Fan Zhang is an Assistant Professor in the Department of Computer Science at Yale University. He holds a Ph.D. from Cornell University, advised by Ari Juels, and a B.S. from Tsinghua University. His research focuses on computer security, applied cryptography, decentralized systems, blockchains, and trusted execution environments (TEEs). He leads the Decentralized Systems Group at Yale and is affiliated with the IC3, CDCC, and CADMY centers. He teaches courses on blockchain and real-world cryptography. Key research interests include blockchain decentralization, privacy-preserving protocols, and secure distributed systems. Notable contributions include the Town Crier oracle system (acquired by Chainlink), the DECO TLS protocol, and foundational work on transaction order fairness and MEV mitigation. Awards include the Ethereum Foundation Academic Grant (2022) and the IBM PhD Fellowship (2018-2020). His work has been published in top venues like CCS, S&P, CRYPTO, and USENIX Security.
Stefano Grivet Talocia is a Full Professor in the Department of Electronics and Telecommunications at Polytechnic University of Turin. He serves as Director of the Doctoral School, is a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory, and holds positions on the University Committee for Research and the Commission for the Promotion of Library, Archive and Museum Heritage. He is also President of the Doctoral School Council. His educational background includes a Laurea degree (summa cum laude) in Electronic Engineering (1994) and a Ph.D. in Electronic and Communication Engineering (1998), both from Polytechnic University of Torino. From 1994 to 1996, he worked at NASA/Goddard Space Flight Center in Greenbelt, MD, USA. Professor Grivet Talocia's research focuses on passive macro-modeling of concentrated and distributed interconnect structures for Signal/Power Integrity, order reduction techniques, and modeling and simulation of fields, circuits, and their interactions. His work spans several key areas including fast simulation of transmission lines (TOPLine technique), macromodeling and model order reduction, simulation methods for fields and circuits, passivity enforcement of lumped macromodels, waveform relaxation techniques, and wavelet applications. His research has significant applications in electromagnetic compatibility and signal integrity verification of complex electronic systems. His recent publications demonstrate strong trends in model order reduction techniques applied to power integrity verification, advanced macromodeling for electromagnetic compatibility, nonlinear circuit analysis, uncertainty quantification in PCB design, and power electronics modeling. These works consistently address practical engineering challenges in high-speed electronic design with emphasis on computational efficiency and accuracy. URSI Young Scientist Award (1999) Best symposium paper (2006) Three IBM Shared University Research Awards (2007-2009) IEEE Transactions on Advanced Packaging Best Paper Award (2007) Best EPEP conference paper awards (2007, 2008) Best Associate Editor Award - IEEE Transactions (2020) Best Conference Paper Award (2020) Three Intel SRS Grants (2022-2024) IEEE Fellow (2018) Professor Grivet Talocia actively supervises PhD students working on cutting-edge topics including machine learning applications in signal integrity, model reduction techniques, and electromagnetic compatibility. He has secured significant research funding through competitive grants including PRIN projects and multiple industry-sponsored research contracts with major technology companies such as IBM, Intel, Nokia, Hitachi, and Infineon. His technology transfer activities include co-founding the spin-off IdemWorks (acquired by CST in 2016) and maintaining active collaborations with industry partners. He leads the EMC Group (Electromagnetic Compatibility) within the Department of Electronics and Telecommunications and has developed the autoCircuits web service for automated generation of circuit theory problems. His research has been recognized by inclusion in the top 2% worldwide researcher catalog (Stanford) since 2019.
Dr. Liyi Zhou is a Lecturer in the School of Computer Science at the University of Sydney, specializing in systems security, blockchain, and AI. His research focuses on developing automated and adaptive security tools using machine learning and reinforcement learning. He co-founded D23E.ch, a platform addressing blockchain security and privacy challenges. Research interests include AI-driven vulnerability detection, large security models, real-time intrusion prevention, advanced program analysis (fuzzing/symbolic execution), and privacy-preserving systems. He actively recruits PhD students for projects advancing AI in cybersecurity. Notable achievements include pioneering 'sandwich attacks' discovery in DeFi protocols, contributing to Ethereum Foundation grants, and receiving bug bounties from Flashbots and Ethereum Foundation for vulnerability disclosures. His work has been published in venues like IEEE S&P, USENIX Security, and SIGMETRICS. Teaching includes the course INFO2222. He seeks collaborations and funding to bridge academic research with real-world industry problems, emphasizing practical impact.
Daniel B. Szyld is a Professor in the Department of Mathematics at Temple University's College of Science and Technology. He is co-Director of the High-Performance Computing for Scientific Applications Professional Science Master’s program and a member of the Center for Computational Mathematics and Modeling. He holds leadership roles as President of the International Linear Algebra Society (ILAS, 2020–2026) and as a Board of Trustees member at ICERM (2024–2028), and previously served as Vice-President of SIAM (2014–2015). His research interests include Numerical Analysis , Scientific Computing , Numerical Linear Algebra , Iterative Methods , Preconditioning , Domain Decomposition , and High-Performance Computing . His work often focuses on Krylov subspace methods like GMRES, block solvers, and asynchronous algorithms, with applications in large-scale scientific simulations. The 15 most recent publications reflect a strong focus on enhancing the stability, convergence, and performance of iterative solvers, especially GMRES variants and domain decomposition methods. Topics include random sketching, deflation, weighted norms, multisketching in QR factorization, and asynchronous Schwarz methods. These works appear in top journals such as SIAM Journal on Matrix Analysis and Applications , Numerische Mathematik , and Electronic Transactions on Numerical Analysis , often in collaboration with leading researchers in the field. Scientific Awards and Recognitions: Commemorative medal, Charles University of Prague, 1997 Featured in Hall of Fame by Henk van der Vorst, SARA, 2010 Dean's Distinguished Award for Excellence in Research, Temple University, 2011 Fellow, American Mathematical Society, 2017 Fellow, Society for Industrial and Applied Mathematics, 2017 Achievement in Mathematics Award, Temple University, 2018 Faculty Senate Outstanding Service Award, Temple University, 2021 Daniel B. Szyld has served on the editorial boards of numerous prestigious journals, including Mathematics of Computation , Linear Algebra and its Applications , Numerical Linear Algebra with Applications , and was Co-Editor-in-Chief of Electronic Transactions on Numerical Analysis (2005–2013) and Editor-in-Chief of SIAM Journal on Matrix Analysis and Applications (2015–2020). His research has been supported by the National Science Foundation and the Department of Energy. He has advised students and postdocs, though specific names are not listed in the provided text. He is also involved in professional service through societies such as SIAM, AMS, ILAS, and NAM, and advocates for equity and ethical engagement in mathematics. Labs and Research Groups: He is a member of the Center for Computational Mathematics and Modeling at Temple University and co-Director of the High-Performance Computing for Scientific Applications Professional Science Master’s program, indicating active leadership in computational research and training.
Burak Sunguralp Tekin is an Assistant Professor in the Department of Translation and Interpretation at the Faculty of Humanities and Social Sciences, Ankara Yıldırım Beyazıt University. His academic career includes positions as both Lecturer (2021-2022) and Assistant Professor (2022-present) at the same institution. Dr. Tekin's research primarily focuses on human sociality and the interplay of language, bodies, and technology, with particular emphasis on embodied interaction, social practices, and multimodal communication. Dr. Tekin's educational background includes: Doctorate (2014-2019) from University of Basel, Switzerland, with dissertation "Bodies at play: Exploring participation, spectatorship, and morality in videogaming activities" Postgraduate studies (2012-2013) from University of Newcastle Upon Tyne, United Kingdom, with thesis "Smiles and affective alignment in naturally occurring dyadic academic supervisions" Undergraduate degree (2006-2010) from Hacettepe University, Turkey Dr. Tekin's research interests center on social interaction, multimodal communication, and embodied practices. His work examines how people coordinate their actions, bodies, and language in various contexts including gaming, photography, public spaces, and pandemic-related social adaptations. He employs conversation analysis and multimodal interaction analysis to investigate the micro-details of human sociality, with particular attention to how technology mediates social practices. His research bridges sociology, linguistics, and human-computer interaction to understand the embodied nature of social organization. Analysis of Dr. Tekin's recent publications reveals a consistent focus on embodied social interaction across diverse contexts. His work demonstrates sophisticated methodological approaches to studying how people coordinate their movements, manage social spaces, and create meaning through multimodal resources. A notable trend is his examination of how social practices adapt during extraordinary circumstances like the COVID-19 pandemic, as well as his sustained interest in gaming activities and their social organization. His collaborative work with Lorenza Mondada is particularly prominent in the field of conversation analysis. Dr. Tekin has taught various courses including Multimodal Communication, Introduction to Applied Linguistics, Language and Communication, Language Acquisition, Consecutive Interpreting, Intercultural Communication, World Englishes, Applied Linguistics, Turkish Grammar, Language Culture and Society, and Discourse Analysis at both undergraduate and postgraduate levels. His teaching reflects his research expertise in communication, language, and social interaction.
Phil Bernstein is a Distinguished Scientist in the Data Systems Group at Microsoft Research Redmond and an Affiliate Professor at the University of Washington where he occasionally teaches CSEP 545 Transaction Processing. With over four decades of pioneering work in database systems, he has made significant contributions across transaction processing, data integration, and distributed systems. His research interests focus on database systems, transaction processing, and data integration, with recent work on approximate nearest neighbor search over vector databases, improving database servers using disaggregated cloud resources, and the Orleans distributed systems programming framework. Bernstein's work on Orleans (2012-2019) resulted in an open-source framework widely used inside and outside Microsoft, with components addressing indexing, geo-distribution, and transactions. Bernstein has received numerous prestigious awards including being named a Fellow of the ACM and AAAS, receiving the SIGMOD Edgar F. Codd Innovations Award, and election to the National Academy of Engineering and Washington State Academy of Sciences. Fellow of the ACM Fellow of the AAAS SIGMOD Edgar F. Codd Innovations Award Member of the National Academy of Engineering Member of the Washington State Academy of Sciences As an active researcher and academic, Bernstein serves on numerous conference program committees including SIGMOD 2024 (keynotes), VLDB 2024 (Industry), and has held editorial positions for Information Systems and Springer Data-Centric Systems and Applications. His influential books, Principles of Transaction Processing (2009) and Concurrency Control and Recovery in Database Systems, remain foundational texts in the field.
Professor Dahlia Malkhi is a leading academic and researcher in distributed systems and blockchain technology. She currently holds a faculty position at the University of California, Santa Barbara (UCSB), where she heads the Foundations of Financial Technology (FfTech) research lab. Her work focuses on reliability, security, and consensus mechanisms in distributed systems, with a recent emphasis on blockchain innovations like HotStuff, which underpins Diem, Aptos, and other blockchains. She has held influential roles at industry leaders such as Chainlink Labs, Diem Association, VMware, and Microsoft Research. Education: Ph.D. in Computer Science from The Hebrew University of Jerusalem. Past roles include CTO of Diem Association (2019–2022), Principal Researcher at VMware (2014–2019), and Partner Principal Researcher at Microsoft Research (2004–2014). Research Interests: Blockchain consensus algorithms (e.g., HotStuff, Flexible Paxos), Byzantine Fault Tolerance (BFT), secure multi-party computation (FairPlay), and distributed database systems (CorfuDB). Her work bridges academic theory with industrial applications, emphasizing practical scalability and security. Awards: ACM Fellow (2011), IEEE TCDP Outstanding Technical Achievement Award (2021), IBM Faculty Award (2003/2004). She has also held leadership roles in conferences like Usenix ATC and program chairs for multiple distributed systems events. Advising & Grants: Advises projects at Space Computer, Lyquor Labs, and Chainlink Labs. Her research labs and collaborations include work on BBCA-Chain, Lumiere, and BFTBrain, advancing consensus mechanisms in decentralized systems. Labs/Teams: Leads UCSB’s FfTech lab, co-founded VMware Research, and contributed to foundational blockchain projects like DiemBFT and Espresso Systems. Her work impacts technologies such as NSX-T control planes and distributed financial infrastructure.
Professor Chun-Hung Chen is a distinguished academic at George Mason University ’s Volgenau School of Engineering , where he holds the rank of Professor in the Department of Systems Engineering and Operations Research . He has also held professorships at National Taiwan University and visiting roles at institutions like University of Pennsylvania and Microsoft Research Asia . Education: PhD in Decision and Control, Harvard University (1994) MS in Electrical Engineering, National Taiwan University (1989) BS in Control Engineering, National Chiao-Tung University (1987) Research Interests focus on Stochastic Simulation Optimization , particularly his pioneering Optimal Computing Budget Allocation (OCBA) methodology. OCBA enhances simulation efficiency by dynamically allocating computational resources to critical design alternatives, reducing computation time by orders of magnitude. Applications span air transportation , healthcare , power grids , and semiconductor manufacturing . His 15 most recent articles (2022–2025) explore intersections of simulation optimization , artificial intelligence , reinforcement learning , and personalized medicine , emphasizing computational efficiency and stochastic systems in domains like microgrids and organ transplant logistics . Scientific Awards include: IEEE Fellow (2015) K.D. Tocher Medal (2017) Best Paper Awards at IEEE CASE (2019), LOGMS (2019), and IEEE ICC (2021) Harvard’s Eliahu I. Jury Award (1994) Advisory roles include editorial leadership in IIE Transactions , Journal of Simulation , and IEEE Transactions series. He has coordinated graduate programs at George Mason (2006–11, 2015–19) and led conferences like INFORMS International Meeting (2025) and Harvard Control Workshop (2024). His work is funded by organizations such as the National Science Foundation , National Institutes of Health , and Department of Energy , with applications in healthcare logistics and microgrid control .
Christopher Ramsey is an Associate Professor and Interim Chair of the Department of Mathematics and Statistics within the Faculty of Arts and Science at MacEwan University in Edmonton, Alberta. He holds a PhD in Pure Mathematics from the University of Waterloo (2013), an MMath from Waterloo, and a BSc Honours from the University of Regina. Dr. Ramsey's research centers on operator algebras and functional analysis, with particular emphasis on non-selfadjoint operator algebras and multivariable operator theory. His work explores connections between analysis and algebra, studying algebras of infinite matrices and their applications to group theory, dynamical systems, free probability, and quantum information theory. He also investigates aperiodic order and its mathematical structures. His recent publications (2020-2025) demonstrate a consistent focus on operator algebras, with significant contributions to C*-algebras, tensor algebras, and their applications. The research spans theoretical foundations in functional analysis while connecting to diverse fields including symbolic dynamics, aperiodic structures, and quantum information. His work often bridges abstract algebraic structures with concrete analytical problems. Dr. Ramsey has received notable recognition including an NSERC Discovery Grant (2019), a MacEwan University Project Grant (2019), and an NSERC Postdoctoral Fellowship (2013). He serves as Editor-in-Chief of the MacEwan University Student eJournal (MUSe) and Associate Editor of the Canadian Transactions of Operator Theory. As an educator, Dr. Ramsey teaches various mathematics courses and supervises senior students' independent studies. His academic service includes editorial work and active participation in the Canadian Mathematical Society. His research program continues to develop connections between operator algebras and their diverse applications across mathematical disciplines.
Athanasios Rontogiannis is an Associate Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA). He holds a PhD in Signal Processing from the National University of Athens (1997) and has held roles including Research Director at the National Observatory of Athens (2017–2021). His research focuses on signal processing, machine learning, and hyperspectral image analysis. Education: MEng (Electrical Engineering, NTUA, 1991), M.A.Sc. (University of Victoria, Canada, 1993), PhD (Signal Processing, National University of Athens, 1997). Research interests include adaptive algorithms, sparse representations, and tensor models. He has served on editorial boards of IEEE Transactions on Signal Processing and EURASIP journals, receiving an honorary distinction in 2020. He is a Senior Member of IEEE and affiliated with EURASIP and the Technical Chamber of Greece. Key contributions span hyperspectral unmixing, Bayesian algorithms, and space data exploitation. His work integrates machine learning for applications in space science and signal processing.
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.
Dr. Mike Tehranchi is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) . His research focuses on mathematical finance, stochastic processes, and probability theory. He holds a Lecturer position and is actively involved in academic research, with notable contributions to financial models, term structure analysis, and stochastic calculus. His work bridges theoretical probability and applied finance, addressing topics such as interest rate modeling, implied volatility, and optimal investment strategies. Tehranchi’s research often intersects with optimization, statistical methods, and interdisciplinary applications in astrophysics and fluid dynamics. He maintains an active publication record and contributes to the academic community through his role in the Statistical Laboratory. Key research trends in his articles include the analysis of financial derivatives, stochastic processes in market dynamics, and the application of advanced mathematical techniques to real-world financial problems. His work emphasizes rigorous theoretical foundations while addressing practical challenges in quantitative finance. Dr. Tehranchi has no listed students or academic awards in the provided texts. He can be reached via email and is based in Room D1.04 at the Statistical Laboratory.
Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.
Mahnoosh Alizadeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), affiliated with the Institute for Energy Efficiency and the Center for Control, Dynamical Systems and Computation (CCDC). She directs the Smart Infrastructure Systems laboratory and focuses on scalable control frameworks, data analytics, and market mechanisms for sustainable cyber-physical systems in smart grids and electric transportation. PhD in Electrical and Computer Engineering from UC Davis (2014) Recipient of the National Science Foundation CAREER award (2019) Associate Editor for IEEE Transactions on Control of Network Systems and IEEE Open Journal of Control Systems Her research spans theoretical work in networks, optimization, and AI, with applications in smart grids , electric transportation , and resilient infrastructure . She has contributed to safe optimization algorithms, decentralized learning, and game-theoretic approaches in resource allocation. Recent publications highlight advancements in safe optimization (safe linear bandits, conservative linear bandits), decentralized learning (robust federated learning), game theory (General Lotto games, resource allocation), and smart charging (mobility-aware EV scheduling). These works emphasize real-time decision-making under constraints, security, and robustness in cyber-physical systems. NSF Early CAREER Award Northrop Grumman Excellence in Teaching Award Her research group includes PhD students Spencer Hutchinson, Arghavan Zibaei, Nanfei Jiang, and Sajjad Ghiasvand, with alumni placed at institutions like Apple, Toyota, and the University of Colorado.
