Lam Family College of Business & Computer Science Pitch Contest
The Lam Family College of Business (LFCoB) is proud to collaborate with the San Francisco State University chapter of the Association for Computer Machinery (ACM) to host the annual Computer Science Pitch Competition.

Past CS Pitch Contest Winners
Recording of the Event Will Be Available HERE
- First Place: HouseSync - Swastik N. Amatya (Computer Science); Chung Heng Poon (Computer Science)
- Second Place: Laby - Odera Nwosu (Computer Science)
- Third Place: Cardinal - Rehmat Gill (Computer Science)
- First Place: AlignAI - Westly Cho and Natalie Yam
- Second Place: FreshLens - Junghyun Song, Jinwon Choi, and Ronald Tieu
- Third Place: AccoAI - Alex Loughry
- First Place - Andy Poon, Nagamatsu Mai, Eric Su, Iwano Yoshimasa
- Second Place - Jijeong Lee, Marie Shimizu
- Third Place - Sravani Viswanadha, Jean-Michael Arraki, Chantal Gerwe
- Fan Favorite - Raul Cardenas, Tu Le, Zubair Shaik, Ram Francis

The top three pitches win cash prizes!
This is a great opportunity for Computer Science students to hear feedback about their innovative ideas from industry experts and faculty. Students can apply for the University-wide Innovation Pitch Competition with the same idea. We welcome any stage of entrepreneurial ideas from the ideation phase to those that are ready for angels and seed investments.
- First place: $500
- Second place: $300
- Third place: $200
Important Dates and Deadlines:
- Applications Close: Wednesday, March 14, 2025
- Acceptance Notification: Monday, March 17, 2025
- Pitch Contest Day: Friday, March 21, 2025
Event Location
The pitch competition will be held in person at Science (the new Science Building) 210 at 10:00 A.M.
To Enter the Pitch Contest
- Submit your application and pitch deck [Applications Closed].
- Applications will be reviewed, and finalists will be notified through email.
- Selected applicants are invited to present live in front of the judges panel.
- Winners will be announced after judges deliberation on competition day.
Read the full terms and conditions for your application.
Entry Requirements
- At least one of the cofounders and the presenter must be a student currently enrolled, at least part-time, as a Computer Science major, minor or graduate student within the College of Science and Engineering at San Francisco State.
- Prototypes are preferred but not required.
- Teams are limited to between 2 and 6 students.
- Individual submissions are allowed.
Judging
The preliminary round of judging focuses on finding a compelling and convincing innovation or entrepreneurial story, based on a five (5) minute live pitch, followed by a question/answer session with the judges.
The most successful entries typically demonstrate:
- A clear value proposition for a clearly defined target market. Who is the prototypical consumer of your innovation, and why would they pay for your solution? What problem of theirs does your innovation solve? What need(s) does your innovation fulfill?
- A clear explanation of how you are able to solve the consumer’s problem. How or why does your solution work? What enables your solution to fulfill customer needs better than anything/anyone else out there?
- Traction in the marketplace. Is there a demonstrated product-market fit? Do you have evidence that your idea or prototype is desirable to, adopted by, or sought after by a market?
- A well-articulated revenue model. How does this innovation generate revenue? What recurring revenue streams are created? Who’s paying, and how are they paying for your offering?
- A founding team that is set up for success. Does the founding team have the right expertise to execute the innovation? Is this something that the founders will continue to pursue after graduation?
Judges
Naveen Achyuta is a Senior Network Reliability Engineer at Roblox where he's part of the network software team building network systems for Roblox infrastructure. He's also an advisory board member of Supertrace Al, helping them to build the world's first Al network engineer that can help companies to improve their efficiency and reduce downtime.
Dr. Arno Puder is a Professor and Chair of the Department of Computer Science at the San Francisco State University. Prior to joining SF State, he worked for Deutsche Telekom AG and AT&T Labs Research. As department chair, he promotes equitable and diverse pedagogy through socially responsible computing.
Dr. Puder published two books as well as over 60 peer reviewed conference and journal papers. His research interests include Embedded Systems, Mobile Platforms, loT and Reinforcement Learning. His collaborators include Mozilla Research, Microsoft Research, and SAP and he served as an expert witness in a class action suit against prominent mobile app developers.
He is the founder and co-founder of several widely used Open Source projects. Dr. Puder has a Ph.D. in Computer Science from the Goethe University in Frankfurt, Germany.
John Miller is an experienced software engineer and former startup founder in San Francisco's startup tech industry. As an engineer, John has overseen the engineering efforts of numerous successfully launched transportation and mobility software products used by companies including Toyota and BMW. As a founder, John co-founded companies focused on computer-aided design and publishing. He currently works for Ridecell (YC 2012) as a Principal Engineer focused on creating products that use data-driven AI to solve problems for the leasing industry.
As an undergraduate, John also participated in pitch competitions, including making it to the final round of judging in the $50,000 Sontag Entrepreneurship Competition.
Diwakar Reddy Peddinti is a seasoned software engineer and technical lead with over 12 years of diverse experience in domains such as embedded systems, aviation, logistics, and Industrial IoT. Holding a master’s degree in data science, he currently leads technology efforts at a Silicon Valley IIoT unicorn start-up. Diwakar’s extensive expertise encompasses designing enterprise-grade mobile applications, developing robust backend systems, performing data analytics on vast IoT datasets, and creating innovative machine learning and deep learning models.