team: He leads the NeWS Lab (Networked Wireless Systems) at IITH, which focuses on 5G, wireless networks, and IoT systems. The lab is actively involved in developing real-world testbeds and publishing in top-tier conferences and journals.
Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling
Ben Fisch is an Assistant Professor of Computer Science at Yale University's School of Engineering & Applied Science. He is also the co-founder of Espresso Systems, a company focused on blockchain infrastructure. His research focuses on privacy and verifiability in decentralized systems like Bitcoin and Ethereum, with applications in digital finance and healthcare. Dr. Fisch received his B.A. from the University of Pennsylvania and completed his Ph.D. at Stanford University, where he worked with Dan Boneh in the applied cryptography research group. His educational background provided the foundation for his work at the intersection of cryptography, distributed systems, and economics. His research centers on leveraging cryptographic tools such as succinct non-interactive zero-knowledge proofs (zk-SNARKs), private information retrieval, and homomorphic encryption to address challenges in verifiable computation, verifiable storage, and verifiable fairness. He has made significant contributions to verifiable delay functions (VDFs) and proofs of replication, which have been adopted by major blockchain projects including Ethereum 2.0, Chia, and Filecoin. His work on Filecoin's Proofs of Replication has helped the network reach over 1.5 exabytes of storage capacity. His publication record shows a clear trend toward increasingly sophisticated cryptographic protocols for blockchain applications, with recent work focusing on data availability for Bitcoin rollups, efficient folding schemes for pairing-based arguments, and privacy pools with proof-carrying disclosures. His research bridges theoretical cryptography with practical implementations that have real-world impact in decentralized systems. His notable recognition includes: Best Paper Finalist at ACM CCS 2017 for 'Iron: Functional Encryption using Intel SGX' Dr. Fisch's research has led to significant technology transfer, most notably with his work on Verifiable Delay Functions (VDFs) sparking a multimillion dollar industry initiative through the VDF Alliance. His research on Proofs of Replication forms the basis of Filecoin's incentive layer and consensus protocol. His newer SNARK system Basefold is being used by several commercial products. He maintains active collaborations across academia and industry, with publications spanning top conferences in cryptography and security. As co-founder of Espresso Systems, Dr. Fisch leads a team developing next-generation blockchain infrastructure, particularly focusing on sequencing layers for rollups. His work bridges academic research with practical implementation, ensuring that theoretical advances in cryptography find real-world applications in decentralized systems.
David S. Eisenberg is a Professor of Chemistry and Biochemistry and Biological Chemistry at the University of California, Los Angeles, where he also serves as Director of the UCLA-DOE Institute for Genomics and Proteomics and as an HHMI Investigator. His research focuses on protein interactions, particularly the structural basis for conversion of normal proteins to the amyloid state and conversion of prions to the infectious state. Dr. Eisenberg earned his undergraduate degree in biochemical sciences from Harvard College and his D.Phil. degree in theoretical chemistry from Oxford University on a Rhodes Scholarship. His postdoctoral research was on ice and water with Walter Kauzmann at Princeton and in protein crystallography with Richard Dickerson. He joined the UCLA faculty after his postdoctoral studies. Dr. Eisenberg and his research group focus on protein interactions in amyloid and prion diseases. These diseases involve protein aggregation where normal functional proteins convert to abnormal aggregated forms. Systemic amyloid diseases like dialysis-related amyloidosis result from fiber accumulation until organ failure, while neurodegenerative diseases like Alzheimer's, Parkinson's, ALS, and prion conditions appear to be caused by smaller oligomers. In 2005, his team determined the atomic-level structure for the amyloid fiber spine, revealing a 'steric zipper' of two parallel beta sheets packed across a dry interface. Since then, they've determined approximately 90 amyloid spines from 15 disease-related proteins. In 2010, they identified the structure of a toxic amyloid-related oligomer consisting of six anti-parallel beta strands forming a cylindrical barrel. His recent publications demonstrate continued innovation in amyloid research, with focus areas including structural prediction of amyloid formation, mechanisms of tau fibril disassembly in Alzheimer's disease, cryo-EM analysis of amyloid polymorphism, and structure-based design of inhibitors for amyloid toxicity. His work integrates computational, structural, and biochemical approaches to understand protein aggregation across multiple disease contexts. Dr. Eisenberg has received numerous prestigious awards and honors: National Academy of Sciences Member American Philosophical Society Member Institute of Medicine Member Howard Hughes Medical Institute Investigator Biophysical Society Emily M. Gray Award Harvard Westheimer Medal UCLA Seaborg Medal Technion - Israel Institute of Technology Harvey Prize in Human Health As Director of the UCLA-DOE Institute for Genomics and Proteomics and an HHMI Investigator, Dr. Eisenberg leads significant research initiatives in protein structure and aggregation. His laboratory combines X-ray crystallography, bioinformatics, and biochemical techniques to investigate protein interactions, with particular emphasis on amyloid-forming proteins and their role in disease. The Eisenberg Lab, located in Boyer Hall at UCLA, maintains an active research program investigating the structural basis of protein aggregation. The lab continues to build on its landmark discoveries of amyloid structures while exploring new frontiers in understanding protein misfolding diseases and developing potential therapeutic interventions.
Ralph H. Colby serves as Professor of Materials Science and Engineering and Chemical Engineering at Pennsylvania State University's College of Earth and Mineral Sciences, holding the Corning Faculty Fellowship. His research focuses on molecular-level dynamics in complex fluids, particularly polymers, ionomers, and liquid crystalline systems. With over 130 publications and authorship of the textbook Polymer Physics (2003), he directs an active research program examining structure-property relationships in soft matter. B.S. in Materials Science and Engineering, Cornell University (1979) M.S. in Chemical Engineering, Northwestern University (1983) Ph.D. in Chemical Engineering, Northwestern University (1985) Professor Colby's research spans polymer physics, rheology, and materials for energy applications. His group employs mechanical rheology, dielectric spectroscopy, and scattering techniques to investigate ion transport in single-ion conductors for batteries, dynamics of glass-forming liquids, and self-assembly in polyelectrolyte systems. Current work emphasizes structure-property relationships in ionomers, liquid crystalline polymers, and branched architectures. Analysis of recent publications reveals consistent focus on ionomer membranes for energy applications, processing-structure relationships in advanced polymers, and fundamental dynamics of complex fluids. Key trends include increasing integration of computational modeling with experimental characterization, expansion into sustainable materials processing, and growing emphasis on applications in battery technology and biomedical materials. Penn State Faculty Scholar Medal for Outstanding Achievement (2022) Bingham Medal, Society of Rheology (2012) American Chemical Society Fellowship Corning Faculty Fellowship in Materials Science and Engineering Professor Colby leads multiple federally funded projects including NSF's 'Fundamental Studies of Flow-Induced Polymer Crystallization' and DOE's 'Conduction mechanisms and structure of ionomeric single-ion conductors'. His group maintains strong industry partnerships with Corning Incorporated and participates in interdisciplinary initiatives like the Penn State Intercollege Graduate Degree Program in Materials Science and Engineering. Current research includes collaborations on breast cancer adherence interventions in Rwanda and conjugated polymer development for flexible electronics. The Colby Research Group operates specialized facilities for rheological characterization, dielectric spectroscopy, and X-ray scattering at Penn State's Materials Research Institute. The team maintains active collaborations with national laboratories and international research groups, focusing on translating fundamental polymer physics discoveries into practical applications for energy storage and advanced manufacturing.
Tian Li is an Assistant Professor of Computer Science at the University of Chicago. She holds a Ph.D. in Computer Science from Carnegie Mellon University and undergraduate degrees in Computer Science and Economics from Peking University. Her research focuses on distributed optimization, federated learning, and trustworthy machine learning, emphasizing algorithm design that addresses accuracy, scalability, and privacy concerns in practical systems. Key areas of expertise include federated learning systems, privacy-preserving technologies, and scalable distributed algorithms. She has contributed to foundational work on tilted empirical risk minimization and decentralized knowledge propagation. Notable achievements include winning the Best Paper Award at the ICLR Workshop on Secure Machine Learning Systems and First Place in the U.S. Privacy-Enhancing Technologies Pandemic Challenge (2023). Her academic trajectory includes recognition as a Rising Star in Machine Learning/Data Science and participation in prestigious workshops like the EECS Rising Stars Program. Her work bridges theoretical advancements with practical applications, aiming to enhance both the robustness and accessibility of machine learning systems.
Benedikt Bünz is an Assistant Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences. He is also a co-founder and chief scientist of Espresso Systems, where he applies his research expertise to real-world blockchain solutions. His academic work bridges theoretical cryptography with practical blockchain implementations, focusing on enhancing privacy, security, and usability of decentralized systems. Dr. Bünz's research centers around applied cryptography, consensus mechanisms, and game theory as they relate to cryptocurrencies. His work spans zero-knowledge proofs, verifiable delay functions, secure multi-party computation, and privacy-preserving protocols. He has made significant contributions to Bulletproofs, a zero-knowledge proof system deployed on blockchains like Monero, and pioneered research in verifiable delay functions which are now part of Ethereum 2.0's design. His recent work focuses on recursive proof systems, accumulation schemes, and efficient verification techniques for blockchain scalability. His publication record shows a consistent progression from foundational cryptographic primitives to practical blockchain implementations. Recent work demonstrates increasing sophistication in recursive proof systems (ProtoStar, HyperPlonk), novel accumulation techniques (ARC, DewTwo), and foundational work on randomness generation (VDFs). His research consistently bridges theoretical cryptography with real-world blockchain applications, resulting in protocols that are both theoretically sound and practically implementable across multiple blockchain platforms. Dr. Bünz actively contributes to the academic community through teaching and mentorship. He teaches courses on cryptography of blockchains and computer security at NYU, providing students with hands-on experience in blockchain security and cryptographic protocols. His industry engagement through Espresso Systems demonstrates his commitment to translating academic research into practical solutions for the blockchain ecosystem.
Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.
