Find Academic Supervisors

Jorge González AlonsoView profile

Researcher

• Third Language Acquisition
• Bilingualism
• Cognitive Linguistics
• +4 more

Jorge González Alonso is a Researcher at the School of Language and Education in Nebrija University , affiliated with the CINC (Nebrija Research Center on Cognition) and the AcqVA Aurora Center at UiT The Arctic University of Norway. His work bridges linguistics and cognitive neuroscience , focusing on early language learning mechanisms and neural correlates. PhD in Morphological Processing and Representation in Multilingualism (2015, University of the Basque Country) Research Interests : Investigates how the mind and brain process new languages from initial exposure, with emphasis on third language acquisition , bilingualism , neurolinguistic responses , and transfer phenomena . His projects analyze the cognitive frameworks enabling multilingual learning. Article Trends : Recent publications highlight third language acquisition , neurocognitive methodologies (EEG/ERP), virtual reality applications , translation priming , and heritage language processing . Keywords span linguistics , cognitive science , psycholinguistics , and educational technology . Labs & Collaborations : Active in CINC and AcqVA Aurora Center projects, utilizing experimental designs like artificial grammar learning and longitudinal studies to decode language acquisition dynamics.

Tulay DixonView profile

Assistant Professor

• Corpus Linguistics
• Academic Discourse
• Language Attitudes
• +2 more

Tulay Dixon is an Assistant Teaching Professor at Emory University, affiliated with the QTM & Linguistics departments. Her work bridges corpus linguistics , academic discourse , language attitudes , and technology in second language education . Key research areas include: Formality in academic writing (e.g., corpus studies on prescriptive rules). L2 spoken/written production (e.g., adverb placement, refusals). Educational technology (e.g., tools like Vocabulary List Generator, meta-analysis of digital gaming). Methodological rigor (e.g., coding protocols, reliability in corpus studies). She presents at conferences like AAAL, AACL, and CALICO, focusing on corpus-driven pedagogy and research transparency. Software projects include Vocabulary List Generator and Introduction to Research Methods App (IRMA) .

John GoldsmithView profile

Professor

• Computational Linguistics
• Artificial Intelligence
• Phonology
• +3 more

Dr. John Goldsmith is a Professor in the Department of Computer Science at the University of Chicago, specializing in computational linguistics and artificial intelligence. His research focuses on understanding symbolic representation in natural language and genomic sequences through software development, notably the open-source Linguistica project, which automates morphological analysis in diverse languages like Somali and Swahili. He co-authored *Battle in the Mind Fields* (2019), exploring the epistemology and sociology of linguistic theory development. Goldsmith's work bridges computational models with phonological theory, including autosegmental phonology and dynamic computational networks. His contributions span morphological algorithms, unsupervised learning techniques, and the historical analysis of linguistic thought. His research interests include phonology, computational morphology, and formal language theory, with a focus on integrating big data and visualization tools. Goldsmith’s academic career spans decades, reflected in his mentorship through *The Chicago Guide to Your Academic Career* (2010), which advises scholars from graduate studies through tenure. His teaching emphasizes modern pedagogical methods, such as applying graphs and networks in linguistics education. Goldsmith’s awards and grants are not explicitly listed, but his extensive publication record and leadership in computational linguistics highlight his disciplinary impact. He collaborates across departments, linking computer science with linguistics and cognitive science. His current projects explore interdisciplinary approaches to language structure and the evolution of linguistic theories, maintaining a lab focused on computational linguistics innovations.

• Software Verification
• Automated Reasoning
• Code Synthesis
• +3 more

Ruzica Piskac is a Professor of Computer Science at Yale University, leading the Rigorous Software Engineering (ROSE) group. She previously held an Independent Research Group Leader position at the Max Planck Institute for Software Systems (2012–2013) and earned her PhD from EPFL (2011), awarded the Patrick Denantes Prize for her thesis on decision procedures for program synthesis and verification. Her research focuses on improving software reliability and trustworthiness through formal methods, encompassing software verification, automated reasoning, applied cryptography, and code synthesis. Notable honors include multiple Amazon Research Awards, the Yale Ackerman Award, and the Microsoft Research Award. She currently serves as Program Chair for the International Conference on Computer Aided Verification (CAV 2024) and on the Steering Committee for Formal Methods in Computer-Aided Design (FMCAD). Education: PhD in Computer Science (EPFL, 2011), MSc in Computer Science (Saarland University, 2005) Key Projects: ROSE group’s work on legal accountability for automated decisions (soid tool), privacy-preserving model checking, and quantum computing security Professional Activities: Editorial roles for conferences like LPAR, FMCAD, and VMCAI Her research group has graduated five PhD students, four now holding assistant professor roles. Current projects include formal XAI via syntax-guided synthesis, privacy-preserving cryptographic protocols, and quantum circuit vulnerability analysis.

Bettina KönighoferView profile

Assistant Professor

• Artificial Intelligence
• Reinforcement Learning
• Formal Methods
• +3 more

Bettina Könighofer is an Assistant Professor at the Technische Universität Graz (TU Graz), affiliated with the Institute of Information Security. Her academic background includes a BSc, Dipl.-Ing. (Diplom-Ingenieur), and Dr.techn. (Technical Doctorate). She specializes in formal methods applied to AI safety, reinforcement learning, and autonomous systems. Her research emphasizes ensuring safety and ethical compliance in decision-making systems through techniques like shield synthesis and formal verification. Research Interests: Safe Reinforcement Learning Algorithmic Fairness and Ethical AI Formal Verification of Learning Systems Autonomous Systems Safety Legal Accountability in Automated Decision-Making Her recent work explores safety shields for real-time decision enforcement, tools for legal accountability (e.g., soid ), and adaptive architectures for safety-critical systems. Notable projects include contributions to QComp verification competitions and Dagstuhl seminars on trustworthy AI. She holds a leadership role in academic conferences, including co-chairing the AISoLA 2023 Track C2. Her research often intersects with practical applications in mobility systems and regulatory compliance.

Gianluca MartinoView profile

Researcher

• Computer Engineering
• Embedded Systems
• Machine Learning
• +3 more

Gianluca Martino is a Research Assistant at the Technische Universität Hamburg , affiliated with the Institute of Embedded Systems and the Computer Engineering group. His work focuses on hardware acceleration, formal verification, and machine learning applications in digital systems. Role: Research Assistant Institution: Technische Universität Hamburg Department: Computer Engineering Research Interests: Gianluca's research spans hardware acceleration for machine learning, FPGA-based implementations, anomaly detection in critical systems, and formal verification of reactive systems. He explores the intersection of digital design and artificial intelligence. Publications Trends: Recent work emphasizes real-time machine learning on FPGAs, anomaly detection for particle accelerators, and formal verification of hardware using temporal logic. Key areas include embedded systems, neural network deployment, and high-integrity digital design. Contact: Email: gianluca.martino@tuhh.de

Andrés GoensView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Theorem Proving
• +3 more

Andrés Goens is an Assistant Professor at the University of Amsterdam since 2023. He holds a Ph.D. (Dr.rer.nat.) in Computer Science from TU Dresden (2021) and an M.Sc. in Mathematics from RWTH Aachen University (2014). His research focuses on programming languages, formal methods, and theorem proving, with applications to compilers and heterogeneous systems. He investigates efficient execution of concurrent programs in multicore architectures and explores machine learning techniques for compiler optimization. Key research areas include concurrency, weak memory models, and bridging abstract mathematics with practical compiler design. His work often employs theorem provers like Lean to formalize program behavior. Recent publications address e-graphs for variable handling, guided equality saturation, and optimizing virtual networks in distributed systems. Publications span venues like PLDI, POPL, and ISCA, reflecting contributions to programming language theory and compiler infrastructure. His research bridges theoretical foundations with practical system design, aiming to improve both programmer productivity and computational efficiency. Contact details: a.goens@uva.nl (professional) and andres@goens.org (personal).

Xiaokang QiuView profile

Associate Professor

• Programming Languages
• Software Engineering
• Program Verification
• +2 more

Xiaokang Qiu is an Associate Professor at Purdue University's Elmore Family School of Electrical and Computer Engineering. His work bridges theoretical and applied research in programming languages and software engineering. Current Role: Associate Professor at Purdue University Research Focus: Program verification, synthesis, and automated deduction Over the past decade, Xiaokang's research has centered on advancing program synthesis techniques. His work appears in leading venues like POPL, PLDI, and SAS, where he explores: Syntax-guided synthesis for bit-vector operations Concurrent string transformation synthesis Library-based synthesis frameworks Recursive program verification He actively contributes to the academic community as a committee member and session chair at conferences including POPL, VMCAI, and SPLASH. His research addresses fundamental challenges in automating code generation while ensuring formal correctness.

Ruzica PiskacView profile

Professor

• Software Verification
• Security
• Applied Cryptography
• +3 more

Ruzica Piskac is a Professor of Computer Science at Yale University, where she joined the Department of Computer Science in 2013 and leads the Rigorous Software Engineering (ROSE) research group. Her work focuses on formal techniques to improve software reliability and trustworthiness through verification, security, and synthesis approaches. Education: PhD in Computer Science from the Swiss Federal Institute of Technology (EPFL) in 2011 Thesis title: Decision Procedures for Program Synthesis and Verification Previously led an independent research group at the Max Planck Institute for Software Systems in Germany (2012-2013) Research Interests: Piskac's research spans software verification, security and applied cryptography, automated reasoning, and code synthesis. Her work combines formal methods with practical applications in programming languages, particularly focusing on making software systems more reliable and trustworthy. She has developed tools for symbolic execution in Haskell, privacy-preserving formal methods, and verification techniques for configuration files and software updates. Publication Trends: Her recent work shows a strong focus on applying formal verification techniques to emerging challenges including large language models, zero-knowledge proofs, quantum computing security, and legal accountability for AI systems. There's a clear trajectory toward bridging formal methods with practical security and verification challenges across diverse domains. Awards: Multiple Amazon Research Awards Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation Patrick Denantes Prize for her doctoral dissertation Advising and Service: Piskac has graduated five PhD students, four of whom are now assistant professors of computer science. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and is on the Steering Committee of the Formal Methods in Computer-Aided Design conference. Her service includes numerous program committee roles across major programming languages and verification conferences. Research Group: She leads the Rigorous Software Engineering (ROSE) group at Yale, which focuses on developing formal techniques for software reliability and trustworthiness, with projects spanning symbolic execution, privacy-preserving verification, reactive synthesis, and configuration file verification.

Thomas RepsView profile

Professor

• Program Analysis
• Abstract Interpretation
• Model Checking
• +4 more

Thomas Reps is a Professor at the University of Wisconsin-Madison 's Computer Sciences Department. He has held the J. Barkley Rosser Professor and Rajiv and Ritu Batra Chair since joining in 1982. His research spans program analysis , model checking , abstract interpretation , computer security , and quantum computing . Ph.D. in Computer Science from Cornell University (1982), ACM Doctoral Dissertation Award winner Co-founder of GrammaTech, Inc. (1988) Held visiting positions at INRIA (France), University of Copenhagen (Denmark), CNR (Italy), and University Paris Diderot (France) Research Interests include program slicing, interprocedural dataflow analysis, pointer analysis, software model checking, and code instrumentation. His recent work focuses on quantum circuit verification , CFLOBDDs , and unrealizability logic . His scientific awards include: ACM Fellow (2005) Foreign Member, Academia Europaea (2013) ACM SIGPLAN Achievement Award (2017) He has advised Ph.D. students like Akash Lal (SIGPLAN Dissertation Award) and Gogul Balakrishnan (ETAPS Best Paper Award). His work has produced influential papers such as the 1988 PLDI paper on interprocedural slicing (50 most influential PLDI paper, 2004) and the 2003 TOPLAS paper on parametric shape analysis.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. His research lies at the intersection of programming languages and artificial intelligence, with a current focus on neurosymbolic programming, trustworthy AI for healthcare, and AI-enabled software engineering tools. Education & Career: Ph.D. in Computer Science, Stanford University (2008) – advisor Alex Aiken M.S. in Computer Science, Purdue University (2003) – advisor Jens Palsberg B.E. in Computer Science, BITS Pilani (1999) Former faculty at Georgia Institute of Technology and researcher at Intel Labs, Berkeley Research Interests: Naik’s group develops languages, algorithms, and compilers for neurosymbolic programming, an emerging paradigm that unites symbolic reasoning with data-driven learning. Their flagship system is the open-source Scallop language and toolchain, applied to computer vision, cybersecurity, medicine, and bioinformatics. He also investigates AI-assisted programming tools that boost productivity and software quality by marrying traditional program analysis with modern machine learning. Recent Highlights: In 2024 he was named Misra Family Professor; his former student Elizabeth Dinella received the 2025 ACM SIGSOFT Outstanding Dissertation Award; his team released IRIS , an LLM-assisted static analysis framework for security vulnerabilities, and published the first comprehensive book on Neurosymbolic Programming in Scallop . Teaching: He regularly teaches CIS 5470 (Software Analysis) every Fall and CIS 5500 (Database Systems) every Spring, both of which are also delivered in Penn’s MCIT Online and Georgia Tech’s OMSCS programs. Advising & Service: Naik has graduated 8 Ph.D. students and mentored numerous postdocs and undergraduates; many alumni now hold faculty or research positions worldwide. He has served on organizing, program, and steering committees for premier venues such as PLDI, POPL, OOPSLA, SPLASH, ESEC/FSE, ISSTA, SAS, and others.

• Computer Engineering
• Computer Science
• Program Synthesis
• +5 more

Xiaokang Qiu is an Associate Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on program synthesis, formal verification, and hardware/software design with an emphasis on automated reasoning and system resilience. He holds a leadership role in advancing techniques for scalable verification of hardware systems and developing efficient program synthesis methods. His research interests include program synthesis, formal methods, hardware design, and algorithm optimization. He explores applications in concurrent systems, network protocols, and safety-critical systems, often integrating machine learning and automated reasoning approaches. Recent work trends show a focus on leveraging large language models for invariant generation, optimizing network and hardware systems through formal methods, and advancing recursive algorithm verification. His publications span topics from concurrent programming to runtime verification frameworks for autonomous systems. Xiaokang Qiu’s contributions include over 20 peer-reviewed articles since 2004, with recent highlights in 2025-2024 addressing cutting-edge challenges in program synthesis and hardware verification. He maintains an active research lab focusing on resilient autonomous systems and compiler optimizations. Office: BHEE 329 | Email: xkqiu@purdue.edu | Website: https://engineering.purdue.edu/~xqiu/

Gus Hahn-PowellView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +3 more

Dr. Gus Hahn-Powell is an Assistant Professor in the Department of Linguistics at the University of Arizona. He directs the online MS in Human Language Technology (HLT) and the Graduate Certificate Program in Natural Language Processing (NLP). He holds affiliate appointments in the College of Information Science, Cognitive Science GIDP, and Computational Social Science Program. His research focuses on machine reading systems to address scientific information overload, enabling automated discovery through analysis of scientific literature. Education: PhD in Computational Linguistics (University of Arizona), MS in Human Language Technology (UA), MA in Applied Linguistics (University of Alabama), BA in Japanese (UA). Research interests include AI-driven systems for knowledge synthesis, natural language processing, and causal graph generation from texts. His work bridges computational linguistics with information science, emphasizing practical applications in scientific discovery. Recent publications span neural-guided program synthesis, causal graph frameworks, and rule-based information extraction tools like Odinson. No scientific awards listed. Advising and grants: He oversees HLT/MSCS programs but no specific grants or advisees listed here. Collaborative efforts include developing tools for multilingual causal interaction analysis and stakeholder performance modeling. Labs/Teams: Directs the HLT program and collaborates on projects involving interdisciplinary teams across linguistics, computer science, and information science.

• software verification
• automated reasoning
• code synthesis
• +3 more

Ruzica Piskac is a Professor of Computer Science at Yale University's School of Engineering & Applied Science, where she leads the Rigorous Software Engineering (ROSE) group. She joined Yale's Department of Computer Science in 2013 after serving as an Independent Research Group Leader at the Max Planck Institute for Software Systems in Germany. Her educational background includes a PhD from the Swiss Federal Institute of Technology (EPFL) in 2011, where her dissertation won the Patrick Denantes Prize. Her advisor was Viktor Kuncak, and her thesis focused on Decision Procedures for Program Synthesis and Verification . Piskac's research centers on improving software reliability and trustworthiness through formal techniques. Her primary interests span software verification, security and applied cryptography, automated reasoning, and code synthesis. She has developed innovative approaches in symbolic execution for Haskell, privacy-preserving formal methods, functional reactive synthesis, and verification for configuration files. Her work bridges theoretical foundations with practical applications in security, programming languages, and AI systems. Her recent publications (2022-2024) demonstrate a strong focus on applying formal methods to emerging challenges in AI, legal reasoning, quantum computing security, and zero-knowledge proofs. There's a clear trend toward interdisciplinary work connecting formal verification with legal accountability, large language models, and security applications. Multiple Amazon Research Awards (2021-2023) for work on formalizing legal reasoning and automated reasoning Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation Donna L. Dubinsky Associate Professor of Computer Science (2019) Patrick Denantes Prize for her PhD dissertation Piskac has graduated five PhD students, four of whom now hold assistant professor positions in computer science. Her research has been supported by multiple Amazon Research Awards and other funding sources. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and on the Steering Committee of the Formal Methods in Computer-Aided Design conference. Her ROSE group at Yale conducts cutting-edge research in formal methods with applications across software engineering, security, and AI systems.

• Natural Language Processing
• Machine Learning
• Unsupervised Learning
• +4 more

Taylor Berg-Kirkpatrick is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego. He directs the BergLab research group, which focuses on developing machine learning methods for analyzing human language and artifacts, including historical documents, music, and multimodal data. His work bridges natural language processing, computational linguistics, and creative AI applications. Research Interests: Berg-Kirkpatrick's research spans unsupervised learning for language understanding, structured prediction models, and computational analysis of human artifacts. The BergLab specializes in: Deciphering historical documents and languages using computer vision and NLP Developing generative models for music and audio synthesis Creating novel methods for low-resource and unsupervised machine learning Cross-modal analysis between text, audio, and visual data Recent Publication Trends: His 15 most recent publications (2023-2024) demonstrate three key research vectors: advanced music generation using diffusion models and symbolic representations; computational analysis of historical artifacts including cuneiform tablets and early modern books; and foundational NLP work on model architectures, decoding methods, and multimodal systems. The work shows consistent innovation in generative AI, historical document processing, and efficient machine learning techniques. Awards and Honors: NSF CAREER Award for 'Modeling Language Evolution via Deep Probabilistic Factorization' (2022) Advising and Funding: Berg-Kirkpatrick has advised over 10 PhD students to completion on topics spanning NLP, music generation, and historical document analysis. His research is supported by the NSF CAREER grant and focuses on developing unsupervised methods for language evolution modeling. The BergLab maintains several open-source projects including Klavier (piano transcription), Ocular (historical document recognition), and Puck (high-speed parser). Lab Direction: The BergLab at UCSD develops computational methods for analyzing diverse human artifacts through interdisciplinary collaborations with historians, musicians, and archaeologists. Current projects include computational bibliography of early modern books, decipherment of historical ciphers, and generative models for symbolic music.